60 acast.com Environment Variables https://podcasts.castplus.fm/environment-variables en Green Software Foundation Green Software Foundation,Environment Variables,Sustainable software,Software development,Software developer,Developer,AI,AI Energy,software carbon emissions,Software Carbon Intensity,Software Industry,Tech Industry,GreenOps,FinOps,data center Green Software Foundation Each episode we discuss the latest news regarding how to reduce the emissions of software and how the industry is dealing with its own environmental impact. Brought to you by The Green Software Foundation.

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]]> false Asim Hussain info+68dc7cef46a2532cdd8d1c23@mg-eu.acast.com 68dc7cef46a2532cdd8d1c23 environment-variables https://feeds.castplus.fm/environment-variables episodic https://assets.pippa.io/shows/68dc7cef46a2532cdd8d1c23/show-cover.png https://podcasts.castplus.fm/environment-variables Environment Variables https://feeds.acast.com/public/shows/68dc7cef46a2532cdd8d1c23 The Week in Green Software: Green AI Tradeoffs The Week in Green Software: Green AI Tradeoffs Thu, 16 Apr 2026 07:00:00 GMT 53:46 69dff7c26f0d582d7acb4ea0 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-green-ai-tradeoffs 69dff7c26f0d582d7acb4ea0 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-green-ai-tradeoffs full 139 Host Kate Goldenring is joined by Chris Adams to explore how the Software Carbon Intensity (SCI) standard can help companies meet new EU sustainability reporting requirements, shifting green software from best practice to audited obligation. They also discuss research on “green prompting,” showing that specific words can significantly impact AI energy use, along with new tools that reveal real-time energy consumption in AI workflows. They close by examining how rising AI demand is increasing hardware costs and disrupting the refurbished laptop market.


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]]> Host Kate Goldenring is joined by Chris Adams to explore how the Software Carbon Intensity (SCI) standard can help companies meet new EU sustainability reporting requirements, shifting green software from best practice to audited obligation. They also discuss research on “green prompting,” showing that specific words can significantly impact AI energy use, along with new tools that reveal real-time energy consumption in AI workflows. They close by examining how rising AI demand is increasing hardware costs and disrupting the refurbished laptop market.


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]]> The Week in Green Software: Does Faster AI mean Greener AI? The Week in Green Software: Does Faster AI mean Greener AI? Thu, 09 Apr 2026 07:00:00 GMT 33:15 69d6ae10e257f11e0396d361 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-does-faster-ai-mean-greener-ai 69d6ae10e257f11e0396d361 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-does-faster-ai-mean-greener-ai full 138 Hosts Adi and Valeria cover new research showing that faster AI models do not always use less energy, especially when multiple models run in parallel. They highlight how the lack of standardized metrics makes it difficult to fairly compare energy efficiency and can lead to inconsistent reporting. They also discuss a curated set of open-source tools for measuring software energy use and why measurement is a critical but challenging first step. Finally, they explore a broader view of digital sustainability beyond carbon and highlight the launch of the Green Software Foundation website as a key resource.


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]]> Hosts Adi and Valeria cover new research showing that faster AI models do not always use less energy, especially when multiple models run in parallel. They highlight how the lack of standardized metrics makes it difficult to fairly compare energy efficiency and can lead to inconsistent reporting. They also discuss a curated set of open-source tools for measuring software energy use and why measurement is a critical but challenging first step. Finally, they explore a broader view of digital sustainability beyond carbon and highlight the launch of the Green Software Foundation website as a key resource.


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]]> <![CDATA[The Week in Green Software: Local AI & Lean Observability]]> Thu, 02 Apr 2026 07:00:00 GMT 32:27 69cbe3a1bfb99db0bc61b009 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-local-ai-lean-observability 69cbe3a1bfb99db0bc61b009 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-local-ai-lean-observability full 137 Hosts Kate and Tzviya unpack the latest news This Week in Green Software, focusing on how sustainability is increasingly being integrated into everyday engineering decisions. They explore the push toward earlier measurement and accountability, the role of tooling in helping developers understand energy use, and the growing influence of policy and industry initiatives. The conversation highlights both the challenges and opportunities in making sustainability a standard part of software design, rather than an afterthought.


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]]> Hosts Kate and Tzviya unpack the latest news This Week in Green Software, focusing on how sustainability is increasingly being integrated into everyday engineering decisions. They explore the push toward earlier measurement and accountability, the role of tooling in helping developers understand energy use, and the growing influence of policy and industry initiatives. The conversation highlights both the challenges and opportunities in making sustainability a standard part of software design, rather than an afterthought.


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]]> The Week in Green Software: Hourly Carbon Accounting The Week in Green Software: Hourly Carbon Accounting Thu, 26 Mar 2026 08:00:00 GMT 47:15 69c299be1a160b44db032caf false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-hourly-carbon-accounting 69c299be1a160b44db032caf 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-hourly-carbon-accounting full 136 This Week in Green Software, hosts Oli, Adi, and Valeria explore the growing importance of hourly carbon accounting and what it means for building truly sustainable software. They discuss how moving beyond annual averages to real-time energy data can unlock more accurate insights, better decision-making, and smarter workload placement. The conversation highlights the challenges of measurement, the role of tooling and standards, and why aligning software systems with cleaner energy in the moment could be a key step toward reducing digital emissions.


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]]> This Week in Green Software, hosts Oli, Adi, and Valeria explore the growing importance of hourly carbon accounting and what it means for building truly sustainable software. They discuss how moving beyond annual averages to real-time energy data can unlock more accurate insights, better decision-making, and smarter workload placement. The conversation highlights the challenges of measurement, the role of tooling and standards, and why aligning software systems with cleaner energy in the moment could be a key step toward reducing digital emissions.


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]]> The Week in Green Software: Energy effects of War in Iran The Week in Green Software: Energy effects of War in Iran Thu, 19 Mar 2026 08:00:00 GMT 45:29 69b987b2d01741341b4d1484 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-measuring-for-action-vs-measuring 69b987b2d01741341b4d1484 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-measuring-for-action-vs-measuring full 135 TWiGS hosts Tzviya and Kate are joined by Ryan Sholin of Electricity Maps to discuss how global events impact electricity availability, cost, and carbon intensity. They highlight that improving efficiency and better utilizing existing power grids could reduce the need for new energy infrastructure. The conversation connects energy awareness with software design decisions, emphasizing that developers and organizations can play a role in sustainability by aligning workloads with cleaner energy and understanding the broader energy context behind digital systems.


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]]> TWiGS hosts Tzviya and Kate are joined by Ryan Sholin of Electricity Maps to discuss how global events impact electricity availability, cost, and carbon intensity. They highlight that improving efficiency and better utilizing existing power grids could reduce the need for new energy infrastructure. The conversation connects energy awareness with software design decisions, emphasizing that developers and organizations can play a role in sustainability by aligning workloads with cleaner energy and understanding the broader energy context behind digital systems.


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]]> The Week in Green Software: Shift-Left Sustainability The Week in Green Software: Shift-Left Sustainability Thu, 12 Mar 2026 08:00:00 GMT 37:39 69b196e894cfbd3a45525366 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-shift-left-sustainability 69b196e894cfbd3a45525366 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-shift-left-sustainability full 134 This Week in Green Software, Valeria, Adi, and Oli come together to explore the concept of Shift-Left Sustainability—the idea that environmental impact should be considered early in the software development lifecycle rather than after deployment. They discuss how better measurement, developer tooling, and practical incentives can help teams build more efficient and environmentally responsible software, why sustainability should be treated more like security, and highlight growing government interest in digital sustainability.


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]]> This Week in Green Software, Valeria, Adi, and Oli come together to explore the concept of Shift-Left Sustainability—the idea that environmental impact should be considered early in the software development lifecycle rather than after deployment. They discuss how better measurement, developer tooling, and practical incentives can help teams build more efficient and environmentally responsible software, why sustainability should be treated more like security, and highlight growing government interest in digital sustainability.


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]]> The Week in Green Software: Who Pays for AI’s Energy Footprint? The Week in Green Software: Who Pays for AI’s Energy Footprint? Thu, 05 Mar 2026 08:00:00 GMT 50:04 69a8a0bcf413fba64f6ac968 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-who-pays-for-ais-energy-footprint 69a8a0bcf413fba64f6ac968 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-who-pays-for-ais-energy-footprint full 133 Host Kate Goldenring is joined by Chris Adams and Tzviya Siegman for a news round-up on sustainable software. They dig into EnergyNet and the idea of routing electricity more like the internet, unpack the latest AI energy and greenwashing debates, and look at policy and research angles — from proactive water planning for data centers to a cap-and-trade style proposal for AI efficiency.


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]]> Host Kate Goldenring is joined by Chris Adams and Tzviya Siegman for a news round-up on sustainable software. They dig into EnergyNet and the idea of routing electricity more like the internet, unpack the latest AI energy and greenwashing debates, and look at policy and research angles — from proactive water planning for data centers to a cap-and-trade style proposal for AI efficiency.


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]]> The Week in Green Software: More New Hosts! The Week in Green Software: More New Hosts! Thu, 26 Feb 2026 08:00:00 GMT 49:11 699de8de0e248fdc4f446e50 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-more-new-hosts 699de8de0e248fdc4f446e50 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-more-new-hosts full 132 Asim Hussain introduces more new co-hosts on this episode of TWiGS as they explore the evolving intersection of AI, infrastructure, and sustainability. The discussion covers the growing energy demands of AI workloads, the tension between innovation and environmental impact, and the role of standards and policy in guiding responsible growth. From data center expansion to practical steps engineers can take today, the hosts share insights on how the tech industry can balance rapid advancement with measurable climate accountability.


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]]> Asim Hussain introduces more new co-hosts on this episode of TWiGS as they explore the evolving intersection of AI, infrastructure, and sustainability. The discussion covers the growing energy demands of AI workloads, the tension between innovation and environmental impact, and the role of standards and policy in guiding responsible growth. From data center expansion to practical steps engineers can take today, the hosts share insights on how the tech industry can balance rapid advancement with measurable climate accountability.


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]]> The Week in Green Software: New Hosts! The Week in Green Software: New Hosts! Thu, 19 Feb 2026 08:00:00 GMT 51:27 6994d46dc4b7a0510c70b4fd false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-new-hosts 6994d46dc4b7a0510c70b4fd 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-new-hosts full 131 This Week in Green Software, Chris Adams is joined by new co-hosts Kate Goldenring and Tzviya Siegman to explore the latest stories on their radars. They unpack Microsoft’s community-first AI infrastructure pledge, the rise of gas-powered data centers, and the hidden embodied emissions behind AI models and storage hardware. The conversation also dives into the energy cost of AI prompts, new research measuring real browser energy use, and emerging models like billing AI by the kilowatt-hour. Together, they examine how transparency, standards, and smarter engineering decisions can shape a more sustainable digital future.


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]]> This Week in Green Software, Chris Adams is joined by new co-hosts Kate Goldenring and Tzviya Siegman to explore the latest stories on their radars. They unpack Microsoft’s community-first AI infrastructure pledge, the rise of gas-powered data centers, and the hidden embodied emissions behind AI models and storage hardware. The conversation also dives into the energy cost of AI prompts, new research measuring real browser energy use, and emerging models like billing AI by the kilowatt-hour. Together, they examine how transparency, standards, and smarter engineering decisions can shape a more sustainable digital future.


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]]> Backstage: Carmen Backstage: Carmen Thu, 12 Feb 2026 08:00:00 GMT 23:09 698b5aa5ba80cf1ecb051e79 false https://shows.acast.com/environment-variables/episodes/backstage-carmen 698b5aa5ba80cf1ecb051e79 68dc7cef46a2532cdd8d1c23 backstage-carmen full 130 Chris Skipper hosts Florent Morel and Joseph Cook to discuss Carmen on this Backstage episode. Built at Amadeus and now part of the GSF ecosystem, Carmen helps organizations measure software carbon emissions at both infrastructure and application levels using existing observability and FinOps data, all powered by the GSF Impact Framework. They discuss why granular, team-level emissions data matters, how Carmen works in practice, and how standardized, transparent measurements can turn sustainability insights into concrete engineering action.


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]]> Chris Skipper hosts Florent Morel and Joseph Cook to discuss Carmen on this Backstage episode. Built at Amadeus and now part of the GSF ecosystem, Carmen helps organizations measure software carbon emissions at both infrastructure and application levels using existing observability and FinOps data, all powered by the GSF Impact Framework. They discuss why granular, team-level emissions data matters, how Carmen works in practice, and how standardized, transparent measurements can turn sustainability insights into concrete engineering action.


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]]> Space and Commitment to Green Software Space and Commitment to Green Software Thu, 05 Feb 2026 08:00:00 GMT 54:13 69823b66143ae987af392daa false https://shows.acast.com/environment-variables/episodes/space-and-commitment-to-green-software 69823b66143ae987af392daa 68dc7cef46a2532cdd8d1c23 space-and-commitment-to-green-software full 129 Anne Currie hosts Anna Forlati to discuss why sustainability is not a cost center but a business advantage. Drawing on her journey from UX designer to Head of Digital Sustainability and Impact, Anna explores how inclusive design, ESG strategy, and cultural change can make digital products more resilient, ethical, and profitable. From B Corps and EU regulation to GreenOps and AI efficiency, the conversation reframes sustainability as a mindset shift that aligns purpose, performance, and long term value.


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]]> Anne Currie hosts Anna Forlati to discuss why sustainability is not a cost center but a business advantage. Drawing on her journey from UX designer to Head of Digital Sustainability and Impact, Anna explores how inclusive design, ESG strategy, and cultural change can make digital products more resilient, ethical, and profitable. From B Corps and EU regulation to GreenOps and AI efficiency, the conversation reframes sustainability as a mindset shift that aligns purpose, performance, and long term value.


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]]> How to do Greener Prompting with AI and GreenPT How to do Greener Prompting with AI and GreenPT Thu, 29 Jan 2026 08:00:00 GMT 58:08 6979dd3e3718a16cebd31d74 false https://shows.acast.com/environment-variables/episodes/how-to-do-greener-prompting-with-ai-and-greenpt 6979dd3e3718a16cebd31d74 68dc7cef46a2532cdd8d1c23 how-to-do-greener-prompting-with-ai-and-greenpt full 128 In this episode, host Chris Adams is joined by Wilco Burggraaf and Robert Keus of GreenPT to unpack what greener prompting and transparent AI actually look like in practice. They discuss why most AI services hide their environmental impact, how GreenPT exposes real energy and carbon data to users, and why user behavior plays a major role in AI’s footprint. The conversation explores prompt length, session design, model efficiency, and the limits of chat-based AI, making a strong case for transparency, better defaults, and more purposeful use of AI if it’s going to scale responsibly.


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]]> In this episode, host Chris Adams is joined by Wilco Burggraaf and Robert Keus of GreenPT to unpack what greener prompting and transparent AI actually look like in practice. They discuss why most AI services hide their environmental impact, how GreenPT exposes real energy and carbon data to users, and why user behavior plays a major role in AI’s footprint. The conversation explores prompt length, session design, model efficiency, and the limits of chat-based AI, making a strong case for transparency, better defaults, and more purposeful use of AI if it’s going to scale responsibly.


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]]> Azure API Management Azure API Management Thu, 22 Jan 2026 08:00:00 GMT 31:15 6970e09f08791c965c6f55d7 false https://shows.acast.com/environment-variables/episodes/azure-api-management 6970e09f08791c965c6f55d7 68dc7cef46a2532cdd8d1c23 azure-api-management full 127 Chris Adams speaks with Tom Kerkhove of the Microsoft Azure API Management team about how thoughtful API design can reduce energy use and improve system efficiency. They discuss how API gateways, caching, throttling, and observability can cut unnecessary compute and data transfer, while also improving reliability and developer experience. The conversation shows how small architectural decisions at the API layer can have an outsized impact on cost, performance, and sustainability at scale.


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]]> Chris Adams speaks with Tom Kerkhove of the Microsoft Azure API Management team about how thoughtful API design can reduce energy use and improve system efficiency. They discuss how API gateways, caching, throttling, and observability can cut unnecessary compute and data transfer, while also improving reliability and developer experience. The conversation shows how small architectural decisions at the API layer can have an outsized impact on cost, performance, and sustainability at scale.


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]]> The Green Shift: Transitioning .NET Services Across Architectures The Green Shift: Transitioning .NET Services Across Architectures Thu, 25 Dec 2025 19:38:49 GMT 44:49 694d92c930165a956d6923c7 false https://shows.acast.com/environment-variables/episodes/the-green-shift-transitioning-net-services-across-architectu 694d92c930165a956d6923c7 68dc7cef46a2532cdd8d1c23 the-green-shift-transitioning-net-services-across-architectu full 126 Anne Currie is joined by Sara Bergman to explore what the shift to greener computing really looks like in practice, using .NET and modern CPU architectures as a concrete example. They unpack why moving from traditional x64 systems to more efficient ARM-based platforms can cut costs and carbon, how runtime environments like .NET make architectural transitions easier, and why staying up to date with platforms is essential for performance, security, and sustainability. Along the way, the conversation connects DevOps, modernization, and energy efficiency into a clear message: the green shift starts with building systems that are designed to change.


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]]> Anne Currie is joined by Sara Bergman to explore what the shift to greener computing really looks like in practice, using .NET and modern CPU architectures as a concrete example. They unpack why moving from traditional x64 systems to more efficient ARM-based platforms can cut costs and carbon, how runtime environments like .NET make architectural transitions easier, and why staying up to date with platforms is essential for performance, security, and sustainability. Along the way, the conversation connects DevOps, modernization, and energy efficiency into a clear message: the green shift starts with building systems that are designed to change.


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]]> The Week in Green Software: Tokens, Antarctica and SCI for AI The Week in Green Software: Tokens, Antarctica and SCI for AI Thu, 18 Dec 2025 08:00:00 GMT 54:03 694070ae80257c9e35039cd1 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-tokens-antarctica-and-sci-for-ai 694070ae80257c9e35039cd1 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-tokens-antarctica-and-sci-for-ai full 125 This Week in Green Software, Chris Adams and Asim Hussain round up the latest stories shaping sustainable tech. From new research on AI and energy use to policy shifts, tooling updates, and signals from the wider climate and software communities, the discussion connects the dots on what matters right now and why. It’s a fast-moving snapshot of the trends, tensions, and progress driving green software forward.


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]]> This Week in Green Software, Chris Adams and Asim Hussain round up the latest stories shaping sustainable tech. From new research on AI and energy use to policy shifts, tooling updates, and signals from the wider climate and software communities, the discussion connects the dots on what matters right now and why. It’s a fast-moving snapshot of the trends, tensions, and progress driving green software forward.


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]]> Root and Branch and SCI-Web Root and Branch and SCI-Web Thu, 11 Dec 2025 08:00:00 GMT 1:00:48 6938c7180e4329eef0f7e7dd false https://shows.acast.com/environment-variables/episodes/root-and-branch-and-sci-web 6938c7180e4329eef0f7e7dd 68dc7cef46a2532cdd8d1c23 root-and-branch-and-sci-web full 124 Host Chris Adams talks with Adam Newman and Oli Winks of Root & Branch about their new Software Carbon Intensity Web model for measuring the real carbon footprint of websites. They break down why current methods miss the mark, how their bottom-up approach captures actual energy use across servers, networks and devices, and why better measurement can lead to smarter, lower-carbon choices for teams. It’s a candid look at what it really takes to make the web greener, and the tools that can help developers get there.


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]]> Host Chris Adams talks with Adam Newman and Oli Winks of Root & Branch about their new Software Carbon Intensity Web model for measuring the real carbon footprint of websites. They break down why current methods miss the mark, how their bottom-up approach captures actual energy use across servers, networks and devices, and why better measurement can lead to smarter, lower-carbon choices for teams. It’s a candid look at what it really takes to make the web greener, and the tools that can help developers get there.


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]]> The Week in Green Software: Thirsty AI The Week in Green Software: Thirsty AI Thu, 04 Dec 2025 08:00:00 GMT 47:15 693048d58f1b8d099525d7b9 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-thirsty-ai 693048d58f1b8d099525d7b9 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-thirsty-ai full 123 When artificial intelligence grows, so does its thirst. Chris Skipper hosts sustainability expert Valeria Salis, digging into the hidden cost of powering AI: the massive volumes of water needed to cool the data centers. From submerged servers off China’s coast to European communities pushing back on tech infrastructure, they discuss the environmental trade-offs and the push for solutions that keep innovation flowing without draining local resources.


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]]> When artificial intelligence grows, so does its thirst. Chris Skipper hosts sustainability expert Valeria Salis, digging into the hidden cost of powering AI: the massive volumes of water needed to cool the data centers. From submerged servers off China’s coast to European communities pushing back on tech infrastructure, they discuss the environmental trade-offs and the push for solutions that keep innovation flowing without draining local resources.


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]]> Software Architecture for Sustainability Software Architecture for Sustainability Thu, 16 Oct 2025 08:00:00 GMT 44:22 68ee7dc406cd1a7c239d46e7 false https://shows.acast.com/environment-variables/episodes/software-architecture-for-sustainability 68ee7dc406cd1a7c239d46e7 68dc7cef46a2532cdd8d1c23 software-architecture-for-sustainability Guest host Anne Currie speaks with Karthik Vaidhyanathan, Assistant Professor at IIIT Hyderabad, about integrating sustainability into AI development. full 122 Guest host Anne Currie speaks with Karthik Vaidhyanathan, Assistant Professor at IIIT Hyderabad, about integrating sustainability into AI development. They discuss how the world can balance digital growth with renewable energy goals and how AI systems can be designed to be energy-efficient rather than energy-intensive. Karthik shares insights from his research on sustainable AI and MLOps, including dynamically selecting and retraining models to cut energy use and costs without compromising performance. The conversation underscores the importance of dynamic system design and collaboration across academia, industry, and government to make sustainability a core principle in software engineering.


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]]> Guest host Anne Currie speaks with Karthik Vaidhyanathan, Assistant Professor at IIIT Hyderabad, about integrating sustainability into AI development. They discuss how the world can balance digital growth with renewable energy goals and how AI systems can be designed to be energy-efficient rather than energy-intensive. Karthik shares insights from his research on sustainable AI and MLOps, including dynamically selecting and retraining models to cut energy use and costs without compromising performance. The conversation underscores the importance of dynamic system design and collaboration across academia, industry, and government to make sustainability a core principle in software engineering.


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]]> The Week in Green Software: Sustainability along the DevOps Lifecycle The Week in Green Software: Sustainability along the DevOps Lifecycle Thu, 09 Oct 2025 08:24:09 GMT 58:44 68e7710ed798804c9ea3b377 false https://shows.acast.com/environment-variables/episodes/the-week-in-green-software-sustainability-along-the-devops-l 68e7710ed798804c9ea3b377 68dc7cef46a2532cdd8d1c23 the-week-in-green-software-sustainability-along-the-devops-l Guest host Anne Currie is joined by software engineer and sustainability advocate Julian Gommlich to explore how green practices can be embedded throughout the DevOps lifecycle. full 121 Guest host Anne Currie is joined by software engineer and sustainability advocate Julian Gommlich to explore how green practices can be embedded throughout the DevOps lifecycle. They discuss how modern operational practices like continuous delivery, automation, and agile iteration naturally align with sustainability goals, helping teams build more efficient, resilient, and energy-aware systems. The conversation covers real-world examples, from migrating to newer, more efficient software versions to understanding the carbon impact of data centers, and highlights why adopting a DevOps mindset is crucial for driving both environmental and business value in today’s rapidly changing digital landscape.


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]]> Guest host Anne Currie is joined by software engineer and sustainability advocate Julian Gommlich to explore how green practices can be embedded throughout the DevOps lifecycle. They discuss how modern operational practices like continuous delivery, automation, and agile iteration naturally align with sustainability goals, helping teams build more efficient, resilient, and energy-aware systems. The conversation covers real-world examples, from migrating to newer, more efficient software versions to understanding the carbon impact of data centers, and highlights why adopting a DevOps mindset is crucial for driving both environmental and business value in today’s rapidly changing digital landscape.


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]]> Building Energy Awareness into Operating Systems Building Energy Awareness into Operating Systems Thu, 02 Oct 2025 07:00:00 GMT 1:05:29 68dcd8ddd5c8a1f9b3427a77 false https://shows.acast.com/environment-variables/episodes/building-energy-awareness-into-operating-systems 68dcd8ddd5c8a1f9b3427a77 68dc7cef46a2532cdd8d1c23 building-energy-awareness-into-operating-systems Host Chris Adams speaks with Didi Hoffmann, CTO of Green Coding Solutions, about building energy awareness into operating systems and making sustainability a first-class concern in software development. full 120 Host Chris Adams speaks with Didi Hoffmann, CTO of Green Coding Solutions, about building energy awareness into operating systems and making sustainability a first-class concern in software development. They discuss Didi’s journey from Linux kernel programming to climate-focused tech and many more!


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]]> Host Chris Adams speaks with Didi Hoffmann, CTO of Green Coding Solutions, about building energy awareness into operating systems and making sustainability a first-class concern in software development. They discuss Didi’s journey from Linux kernel programming to climate-focused tech and many more!


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]]> Sustainable AI Sustainable AI Fri, 26 Sep 2025 04:37:00 GMT 43:34 p1kp7kn0 false https://podcasts.castplus.fm/e/qn0qymv8-sustainable-ai 68dc7d0209b1c365e49ab086 68dc7cef46a2532cdd8d1c23 Guest host Anne Currie speaks with Boris Gamazaychikov, Head of AI Sustainability at Salesforce, about aligning artificial intelligence with environmental responsibility. They explore the wide range of energy impacts across AI models, the development of the AI Energy Score benchmarking tool, and why transparency is essential for sustainable choices. 119

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]]> Backstage: The Green Software Movement Platform Backstage: The Green Software Movement Platform Thu, 11 Sep 2025 09:46:00 GMT 29:44 713pmvk0 false https://podcasts.castplus.fm/e/x814klmn-backstage-the-green-software-movement-platform 68dc7d0209b1c365e49ab057 68dc7cef46a2532cdd8d1c23 full 118 WIN FREE TICKETS TO GREEN IO LONDON:


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]]> WIN FREE TICKETS TO GREEN IO LONDON:


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]]> <![CDATA[The Week in Green Software: AI Energy Scores & Leaderboards]]> Thu, 21 Aug 2025 07:00:00 GMT 1:04:39 z1r3p6r0 false https://podcasts.castplus.fm/e/18pvmy18-the-week-in-green-software-ai-energy-scores-leaderboards 68dc7d0409b1c365e49ab1ac 68dc7cef46a2532cdd8d1c23 Host Chris Adams is joined by Asim Hussain to explore the latest news from The Week in Green Software. They look at Hugging Face’s AI energy tools, Mistral’s lifecycle analysis, and the push for better data disclosure in the pursuit for AI sustainability. They discuss how prompt design, context windows, and model choice impact emissions, as well as the role of emerging standards like the Software Carbon Intensity for AI, and new research on website energy use. 117 Host Chris Adams is joined by Asim Hussain to explore the latest news from The Week in Green Software. They look at Hugging Face’s AI energy tools, Mistral’s lifecycle analysis, and the push for better data disclosure in the pursuit for AI sustainability. They discuss how prompt design, context windows, and model choice impact emissions, as well as the role of emerging standards like the Software Carbon Intensity for AI, and new research on website energy use. 

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TRANSCRIPT BELOW:

Asim Hussain: ChatGPT, they're all like working towards a space of how do we build a tool where people can literally pour junk into it, and it will figure something out. 

Whereas what we should be doing, is how do you use that context window very carefully. And it is like programming. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to this week in Green Software where we look at the latest news in sustainable software development. I am joined once again by my friend and partner in crime or occasionally crimes, Asim Hussain, of the Green Software Foundation. My name is Chris Adams. I am the Director of Policy and Technology at the Green Web Foundation, no longer the executive director there,

and, as we've moved to a co-leadership model. And, Asim, really lovely to see you again, and I believe this is the first time we've been on a video podcast together, right?

Asim Hussain: Yeah. I have to put clothes on now, so, so that's,

Chris Adams: That raises all kinds of questions to how intimate our podcast discussions were before. Maybe they had a different meaning to you than they did to me, actually.

Asim Hussain: Maybe you didn't know I was naked, but anyway.

Chris Adams: No, and that makes it fine. That's what, that's what matters. I also have to say, this is the first time we get to, I like the kind of rocking the Galactus style headphones that you've got on here.

Asim Hussain: These are my, yeah, no, these are old ones that I posted recently. I actually repaired them. I got my soldering iron and I repaired the jack at the end there. So, I'm very proud of myself for having repaired. I had the right to repair. Chris. I had the right to repair it.

Chris Adams: Yeah. This is why policy matters.

Asim Hussain: I also have the capability.

Chris Adams: Good. So you can get, so, good on you for saving a bunch of embodied carbon and, how that's calculated is something we might touch on. So, yes. So if you are new to this podcast, my friends, we're just gonna be reviewing some of the news and stories that are kinda showed up on our respective radars as we work in our kind of corresponding roles in both the Green Software Foundation and the Green Web Foundation.

And hopefully this will be somewhat interesting or at least diverting to people as they wash their dishes whilst listening to us. So that's the plan. Asim, should I give you a chance to just briefly introduce what you do at the Green Software Foundation before I go into this?

'Cause I realized, I've just assumed that everyone knows who you are. And I know who you are, but maybe there's people who are listening for the first time, for example.

Asim Hussain: Oh yeah. So, yeah. So my name's Asim Hussain. I am a technologist by trade. I've been building software for several decades now. I formed the green software, yeah, Green Software Foundation, you know, four years ago. And, now I'm the executive director and I'm basically in charge of, yeah, just running the foundation and making sure we deliver against our vision of a future where software has zero harmful environmental impacts.

Chris Adams: That's a noble goal to be working for. And Asim, I wanted to check. How long is it now? Is it three years or four years? 'Cause we've been doing this a while.

Asim Hussain: We, yeah. So we just fin, well, four years was May, so yeah, four years. So next birthday's the fifth birthday.

Chris Adams: Wow. Time flies when 

the world is burning, I suppose. 

Alright, so anyway, as per usual, what we'll do, we share all the show notes and any links that we discuss or projects we discuss, we'll do our damnedest to make sure that they're available for anyone who wants to continue their quest and learning more about sustainability in the field of software.

And I suppose, Asim, it looks like you're sitting comfortably now. Should we start looking at some of the news stories?

Asim Hussain: Let's go for it.

Chris Adams: Alright. Okay. The first one we have, is a story from Hugging Face. This is actually a few months back, but it's one to be aware of if it missed you the first time. So, Hugging Face released a new tool called Chat UI Energy that essentially lets you see, the energy impact live from using a kind of chat session,

a bit like ChatGPT or something like that. Asim, I think we both had a chance to play around with this, and we'll share a link to the actual story around this as well as the actual repo that's online. What do you think of this? what's your immediate take when you see this and have a little poke around with this? 

Asim Hussain: Well, it's good. I wanna make sure. It's a really nice addition to a chat interface. So just so the audience who's not seeing it, every time you do a prompt, it tells you the energy in, well, in watt hours, what I'm seeing right now. But then also, you know, some other stats as well.

And then also kind of how much of a phone charge it is. And that's probably the most surprising one. I just did a prompt, which was 5.7% of a phone charge, which was, that's pretty significant. Actually, I dunno, is that significant? So, one of the things is, I'm trying to, what I'm trying to find out from it though is how does that calculation, 'cause that's my world, it's like, how does, what do you really mean by a calculation?

Is it cumulative? Is it session based? Is it just, you know, how, what have you calculated in terms of the energy emissions? The little info on the side is just the energy of the GPU during inference. So it's not the energy of kind of anything else in the entire user journey of me using a UI to ask a prompt.

But we also know that's probably the most significant. And I'm kind of quite interested in figuring out, as I'm prompting it, I'm one, I'm, one of the things I'm seeing is that every single prompt is actually, the emissions are bigger than the previous prompt. Oh no, it's not actually, that's not true.

Yeah, it is.

Chris Adams: Ah, this is the thing you've been mentioning about cumulative, 

Asim Hussain: Cumulative. Yeah. Which is a confusing one. 'Cause I've had a lot of people who are really very good AI engineers go, "Asim, no, that's not true." And other people going, "yeah, it kind of is true." But they've just optimized it to the point where the point at which you get hit with that is at a much larger number.

But the idea is that there's, there, it used to be an n squared issue for your prompt and your prompt session history. So every time you put a new prompt in all of your past session history was sent with your next prompt. And if you are actually building, like a your own chat system, if you are actually building like your own chat solution for your company or wherever, that is typically how you would implement it as a very toy solution to begin with is just, you know, take all the texts that was previous and the new text and send it, in the next session.

But I think what, they were explaining to me, which was actually in the more advanced solutions, you know, the ones from Claude or ChatGPT, there's a lot of optimization that happens behind the scenes. So it doesn't really, it doesn't really happen that way, but I was trying to figure out whether it happens with this interface and I haven't quite figured it out yet.

Chris Adams: Oh, okay. So I think what you might be referring to is the fact that when you have like a GPU card or something like that, there's like new tokens and kind of cashed tokens, which are priced somewhat differently now. And this is one of the things that we've seen.

'Cause it's using maybe a slightly different kind of memory, which might be slightly faster or is slightly kind of is slightly lower cost to service in that sense. Yeah. Okay. So this is one thing that we don't see. What I, the good news is we can share a link to this, for anyone listening, this source code is all on GitHub, so we can have a look at some of this.

And one of the key things you'll see actually is, well this is sending a message. When you see the actual numbers update, the, it's not actually, what it's actually doing is it's calculating all this stuff client site based on how big each model is likely to be. 'Cause when you look at this, you can A,

Asim Hussain: It's a model.

Chris Adams: You can work out the, I mean, so when people talk about should I be using the word please or thank you, and am I making the things worse by treating this like a human or should I just be prompting the machine like a machine, is there a carbon footprint to that? This will display some numbers that you can see there, but this has all been calculated inside your browser rather than actually on the server.

So like you said, Asim, there is a bit of a model that's taking place here, but as a kind of way to like mess around and kind of have a way into this. This is quite interesting and even now it's kind of telling that there are so few providers that make any of this available, right now. We're still struggling even in like the third quarter of 2025,

to have a commercial service that will expose these numbers to you in a way that you can actually meaningfully change the environmental footprint of through either your prompting behavior or well maybe model choice. But that's one of the key things that I see. I can't think, I can't think of any large commercial service that's doing this.

The only one is possibly GreenPT,

which is basically put a front end on Scaleway's, inference service and I'm not sure how much is being exposed there for them to make some assumptions as well.

Asim Hussain: Do you know how bad, do you know how,

I feel very uncomfortable with the idea of a future where a whole bunch of people are not saying please or thank you, and the reason for it is they're proudly saying, "well, I care about, I care about sustainability, so I'm not gonna say please or thank you anymore 'cause it's costing too many, too much carbon." I find that very uncomfortable. I personally, I don't wanna, we could, choose not to say please or thank you in all of our communications because it causes, emissions no matter what you do. I don't know.

Chris Adams: I'm glad you weren't there, Asim. 'Cause I was thinking about that too. There's a carbon cost to breathing out and if, you, I guess maybe that's 'cause we're both English and it's kinda hardwired into us. It's like the same way that, you know, if you were to step on my toe, I would apologize to you stepping on my toe because I'm just English and I, and it's a muscle memory, kind of like impulsing.

Okay.

Asim Hussain: Yeah.

Chris Adams: That's, what we found. We will share some couple, a couple of links to both the news article, the project on Hugging Face, and I believe it's also on GitHub, so we can like, check this out and possibly make a PR to account for the different kinds of caching that we just discussed to see if that does actually make a meaningful difference on this.

For other people who are just looking, curious about this, this is one of the tools which also allows you to look at a, basically not only through weird etiquette, how etiquette can of impact the carbon footprint of using a tool, but also your choice of model. So some models might be, say 10 times the size of something, but if they're 10, if they're not 10 times as good, then there's an open question about whether it's really worth using them, for example.

And I guess that might be a nice segue to the next story that we touch on. But Asim, I'll let you, you gotta say something. I

Asim Hussain: No, I was gonna say, because I, this is, 'cause I've been diving into this like a lot recently, which is, you know, how do you efficiently use AI? Because I think a lot of the, a lot of the content that's out there about, you know, oh, AI's emissions and what to do to reduce AI's emissions, there are all the choices that as a consumer of AI, you have absolutely no ability to affect. I mean, unless you are somebody who's quite comfortable, you know, taking an open source model and rolling out your own infrastructure or this or that or the other. If you're just like an everyday, not even an everyday person, but just somebody who works in a company who's, you know, the company bought Claude, you know, you're using Claude,

end of story, what are you, like, what do you do? And I think that's really, it is a really interesting area. I might just derail our whole conversation to talk about this, but I think it's a really interesting area because, what it's really boiling down to is your use of the context window.

And so you have a certain number of tokens in a chat before that chat implodes, and you can't use that chat anymore. And historically, those number of tokens were quite low. Relative to, because of all the caching stuff hadn't been invented yet and this and that and the other. So the tokens were quite low.

What, didn't mean they didn't mean they were, the prompts were cheaper before. I think they were still causing a lot of emissions. But because they've improved the efficiency and rather than just said, I've improved the efficiency, leave it at that, I've improved the efficiency, Jevons paradox, I've improved the efficiency,

let's just give people more tokens to play around with before we lock them out. 

So the game that we're always playing is how to actually efficiently use that context. And the please or thank you question is actually, see this is, I don't think it's that good one. 'Cause it's two tokens in a context window of a million now, is what's coming down the pipeline.

The whole game. And I think this is where we're coming from as you know, if you wanna be in the green software space and actually have something positive to say about how to actually have a relationship with AI, it's all about managing that context. 'Cause the way context works is you're just trying to, it's like you've got this intern and if you flash a document at this intern, you can't then say, "oh, ignore that.

Forget it I didn't mean to show you that." It's too late. They've got it and it's in their memory and you can't get rid of it. the only solution is to literally execute that intern and bury their body and get a new intern and then make sure they see the information in the order and only the information they need to see so that when you finally ask 'em that question, they give you the right answer. And so what a lot of people do is they just, because there's a very limited understanding of how to play, how to understand, how to play with this context space, what people end up doing is they're just going, "listen, here's my entire fricking document. It's actually 50,000 words long. You've got it, and now I'm gonna ask you, you know, what did I do last Thursday?"

So it's, and all of that context is wasted. And I think that's, and it's also like a very simplistic way of using an AI, which is why like a lot of companies are, kind of moving towards that space because they know that it means their end user doesn't have to be very well versed in the use of the tool in order to get benefit out of it.

So that's why ChatGPT, they're all like working towards a space of how do we build a tool where people can literally pour junk into it, and It will figure something out.

 Whereas what we should be doing and what I'm like, and I think it's not only what we should be doing, it's, what the people who are like really looking at how to actually get real benefit from AI,

is how do you use that context window very carefully. And it is like programming. It is really like program. That's what, that's my experience with it so far. It's like, I want this, I need to feed this AI information. It's gonna get fed in an order that matters. It's gonna get fed in a format that matters.

I need to make sure that the context I'm giving it is exactly right and minimal. Minimal for the question that I wanna answer, get it answered at the end of it. So we're kind of in this like space of abundance where, because every AI provider's like, "well do what you want. Here's a million tokens.

Do what you want, do what you want."

And they're all, we're all just chucking money. These we're just chucking all our context tokens at it. They're burning money on the other side because they're not about making a profit at the moment. They're just about becoming the winner. So they don't really care about kind of profitability to that level.

So what us It's all about, I'm just getting back to it again. I think, we need to eventually be telling that story of like, how do you actually use the context window very carefully? And again, it's annoyed me that the conversation has landed at please and thank you. 'Cause the actual conversation should be, you know, turning that Excel file into a CSV because it knows how to parse a CSV and it uses fewer tokens to parse a CSV than an Excel file. Don't dump the whole Excel file, export the sheet that you need in order for it to, answer that question. If you f up, don't just kill the session and start a new session.

This is, there's this advice that we need to be giving that I don't even know yet.

Chris Adams: MVP. Minimal viable prompt.

Asim Hussain: Minimal viable prompt! Yeah. What is the minimal viable prompt and the, what's frustrating me is that like one of the things that we use Claude and I use Claude a lot, and Claude's got a very limited context window and I love that.

It was like Twitter when you had to, remember Twitter when you had to like have 160 characters?

It was beautiful.

Chris Adams: to 280, and then you're prepared to be on that website, you can be as, you can monologue as much as you want

Asim Hussain: Yeah. You can now monologue, but it was beautiful having to express an idea in this short, like short, I love that whole, how do I express this complex thing in a tweet? And so with the short context windows, were kind of forced to do that, and now I'm really scared because now everybody, Claude literally two days ago has now gone, right, you've got a million context window, and I'm like, oh, damn it.

Now I don't even, now I don't have personally

Chris Adams: That's a million token context window when you say that. Right. So that's enough for a small book basically. I can dump entire book into it, then ask questions about it. Okay. Well, I guess it depends on the size of your book really, but yeah, so that's, what you're referring to when you talk about a million context window there.

Asim Hussain: Yeah, yeah. And it's kind of an energy question, but the energy doesn't really, kind of, knowing how much, like I've just looked at chat UI window and I've checked a couple of prompts and it's told me the energy, and it's kinda that same world.

It's just it's just there to make me feel guilty, whereas the actual advice you should be getting is well, actually no, I, what do I do? How am I supposed to prompt this thing to actually make it consume less energy? And that's the,

Chris Adams: Oh, I see. So this is basically, so this is, you're showing me the thing and now you're making me feel bad. And this may be why various providers have hosted chat tools who want people to use them more, don't automatically ship the features that make people feel bad without giving 'em a thing they can actually do to improve that experience.

And it may be that it's harder to share some of the guidance like you've just shared about making minimum viable prompt or kind of clear prompt. I mean, to be honest, in defence of Anthropic, they do actually have some pretty good guidance now, but I'm not aware of any of it that actually talks about in terms of here's how to do it for the lowest amount of potential tokens, for example.

Asim Hussain: No, I don't see them. I don't see them. I mean, they, yeah, they do have like stuff, which is how to optimize your context window, but at the same time, they're living in this world where everybody's now 

working to a bigger, that's what they have to do.

And I don't know, it's kinda like, where do we, because we, 'cause the AI advice we would typically have given in the past, or we would typically give is listen, just run your AI in a cleaner region. And you are like, well, I can't bloody do that with Anthropic, can I? It's just, it's whatever it is, it's, you know.

Chris Adams: That's a soluble problem though. Like,

Asim Hussain: Like what I'm just saying or,

Chris Adams: Yeah. You know, but like the idea they're saying, "Hey, I want to use the service. And I want to have some control over where this is actually served from."

That is a thing that you can plausibly do. And that's maybe a thing that's not exposed by end users, but that is something that is doable.

And, I mean, we can touch on, we actually did speak about, we've got Mistral's LCA reporting as one of the things, where they do offer some kind of control, not directly, but basically by saying, "well, because we run our stuff in France, we're already using a low carbon grid."

So it's almost like by default you're choosing this rather than you explicitly opting in to have like the kind of greener one by, the greener one through an active choice,

I suppose.

Asim Hussain: They're building some data centers over there as well, aren't they? So it's a big, it's a big advantage for Mistral to be in France, to be honest with you. It's yeah, they're in

Chris Adams: this definitely does help, there's, I mean, okay. Well, we had this on our list, actually, so maybe this is something we can talk about for our next story, because another one on our list since we last spoke was actually a blog post from Mistral.ai talking about, they refer to, in a rather grandiose terms, our contribution to a global environmental standard for AI.

And this is them sharing for the first time something like a lifecycle analysis data about using their models. And, it's actually one that has, it's not just them who've been sharing this. They actually did work with a number of organizations, both France's agency, ADM. They were following a methodology specifically set out by AFNOR, which is a little bit like one of the French kind of, environmental agency, the frugal AI methodology.

And they've also, they were working with I think, two organizations. I think it's Sopra Steria, and I forget the name of the other one who was mentioned here, but it's not just like a kind of throwaway quote from say Sam Altman. It's actually, yeah, here we are is working with Hubblo, which is a nonprofit consultancy based in Paris and Resilio who are a Swiss organization, who are actually also quite, who are quite very well respected and peer reviewed inside this.

So you had something, some things to share about this one as well. 'Cause I, this felt like it was a real step forward from commercial operators, but still falling somewhat short of where we kind of need to be. So, Asim, what, when you read this, what were the first things that occurred to you, I suppose, were there any real takeaways for you?

Asim Hussain: Well, I'd heard about this, on the grapevine, last year because I think, one of the researchers from Resilio was at greenIO, yeah, in Singapore. And I was there and he gave a little a sneak. They didn't say who it was gonna be, they didn't say it was Mistral, but they said, we are working on one.

And he had like enough to tease some of the aspects of it. I suspect once it's got released, some of the actual detail work has not, that's what I'm, I think I'm, unless I, unless there's a paper I'm missing. But yeah, there is kind of more work I think here that didn't end up to actually get released once it's, once it got announced, but there was, it was a large piece of work.

It's good. It's the first AI company in the world of this, you know, size that has done any work in this space and released it. Other than like a flippant comment from Sam Altman, "I heard some people seem to care about the emission, energy consumption of AI." So, so that's good. And I think we're gonna use this, it's gonna be used in as a, as I'd say, a proxy or an analog for kind of many other, situations.

I think it's, it is lacking a little bit in the detail. But that's okay. I think we, every single company that starts, we should celebrate every organization that leads forward with some of this stuff. it's always very, when you're inside these organizations, It's always a very hard headwind to push against.

'Cause there's a lot of negative reasons to release stuff like this, especially when you're in a very competitive space like AI. So they took the lead, we just celebrate that. I think we're going to, there's some data here that we can use as models for other, as, you know, when we now want to look at what are the emissions of Anthropic or OpenAI or Gemini or something like that,

there's some more, you know, analogs that we can use. But also not a huge amount of surprise, I'd say, it's kind of a training and inference,

Chris Adams: Yep.

That turns be where the environmental footprint is.

Asim Hussain: Yeah. Training and inference, which is kind of, which is good. I mean, I think obviously hardware and embodied impacts is, they kind of separate kind of the two together.

I suspect, the data center construction is probably gonna be, I don't know

 that is quite low. Yeah, yeah,

Chris Adams: I looked at this, I mean this is, it's been very difficult to actually find any kind of meaningful numbers to see what share this might actually make. 'Cause as the energy gets cleaner, it's likely that this will be a larger share of emissions. But one thing that was surprising here was like, this is, you know, France, which is a relatively cr clean grid, like maybe between 40 and say 60 grams of CO2 per kilowatt hour, which is, that's 10 times better than the global average, right?

Or maybe 9, between 8 and 10 times cleaner than the global average. And even then it's, so with the industry being that clean, you would expect the embodied emissions from like data centers and stuff to represent a larger one. But the kind of high level, kind of pretty looking graphic that we see here shows that in, it's less than 2% across all these different kind of impact criteria like carbon emissions or water consumption or materials, for example.

This is one thing that, I was expecting it to be to be larger, to be honest. The other thing that I noticed when I looked at this is that, dude, there's no energy numbers. 

Asim Hussain: Oh, yeah. 

Chris Adams: Yeah. And this is the thing that it feels like a, this is the thing that everyone's continually asking for.

Asim Hussain: It's an LCA. So they use the LCAs specification, so

Chris Adams: That's, a very good point. You're right. that is, that's a valid response, I suppose. 'Cause energy by itself doesn't have a, doesn't have a carbon footprint, but the results of generating that energy does, electricity does have that impact. So yeah.

Okay. Maybe that's 

For 

Asim Hussain: the audience, they use like a well known, well respected, standardized way of reporting the lifecycle emissions using the LCA lifecycle analysis methodology, which is like an ISO certified standard of doing it. So they adhere to a standard.

Chris Adams: So this actually made me realize, if this is basically here and you are a customer of a AI provider, 'cause we were looking at this ourselves trying to figure out, okay, well what people speak to us about a AI policies? And we realized well, we should probably, you know, what would you want to have inside one?

The fact that you have a provider here who's actually done this work, does suggest that for that it's possible to actually request this information if you're a customer under NDAs. In the same way that with, if you're speaking to Amazon or probably any of the large providers, if you're spending enough money with them, you can have information that is disclosed to you directly under NDA.

So it may not be great for the world to see, but if you are an organization and you are using, say, Mistral, for example, or Mistral services, this would make me think that they're probably more able to provide much more detailed information so that you can at least make some informed decisions in a way that you might not be able to get from some of the other competing providers.

So maybe that's one thing that we actually do see that is a kind of. Not really a published benefit in this sense, but it's something that you're able to do if you are in a decision making position yourself and you're looking to choose a particular provider, for example.

Asim Hussain: I mean, you should always be picking the providers who've actually got some, you know,

Chris Adams: optimize for disclosure,

Asim Hussain: optimize for disclosure. Yeah. Always be picking the providers if you optimize for disclosure. I mean, if we, the people listening to this, that is the thing that you can do. And Mistral, They're also, they have some arguments in here as well, which is kind of, they did kind of also surface that it is like a pretty linear relationship between your emissions and the size of the model, which is a very useful piece of information for us to know, as a consumer.

Because then we can go, well actually I've heard all these stories about use Smaller models use smaller models and now you actually have some data behind it, which is supporting the fact that, yeah, using a smaller model isn't, it's not got some weird non-linearity to it, so a half size model is only like 10% less, emissions.

A half size model is half the emissions. So that's pretty, that's a pretty good thing to know. Helps Mistral, the fact that they have a lot of small models that you can pick and choose, is not, so a lot of this stuff really benefits Mistral. They are the kind of the kind of organization which has a product offering which is benefited, which does benefit a sustainability community.

So they have like small models you can use. I think, I wonder actually, Chris, 'cause they do say that they're building their own data center in France, but they've never said where there exists, where they until now, where they've been running their AI. So that might be the reason for, they might have been running it in East Coast US or something

like 

Chris Adams: I think that would be quite unlike, wouldn't be very likely, given that most of their provider, most of their customers are based in probably Western Europe still. Right. There is very much a kinda like Gaelic kind of flavor to the tooling. And I've, I mean actually Mistral, or Mistral's tools are ones which I've been using myself personally over the last, like few months, for example.

And it's also worth bearing in mind that they, took on a significant amount of investment from Microsoft a few years back and I would be very surprised if they weren't, or if they weren't using a French data center serving French providers. 'Cause if you were to choose between two countries, okay, if, France or like France actually has, and since 2021, I believe, has had actually a law specifically about measuring the environmental footprint of digital services.

So they've got things that they, I think it's called, I'm going to, I'm just gonna share a link to that, to the name of the law because I'm gonna butcher the French pronunciation, but it basically, it translates to Reduce the Environmental Footprint of Digital Services Law.

That's pretty much it. And that's where, as a follow on from that, that's what, that's what the RGESN, the kind of general guidance that it shares across kind of government websites in general for France. They've already got a bunch of this stuff out there for like how to do greener IT. I suspect that France is probably gonna be one of, well, probably the premier country, if you'd run, be running a startup to see something like this happening much more so than, well probably the US right now, especially given the current kind of push with its current kind of federal approach, which is basically calling into doubt climate change in the wider sense basically.

We were talking about disclosure, right? And we said an optimization for disclosure. And that's probably a nice segue to talk about, another link we had here, which was the energy score leaderboard. Because this is one thing that we frequently point to. And this is one thing that we've suggested in my line of work, that if you are looking to find some particular models, one of the places to look would be the AI Energy Score Leaderboard, which is actually maintained by Hugging Face.

And, I share this 'cause it's one of the few places where you can say, I'm looking for a model to help me maybe do something like image generation or captioning text or generating text or doing various things like this. And you can get an idea of how much power these use on a standardized setup.

Plus, how satisfied, you know, what the kind of satisfaction score might be, based on these tools and based on a kind of standardized set of like tests, I suppose. The thing is though, this looks like it hasn't been updated since February. So for a while I was thinking, oh, Jesus, does this mean we actually need to, do we have to be careful about who we, how we recommend this?

But it turns out that there's a new release that will be coming out in September. It's updated every six months. And, now that I do have to know about AI, this is one thing that I'm looking forward to 

seeing some of the releases on because if you look at the leaderboard for various slices, you'll see things like Microsoft Phi 1 or Google Gemma 2 or something like that.

Asim Hussain: That quite old?

Chris Adams: yeah, these are old now, it's six months in generative AI land is quite a long time. There's Phi 4 now, for example, and there's a bunch of these out there. So I do hope that we'll see this actually. And if you feel the same way, then 

yeah, go on.

Asim Hussain: Is it, 'cause, is I always assume this was like a, live leaderboard. So as soon as a model, I suppose once a model, like the emissions of a model are linked to the model and the version of it. So once you've computed that and put on the leaderboard, it's not gonna change. So then it's just the case of as new models come out, you just measure and it just sees how it goes on the leaderboard.

Because I'm seeing something here. I'm, I thought open, I'm seeing OpenAI, GPT. Isn't that the one they just released?

Chris Adams: No, you're thinking GPT-OSS, perhaps

Asim Hussain: Oh.

Chris Adams: One thing they had from a while ago. So that one, for example, came out less than two weeks ago, I believe. That isn't showing up here.

Asim Hussain: That isn't showing up

Chris Adams: The, I'm, I was actually looking at this thinking, oh, hang on, it's six months, something being updated, six months,

that's, it'd be nice if there was a way, a faster way to expedite kind of getting things disclosed to this. For example, let's say I'm working in a company and I've, someone's written in a policy that says only choose models that disclose in the public somewhere. This is one of the logical places where you might be looking for this stuff right now, for example, and there's a six month lag, and I can totally see a bunch of people saying, no, I don't wanna do that.

But right now there's a six month kind of update process for this.

Asim Hussain: In the AI realm is an eternity. Yeah.

Chris Adams: Yeah. But at the same time, this is, it feels like a thing that this is a thing that should be funded, right? I mean, it's, it feels :I wish there was a mechanism by which organizations that do want to list the things, how to make them to kind of pay for something like that so they can actually get this updated so that you've actually got some kind of meaningful, centralized way to see this.

Because whether we like it or not, people are basically rolling this stuff out, whether we like it or not, and I feel In the absence of any kind of meaningful information or very patchy disclosure, you do need something. And like this is one of the best resources I've seen so far, but it would be nice to have it updated.

So this is why I'm looking forward to seeing what happens in September. And if you think, if you too realize that like models and timely access to information models might be useful, it's worth getting in touch with these folks here because, I asked 'em about this when I was trying to see when they were, what the update cycle was.

And basically the thing they said was like, yeah, we're, really open to people speaking to us to figure out a way to actually create a faster funded mechanism for actually getting things listed so that you can have this stuff visible. Because as I'm aware, as I understand it, this is a labor of love by various people, you know, between their day jobs, basically.

So it's not like they've got two or three FTE all day long working on this, but it's something that is used by hundreds of people. It's the same kind of open source problem that we see again and again. But this is like one of the pivotal data sources that you could probably cite in the public domain right now.

So this is something that would be really nice to actually have resolved.

Asim Hussain: Because there is actually, 'cause the way Hugging Face works is, they have a lab and they have their own infrastructure. Is that how it works? Yeah. So that's

Chris Adams: this would, that was be, that was either, that was physically theirs, or it was just some space. 

Asim Hussain: Spin up. But yeah. But yeah, but they have to effectively like to get the score here. It's not self certified, I presume, but there's a, you know, each of these things has got to get run against the benchmark. So there's basically, if I remember, there was a way of like self certifying.

There was literally a way for

Chris Adams: You could upload your stuff.

Asim Hussain: Yeah. OpenAI could disclose to the Hugging Face to the, what the emissions of, you know, what the energy of it was. But most of it is, there's actually, you gotta run against the H100 and there's a benchmark

Chris Adams: Yep, exactly. So there's a bit of manual. There's a bit of manual steps to do that, and this is precisely the thing that you'd expect that really, it's not like an insoluble problem to have some way to actually expedite this so that people across the industry have some mechanism to do this. 'cause right now it's really hard to make informed decisions about either model choice or anything like that.

Even if you were to architect a more responsibly designed system, particularly in terms of environmental impact here.

Asim Hussain: Because if you were to release a new model and you wanted it listed in the leaderboard, you would have to run every other model against. Why would you need to do that? You need to

Chris Adams: You wouldn't need to do that. You just need to, you, because you don't have control over when it's released, you have to wait six months until the people who are working in that get round to doing that.

Asim Hussain: Just the time. It's just a time. Yeah. Someone's

Chris Adams: If you're gonna spend like a millions of dollars on something like this, it feels like this is not, even if you were to drop say, if, even if it was to cost, maybe say a figure in the low thousands to do something like this, just to get that listed and get that visible, that would be worth it.

So that you've actually got like a functioning way for people to actually disclose this information, to inform decisions. 'Cause right now there's, nothing that's easy to find. This is probably the easiest option I've seen so far and we've only just seen like the AI code of practice that's actually kind of been kind of pub that came into effect in August in Europe for example.

But even then, you still don't really have that much in the way of like public ways to filter or look for something based on the particular task you're trying to achieve.

I wanted to ask you actually, Asim, so I think, I can't remember last time if I was speaking to you, if this came up, I know that in your, with your GSF hat on, there's been some work to create a software carbon intensity for AI spec, right. Now, I know that there's a thing where like court cases, you don't wanna kind of prejudice the discussions too much by having things internally.

Although you're probably not, there isn't like AI court, you can be in contempt of, but I mean, yeah, not yet, but, who knows? Give it another six months. Is there anything that, is there anything, any, juicy gossip or anything you can share that people have been learning? 'cause like you folks have been diving into this with a bunch of domain experts so far, and this isn't my, like, while I do some of this, I'm not involved in those discussions.

So I mean, and I'm aware that there has been a bunch of work trying to figure out, okay, how do you standardize around this? What do you measure? You know, do you count tokens? Do you count like a prompt? What's the thing? Is there anything that you can share that you're allowed to talk about before it goes?

Asim Hussain: Yeah. I think, we, I think that what we've landed on is that as long as I'm not discussing stuff which is in, you know, active discussion and it's kind of made its way into the spec and there's been, you know, broad consensus over, I think it's pretty safe to talk about it.

If there's something that's kind of, and what we do, we do everything in GitHub. So if there's something which is like, I won't, I won't discuss anything which has only been discussed in like an issue or a discussion or comment thread or something. If it's actually made its way into the actual spare, that's pretty safe.

So yeah, the way it's really landed is that there's, there was a lot of conversations at the start. There was a lot of conversations and I was very confused. I didn't really know where things were gonna end up with. But you know, at the start there was a lot of conversations around well, how do we deal with training?

How do we deal with training? There's this thing called inference. And it's interesting 'cause when we look at a lot of other specs that have been created, even the way the Mistral LCA was done, so they, they gave a per inference, or per request. I've forgotten what they did. It, they didn't do per token.

So per

Chris Adams: they do per chat session or per task, right. I think it's something along those lines. Yeah.

Asim Hussain: Something along that, it wasn't a per token thing. But even then they, they added the training cost to it. And like those, some of the questions we were adding, can you add, is there a way of adding like the training? The training happened like ages ago. Can you somehow, is there a function that you can use to amortize that training to like future inference runs?

And we explored like lots of conversations. There's like a decay function. So if you were the first person to use a new model, the emissions per token would be higher because you are amortizing more of the training cost and the older models, the, so you explored like a decay function, we explored, yeah.

There's lots of ideas.

Chris Adams: Similar to the embodied usage, essentially like what we have with embodied versus, embodied carbon versus like use time carbon. You're essentially doing the same thing for training, being like the embodied bit and inference being the usage. And if you had training and you had three inferences, each of those inferences is massive.

Like in terms of the car embodied carbon, if there's like a billion, it's gonna much lower per, for each one.

Asim Hussain: But then you get into really weird problems because I mean it, we do that with the embodied carbon hardware, but we do that by saying, do you know what? The lifespans gone be four years and that's it. And we're just gonna pretend it's an equal waiting every single day for four years.

Chris Adams: Not with the GHG protocol. You can't do it with the GHG protocol. You can't amortize it out like that. You can, you have to do it the same year, so it, your emissions look awful one year

Asim Hussain: Ah, the year that you bought it from. 

Chris Adams: So this is actually one of the reasons, but yeah, this is actually one of the problems with the kind of default way of measuring embodied carbon versus other things inside this is, it's not, like Facebook for example, they've proposed another way of measuring it, which does that, this kind of amortization approach, which is quite a bit closer to how you might do, I guess, like typical amortization of capital, capital

Asim Hussain: Cap, yeah.

Chris Adams: So that's the, that's the difference in the models. And this is, these are some of the kind of honestly sometimes tedious details that actually have quite a significant impact. Because if you did have to, that's gonna have totally different incentive incentives. If you, especially at the beginning of something, if you said, well, if you pay the full cost, then you are incentivized not to use this shiny new model.

'Cause it makes you look awful compared to you using an existing one for example.

Asim Hussain: And that's one of the other questions like, is like, how do you, I mean, a lot of these questions were coming up like what do you... A we never, we didn't pick that solution. and we also didn't pick the solution of we had the, we actually had the conversation of you amortize it over a year, and then there's a cliff.

And then that was like, we're gonna incentivize people to use older models with this idea that older models were the thing. 

There were questions that pop up all the time. Like, what do you do when you have an open source model? If you were to, if I was to fine tune an open source model and then make a service based off of that, is the emissions of the model the open source model that I got Llama whatever it was, am I responsible for that?

Or is the,

and there was like, if you were to say, if you were to say no, then you're incentivizing people to just like open source their models and go, "meh well the emissions are free now 'cause I'm using an open source model." So there's lots of these, it's very nuanced. Kind of the, a lot of the conversations we have in the standards space, is like a small decision can actually have a cascading series of unintended consequences.

So the thing that we really like sat down was like, what, well, what actually, what do you want to incentivize? Let's just start there. What do we want to incentivize? Okay, we've listed those things we wanna incentivize. Right. Now, let's design a metric, which through no accident incentivizes those things. And where they ended up was basically two,

there's gonna be two measures. So we didn't, we didn't solve the training one because there isn't a solution to it. It's a different audience cares about the training emissions than that doesn't, consumers, it's not important to you because it doesn't really matter. It doesn't change how you behave with a model.

It doesn't change how you prompt a model just because it had some training emissions in the past. What matters to you most is your direct emissions from your actions you're performing at that given moment in time. So it's likely gonna be like two SCI scores for AI, a consumer and a provider. So the consumer is like inference plus everything else.

and also what is the functional unit? There's a lot of conversations here as well, and that's likely to land that now very basically the same as how you sell an AI model. So if you are an LLM, you're typically selling by token. And so why for us to pick something which isn't token in a world where everybody else is thinking token, token, token, token, it would be a very strange choice and it would make the decision really hard for people when they're evaluating certain models. They'd be like, oh, it's this many dollars per token for this one and this many dollars per token for that one. But it's a carbon per growth. And it's a carbon per growth,

I can't rationalize that. Where, if it's well look, that's $2 per token, but one gram per token of emissions and that's $4 per token, but half a gram per token for emissions. I can evaluate the kind of cost, carbon trade off, like a lot easier. The cognitive load is a lot easier.

Chris Adams: So you're normalizing on the same units, essentially, right?

Asim Hussain: Yeah. As how, however it's sold, however, it's, 'cause that's sort of, it's a fast, AI is also a very fast moving space and we dunno where it's gonna land in six months, but we are pretty sure that people are gonna figure out how to sell it, in a way that makes sense. So lining up the carbon emissions to how it's sold.

And the provider one is going to be, that's gonna include like the training emissions, but also like data and everything else. And that's gonna be probably per version of an AI. And that will, so you can imagine like OpenAI, like ChatGPT would have a consumer score of carbon per token and also a provider score of ChatGPT 5 has, and it's gonna be probably like per flop or something,

so per flop of generating ChatGPT 5, it was this many, this much carbon. And that's really like how it's gonna,

 it's also not gonna be total totals are like, forget about totals. Totals are pointless when it comes to, to change the behavior.

 You really want to have a, there's this thing called neural scaling laws.

The paper.

Chris Adams: Is that the one that you double the size of the model when it's supposed to double the performance? Is that the thing? 

Asim Hussain: It's not double, but yeah, got relationship. Yeah. So there's this logarithmic, perfectly logarithmic relationship between model accuracy and model size, model accuracy, and the data, the number of training you put into it, and model size and the amount of compute you put into, it's all logarithmic.

So it's often used as the reason, the rationale for like why we need to, yeah, larger models is because we can prove it. So, but that basically comes down to like really then, you know, like if like I care more about, but for instance, I don't particularly, it doesn't matter to me how much, it's not that important to know the total training emissions of ChatGPT 5 versus ChatGPT 4.

What's far more useful, is to know, well, what was the carbon per flop of training for 4 versus the carbon per flop of training for 5? 'Cause then that gives you more interesting information. Have you, did you,

Chris Adams: What does that allow?

Asim Hussain: Bother to do anything? Huh?

Chris Adams: Yeah. What does that allow me to do? If I know if 5 is 10 times worse per flop than 4, 

what that incentivize me to do differently? 'Cause I think I might need a bit of hand help here making this call here.

Asim Hussain: Because I think, 'cause it, what, let's say ChatGPT 6 is going to come along. The one thing we know absolutely sure is it's just gonna be in terms of total bigger than ChatGPT 5. So as like a metric, it's not, if you are an engineer, if you are somebody trying to make decisions regarding what do I do to actually train this model with causing less emissions, it doesn't really help me because it's just, a number that goes higher and higher.

Chris Adams: Oh, it's a bit like carbon intensity of a firm versus, absolute emissions. Is that the much, the argument you're using? So it doesn't matter that Amazon's emissions have increased by 20%, the argument is well, at least if they've got more efficient per dollar of revenue, then that's still improvement.

That's the line of reasoning that's using, right?

Asim Hussain: Yeah. So it's, 

because of the way the SCI is, it's not if you want to do a total, there are LCAs, like the thing that Mistral did, there's existing standards that are very well used. They're very well respected. There's a lot of, there's a lot of information about how to do them.

You can just use those mechanisms to calculate a total. What the SCI is all about is what is a, 

KPI that a team can use and they can optimize against, so over time, the product gets more and more efficient? 

Obviously, you should also be calculating your totals and be making a decision based upon both.

But just having a total is, I've gotta be honest with you, it's just, I don't see totals having, in terms of changing behavior, I don't think it changes any behavior. Full stop.

Chris Adams: Okay. I wanna put aside the whole, we live in a physical world with physical limits and everything like that, but I think the argument you're making is essentially that, because the, you need something to at least allow you to course correct on the way to reducing emissions in absolute terms, for example. And your argument you're making is if you at least have an efficiency figure, that's something you can kind of calibrate and change over time in a way that you can't with absolute figures, which might be like having a, you know, a budget between now and 2030, for example.

That's the thinking behind it, right?

Asim Hussain: Yeah. I mean, if you, I've actually got an example here from 'cause we, so we don't have actual compute. They, no, no one's ever disclosed like the actual compute that they used per model. But they have, or they used to disclose the number of parameters per model. And we know that there's a relationship.

So there's a really interesting, so for 2, 3 and 4, we have some idea regarding the training emissions and the parameters, not from a disclosure, from like research as well, so between, but when you compute the emissions per billion parameters of the model, so per billion parameters of the model, GPT two was 33.3 tons of carbon per billion parameters of the model.

Chris Adams: Okay.

Asim Hussain: GPT-3 went down to 6.86 tons of carbon per billion parameters. So it went down from 33 to 6. So that was a good thing. It feels like a good thing, but we know the total emissions of 3 was higher. Interestingly, GPT-4 went up to 20 tons of carbon per billion parameters. So that's like an interesting thing to know.

It's like you did something efficient between two and three. You did something good. Whatever it was, we don't know what it was, we did something good actually the carbon emissions per parameter reduced. Then you did something. Maybe it was bad. Maybe I, some, maybe it was necessary. Maybe it was architectural. But for some reason your emissions,

Chris Adams: You became massively less efficient in the set, in that 

next 

Asim Hussain: In terms of carbon. In terms of carbon, you became a lot less efficient in GPT-4. We have no information about GPT 5. I hope it's less than 20 metric tons per billion parameters.

Chris Adams: I think I'm starting to wanna step, follow your argument and I'm not, I'm not gonna say I agree with it or not, but I, the, I think the argument you're making is essentially by switching from, you know, that that in itself is a useful signal that you can then do something with. there was maybe like a regression or a bug that happened in that one that you can say, well, what change that I need to do so I can actually start working my way towards, I don't know, us careering less forcefully towards oblivion, for example, or something like that.

Right.

Asim Hussain: Yeah.

Chris Adams: Okay. That makes, I think I understand that now. And, let's, and I suppose the question I should ask from following on from that is that this is, some of this is, we're talking about, we got into this, 'cause we were talking about the SCI for AI, this kind of standard or presumably an ISO standard that we published.

Is there a kind of rough like roadmap for when this is gonna be in the public domain, for example, or people might be requesting this in commercial agreements or something like that?

Asim Hussain: I mean, I can tell you what my hope is. So I think, I mean, cause everything is based upon consensus and if anybody objects then everything or all the plans basically, you know, put on the back burner. But everything's looking very positive. I'm very hopeful that by the end of Q3, so the end of September, we will have gone into draft and then, there hasn't been a full agreement yet as to what we'll actually publish for that. But I'm hoping we'll be able to actually publish the whole specification, because what we wanna start doing is get, I mean this maybe if anybody's interested, we wanna start running case studies because right now it's like the outline of what we want the calculation to be is being agreed on.

But we need a lot of use cases of very different types of products that have computed using it. Not just, you know, I'm a major player and I've got a gazillion servers and we also want, need people, there's lots of organizations we're talking to or listen, we've just, we are, AI is not our central business, but we've built like AI solutions internally and we want to be able to measure that.

Or even smaller organizations or people who are not even training in AI, but just consuming APIs then build like an AI solution on top of that. So there's like a whole range of things that we wanna measure and we want to publish, go into draft in September, and then work on a number of case studies. Hopefully, dream,

my dream, and I, no one holds me to this is by kind of Q1, Q2 next year where we're out and we start the ISO process then, but when we come out, we want to come out with here's a specification. It'll come out with a training course that you can take to learn how to compute the specification. It will come out with a tooling.

So you can just plug in values and then you'll be able to get your numbers and also come out with a number of case studies from organizations who, this is how exactly we calculated it, and maybe you can learn from, how we did it. So that's our goal.

Chris Adams: Okay, well that, so we're looking at basically, okay, first half of 2026, so there's still time to be involved and there's, and presumably later on in Q3, Q4, some of this will be going out in public for people to kind of respond to or have this some, something like the consultation there.

Asim Hussain: Yeah, It'd be a public consultation coming up soon.

Chris Adams: This is useful to know because this takes it to our last story we were looking at, which is actually also talking about the challenges related to the working on the environmental footprint of other things, particularly websites.

This is our final link of the podcast, which is a link to, the IEEE, where there's a post by, I believe it's Janne Kalliola. And, oh dear. I'm not gonna pronounce the other person's name very well. Juho Vepsäläinen. Oh dear. I'm so sorry for mispronouncing your names. I'm drawing attention to this 'cause this is the first time In a while I've seen a peer reviewed article in the IEEE specifically, which I think is the.

It's the Instutute of Electrical and Electronics Engineers. I forget what it stands for. Yes, thank you. They looked at both, Firefox Profiler and Website Carbon. They basically started looking at the environmental footprint, what kind of, what does using these website calculators actually tell you and what can you use?

And they had some recommendations about, okay, we've tried using these tools, what can we learn from that? And the thing that was actually particularly interesting was that they were using Firefox's Firefox profiler specifically to look at the footprint of, they're basically saying that there's two, three insights that have probably come away from this, which I thought was interesting.

One of them, it's really hard to get meaningful numbers around data transfer, which I think is actually something that we've shared and we've covered in a lot of detail and I'm finding very helpful for future discussions around creating something like a software, carbon intensity for Web for this.

But the other thing they did was they spoke about the use of, like tools out there, like profilers, which do provide this direct measurement that does give you some meaningful numbers. But when you look at the charts, the differences aren't that high. For example, they were showing comparisons with things like website carbon, which shows massively different, massively different kind of readings for the carbon footprint of one site versus another.

And then when they used other tools like say Firefox Profiler, the differences were somewhat more modest between these two things. So this kind of gives the impression that tool, some of the tools that use like the soft, the sustainable web design model may, they may be overestimating the effectiveness of changes you might be making as an engineer versus what gets measured directly.

Now, there's obviously a elephant in the room and that this isn't measuring what's happening server side, but this is the first time I've seen a really, kind of a real deep dive by, some people who are actually looking into this to come up with some things you can, you can test, I suppose, or you can kind of, you can like, reproduce to see if they get, you're getting the same numbers from these people here.

And, this is actually quite a useful, it's, I found it quite noteworthy and really nice to see and I would've found out about it because, Janne actually shared it inside the Climateaction.tech Slack.

Asim Hussain: So it was a paper inside IEEE or, an article inside that

Chris Adams: It's, a paper. So it's a peer reviewed paper in volume 13 of IEEE and they basically, they talk about the current state of the art, how people currently try to measure energy consumption on the Web. Then they talk about some of the tools you can use for the end user devices. Talk about some of the issues related to trying to go on just data transfer alone and why that isn't necessarily the best thing to be using, but, what kind of statements you could plausibly make.

But as someone who ends up, you know, we, the organization I work for, we implemented the sustainable web design model for this. Having something like this is so, so useful because we can now cite other peer reviewed work that's in the public domain that we can say, hey, we need to update this, based on this, or possibly do some, or an idea, which I believe that Professor Daniel Sheen shared with me.

He said, well, if we know, if we've got figures for the top million websites, the top thousand websites, maybe you could actually just experimentally validate those versus what you have in the, in a model already. So you can get better numbers for this. There's a bunch of steps. Yeah, exactly. If you were to measure the top thousand ones compared to the model figures, then that will give you an idea of the gap between the model figure and the ground truth, so you can end up with a slightly better, a better figure.

There's a bunch of things that you could do out there, which would, might make it easier to make these, this tooling much, much easier to use and much more likely to give people the signals they are craving to kind of build websites in a more kind of climate compatible fashion, for example.

Asim Hussain: And I think it's important because I think people like when you use a, when you use a tool and it gives you a, it gives you a value, it's incentivizing a behavior. And it might be incentivizing the wrong behavior. And it's, and I think that's one of the things I find that when people get excited about a measurement, I don't, because I'm, I need to know the details behind it.

'Cause I know that if you're a little bit wrong, you're incentivizing the wrong thing. And you shouldn't just, you shouldn't just take it face value. But it's really hard. I also, in the sense it's really bloody hard even for the tool makers to even figure out what to do here.

So this isn't really a, you know, but it's not really criticism of anybody. But, yeah, it's just really hard to figure this stuff out. But the Firefox stuff is using yours isn't, it's using CO2.js, isn't it?

Chris Adams: I'm not sure if this actually uses the carbon figures we use 'cause we're just, we basically package up the numbers from Ember, which is a non-profit think tank who already published stuff. I can't remember if this one is actually use using the energy or the carbon figures basically.

But we update the carbon figures, every month anyway. So it may, it might be our, I'll need to kind of check if they measure in terms, if they, I think they report this in energy, not carbon actually. It's what they used inside this. 

Actually, I'll need to reread and we're coming up to time actually.

Asim Hussain: Here we come time, so this, but also I think maybe just call out a little bit. So we are gonna be running the, and you are leading it, the SCI for Web assembly shortly in the foundation. And I think this is, this can be a very, this looks, my brief scan of it, like a very important pre-read, I presume for a lot of the people who are gonna be attending that assembly.

Chris Adams: Yeah, I'm actually really pleased this came out. That was initially what I saw, oh great, this is a really nice, concise piece that covers this. This was another piece from Daniel Sheen talking about, okay, well how do you measure network figures, for example? 'cause he's put some really, good interesting stuff inside that we don't have enough time to talk about, but it's a really, but we'll share links to that inside that because yes, this is something that we'll be doing and I'm looking forward to doing it.

And oh, I've just realized we've gone way over.

Asim Hussain: We're well over. You've gotta go, on. Let's just, let's wrap

Chris Adams: Dude, really lovely catching up with you again. Oh, the final thing I need to give is this, just quickly talking about this GSM, the Green Software Movement thing that you were talking about here. Maybe I can just give you space to do that before we cl before we wrap up.

'Cause I know this is the project you're working on at the moment.

Asim Hussain: Yeah. So the movement is a platform that we've created, so it's movement.greensoftware.foundation. So this is where we, will be putting a lot more of our tension moving forward in terms of engaging with the broader community. It's also where all of our training is going to be.

So our current training is moving over there, and we just now have a, now that we've got like a real platform to publish training to. We're gonna get training for all of our products and services, so for SCI, Impact Framework, SOFT, RTC. We're gonna do training for all of them and have them available on the platform.

And you'll be able to go in, you'll be able to learn about the products that we've created, learn about the foundation, get certified for your training. But also it's a platform where you can connect with other people as well. So you can meet people, have chats, have conversations, connect with people who are local to you.

We've had over 130,000 people take our previous training, which unfortunately is on a previous, another platform. So we're gonna be trying to move everybody over. So hopefully our goal is ultimately for this to be the platform where you go, at least from terms of the Green Software Foundation to learn about our products, our standards get involved would be, our champions programs moving over there as well.

And we're just kind of like having, this will be where we put a lot of our effort moving forward, and I recommend people go to it, join, sign up, take the training, and connect with others.

Chris Adams: Alright. Okay. Well, Asim, lovely catching up with you. And I hope you have a lovely rest of the week. And I guess I'll see you in the Slacks or the Zulips or whichever online tools we use to across paths.

Asim Hussain: Zulips. I don't know what that is. Yeah. Sounds good. right, mate.

Chris Adams: our open source chat tool inside the Green Web Foundation. It runs on Django and it's wonderful.

Yeah, it's really good. I cannot recommend it enough. If you are using Slack and you are sick of using Slack, then use Zulips. Zulips is wonderful. Yeah. It's really, good.

Asim Hussain: I can check it out. Yeah. All right.

Chris Adams: Take man. See you Bye.

Asim Hussain: Bye. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 

 



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]]> Host Chris Adams is joined by Asim Hussain to explore the latest news from The Week in Green Software. They look at Hugging Face’s AI energy tools, Mistral’s lifecycle analysis, and the push for better data disclosure in the pursuit for AI sustainability. They discuss how prompt design, context windows, and model choice impact emissions, as well as the role of emerging standards like the Software Carbon Intensity for AI, and new research on website energy use. 

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TRANSCRIPT BELOW:

Asim Hussain: ChatGPT, they're all like working towards a space of how do we build a tool where people can literally pour junk into it, and it will figure something out. 

Whereas what we should be doing, is how do you use that context window very carefully. And it is like programming. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to this week in Green Software where we look at the latest news in sustainable software development. I am joined once again by my friend and partner in crime or occasionally crimes, Asim Hussain, of the Green Software Foundation. My name is Chris Adams. I am the Director of Policy and Technology at the Green Web Foundation, no longer the executive director there,

and, as we've moved to a co-leadership model. And, Asim, really lovely to see you again, and I believe this is the first time we've been on a video podcast together, right?

Asim Hussain: Yeah. I have to put clothes on now, so, so that's,

Chris Adams: That raises all kinds of questions to how intimate our podcast discussions were before. Maybe they had a different meaning to you than they did to me, actually.

Asim Hussain: Maybe you didn't know I was naked, but anyway.

Chris Adams: No, and that makes it fine. That's what, that's what matters. I also have to say, this is the first time we get to, I like the kind of rocking the Galactus style headphones that you've got on here.

Asim Hussain: These are my, yeah, no, these are old ones that I posted recently. I actually repaired them. I got my soldering iron and I repaired the jack at the end there. So, I'm very proud of myself for having repaired. I had the right to repair. Chris. I had the right to repair it.

Chris Adams: Yeah. This is why policy matters.

Asim Hussain: I also have the capability.

Chris Adams: Good. So you can get, so, good on you for saving a bunch of embodied carbon and, how that's calculated is something we might touch on. So, yes. So if you are new to this podcast, my friends, we're just gonna be reviewing some of the news and stories that are kinda showed up on our respective radars as we work in our kind of corresponding roles in both the Green Software Foundation and the Green Web Foundation.

And hopefully this will be somewhat interesting or at least diverting to people as they wash their dishes whilst listening to us. So that's the plan. Asim, should I give you a chance to just briefly introduce what you do at the Green Software Foundation before I go into this?

'Cause I realized, I've just assumed that everyone knows who you are. And I know who you are, but maybe there's people who are listening for the first time, for example.

Asim Hussain: Oh yeah. So, yeah. So my name's Asim Hussain. I am a technologist by trade. I've been building software for several decades now. I formed the green software, yeah, Green Software Foundation, you know, four years ago. And, now I'm the executive director and I'm basically in charge of, yeah, just running the foundation and making sure we deliver against our vision of a future where software has zero harmful environmental impacts.

Chris Adams: That's a noble goal to be working for. And Asim, I wanted to check. How long is it now? Is it three years or four years? 'Cause we've been doing this a while.

Asim Hussain: We, yeah. So we just fin, well, four years was May, so yeah, four years. So next birthday's the fifth birthday.

Chris Adams: Wow. Time flies when 

the world is burning, I suppose. 

Alright, so anyway, as per usual, what we'll do, we share all the show notes and any links that we discuss or projects we discuss, we'll do our damnedest to make sure that they're available for anyone who wants to continue their quest and learning more about sustainability in the field of software.

And I suppose, Asim, it looks like you're sitting comfortably now. Should we start looking at some of the news stories?

Asim Hussain: Let's go for it.

Chris Adams: Alright. Okay. The first one we have, is a story from Hugging Face. This is actually a few months back, but it's one to be aware of if it missed you the first time. So, Hugging Face released a new tool called Chat UI Energy that essentially lets you see, the energy impact live from using a kind of chat session,

a bit like ChatGPT or something like that. Asim, I think we both had a chance to play around with this, and we'll share a link to the actual story around this as well as the actual repo that's online. What do you think of this? what's your immediate take when you see this and have a little poke around with this? 

Asim Hussain: Well, it's good. I wanna make sure. It's a really nice addition to a chat interface. So just so the audience who's not seeing it, every time you do a prompt, it tells you the energy in, well, in watt hours, what I'm seeing right now. But then also, you know, some other stats as well.

And then also kind of how much of a phone charge it is. And that's probably the most surprising one. I just did a prompt, which was 5.7% of a phone charge, which was, that's pretty significant. Actually, I dunno, is that significant? So, one of the things is, I'm trying to, what I'm trying to find out from it though is how does that calculation, 'cause that's my world, it's like, how does, what do you really mean by a calculation?

Is it cumulative? Is it session based? Is it just, you know, how, what have you calculated in terms of the energy emissions? The little info on the side is just the energy of the GPU during inference. So it's not the energy of kind of anything else in the entire user journey of me using a UI to ask a prompt.

But we also know that's probably the most significant. And I'm kind of quite interested in figuring out, as I'm prompting it, I'm one, I'm, one of the things I'm seeing is that every single prompt is actually, the emissions are bigger than the previous prompt. Oh no, it's not actually, that's not true.

Yeah, it is.

Chris Adams: Ah, this is the thing you've been mentioning about cumulative, 

Asim Hussain: Cumulative. Yeah. Which is a confusing one. 'Cause I've had a lot of people who are really very good AI engineers go, "Asim, no, that's not true." And other people going, "yeah, it kind of is true." But they've just optimized it to the point where the point at which you get hit with that is at a much larger number.

But the idea is that there's, there, it used to be an n squared issue for your prompt and your prompt session history. So every time you put a new prompt in all of your past session history was sent with your next prompt. And if you are actually building, like a your own chat system, if you are actually building like your own chat solution for your company or wherever, that is typically how you would implement it as a very toy solution to begin with is just, you know, take all the texts that was previous and the new text and send it, in the next session.

But I think what, they were explaining to me, which was actually in the more advanced solutions, you know, the ones from Claude or ChatGPT, there's a lot of optimization that happens behind the scenes. So it doesn't really, it doesn't really happen that way, but I was trying to figure out whether it happens with this interface and I haven't quite figured it out yet.

Chris Adams: Oh, okay. So I think what you might be referring to is the fact that when you have like a GPU card or something like that, there's like new tokens and kind of cashed tokens, which are priced somewhat differently now. And this is one of the things that we've seen.

'Cause it's using maybe a slightly different kind of memory, which might be slightly faster or is slightly kind of is slightly lower cost to service in that sense. Yeah. Okay. So this is one thing that we don't see. What I, the good news is we can share a link to this, for anyone listening, this source code is all on GitHub, so we can have a look at some of this.

And one of the key things you'll see actually is, well this is sending a message. When you see the actual numbers update, the, it's not actually, what it's actually doing is it's calculating all this stuff client site based on how big each model is likely to be. 'Cause when you look at this, you can A,

Asim Hussain: It's a model.

Chris Adams: You can work out the, I mean, so when people talk about should I be using the word please or thank you, and am I making the things worse by treating this like a human or should I just be prompting the machine like a machine, is there a carbon footprint to that? This will display some numbers that you can see there, but this has all been calculated inside your browser rather than actually on the server.

So like you said, Asim, there is a bit of a model that's taking place here, but as a kind of way to like mess around and kind of have a way into this. This is quite interesting and even now it's kind of telling that there are so few providers that make any of this available, right now. We're still struggling even in like the third quarter of 2025,

to have a commercial service that will expose these numbers to you in a way that you can actually meaningfully change the environmental footprint of through either your prompting behavior or well maybe model choice. But that's one of the key things that I see. I can't think, I can't think of any large commercial service that's doing this.

The only one is possibly GreenPT,

which is basically put a front end on Scaleway's, inference service and I'm not sure how much is being exposed there for them to make some assumptions as well.

Asim Hussain: Do you know how bad, do you know how,

I feel very uncomfortable with the idea of a future where a whole bunch of people are not saying please or thank you, and the reason for it is they're proudly saying, "well, I care about, I care about sustainability, so I'm not gonna say please or thank you anymore 'cause it's costing too many, too much carbon." I find that very uncomfortable. I personally, I don't wanna, we could, choose not to say please or thank you in all of our communications because it causes, emissions no matter what you do. I don't know.

Chris Adams: I'm glad you weren't there, Asim. 'Cause I was thinking about that too. There's a carbon cost to breathing out and if, you, I guess maybe that's 'cause we're both English and it's kinda hardwired into us. It's like the same way that, you know, if you were to step on my toe, I would apologize to you stepping on my toe because I'm just English and I, and it's a muscle memory, kind of like impulsing.

Okay.

Asim Hussain: Yeah.

Chris Adams: That's, what we found. We will share some couple, a couple of links to both the news article, the project on Hugging Face, and I believe it's also on GitHub, so we can like, check this out and possibly make a PR to account for the different kinds of caching that we just discussed to see if that does actually make a meaningful difference on this.

For other people who are just looking, curious about this, this is one of the tools which also allows you to look at a, basically not only through weird etiquette, how etiquette can of impact the carbon footprint of using a tool, but also your choice of model. So some models might be, say 10 times the size of something, but if they're 10, if they're not 10 times as good, then there's an open question about whether it's really worth using them, for example.

And I guess that might be a nice segue to the next story that we touch on. But Asim, I'll let you, you gotta say something. I

Asim Hussain: No, I was gonna say, because I, this is, 'cause I've been diving into this like a lot recently, which is, you know, how do you efficiently use AI? Because I think a lot of the, a lot of the content that's out there about, you know, oh, AI's emissions and what to do to reduce AI's emissions, there are all the choices that as a consumer of AI, you have absolutely no ability to affect. I mean, unless you are somebody who's quite comfortable, you know, taking an open source model and rolling out your own infrastructure or this or that or the other. If you're just like an everyday, not even an everyday person, but just somebody who works in a company who's, you know, the company bought Claude, you know, you're using Claude,

end of story, what are you, like, what do you do? And I think that's really, it is a really interesting area. I might just derail our whole conversation to talk about this, but I think it's a really interesting area because, what it's really boiling down to is your use of the context window.

And so you have a certain number of tokens in a chat before that chat implodes, and you can't use that chat anymore. And historically, those number of tokens were quite low. Relative to, because of all the caching stuff hadn't been invented yet and this and that and the other. So the tokens were quite low.

What, didn't mean they didn't mean they were, the prompts were cheaper before. I think they were still causing a lot of emissions. But because they've improved the efficiency and rather than just said, I've improved the efficiency, leave it at that, I've improved the efficiency, Jevons paradox, I've improved the efficiency,

let's just give people more tokens to play around with before we lock them out. 

So the game that we're always playing is how to actually efficiently use that context. And the please or thank you question is actually, see this is, I don't think it's that good one. 'Cause it's two tokens in a context window of a million now, is what's coming down the pipeline.

The whole game. And I think this is where we're coming from as you know, if you wanna be in the green software space and actually have something positive to say about how to actually have a relationship with AI, it's all about managing that context. 'Cause the way context works is you're just trying to, it's like you've got this intern and if you flash a document at this intern, you can't then say, "oh, ignore that.

Forget it I didn't mean to show you that." It's too late. They've got it and it's in their memory and you can't get rid of it. the only solution is to literally execute that intern and bury their body and get a new intern and then make sure they see the information in the order and only the information they need to see so that when you finally ask 'em that question, they give you the right answer. And so what a lot of people do is they just, because there's a very limited understanding of how to play, how to understand, how to play with this context space, what people end up doing is they're just going, "listen, here's my entire fricking document. It's actually 50,000 words long. You've got it, and now I'm gonna ask you, you know, what did I do last Thursday?"

So it's, and all of that context is wasted. And I think that's, and it's also like a very simplistic way of using an AI, which is why like a lot of companies are, kind of moving towards that space because they know that it means their end user doesn't have to be very well versed in the use of the tool in order to get benefit out of it.

So that's why ChatGPT, they're all like working towards a space of how do we build a tool where people can literally pour junk into it, and It will figure something out.

 Whereas what we should be doing and what I'm like, and I think it's not only what we should be doing, it's, what the people who are like really looking at how to actually get real benefit from AI,

is how do you use that context window very carefully. And it is like programming. It is really like program. That's what, that's my experience with it so far. It's like, I want this, I need to feed this AI information. It's gonna get fed in an order that matters. It's gonna get fed in a format that matters.

I need to make sure that the context I'm giving it is exactly right and minimal. Minimal for the question that I wanna answer, get it answered at the end of it. So we're kind of in this like space of abundance where, because every AI provider's like, "well do what you want. Here's a million tokens.

Do what you want, do what you want."

And they're all, we're all just chucking money. These we're just chucking all our context tokens at it. They're burning money on the other side because they're not about making a profit at the moment. They're just about becoming the winner. So they don't really care about kind of profitability to that level.

So what us It's all about, I'm just getting back to it again. I think, we need to eventually be telling that story of like, how do you actually use the context window very carefully? And again, it's annoyed me that the conversation has landed at please and thank you. 'Cause the actual conversation should be, you know, turning that Excel file into a CSV because it knows how to parse a CSV and it uses fewer tokens to parse a CSV than an Excel file. Don't dump the whole Excel file, export the sheet that you need in order for it to, answer that question. If you f up, don't just kill the session and start a new session.

This is, there's this advice that we need to be giving that I don't even know yet.

Chris Adams: MVP. Minimal viable prompt.

Asim Hussain: Minimal viable prompt! Yeah. What is the minimal viable prompt and the, what's frustrating me is that like one of the things that we use Claude and I use Claude a lot, and Claude's got a very limited context window and I love that.

It was like Twitter when you had to, remember Twitter when you had to like have 160 characters?

It was beautiful.

Chris Adams: to 280, and then you're prepared to be on that website, you can be as, you can monologue as much as you want

Asim Hussain: Yeah. You can now monologue, but it was beautiful having to express an idea in this short, like short, I love that whole, how do I express this complex thing in a tweet? And so with the short context windows, were kind of forced to do that, and now I'm really scared because now everybody, Claude literally two days ago has now gone, right, you've got a million context window, and I'm like, oh, damn it.

Now I don't even, now I don't have personally

Chris Adams: That's a million token context window when you say that. Right. So that's enough for a small book basically. I can dump entire book into it, then ask questions about it. Okay. Well, I guess it depends on the size of your book really, but yeah, so that's, what you're referring to when you talk about a million context window there.

Asim Hussain: Yeah, yeah. And it's kind of an energy question, but the energy doesn't really, kind of, knowing how much, like I've just looked at chat UI window and I've checked a couple of prompts and it's told me the energy, and it's kinda that same world.

It's just it's just there to make me feel guilty, whereas the actual advice you should be getting is well, actually no, I, what do I do? How am I supposed to prompt this thing to actually make it consume less energy? And that's the,

Chris Adams: Oh, I see. So this is basically, so this is, you're showing me the thing and now you're making me feel bad. And this may be why various providers have hosted chat tools who want people to use them more, don't automatically ship the features that make people feel bad without giving 'em a thing they can actually do to improve that experience.

And it may be that it's harder to share some of the guidance like you've just shared about making minimum viable prompt or kind of clear prompt. I mean, to be honest, in defence of Anthropic, they do actually have some pretty good guidance now, but I'm not aware of any of it that actually talks about in terms of here's how to do it for the lowest amount of potential tokens, for example.

Asim Hussain: No, I don't see them. I don't see them. I mean, they, yeah, they do have like stuff, which is how to optimize your context window, but at the same time, they're living in this world where everybody's now 

working to a bigger, that's what they have to do.

And I don't know, it's kinda like, where do we, because we, 'cause the AI advice we would typically have given in the past, or we would typically give is listen, just run your AI in a cleaner region. And you are like, well, I can't bloody do that with Anthropic, can I? It's just, it's whatever it is, it's, you know.

Chris Adams: That's a soluble problem though. Like,

Asim Hussain: Like what I'm just saying or,

Chris Adams: Yeah. You know, but like the idea they're saying, "Hey, I want to use the service. And I want to have some control over where this is actually served from."

That is a thing that you can plausibly do. And that's maybe a thing that's not exposed by end users, but that is something that is doable.

And, I mean, we can touch on, we actually did speak about, we've got Mistral's LCA reporting as one of the things, where they do offer some kind of control, not directly, but basically by saying, "well, because we run our stuff in France, we're already using a low carbon grid."

So it's almost like by default you're choosing this rather than you explicitly opting in to have like the kind of greener one by, the greener one through an active choice,

I suppose.

Asim Hussain: They're building some data centers over there as well, aren't they? So it's a big, it's a big advantage for Mistral to be in France, to be honest with you. It's yeah, they're in

Chris Adams: this definitely does help, there's, I mean, okay. Well, we had this on our list, actually, so maybe this is something we can talk about for our next story, because another one on our list since we last spoke was actually a blog post from Mistral.ai talking about, they refer to, in a rather grandiose terms, our contribution to a global environmental standard for AI.

And this is them sharing for the first time something like a lifecycle analysis data about using their models. And, it's actually one that has, it's not just them who've been sharing this. They actually did work with a number of organizations, both France's agency, ADM. They were following a methodology specifically set out by AFNOR, which is a little bit like one of the French kind of, environmental agency, the frugal AI methodology.

And they've also, they were working with I think, two organizations. I think it's Sopra Steria, and I forget the name of the other one who was mentioned here, but it's not just like a kind of throwaway quote from say Sam Altman. It's actually, yeah, here we are is working with Hubblo, which is a nonprofit consultancy based in Paris and Resilio who are a Swiss organization, who are actually also quite, who are quite very well respected and peer reviewed inside this.

So you had something, some things to share about this one as well. 'Cause I, this felt like it was a real step forward from commercial operators, but still falling somewhat short of where we kind of need to be. So, Asim, what, when you read this, what were the first things that occurred to you, I suppose, were there any real takeaways for you?

Asim Hussain: Well, I'd heard about this, on the grapevine, last year because I think, one of the researchers from Resilio was at greenIO, yeah, in Singapore. And I was there and he gave a little a sneak. They didn't say who it was gonna be, they didn't say it was Mistral, but they said, we are working on one.

And he had like enough to tease some of the aspects of it. I suspect once it's got released, some of the actual detail work has not, that's what I'm, I think I'm, unless I, unless there's a paper I'm missing. But yeah, there is kind of more work I think here that didn't end up to actually get released once it's, once it got announced, but there was, it was a large piece of work.

It's good. It's the first AI company in the world of this, you know, size that has done any work in this space and released it. Other than like a flippant comment from Sam Altman, "I heard some people seem to care about the emission, energy consumption of AI." So, so that's good. And I think we're gonna use this, it's gonna be used in as a, as I'd say, a proxy or an analog for kind of many other, situations.

I think it's, it is lacking a little bit in the detail. But that's okay. I think we, every single company that starts, we should celebrate every organization that leads forward with some of this stuff. it's always very, when you're inside these organizations, It's always a very hard headwind to push against.

'Cause there's a lot of negative reasons to release stuff like this, especially when you're in a very competitive space like AI. So they took the lead, we just celebrate that. I think we're going to, there's some data here that we can use as models for other, as, you know, when we now want to look at what are the emissions of Anthropic or OpenAI or Gemini or something like that,

there's some more, you know, analogs that we can use. But also not a huge amount of surprise, I'd say, it's kind of a training and inference,

Chris Adams: Yep.

That turns be where the environmental footprint is.

Asim Hussain: Yeah. Training and inference, which is kind of, which is good. I mean, I think obviously hardware and embodied impacts is, they kind of separate kind of the two together.

I suspect, the data center construction is probably gonna be, I don't know

 that is quite low. Yeah, yeah,

Chris Adams: I looked at this, I mean this is, it's been very difficult to actually find any kind of meaningful numbers to see what share this might actually make. 'Cause as the energy gets cleaner, it's likely that this will be a larger share of emissions. But one thing that was surprising here was like, this is, you know, France, which is a relatively cr clean grid, like maybe between 40 and say 60 grams of CO2 per kilowatt hour, which is, that's 10 times better than the global average, right?

Or maybe 9, between 8 and 10 times cleaner than the global average. And even then it's, so with the industry being that clean, you would expect the embodied emissions from like data centers and stuff to represent a larger one. But the kind of high level, kind of pretty looking graphic that we see here shows that in, it's less than 2% across all these different kind of impact criteria like carbon emissions or water consumption or materials, for example.

This is one thing that, I was expecting it to be to be larger, to be honest. The other thing that I noticed when I looked at this is that, dude, there's no energy numbers. 

Asim Hussain: Oh, yeah. 

Chris Adams: Yeah. And this is the thing that it feels like a, this is the thing that everyone's continually asking for.

Asim Hussain: It's an LCA. So they use the LCAs specification, so

Chris Adams: That's, a very good point. You're right. that is, that's a valid response, I suppose. 'Cause energy by itself doesn't have a, doesn't have a carbon footprint, but the results of generating that energy does, electricity does have that impact. So yeah.

Okay. Maybe that's 

For 

Asim Hussain: the audience, they use like a well known, well respected, standardized way of reporting the lifecycle emissions using the LCA lifecycle analysis methodology, which is like an ISO certified standard of doing it. So they adhere to a standard.

Chris Adams: So this actually made me realize, if this is basically here and you are a customer of a AI provider, 'cause we were looking at this ourselves trying to figure out, okay, well what people speak to us about a AI policies? And we realized well, we should probably, you know, what would you want to have inside one?

The fact that you have a provider here who's actually done this work, does suggest that for that it's possible to actually request this information if you're a customer under NDAs. In the same way that with, if you're speaking to Amazon or probably any of the large providers, if you're spending enough money with them, you can have information that is disclosed to you directly under NDA.

So it may not be great for the world to see, but if you are an organization and you are using, say, Mistral, for example, or Mistral services, this would make me think that they're probably more able to provide much more detailed information so that you can at least make some informed decisions in a way that you might not be able to get from some of the other competing providers.

So maybe that's one thing that we actually do see that is a kind of. Not really a published benefit in this sense, but it's something that you're able to do if you are in a decision making position yourself and you're looking to choose a particular provider, for example.

Asim Hussain: I mean, you should always be picking the providers who've actually got some, you know,

Chris Adams: optimize for disclosure,

Asim Hussain: optimize for disclosure. Yeah. Always be picking the providers if you optimize for disclosure. I mean, if we, the people listening to this, that is the thing that you can do. And Mistral, They're also, they have some arguments in here as well, which is kind of, they did kind of also surface that it is like a pretty linear relationship between your emissions and the size of the model, which is a very useful piece of information for us to know, as a consumer.

Because then we can go, well actually I've heard all these stories about use Smaller models use smaller models and now you actually have some data behind it, which is supporting the fact that, yeah, using a smaller model isn't, it's not got some weird non-linearity to it, so a half size model is only like 10% less, emissions.

A half size model is half the emissions. So that's pretty, that's a pretty good thing to know. Helps Mistral, the fact that they have a lot of small models that you can pick and choose, is not, so a lot of this stuff really benefits Mistral. They are the kind of the kind of organization which has a product offering which is benefited, which does benefit a sustainability community.

So they have like small models you can use. I think, I wonder actually, Chris, 'cause they do say that they're building their own data center in France, but they've never said where there exists, where they until now, where they've been running their AI. So that might be the reason for, they might have been running it in East Coast US or something

like 

Chris Adams: I think that would be quite unlike, wouldn't be very likely, given that most of their provider, most of their customers are based in probably Western Europe still. Right. There is very much a kinda like Gaelic kind of flavor to the tooling. And I've, I mean actually Mistral, or Mistral's tools are ones which I've been using myself personally over the last, like few months, for example.

And it's also worth bearing in mind that they, took on a significant amount of investment from Microsoft a few years back and I would be very surprised if they weren't, or if they weren't using a French data center serving French providers. 'Cause if you were to choose between two countries, okay, if, France or like France actually has, and since 2021, I believe, has had actually a law specifically about measuring the environmental footprint of digital services.

So they've got things that they, I think it's called, I'm going to, I'm just gonna share a link to that, to the name of the law because I'm gonna butcher the French pronunciation, but it basically, it translates to Reduce the Environmental Footprint of Digital Services Law.

That's pretty much it. And that's where, as a follow on from that, that's what, that's what the RGESN, the kind of general guidance that it shares across kind of government websites in general for France. They've already got a bunch of this stuff out there for like how to do greener IT. I suspect that France is probably gonna be one of, well, probably the premier country, if you'd run, be running a startup to see something like this happening much more so than, well probably the US right now, especially given the current kind of push with its current kind of federal approach, which is basically calling into doubt climate change in the wider sense basically.

We were talking about disclosure, right? And we said an optimization for disclosure. And that's probably a nice segue to talk about, another link we had here, which was the energy score leaderboard. Because this is one thing that we frequently point to. And this is one thing that we've suggested in my line of work, that if you are looking to find some particular models, one of the places to look would be the AI Energy Score Leaderboard, which is actually maintained by Hugging Face.

And, I share this 'cause it's one of the few places where you can say, I'm looking for a model to help me maybe do something like image generation or captioning text or generating text or doing various things like this. And you can get an idea of how much power these use on a standardized setup.

Plus, how satisfied, you know, what the kind of satisfaction score might be, based on these tools and based on a kind of standardized set of like tests, I suppose. The thing is though, this looks like it hasn't been updated since February. So for a while I was thinking, oh, Jesus, does this mean we actually need to, do we have to be careful about who we, how we recommend this?

But it turns out that there's a new release that will be coming out in September. It's updated every six months. And, now that I do have to know about AI, this is one thing that I'm looking forward to 

seeing some of the releases on because if you look at the leaderboard for various slices, you'll see things like Microsoft Phi 1 or Google Gemma 2 or something like that.

Asim Hussain: That quite old?

Chris Adams: yeah, these are old now, it's six months in generative AI land is quite a long time. There's Phi 4 now, for example, and there's a bunch of these out there. So I do hope that we'll see this actually. And if you feel the same way, then 

yeah, go on.

Asim Hussain: Is it, 'cause, is I always assume this was like a, live leaderboard. So as soon as a model, I suppose once a model, like the emissions of a model are linked to the model and the version of it. So once you've computed that and put on the leaderboard, it's not gonna change. So then it's just the case of as new models come out, you just measure and it just sees how it goes on the leaderboard.

Because I'm seeing something here. I'm, I thought open, I'm seeing OpenAI, GPT. Isn't that the one they just released?

Chris Adams: No, you're thinking GPT-OSS, perhaps

Asim Hussain: Oh.

Chris Adams: One thing they had from a while ago. So that one, for example, came out less than two weeks ago, I believe. That isn't showing up here.

Asim Hussain: That isn't showing up

Chris Adams: The, I'm, I was actually looking at this thinking, oh, hang on, it's six months, something being updated, six months,

that's, it'd be nice if there was a way, a faster way to expedite kind of getting things disclosed to this. For example, let's say I'm working in a company and I've, someone's written in a policy that says only choose models that disclose in the public somewhere. This is one of the logical places where you might be looking for this stuff right now, for example, and there's a six month lag, and I can totally see a bunch of people saying, no, I don't wanna do that.

But right now there's a six month kind of update process for this.

Asim Hussain: In the AI realm is an eternity. Yeah.

Chris Adams: Yeah. But at the same time, this is, it feels like a thing that this is a thing that should be funded, right? I mean, it's, it feels :I wish there was a mechanism by which organizations that do want to list the things, how to make them to kind of pay for something like that so they can actually get this updated so that you've actually got some kind of meaningful, centralized way to see this.

Because whether we like it or not, people are basically rolling this stuff out, whether we like it or not, and I feel In the absence of any kind of meaningful information or very patchy disclosure, you do need something. And like this is one of the best resources I've seen so far, but it would be nice to have it updated.

So this is why I'm looking forward to seeing what happens in September. And if you think, if you too realize that like models and timely access to information models might be useful, it's worth getting in touch with these folks here because, I asked 'em about this when I was trying to see when they were, what the update cycle was.

And basically the thing they said was like, yeah, we're, really open to people speaking to us to figure out a way to actually create a faster funded mechanism for actually getting things listed so that you can have this stuff visible. Because as I'm aware, as I understand it, this is a labor of love by various people, you know, between their day jobs, basically.

So it's not like they've got two or three FTE all day long working on this, but it's something that is used by hundreds of people. It's the same kind of open source problem that we see again and again. But this is like one of the pivotal data sources that you could probably cite in the public domain right now.

So this is something that would be really nice to actually have resolved.

Asim Hussain: Because there is actually, 'cause the way Hugging Face works is, they have a lab and they have their own infrastructure. Is that how it works? Yeah. So that's

Chris Adams: this would, that was be, that was either, that was physically theirs, or it was just some space. 

Asim Hussain: Spin up. But yeah. But yeah, but they have to effectively like to get the score here. It's not self certified, I presume, but there's a, you know, each of these things has got to get run against the benchmark. So there's basically, if I remember, there was a way of like self certifying.

There was literally a way for

Chris Adams: You could upload your stuff.

Asim Hussain: Yeah. OpenAI could disclose to the Hugging Face to the, what the emissions of, you know, what the energy of it was. But most of it is, there's actually, you gotta run against the H100 and there's a benchmark

Chris Adams: Yep, exactly. So there's a bit of manual. There's a bit of manual steps to do that, and this is precisely the thing that you'd expect that really, it's not like an insoluble problem to have some way to actually expedite this so that people across the industry have some mechanism to do this. 'cause right now it's really hard to make informed decisions about either model choice or anything like that.

Even if you were to architect a more responsibly designed system, particularly in terms of environmental impact here.

Asim Hussain: Because if you were to release a new model and you wanted it listed in the leaderboard, you would have to run every other model against. Why would you need to do that? You need to

Chris Adams: You wouldn't need to do that. You just need to, you, because you don't have control over when it's released, you have to wait six months until the people who are working in that get round to doing that.

Asim Hussain: Just the time. It's just a time. Yeah. Someone's

Chris Adams: If you're gonna spend like a millions of dollars on something like this, it feels like this is not, even if you were to drop say, if, even if it was to cost, maybe say a figure in the low thousands to do something like this, just to get that listed and get that visible, that would be worth it.

So that you've actually got like a functioning way for people to actually disclose this information, to inform decisions. 'Cause right now there's, nothing that's easy to find. This is probably the easiest option I've seen so far and we've only just seen like the AI code of practice that's actually kind of been kind of pub that came into effect in August in Europe for example.

But even then, you still don't really have that much in the way of like public ways to filter or look for something based on the particular task you're trying to achieve.

I wanted to ask you actually, Asim, so I think, I can't remember last time if I was speaking to you, if this came up, I know that in your, with your GSF hat on, there's been some work to create a software carbon intensity for AI spec, right. Now, I know that there's a thing where like court cases, you don't wanna kind of prejudice the discussions too much by having things internally.

Although you're probably not, there isn't like AI court, you can be in contempt of, but I mean, yeah, not yet, but, who knows? Give it another six months. Is there anything that, is there anything, any, juicy gossip or anything you can share that people have been learning? 'cause like you folks have been diving into this with a bunch of domain experts so far, and this isn't my, like, while I do some of this, I'm not involved in those discussions.

So I mean, and I'm aware that there has been a bunch of work trying to figure out, okay, how do you standardize around this? What do you measure? You know, do you count tokens? Do you count like a prompt? What's the thing? Is there anything that you can share that you're allowed to talk about before it goes?

Asim Hussain: Yeah. I think, we, I think that what we've landed on is that as long as I'm not discussing stuff which is in, you know, active discussion and it's kind of made its way into the spec and there's been, you know, broad consensus over, I think it's pretty safe to talk about it.

If there's something that's kind of, and what we do, we do everything in GitHub. So if there's something which is like, I won't, I won't discuss anything which has only been discussed in like an issue or a discussion or comment thread or something. If it's actually made its way into the actual spare, that's pretty safe.

So yeah, the way it's really landed is that there's, there was a lot of conversations at the start. There was a lot of conversations and I was very confused. I didn't really know where things were gonna end up with. But you know, at the start there was a lot of conversations around well, how do we deal with training?

How do we deal with training? There's this thing called inference. And it's interesting 'cause when we look at a lot of other specs that have been created, even the way the Mistral LCA was done, so they, they gave a per inference, or per request. I've forgotten what they did. It, they didn't do per token.

So per

Chris Adams: they do per chat session or per task, right. I think it's something along those lines. Yeah.

Asim Hussain: Something along that, it wasn't a per token thing. But even then they, they added the training cost to it. And like those, some of the questions we were adding, can you add, is there a way of adding like the training? The training happened like ages ago. Can you somehow, is there a function that you can use to amortize that training to like future inference runs?

And we explored like lots of conversations. There's like a decay function. So if you were the first person to use a new model, the emissions per token would be higher because you are amortizing more of the training cost and the older models, the, so you explored like a decay function, we explored, yeah.

There's lots of ideas.

Chris Adams: Similar to the embodied usage, essentially like what we have with embodied versus, embodied carbon versus like use time carbon. You're essentially doing the same thing for training, being like the embodied bit and inference being the usage. And if you had training and you had three inferences, each of those inferences is massive.

Like in terms of the car embodied carbon, if there's like a billion, it's gonna much lower per, for each one.

Asim Hussain: But then you get into really weird problems because I mean it, we do that with the embodied carbon hardware, but we do that by saying, do you know what? The lifespans gone be four years and that's it. And we're just gonna pretend it's an equal waiting every single day for four years.

Chris Adams: Not with the GHG protocol. You can't do it with the GHG protocol. You can't amortize it out like that. You can, you have to do it the same year, so it, your emissions look awful one year

Asim Hussain: Ah, the year that you bought it from. 

Chris Adams: So this is actually one of the reasons, but yeah, this is actually one of the problems with the kind of default way of measuring embodied carbon versus other things inside this is, it's not, like Facebook for example, they've proposed another way of measuring it, which does that, this kind of amortization approach, which is quite a bit closer to how you might do, I guess, like typical amortization of capital, capital

Asim Hussain: Cap, yeah.

Chris Adams: So that's the, that's the difference in the models. And this is, these are some of the kind of honestly sometimes tedious details that actually have quite a significant impact. Because if you did have to, that's gonna have totally different incentive incentives. If you, especially at the beginning of something, if you said, well, if you pay the full cost, then you are incentivized not to use this shiny new model.

'Cause it makes you look awful compared to you using an existing one for example.

Asim Hussain: And that's one of the other questions like, is like, how do you, I mean, a lot of these questions were coming up like what do you... A we never, we didn't pick that solution. and we also didn't pick the solution of we had the, we actually had the conversation of you amortize it over a year, and then there's a cliff.

And then that was like, we're gonna incentivize people to use older models with this idea that older models were the thing. 

There were questions that pop up all the time. Like, what do you do when you have an open source model? If you were to, if I was to fine tune an open source model and then make a service based off of that, is the emissions of the model the open source model that I got Llama whatever it was, am I responsible for that?

Or is the,

and there was like, if you were to say, if you were to say no, then you're incentivizing people to just like open source their models and go, "meh well the emissions are free now 'cause I'm using an open source model." So there's lots of these, it's very nuanced. Kind of the, a lot of the conversations we have in the standards space, is like a small decision can actually have a cascading series of unintended consequences.

So the thing that we really like sat down was like, what, well, what actually, what do you want to incentivize? Let's just start there. What do we want to incentivize? Okay, we've listed those things we wanna incentivize. Right. Now, let's design a metric, which through no accident incentivizes those things. And where they ended up was basically two,

there's gonna be two measures. So we didn't, we didn't solve the training one because there isn't a solution to it. It's a different audience cares about the training emissions than that doesn't, consumers, it's not important to you because it doesn't really matter. It doesn't change how you behave with a model.

It doesn't change how you prompt a model just because it had some training emissions in the past. What matters to you most is your direct emissions from your actions you're performing at that given moment in time. So it's likely gonna be like two SCI scores for AI, a consumer and a provider. So the consumer is like inference plus everything else.

and also what is the functional unit? There's a lot of conversations here as well, and that's likely to land that now very basically the same as how you sell an AI model. So if you are an LLM, you're typically selling by token. And so why for us to pick something which isn't token in a world where everybody else is thinking token, token, token, token, it would be a very strange choice and it would make the decision really hard for people when they're evaluating certain models. They'd be like, oh, it's this many dollars per token for this one and this many dollars per token for that one. But it's a carbon per growth. And it's a carbon per growth,

I can't rationalize that. Where, if it's well look, that's $2 per token, but one gram per token of emissions and that's $4 per token, but half a gram per token for emissions. I can evaluate the kind of cost, carbon trade off, like a lot easier. The cognitive load is a lot easier.

Chris Adams: So you're normalizing on the same units, essentially, right?

Asim Hussain: Yeah. As how, however it's sold, however, it's, 'cause that's sort of, it's a fast, AI is also a very fast moving space and we dunno where it's gonna land in six months, but we are pretty sure that people are gonna figure out how to sell it, in a way that makes sense. So lining up the carbon emissions to how it's sold.

And the provider one is going to be, that's gonna include like the training emissions, but also like data and everything else. And that's gonna be probably per version of an AI. And that will, so you can imagine like OpenAI, like ChatGPT would have a consumer score of carbon per token and also a provider score of ChatGPT 5 has, and it's gonna be probably like per flop or something,

so per flop of generating ChatGPT 5, it was this many, this much carbon. And that's really like how it's gonna,

 it's also not gonna be total totals are like, forget about totals. Totals are pointless when it comes to, to change the behavior.

 You really want to have a, there's this thing called neural scaling laws.

The paper.

Chris Adams: Is that the one that you double the size of the model when it's supposed to double the performance? Is that the thing? 

Asim Hussain: It's not double, but yeah, got relationship. Yeah. So there's this logarithmic, perfectly logarithmic relationship between model accuracy and model size, model accuracy, and the data, the number of training you put into it, and model size and the amount of compute you put into, it's all logarithmic.

So it's often used as the reason, the rationale for like why we need to, yeah, larger models is because we can prove it. So, but that basically comes down to like really then, you know, like if like I care more about, but for instance, I don't particularly, it doesn't matter to me how much, it's not that important to know the total training emissions of ChatGPT 5 versus ChatGPT 4.

What's far more useful, is to know, well, what was the carbon per flop of training for 4 versus the carbon per flop of training for 5? 'Cause then that gives you more interesting information. Have you, did you,

Chris Adams: What does that allow?

Asim Hussain: Bother to do anything? Huh?

Chris Adams: Yeah. What does that allow me to do? If I know if 5 is 10 times worse per flop than 4, 

what that incentivize me to do differently? 'Cause I think I might need a bit of hand help here making this call here.

Asim Hussain: Because I think, 'cause it, what, let's say ChatGPT 6 is going to come along. The one thing we know absolutely sure is it's just gonna be in terms of total bigger than ChatGPT 5. So as like a metric, it's not, if you are an engineer, if you are somebody trying to make decisions regarding what do I do to actually train this model with causing less emissions, it doesn't really help me because it's just, a number that goes higher and higher.

Chris Adams: Oh, it's a bit like carbon intensity of a firm versus, absolute emissions. Is that the much, the argument you're using? So it doesn't matter that Amazon's emissions have increased by 20%, the argument is well, at least if they've got more efficient per dollar of revenue, then that's still improvement.

That's the line of reasoning that's using, right?

Asim Hussain: Yeah. So it's, 

because of the way the SCI is, it's not if you want to do a total, there are LCAs, like the thing that Mistral did, there's existing standards that are very well used. They're very well respected. There's a lot of, there's a lot of information about how to do them.

You can just use those mechanisms to calculate a total. What the SCI is all about is what is a, 

KPI that a team can use and they can optimize against, so over time, the product gets more and more efficient? 

Obviously, you should also be calculating your totals and be making a decision based upon both.

But just having a total is, I've gotta be honest with you, it's just, I don't see totals having, in terms of changing behavior, I don't think it changes any behavior. Full stop.

Chris Adams: Okay. I wanna put aside the whole, we live in a physical world with physical limits and everything like that, but I think the argument you're making is essentially that, because the, you need something to at least allow you to course correct on the way to reducing emissions in absolute terms, for example. And your argument you're making is if you at least have an efficiency figure, that's something you can kind of calibrate and change over time in a way that you can't with absolute figures, which might be like having a, you know, a budget between now and 2030, for example.

That's the thinking behind it, right?

Asim Hussain: Yeah. I mean, if you, I've actually got an example here from 'cause we, so we don't have actual compute. They, no, no one's ever disclosed like the actual compute that they used per model. But they have, or they used to disclose the number of parameters per model. And we know that there's a relationship.

So there's a really interesting, so for 2, 3 and 4, we have some idea regarding the training emissions and the parameters, not from a disclosure, from like research as well, so between, but when you compute the emissions per billion parameters of the model, so per billion parameters of the model, GPT two was 33.3 tons of carbon per billion parameters of the model.

Chris Adams: Okay.

Asim Hussain: GPT-3 went down to 6.86 tons of carbon per billion parameters. So it went down from 33 to 6. So that was a good thing. It feels like a good thing, but we know the total emissions of 3 was higher. Interestingly, GPT-4 went up to 20 tons of carbon per billion parameters. So that's like an interesting thing to know.

It's like you did something efficient between two and three. You did something good. Whatever it was, we don't know what it was, we did something good actually the carbon emissions per parameter reduced. Then you did something. Maybe it was bad. Maybe I, some, maybe it was necessary. Maybe it was architectural. But for some reason your emissions,

Chris Adams: You became massively less efficient in the set, in that 

next 

Asim Hussain: In terms of carbon. In terms of carbon, you became a lot less efficient in GPT-4. We have no information about GPT 5. I hope it's less than 20 metric tons per billion parameters.

Chris Adams: I think I'm starting to wanna step, follow your argument and I'm not, I'm not gonna say I agree with it or not, but I, the, I think the argument you're making is essentially by switching from, you know, that that in itself is a useful signal that you can then do something with. there was maybe like a regression or a bug that happened in that one that you can say, well, what change that I need to do so I can actually start working my way towards, I don't know, us careering less forcefully towards oblivion, for example, or something like that.

Right.

Asim Hussain: Yeah.

Chris Adams: Okay. That makes, I think I understand that now. And, let's, and I suppose the question I should ask from following on from that is that this is, some of this is, we're talking about, we got into this, 'cause we were talking about the SCI for AI, this kind of standard or presumably an ISO standard that we published.

Is there a kind of rough like roadmap for when this is gonna be in the public domain, for example, or people might be requesting this in commercial agreements or something like that?

Asim Hussain: I mean, I can tell you what my hope is. So I think, I mean, cause everything is based upon consensus and if anybody objects then everything or all the plans basically, you know, put on the back burner. But everything's looking very positive. I'm very hopeful that by the end of Q3, so the end of September, we will have gone into draft and then, there hasn't been a full agreement yet as to what we'll actually publish for that. But I'm hoping we'll be able to actually publish the whole specification, because what we wanna start doing is get, I mean this maybe if anybody's interested, we wanna start running case studies because right now it's like the outline of what we want the calculation to be is being agreed on.

But we need a lot of use cases of very different types of products that have computed using it. Not just, you know, I'm a major player and I've got a gazillion servers and we also want, need people, there's lots of organizations we're talking to or listen, we've just, we are, AI is not our central business, but we've built like AI solutions internally and we want to be able to measure that.

Or even smaller organizations or people who are not even training in AI, but just consuming APIs then build like an AI solution on top of that. So there's like a whole range of things that we wanna measure and we want to publish, go into draft in September, and then work on a number of case studies. Hopefully, dream,

my dream, and I, no one holds me to this is by kind of Q1, Q2 next year where we're out and we start the ISO process then, but when we come out, we want to come out with here's a specification. It'll come out with a training course that you can take to learn how to compute the specification. It will come out with a tooling.

So you can just plug in values and then you'll be able to get your numbers and also come out with a number of case studies from organizations who, this is how exactly we calculated it, and maybe you can learn from, how we did it. So that's our goal.

Chris Adams: Okay, well that, so we're looking at basically, okay, first half of 2026, so there's still time to be involved and there's, and presumably later on in Q3, Q4, some of this will be going out in public for people to kind of respond to or have this some, something like the consultation there.

Asim Hussain: Yeah, It'd be a public consultation coming up soon.

Chris Adams: This is useful to know because this takes it to our last story we were looking at, which is actually also talking about the challenges related to the working on the environmental footprint of other things, particularly websites.

This is our final link of the podcast, which is a link to, the IEEE, where there's a post by, I believe it's Janne Kalliola. And, oh dear. I'm not gonna pronounce the other person's name very well. Juho Vepsäläinen. Oh dear. I'm so sorry for mispronouncing your names. I'm drawing attention to this 'cause this is the first time In a while I've seen a peer reviewed article in the IEEE specifically, which I think is the.

It's the Instutute of Electrical and Electronics Engineers. I forget what it stands for. Yes, thank you. They looked at both, Firefox Profiler and Website Carbon. They basically started looking at the environmental footprint, what kind of, what does using these website calculators actually tell you and what can you use?

And they had some recommendations about, okay, we've tried using these tools, what can we learn from that? And the thing that was actually particularly interesting was that they were using Firefox's Firefox profiler specifically to look at the footprint of, they're basically saying that there's two, three insights that have probably come away from this, which I thought was interesting.

One of them, it's really hard to get meaningful numbers around data transfer, which I think is actually something that we've shared and we've covered in a lot of detail and I'm finding very helpful for future discussions around creating something like a software, carbon intensity for Web for this.

But the other thing they did was they spoke about the use of, like tools out there, like profilers, which do provide this direct measurement that does give you some meaningful numbers. But when you look at the charts, the differences aren't that high. For example, they were showing comparisons with things like website carbon, which shows massively different, massively different kind of readings for the carbon footprint of one site versus another.

And then when they used other tools like say Firefox Profiler, the differences were somewhat more modest between these two things. So this kind of gives the impression that tool, some of the tools that use like the soft, the sustainable web design model may, they may be overestimating the effectiveness of changes you might be making as an engineer versus what gets measured directly.

Now, there's obviously a elephant in the room and that this isn't measuring what's happening server side, but this is the first time I've seen a really, kind of a real deep dive by, some people who are actually looking into this to come up with some things you can, you can test, I suppose, or you can kind of, you can like, reproduce to see if they get, you're getting the same numbers from these people here.

And, this is actually quite a useful, it's, I found it quite noteworthy and really nice to see and I would've found out about it because, Janne actually shared it inside the Climateaction.tech Slack.

Asim Hussain: So it was a paper inside IEEE or, an article inside that

Chris Adams: It's, a paper. So it's a peer reviewed paper in volume 13 of IEEE and they basically, they talk about the current state of the art, how people currently try to measure energy consumption on the Web. Then they talk about some of the tools you can use for the end user devices. Talk about some of the issues related to trying to go on just data transfer alone and why that isn't necessarily the best thing to be using, but, what kind of statements you could plausibly make.

But as someone who ends up, you know, we, the organization I work for, we implemented the sustainable web design model for this. Having something like this is so, so useful because we can now cite other peer reviewed work that's in the public domain that we can say, hey, we need to update this, based on this, or possibly do some, or an idea, which I believe that Professor Daniel Sheen shared with me.

He said, well, if we know, if we've got figures for the top million websites, the top thousand websites, maybe you could actually just experimentally validate those versus what you have in the, in a model already. So you can get better numbers for this. There's a bunch of steps. Yeah, exactly. If you were to measure the top thousand ones compared to the model figures, then that will give you an idea of the gap between the model figure and the ground truth, so you can end up with a slightly better, a better figure.

There's a bunch of things that you could do out there, which would, might make it easier to make these, this tooling much, much easier to use and much more likely to give people the signals they are craving to kind of build websites in a more kind of climate compatible fashion, for example.

Asim Hussain: And I think it's important because I think people like when you use a, when you use a tool and it gives you a, it gives you a value, it's incentivizing a behavior. And it might be incentivizing the wrong behavior. And it's, and I think that's one of the things I find that when people get excited about a measurement, I don't, because I'm, I need to know the details behind it.

'Cause I know that if you're a little bit wrong, you're incentivizing the wrong thing. And you shouldn't just, you shouldn't just take it face value. But it's really hard. I also, in the sense it's really bloody hard even for the tool makers to even figure out what to do here.

So this isn't really a, you know, but it's not really criticism of anybody. But, yeah, it's just really hard to figure this stuff out. But the Firefox stuff is using yours isn't, it's using CO2.js, isn't it?

Chris Adams: I'm not sure if this actually uses the carbon figures we use 'cause we're just, we basically package up the numbers from Ember, which is a non-profit think tank who already published stuff. I can't remember if this one is actually use using the energy or the carbon figures basically.

But we update the carbon figures, every month anyway. So it may, it might be our, I'll need to kind of check if they measure in terms, if they, I think they report this in energy, not carbon actually. It's what they used inside this. 

Actually, I'll need to reread and we're coming up to time actually.

Asim Hussain: Here we come time, so this, but also I think maybe just call out a little bit. So we are gonna be running the, and you are leading it, the SCI for Web assembly shortly in the foundation. And I think this is, this can be a very, this looks, my brief scan of it, like a very important pre-read, I presume for a lot of the people who are gonna be attending that assembly.

Chris Adams: Yeah, I'm actually really pleased this came out. That was initially what I saw, oh great, this is a really nice, concise piece that covers this. This was another piece from Daniel Sheen talking about, okay, well how do you measure network figures, for example? 'cause he's put some really, good interesting stuff inside that we don't have enough time to talk about, but it's a really, but we'll share links to that inside that because yes, this is something that we'll be doing and I'm looking forward to doing it.

And oh, I've just realized we've gone way over.

Asim Hussain: We're well over. You've gotta go, on. Let's just, let's wrap

Chris Adams: Dude, really lovely catching up with you again. Oh, the final thing I need to give is this, just quickly talking about this GSM, the Green Software Movement thing that you were talking about here. Maybe I can just give you space to do that before we cl before we wrap up.

'Cause I know this is the project you're working on at the moment.

Asim Hussain: Yeah. So the movement is a platform that we've created, so it's movement.greensoftware.foundation. So this is where we, will be putting a lot more of our tension moving forward in terms of engaging with the broader community. It's also where all of our training is going to be.

So our current training is moving over there, and we just now have a, now that we've got like a real platform to publish training to. We're gonna get training for all of our products and services, so for SCI, Impact Framework, SOFT, RTC. We're gonna do training for all of them and have them available on the platform.

And you'll be able to go in, you'll be able to learn about the products that we've created, learn about the foundation, get certified for your training. But also it's a platform where you can connect with other people as well. So you can meet people, have chats, have conversations, connect with people who are local to you.

We've had over 130,000 people take our previous training, which unfortunately is on a previous, another platform. So we're gonna be trying to move everybody over. So hopefully our goal is ultimately for this to be the platform where you go, at least from terms of the Green Software Foundation to learn about our products, our standards get involved would be, our champions programs moving over there as well.

And we're just kind of like having, this will be where we put a lot of our effort moving forward, and I recommend people go to it, join, sign up, take the training, and connect with others.

Chris Adams: Alright. Okay. Well, Asim, lovely catching up with you. And I hope you have a lovely rest of the week. And I guess I'll see you in the Slacks or the Zulips or whichever online tools we use to across paths.

Asim Hussain: Zulips. I don't know what that is. Yeah. Sounds good. right, mate.

Chris Adams: our open source chat tool inside the Green Web Foundation. It runs on Django and it's wonderful.

Yeah, it's really good. I cannot recommend it enough. If you are using Slack and you are sick of using Slack, then use Zulips. Zulips is wonderful. Yeah. It's really, good.

Asim Hussain: I can check it out. Yeah. All right.

Chris Adams: Take man. See you Bye.

Asim Hussain: Bye. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 

 



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]]> LLM Energy Transparency with Scott Chamberlin LLM Energy Transparency with Scott Chamberlin Thu, 07 Aug 2025 07:00:00 GMT 1:00:44 k08m93w1 false https://podcasts.castplus.fm/e/r87y5xm8-llm-energy-transparency-with-scott-chamberlin 68dc7d0e46a2532cdd8d25f7 68dc7cef46a2532cdd8d1c23 In this episode of Environment Variables, host Chris Adams welcomes Scott Chamberlin, co-founder of Neuralwatt and ex-Microsoft Software Engineer, to discuss energy transparency in large language models (LLMs). They explore the challenges of measuring AI emissions, the importance of data center transparency, and projects that work to enable flexible, carbon-aware use of AI. Scott shares insights into the current state of LLM energy reporting, the complexities of benchmarking across vendors, and how collaborative efforts can help create shared metrics to guide responsible AI development. 116 In this episode of Environment Variables, host Chris Adams welcomes Scott Chamberlin, co-founder of Neuralwatt and ex-Microsoft Software Engineer, to discuss energy transparency in large language models (LLMs). They explore the challenges of measuring AI emissions, the importance of data center transparency, and projects that work to enable flexible, carbon-aware use of AI. Scott shares insights into the current state of LLM energy reporting, the complexities of benchmarking across vendors, and how collaborative efforts can help create shared metrics to guide responsible AI development.

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TRANSCRIPT BELOW:

Scott Chamberlin: Every AI factory is going to be power constrained in the future. And so what does compute look like if power is the number one limiting factor that you have to deal with? 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. We talk a lot about transparency on this podcast when talking about green software, because if you want to manage the environmental impact of software, it really helps if you can actually measure it.

And as we've covered on this podcast before, measurement can very quickly become quite the rabbit hole to go down, particularly in new domains such as generative AI. So I'm glad to have our guest, Scott Chamberlain today here to help us navigate as we plum these depths. Why am I glad in particular?

Well, in previous lives, Scott not only built the Microsoft Windows operating system power and carbon tracking tooling, getting deep into the weeds of measuring how devices consume electricity, but he was also key in helping Microsoft Azure work out their own internal carbon accounting standards. He then moved on to working at Intel to work on a few related projects, including work to expose these kinds of numbers in usable form to developers when people when making the chips that go in these servers. His new project Neuralwatt is bringing more transparency and control to AI language models.

And a few weeks back when I was asking on LinkedIn for pointers on how to understand the energy usage from LLMs I use, he shared a link to a very cool demo showing basically the thing I was asking for: real-time energy usage figures from Nvidia cards directly in the interface of a chat tool. The video's in the show notes if you're curious.

And it is really, cool. So Scott, thank you so much for joining us. Is there anything else that I missed that you'd like to add for the intro before we dive into any of this stuff?

Scott Chamberlin: No, that sounds good.

Chris Adams: Cool. Well, Scott, thank you very much once again for joining us. If you are new to this podcast, just a reminder, we'll try and share a link to every single project in the show notes.

So if there are things that are particularly interest, go to podcast.greensoftware.foundation and we'll do our best to make sure that we have links to any papers, projects, or demos like we said. Alright, Scott, I've done a bit of an intro about your background and everything like that, and you're calling me from a kind of pleasingly green room today.

So maybe I should ask you, can I ask where you're calling from today and a little bit about like the place?

Scott Chamberlin: So I live in the mountains just west of Denver, Colorado, in a small town called Evergreen. I moved here in the big reshuffles just after the pandemic, like a lot of people wanted to shift to a slightly different lifestyle. And so yeah, my kids are growing here, going to high school here, and yeah, super enjoy it.

It gives me quick ability to get outside right outside my door.

Chris Adams: Cool. All right. Thank you very much for that. So it's a green software podcast and you're calling from Evergreen as well, in a green room, right? Wow.

Scott Chamberlin: That's right. I have a, I actually have a funny story I want to share from the first time I was on this podcast. It was me and Henry Richardson from Watttime talking about carbon awareness. And I made some focus on how the future, I believe, everything's going to be carbon aware. And I used a specific example of my robot vacuum of like, it's certainly gonna be charging in a carbon aware way at some point in the future.

I shared the podcast with my dad and he listened to it and he comes back to me and says, "Scott, the most carbon reduced vacuum is a broom."

Chris Adams: Well, it, he's not wrong. I mean, it's a, it's manual but it does definitely solve the problem and it's definitely got lower embedded carbon, that's for sure, actually.

Scott Chamberlin: Yeah.

Chris Adams: Cool. So Scott, thank you very much for that. Now, I spoke a little bit about your kind of career working in ginormous trillion dollar or multi-billion dollar tech companies, but you are now working at a startup Neuralwatt, but you mentioned before, like during, in our prep call, you said that actually after leaving a couple of the big corporate jobs, you spent a bit of time working on like, building your own version of like what a cloud it might be.

And I, we kind of ended up calling it like, what I called it Scott Cloud, like the most carbon aware, battery backed up, like really, kind of green software, cloud possible and like pretty much applying everything you learned in your various roles when you were basically paid to become an expert in this.

Can you talk a little bit about, okay, first of all, if it's, if I should be calling it something other than Scott Cloud and like are there any particular takeaways you did from that? Because that's had like quite an interesting project and that's probably what I think half of the people who listened to this podcast, if they had essentially a bunch of time to build this, they'd probably build something similar.

So yeah. Talk. I mean, why did you build that and, yeah, what are the, were there any things you learned that you'd like to share from there?

Scott Chamberlin: Sure. So, I think it's important to know that I had spent basically every year from about 2019 through about 2022, trying to work to add features to existing systems to make them more, have less environmental impact, lower CO2, both embodied as well as runtime carbon.

And I think it's, I came to realize that adding these systems on to existing systems is always going to come with a significant amount of compromises or significant amount of challenges because, I mean, I think it's just a core principle of carbon awareness is that there is going to be some trade off with how the system was already designed.

And a lot of times it's fairly challenging to navigate those trade offs. I tend to approach them fairly algorithmically, doing optimization on them, but I had always in the back of my mind thought about what would a system look like if the most important principle that we were designing the system from was to minimize emissions? Like if that was the number one thing, and then say performance came second, reliability came second, security has to come first before everything. There's not a lot of tradeoffs you have to make with carbon awareness and security. So I started thinking, I'm like, "what does a data center architecture look like if this is the most important thing?"

So of course, starts with the lowest, it's not the lowest, it's the highest performance-per-watt hardware you can get your hands on. And so really serving the landscape of really what that looked like. Architecting all the, everything we know about carbon awareness into the platform so that developers don't necessarily have to put it into their code, but get to take advantage of it in a fairly transparent and automatic way. And so you end up having things like location shifting as a fundamental principle of how your platform looks to a developer. So, as the idea was, we'd have a data center in France and a data center in the Pacific Northwest of the United States, where you have fairly non-correlated solar and wind values, but you also have very green base loads, so you're not trying to overcome your base load from the beginning.

But that time shifting was basically transparent to the platform. I mean, not time shifting, I'm sorry. Location shifting was transparent to the platform. And then time shifting was implemented for the appropriate parts. but it was all done with just standard open source software, in a way that we minimized carbon while taking a little bit of a hit on performance a little bit of a hit on latency, but in a way the developer could continue to focus on performance and latency, but got all the benefits of carbon reduction at the same time.

Chris Adams: Ah, okay. So when you said system, you weren't talking about like just maybe like an orchestrator, like Kubernetes that just spins up virtual machines. You're talking about going quite a bit deeper down into that then, like looking at hardware itself?

Scott Chamberlin: I started the hardware itself. 'Cause you have to have batteries, you have to have ability to store renewable energy when it's available. You have to have low power chips. You have to have low powered networking. You have to have redundancy. 

And there's always these challenges when you talk about shifting in carbon awareness of, I guess the word is, leaving your resource, your capital resources idle.

So you have to take costs into account with that. And so the goal, but the other challenge that I wanted to do was the goal was have this all location based, very basic carbon accounting, and have as close to theoretically possible minimizing the carbon, as you can. Because it's not possible to get to zero without market based mechanics in when you're dealing with actual hardware.

So get as close to net zero as possible from a location based very, basic emissions accounting. So that was kind of the principle. And so, on that journey, we got pretty far to the point of ready to productize it, but then we decided to really pivot around energy and AI, which is where I'm at now.

But, so I don't have a lot of numbers of what that actual like net, close to the zero theoretically, baseline is. But I'm pretty close. It's like drastically smaller than what we are using in, say, Hyperscale or public cloud today. 

Chris Adams: Oh, I see. Okay. So you basically, so rather than retrofitting a bunch of like green ideas onto, I guess Hyperscale big box out outta town style data centers, which already have a bunch of assumptions already made into them, you, it was almost like a clean sheet of paper, basically. You're working with that and that's the thing you spend a bunch of time into. And it sounds like if you were making some of this stuff transparent, it was almost like it wasn't really a developer's job to figure out, know what it was like shifting a piece of code to run in, say, Oregon versus France, for example, that would, that, the system would take care of that stuff.

You would just say, I just want you to run this in the cleanest possible fashion and don't, and as long as you respect my requirements about security or where the data's allowed to go, and it would take care of the rest. Basically that was the idea behind some of that, right? 

Scott Chamberlin: That's the goal because in the many years I've been spending on this, like there's a great set of passionate developers that want to like minimize the emissions of the code, but it's a small percent, and I think the real change happens is if you make it part of the platform that you get a majority of the benefit, maybe, 80th percentile of the benefit, by making it automatic in a way.

Chris Adams: The default?

Yeah. 

Scott Chamberlin: My software behaves as expected, but I get all the benefits of carbon reduction automatically. 'Cause developers already have so much to care about. And again, like, it's not every developer actually is able to make the trade offs between performance and CO2 awareness appropriately.

Right. It's really hard and we haven't made it easy for people. So that was the goal. Like how do you actually like enable the system to do that for you while the developer can focus on the demands, the principles that they're used to focusing on, making their software fast, making their software secure, making it reliable, making it have good user experience, that kind of stuff. 

Chris Adams: Ah, that's interesting though. That's almost like, so like the kind of green aspect is almost like a implementation detail that doesn't necessarily need to be exposed to the developers somewhat in a way that when people talk about, say, designing systems for end users to use, there's a whole discussion about whether you, whether it's fair to expect someone to feel terrible for using Zoom and using Netflix, when really like, it makes more sense to actually do the work yourself as a designer or as a developer to design the system so by default is green. So rather than trying to get people to change their behavior massively, you're essentially going with the fact that people are kind of frail, busy, distracted people, and you're working at that level almost.

Scott Chamberlin: Yeah, I think that's the exact right term. It is green by default. And that phrase, when I started working on this in Windows, 

so you know, like you referred to earlier, like I created all the carbon aware features in Windows and there was a debate early on like how do we enable these? Like should the carbon awareness feature, should it be a user experience?

I mean, should the user be able to opt in, opt out, that kind of stuff? And it was actually my boss, I was talking to this, he's like, "if you're doing this, it has to be the default," right? And so, you're never going to make the impact on any system if somebody, at the scale we really need to make this impact on, if people have to opt in. It has to be the default. And then sure, they can opt out if there's certain reasons that they want a different behavior. But green by default has to be the main way we make impact. 

Chris Adams: That's actually quite an interesting, like framing because particularly when you talk about carbon aware and at devices themselves, this is something that we've seen with like a, I guess there is a, there, there's a default and then there's maybe like you, the thing you said before about it's really important to leave people in control so they can override that, feels like quite an important thing.

'Cause I remember when Apple rolled out the whole kind of carbon away charging for their phones, for example. Some people are like, "oh, ah, this is really cool. Things have, are slightly greener by default based on what Apple have showed me." But there are some other people who absolutely hated this because the user experience from their point of view is basically, I've got a phone, I need to charge it up, and I plugged it into my wall.

And then overnight it's been a really, high carbon grid period. So my phone hasn't been charged up and I woke up and now I've go to work and I've got no phone charger. And it just feels like this is exactly the thing. Like if you don't provide the, like a sensible kind of get out clause, then that can lead to a really, awful experience as well.

So there is like quite a lot of thought that needs to guess go into that kind of default, I suppose.

Scott Chamberlin: Definitely. Like the user experience of all of these things have to ultimately satisfy the expectations and needs of the users, right. You're, it is 

another like learning experience we had, it was a deep, it was really a thought experiment, right? When we were working on some of the, and Windows is actually, we were working on the ability to change the timer for how fast the device goes to sleep.

Because there's a drastic difference even in between an active mode, and the sleep state that, it's basically when the device will turn on if you touch the mouse, screen's off, it goes into low power state. And so one of the changes we made in Windows was to lower that value from the defaults.

And it's fairly complex about how these defaults get set. Basically, they're set by the OEMs and different power profiles. But we wanted to lower the default that all software was provided. And we did some analysis of what the ideal default would be. But the question in the user experience point of view was "if we set this too low, will there be too many people turning it to, basically, entirely off, rather than what the old default was, which was like 10 minutes?" So let's use these values. Theoretically, I can't remember what the exact values are, but old default, 10 minutes, new default three minutes for going from active to sleep.

If people were, if three minutes was not the right value and we got maybe 20% of the people entirely turning it off, is the carbon impact worse for the overall, fleet of Windows devices by those 20% people turning off 'cause we got a bad user experience by changing the default? So we had to do all these analyses, and have this ability to really look for unintended consequences of changing these.

And that's why the user experience is really critical when you're dealing with some of these things.

Chris Adams: Ah, that's, okay, that's quite useful nuance to actually take into account 'cause there is, there's a whole discussion about kind of setting defaults for green, but then there's also some of the other things. And I actually, now that you said that I realize I'm actually just, 'cause I am one of these terrible people who does that because I've, like,

I mean I'm using a Mac. Right. And, you see when people are using a laptop and it starts to dim and they start like touching the touch pat thing to kinda make it brighten again. And you see people do that a few times. There's an application called Caffeine on a Mac, and that basically stops it going to sleep, right. And so that's great. I mean, but It's also then introduces the idea of like, am Is my a DD bad adult brain gonna remember to switch that back off again? Like, this are the things that come up. So this is actually something that I have direct experience, so that is very much hitting true with me, actually.

Okay. So that was the thing you did with, I'm calling it Scott Cloud, but I assume there was another name that we had for that, but that's, that work eventually became something that Neuralwatt. That's like you went from there and move into this stuff, right?

Scott Chamberlin: Right. So, Scott Cloud or Carbon, Net Zero Cloud, was basically a science experiment. And I wanted to deploy it for the purposes of just really seeing, you learn so much when things are in production and you have real users, but before I did it, I started talking to a lot of people I trusted in my network.

And one of my old colleagues from Microsoft and a good friend of mine, he really dug into it and started pushing me on like some serious questions like, "well, what does this really impact in terms of energy?" Like it was a CO2 optimization exercise, was that project. And he's like, "well what's the impact on energy?

What's the impact on AI?" And actually to, Asim Hussain, he is, he's asked the same question. He's like, "you can't release anything today," and this is, let's rewind, like a year ago, he's like, "you can't release anything today that doesn't have some story about AI," right? And this was just a basic just compute platform with nothing specific about AI.

So both of those comments really struck home. I was like, okay, I gotta like figure out this AI stuff we got. And I've gotta answer the energy question, it's wasn't hard 'cause it was already being measured as part of the platform, but I just was focused on CO2. And what it turned out was that there were some really interesting implications once we started to apply some of the optimization techniques to the GPU and how the GPU was being run from energy point of view, that ended up being in, that we, when we looked into it and it ended up being like potentially more impactful in the short term than the overall platform. And so, that colleague Chad Gibson, really convinced me in our discussions to really spin that piece out of the platform as a basis of the startup that we went and decided to build, which we call Neuralwatt now.

So yeah, what Neuralwatt really is, like the legacy of that, all that work, but the pieces that we could really take out of it that were focused on GPU energy optimization, within the context of AI, growth and energy demands, because those are becoming really critical challenges, not just for just businesses, but there are critical challenges that are underlying all of our, the work against green software, underlying all of the work, and around trying to reduce emissions of compute as a whole.

Right? And we're just really looking at a new paradigm with the exponential increase in energy use of compute and what behaviors that's driving in terms of getting new generators online, as well as what is the user experience behaviors when LLMs are built into everything, LLMs or other AIs are built into everything?

And so I felt that was really important to get focused on as quickly as possible. And that's where we really, really jumped off, with Neuralwatt on.

Chris Adams: Oh, I see. 

Okay. So the, basically there is a chunk of like, usage and there's the ability to kind of make an improvement in the existing set of like, like a fleet of servers and everything. Like that's already could have deployed around the world. But you see this thing which is growing really fast.

And if we look at things like the International Energy Agency's own report, AI and Energy, they basically say over the next five years looks like it's gonna be a rough, their various projections are saying it's probably gonna be the same energy use as all data centers. So it makes more sense to try and blunt some of that shift as early as possible.

Or like that's where you felt like you had more chance for leverage essentially. 

Scott Chamberlin: More chance for leverage, more interest in really having an impact. Because, I mean, we were in really in a period of flat growth in terms of energy for data centers prior to the AI boom because the increase in use in data centers was basically equaled out by the improvement in energy efficiency of the systems themselves.

And there's a lot of factors that went into why that was really balancing, relatively balancing out, but the deployment of the GPUs and the deployment of massively parallel compute and utilization of those from the point of view of AI both training and inference, really changed that equation entirely. Right. And so basically from 2019 on, we've basically seen going from relatively flat growth in data centers to very steep ramp in terms of energy growth.

Chris Adams: Okay. Alright. Now we're gonna come back to Neuralwatt for a little bit later. Partly because the demo you shared was pretty actually quite cool actually, and I still haven't had anything that provides that kind of live information. But one thing that I did learn when I was asking about this, and this is probably speaks to your time when you're working in a number of larger companies, is that there is a bit of a art to get large companies who are largely driven by like, say, profits for the next quarter to actually invest in kind of transparency or sustainability measures. And one thing that I do know that when you were working at Microsoft, one thing I saw actually, and this is one thing I was surprised by when I was asked, I was asking on LinkedIn, like, okay, well if I'm using various AI tools, what's out there that can expose numbers to me?

And there was actually some work by a guy, Will Alpine, providing some metrics on existing AI for an existing kind of AI pipeline. that's one of the only tools I've seen that does expose the numbers or provide the numbers from the actual, the cloud provider themselves. And as I understood it, that wasn't a thing that was almost like a passion project that was funded by some internal kind of carbon fund or something.

Could you maybe talk a little bit about that and how that, and what it's like getting, I guess, large organizations to fund some ideas like that because I found that really interesting to see that, and I, and there was, and as I understand it, the way that there was actually a kind of pool of resources for employees to do that kind of work was actually quite novel.

And not something I've seen in that many, places before.

Scott Chamberlin: Yeah, no, I think that was great work and Will is, want to, I'm a big fan of Will's work and I had the fortune to collaborate with him at that period of both of our careers when really it was, I don't think carbon work is easy to get done anywhere, in my experience, but that, I think Microsoft had a little bit of forethought in terms of designing the carbon tax. And yeah, we did have the ability to really vet a mission vet projects that could have a material impact against Microsoft's net zero goals and get those funded by the carbon tax that was implemented internally.

And so the mechanism was, every, as Microsoft built the capability to audit and report on their carbon, they would assign a dollar value to that from teams and then that money went from those teams budget into a central budget that was then reallocated for carbon reduction goals.

And yeah, I think Will was really at the forefront of identifying that these AI and, we all just really said ML back then, but now we all just say AI, but this GPU energy use was a big driver of the growth and so he really did a ton of work to figure out what that looked like at scale, figure out the mechanics of really exposing it within the Hyperscale cloud environment, taking, essentially like NVIDIA's also done a great job in terms of keeping energy values in.

their APIs and exposed through their chips and through their drivers, so that you can use it fairly easy on GPU. I would say it's more challenging on CPUs to do so, or the rest of the system, but, so he like did a great job in collaboration with those interfaces to get that exposed into the Azure, I think it's the ML studio is what it's called.

So that it has been there for many years, this ability to see and audit your energy values, if you're using the Azure platform. 

Yeah, those super good work.

Chris Adams: Yeah, so this was the thing. I forget the name of it and I'm a bit embarrassed to actually forget it. But, let, I'm just gonna play back to what I think you're saying. 'Cause when I was reading about this is something that I hadn't seen in that many other organizations. So like there's an internal carbon levy, which is basically for every ton that gets emitted, there was like a kind of a dollar amount allocated to that. And that went to like a kind of internal, let's call it a carbon war chest, right? So like there's a bunch of money that you could use. And then any member of staff was basically then able to say, I think we should use some of this to deliver this thing because we think it's gonna provide some savings or it's gonna help us hit our whatever kind of sustainability targets we actually have.

And one of the things that came outta that was essentially, actual meaningful energy report energy figures, if you're using these tools, and this is something that no, the other clouds, you're definitely not gonna get from Amazon right now. Google will show you the carbon but won't show you the energy.

And if you're using chat GPT, you definitely can't see this stuff. But it sounds like the APIs do exist. So it's just a, it has been largely a case of staff being prepared, they're being kind of will inside the system. And people being able to kind of win those, some of those fights to get people to allocate time and money to actually make this thing that's available for people, right? 

Scott Chamberlin: The Nvidia APIs definitely exist. I think the challenge is the methodology and the standards, right? So, within a cloud there's a lot of like complexity around how cycles and compute is getting assigned to users and how do you fairly and accurately count for that? GPUs happen to be a little bit simpler 'cause we tend to allocate a single chip to a single user at a single time.

Whereas in like CPUs, there's a lot of like hyper threading, most clouds are moving to over subscription or even just single hardware threads are 10 are starting to get shared between multiple users. And how do we allocate the, first the energy, all this starts with energy, how to allocate first the energy, and then the CO2 based on a location.

And then, the big complexity in terms of the perception that these clouds want to have around net zero. They're, they want to, everyone wants to say they're net zero for a market-based mechanic. And what's the prevailing viewpoint within the, what is allowed with the GHG protocol or what is the perception that the marketing team wants to have?

Is a lot of the challenges. it tends to, at least in the GPU energy, there's not like huge technical challenges, but there's a lot of like marketing and 

accounting and methodology challenges to overcome.

Chris Adams: So that's interesting. Well, so I did an interview with Kate Goldenring who was working at Fermyon at the time. We'll share a link to that for people and I will also share some links to both the internal carbon levy and how essentially large organizations have funded this kind of like climate kind of green software stuff internally.

'Cause I think other people working inside their companies will kind of want, will find that useful. But I'm just gonna play back to you a little bit about what you said there and then we'll talk a little bit about the, demo you shared with me. So it does seem like, so GPUs like, the thing that's used for AI accelerators, they can provide the numbers.

And that is actually something that's technically possible a lot of the time. And it sounds like that might be kind of tech technically slightly less complex at one level than way the way people sell kind of cloud computing. 'cause when we did the interview with Kate Goldenring, and we'll share the link to that, she basically told, she could have explained to me that, okay, let's say there is a server and it's got maybe, say 32 little processes like, cores inside this, what tends to happen, because not everyone is using all 32 cores at all the same time, you can pretty much get away with selling maybe 40 or 50 cores because not everyone's using all the same tool, all the cores at the same time. And that allows you to basically, essentially sell more compute.

So end up having, you make slightly more money and you end up having a much more kinda like profitable service. And that's been one of the kind of offers of cloud. And also from the perspective of people who are actually customers that is providing a degree of efficiency. So if you have, like, if you don't need to build another server because that one server is able to serve more customers, then there's a kinda hardware efficiency argument.

But it sounds like you're saying that with GPUs, you don't have that kind of over a subscription thing, so you could get the numbers, but there's a whole bunch of other things that might make it a bit more complicated elsewhere, simply because it's a new domain and we are finding out there are new things that happen with GpUs, for example.

Scott Chamberlin: Yeah. So, yeah, that's exactly what I was trying to say. And I think we are seeing emerging GPU over subscription, GPU sharing. So at the end of that will probably change at some point and at scale. It's certainly the technology is there. Like I think NVIDIA's acquisition of run.ai, enables some of this GPU sharing and that, they acquired that company of like six months ago and It's now open source and so people can take advantage of that.

But yes, I think that the core principle is like, from a embodied admissions point of view and in a, green software point of view, it's relatively a good practice to drive up the utilization of these embodied missions you've already like purchased and deployed. There are a lot, some performance implications around doing the sharing that how, it gives back user experience, but today the really, the state of the art is GPU, is that it's mostly just singly allocated and fully utilized when it's utilized or it's not fully utilized, but it's utilized for a single customer, at a time. But that is certainly changing.

Chris Adams: Oh, okay. So I basically, if I'm using a tool, then I'm not sharing it with anyone else in the same way that we typically we'd be doing with cloud and that, okay, that probably helps me understand that cool demo you shared with me then. So maybe we'll just talk a little bit about that. 'cause this was actually pretty, pretty neat when I actually asked that when you showed like, here's a video of literally the thing you wished existed, that was kind of handy.

Right? So, basically if you, we will share the link to the video, but the key thing that Scott shared with me was that using tools like say a chat GPT thing or anthropic where I'm asking questions and I'll see kind of tokens come out in us when I'm asking a question. It we were, we basically saw charts of realtime energy usage and it changing depending on what I was actually doing.

And, maybe you could talk a little bit about actually what's actually going on there and how you came to that. Because it sounds like Neuralwatt wasn't just about trying to provide some transparency. There's actually some other things you can do. So not only do you see it, but you can manage some of the energy use in the middle of a, for like an LLM session, for example, right? 

Scott Chamberlin: So yeah, at the first stage, the question is really just what is, can we measure what's happening today and what does it really look like in terms of how you typically deploy, say, a chat interface or inference system? So, like I was mentioning, we have ability fairly easily because NVIDIA does great work in this space to read those values on the GPU specifically, again, 

there's system implications for what's happening on the CPU what's happening on the network, the discs.

They tend to be outstripped by far because these GPUs use so much energy. 

But so, the first step in really that demo is really just to show what the behavior is like because what we ultimately do within the Neuralwatt code is we take over all of the energy management, all of the system, and We train our own models to basically shift the behavior of servers from one that is focused on maximizing performance for the available energy to balancing the performance for the energy in a energy efficiency mode, essentially. So we are training models that shift the behavior of energy of the computer for energy efficiency.

And so that's why we want to visualize multiple things. We want to visualize what the user experience trade off is. Again, going back to the user experience. You have to have great user experience if you're gonna be doing these things. And we want to visualize the potential gains and the potential value add for our customers in making this shift.

Because, I think we talk about, Jensen Huang made a quote at GTC that we love is that, we are a power constrained industry. 

Every AI factory is going to be power constrained in the future. And so what does compute look like if power is the number one limiting factor that you have to deal with?

So that's why we believe, we really want to enable service to operate differently than what they've done in the past. And we want there to be some, essentially think about, as, like energy awareness, right? That's the word I come back to. Like we want behavior of servers to be energy aware because of these power constraints.

Chris Adams: Ah, okay. Alright. you said a couple of things that I, that kind of, I just want to run by you to check. So, with the, there's this thing, there's all these new awarenesses, there's like carbon aware, then there's grid aware, then there's energy aware. This is clearly like an area where people were trying to figure out what to call things.

But the Neuralwatt, the neural, the thing that you folks are doing was basically okay, yes, you have access to the power and you can make that available, so I'm using something, but I'm just gonna try and run this by you and I might be right and you, I might need you to correct me on this, but it sounds a little bit like the thing that you are allowing to do is almost throttling the power that gets allocated to a given chip. 'Cause if you use, like things like Linux or certain systems they have, like they can introduce limits on the power that is allocated to a particular chip. But if you do that, that can have a unintended effect of making things run a little bit too slowly, for example.

But there, there's a bit of head, there's a bit of headroom there. But if you are able to go from giving absolute power, like, take as much power as you want to, having a kind of finite amount allocated, then you can basically still have a kind of a good, useful experience, but you can reduce it to the amount of power that's actually be consumed. It sounds like you're doing something a little bit like that, but with Neuralwatt thing. So rather than giving it, carte blanche to take all the power, you are kind of asking it to work within a kind of power envelope. That means that you're not having to use quite so much power to do the same kind of work.

Is that it?

Scott Chamberlin: Yeah. So if you go back to the history of like, before we had GPUs everywhere, the CPUs have fairly, let's call 'em like moderate level sophistication of terms of power management. They have sleep states, they have performance states, and there's components that run on the OS that are called, basically CPU governors that govern how a CPU behaves relative to various constraints.

And so, when you allocate a, let's say a Linux VM in the cloud, I don't know why this is, but a lot of 'em get default allocated with a, I'm the name of, it's slipping in my mind, but there's about five default CPU governors in the default Linux Distros, and they get out allocated with the power save one, actually.

And so what it does, it actually limits the top frequencies that you can get to, but it essentially is balancing power and performance is kind of the default that you get allocated. You can check these things, you can change it to a performance mode, which basically is gonna use all of the capability of the processor at a much higher energy use.

And, but on the GPU it's a lot less sophisticated, right? There's, GPUs don't tend to support any sleep states other than just power off and on. And they do have different performance states, but they're not as sophisticated as the CPU has historically been. And so essentially we are inserting ourselves into the OS Neuralwatt and managing it in a more sophisticated manner around exactly how you're describing.

We're making the trade off and we're learning the trade off really through modeling. We're learning the trade off to maintain great user experience, but get power gains, power savings, with our technology, and doing this across the system. So, yes, I think your description essentially, very good. And, we're just essentially adding a level sophistication into the OS, than what exists today.

Chris Adams: Okay. So basically, rather than being able to pull infinite power is, has, like, it's an upper limit by how much it can pull, but you'd probably want to kind of, the reason you're doing some of the training is you're based on how people use this, you'd like the upper limit, the kind of, the upper limit available to what's actually being needed so that you've, you're still giving enough room, but you're not, you're delivering some kind of savings.

Scott Chamberlin: Yeah, and it's important to understand that there's, it's fairly complex, which is why we train models to do this rather than do it, 

Chris Adams: Like sit at one level and just one and done. Yeah.

Scott Chamberlin: Because think about like a LLM, right? So there's essentially two large phases in inference for an LLM. And one of the first phase is really compute heavy, and then the second phase is more memory heavy. And so we can use different, power performance trade-offs in those phases. And understanding what those phases are and what the transition looks like from a reservable state is part of what we do. And then the GPU is just one part of the larger system, right?

It's engaged in the CPU. A lot of these LLMs are engaged in the network. And so how do we balance all the, tradeoffs so to maintain the great user experience for the best amount of power efficiency? That's essentially like what we're, our fitness function is when we're essentially training.

Chris Adams: Ah. I think I understand that now. And, and what you said about the, those two phases, presumably that's like one of, one of it is like taking a model, loading it to something a bit like memory. And then there's a second part which might be accessing, doing the lookups against that memory. Because you need to have the thing, the lookup process when you're seeing the text come out that is quite memory intensive rather than CPU intensive.

So if you're able to change how the power is used to reflect that, then you can deliver some kind of savings inside that. And if you scale that up a data center level that's like, like 10%, 20, I mean, maybe even, yeah. Do you have an idea of like what kind

Scott Chamberlin: We tend to shoot for at least 20% improvements in what I would say performance per unit of energy. So tokens per Joule is the metric I tend to come back to fairly often. But again, how exactly you measure energy on these things, what is the right metric, I think is, I think you need to use a bunch of 'em.

But, I like tokens per Joule 'cause it's fairly simple and it's fairly, it's easy to normalize. But like, it's, it gets super interesting in this conversation about like, inference time, compute and thinking LLMs and stuff like that. 'Cause they're generating tons and tons of tokens and not all of 'em are exposed to, essentially improve their output.

And so people use all their metrics, but they're harder to normalize. So, yeah, long, long story short, I tend to come back to tokens for Joule is my favorite, but,

Chris Adams: So what, so it sounds like the thing that you're working on doing is basically through kind of judicious use of like power envelopes that more accurately match what is actually being required by a GPU or anything like that, you're able to deliver some savings that way. That's essentially, that's one of the things, and like you said before when we were talking about kind of Scott Cloud, that's transparent to the user.

I don't have to be thinking about my prompt or something like that. This is happening in the background, so I don't really, my experience isn't changed, but I am basically receipt of that, 20% of power is basically not being turned into carbon dioxide in the sky, for example, but it's basically the same other than that though.

Scott Chamberlin: That's the goal, right? Essentially we've informed our work continually on number one, user experience has to be great, number two, developer experience has to be great, which means the developer shouldn't have to care about it. So, yeah, it's a single container download, it runs in the background.

It does all the governance in a fairly transparent way. But you know, all throughout as well, like, we actually have CO2 optimization mode as well, so we can do all of this. Fall mode is really energy, but we actually can flip a switch and we get an extra degree of variability where if we're optimizing on CO2, average or marginal emissions. so, we can vary those behaviors of the system relative to the intensity of the carbon in the grid as well. So

Chris Adams: Okay. Or possibly, if not the grid, then the 29 data, 29 gas turbines that are powering that data center today, for example.

Scott Chamberlin: I think that's an emerging problem. And I actually would love to talk to somebody that has a data center that is having a microgrid with a gas turbine, because I actually do believe there's additional optimization available for these little microgrids, that are being deployed alongside these data centers.

If you were to do plummet all the way through in this energy, again, go back to energy awareness, right. Like if your servers knew how your microgrids were behaving relative to the macro grid that they were connected to, like, there's so many interesting optimizations available and, people are looking at this from the point of view of the site infrastructure, but like the reality is all of the load is generated by the compute on the server.

Right. And that's what we're really trying to bring it all the way through to where load originates and the behavior where, while maintain that user experience. So

Chris Adams: Okay. So you said something interesting there, I think, about this part, like the fact that a, you mentioned before that GPU usage is a little bit less sophisticated right now. You said it's either all on and all off. And when you've got something which is like, the power, the multiple thousands of homes worth of power, that can be all on and all off very, quickly.

That's surely gotta have to have some kind of implications, within the data center, but also any poor people connected to the data center. Right? Because, if you are basically making the equivalent to tens of thousands of people disappear from the grid, then reappear from the grid like inside in less than a second, there's gotta be some like a knock on effect for that.

Like, you spoke about like gas turbines. It's like, is there, do you reckon we're gonna see people finding something in the middle to act like a kind of shock absorber for these changes that kind of go through to the grid? Because if you're operating a grid, that feels like the kind of thing that's gonna really mess with you being able to provide like a consistent, kind quality of power to everyone else.

If you've got the biggest use of energy also swinging up and down the most as well, surely.

Scott Chamberlin: Yeah, and it's certainly like a, I don't know if existential problem is the right word, but it's certainly a emerging, very challenging problem, 

Chris Adams: Mm-hmm. 

Scott Chamberlin: within the space of data centers is the essentially like seeking up of some of these behaviors among the GPUs to cause correlated spikes and drops in power and it's not, it has implications within your data center infrastructure and, to the point where we hear from customers that they're no longer deploying some of their UPSs or their battery backups within the GPU clusters because they don't have the electronics to handle the loads shifting so dramatically, to the point where we're also getting emerging challenges in the grid in terms of how these loads ramp up or down and affect, say, I'm not gonna get into where I'm not an expert in terms of the generational, aspects of generation on the grid and maintaining frequency, but it has implications for that as well. But so, we, in the software we can certainly smooth those things out, but there's also, I mean, there's weird behaviors happening right now in terms of trying to manage this.

My favorite, and I don't know if you've heard of this too, Chris, is PyTorch has a mode now where they basically burn just empty cycles to keep the power from dropping down dramatically when, I think it's when weights are sinking, in PyTorch, I'm not exactly sure when it was implemented.

Because i've only read about it, but you know, when you maybe need to sink weights across your network and so some GPUs have to stop, what they've implemented is some busy work so that the power doesn't drop dramatically and cause this really spiky behavior. 

Chris Adams: Ah. 

Scott Chamberlin: So I think what 

Chris Adams: you're referring to, Yeah. So this the PyTorch_no_powerplant_blowup=1 they had, right? Yeah. This, I remember reading about this in semi analysis. It just blew my mind. The idea that you have to essentially, keep it running because the spike would be so damaging to the rest of the grid that they have to kind of simulate some power, so it doesn't, so they don't have that that change propagate through to the rest of the grid, basically.

Scott Chamberlin: Correct. And so, that's one of the, we look at problems like that, there's that problem in terms of the thinking of the way the problem if you train, start a training run

where all the GPUs basically start at the same time and create a surge. And, so, we help with some of those situations in our software.

But yes, I think that some of the behaviors that are getting implemented, like the, no_powerplant_blowup=1 , they're fairly, I would say they're probably not great from a green software point of view because anytime we are, we're, doing busy work, that's an opportunity to reduce energy, reduce CO2 and there probably are ways of just managing that in a bit with a bit more sophistication depending on the amount of, the scale that you're working at, that is, probably may have been more appropriate than that.

 So this is definitely 

Chris Adams: still needs 

Scott Chamberlin: to be looked at a little bit. 

Chris Adams: So like, I mean before like in the pre AI days, there was this notion of like a thundering herd problem where everything tries to use the same connection or do the same at the same time. It sounds like this kind PyTorch_no_powerplant_blowup=1 is essentially like the kind of AI equivalent to like seeing that problem coming and then realizing it's a much greater magnitude and then figuring out, okay, we need to find a elegant solution to this in the long run.

But right now we're just gonna use this thing for now. Because it turns out that having incredibly spiky power usage kind of propagating outside the data center wrecks all kinds of havoc basically. And we probably don't want to do that if we want to keep being connected to the grid.

Scott Chamberlin: Yeah. But at it's really a, spiky behavior at scale is really problematic. Yes.

Chris Adams: Okay, cool. Dude, I'm so sorry. We're totally down this kind of like, this AI energy spikiness rabbit hole, but I guess it is what happens

when 

Scott Chamberlin: it's certainly a, it's certainly customers are really interested in this because it's, it, I mean if we were to like bubble up one level, like there's this core challenge in the AI space where the energy people don't necessarily talk the same language as the software people.

And we, I think that's one place where maybe Hyperscale has a little bit more advantage 'cause it has emerged from software companies, but Hyperscale is not the only game in town and especially when we're going to neo clouds and stuff like that. And so, I think one of our like side goals is really how do we actually enable people talking energy and infrastructure to have the same conversations and create requirements and coordinate with the software people running the loads within the data centers? I think that's the only way to really solve this holistically. So,

Chris Adams: I think you're right. I mean, this is, to bring this to some of the kind of world of green software, I suppose, the Green Software Foundation did a merger with, I think they're called it, SSIA, the Sustainable Servers and Infrastructure Alliance. I think it's something like that. We had them on a couple of episodes a while ago, one where there was a whole discussion about, okay, how do, setting out some hardware standards to have this thing kind of crossing this barrier.

Because, like you said, it does it, as we've learned on this podcast, some changes you might make at AI level can have all these quite significant implications. Not just thinking about like the air quality and climate related issues of having masses and masses of on-premise gas turbines. But there's a whole thing about power quality, which is not something that you've had to think about in terms of relating to other people, but that's something that's clearly needs to be on the agenda as we go forward.

Just like, like responsible developers. I should, before we just kind of go further down there, I should just check, we just, we're just coming up to time. we've spent all this time talking about

 this and like we've mentioned a couple of projects. Are there any other things that aren't related to, like spiky AI power that you are looking at and you find, Hey, I wish, I'm on this podcast, I wish more people knew about this project here or that project there.

Like, are there any things that you are, you've, read about the news or any people's work that you're really ins impressed by and you wish more people knew about right now?

Scott Chamberlin: Yeah, I mean, I think a lot of people probably on this podcast probably know about AI Energy Score. Like I think that's a promising project. Like I really believe that we need to have the ability to understand both the energy and the CO2 implications of some of these models we're using and the ability to compare them and compare the trade-offs.

I do think that, the level of sophistication needs to get a bit higher because it's, right now it's super easy to trade off model size and energy. Like, I can go, single GPU and, but I'm trading off capabilities for that. So how do we, I think on one of my blog posts, it was someone's ideas.

Like you really have to normalize against the capabilities and the energy at the same time for making your decisions about what the right model is for your use cases relative to the energy available to say the CO2 goals you have. So, but yeah, I think eventually they'll get there in that project.

So I think that's a super promising project. 

Chris Adams: We will share a link to that. So we definitely got some of that stuff for the AI Energy Score, 'cause it's entirely open source and you can run it for open models, you can run it for private models and if you are someone with a budget you can require customers to, or you can require suppliers to publish the results to the leaderboard, which would be incredibly useful because this whole thing was about energy transparency and like.

Scott Chamberlin: Yeah,

Chris Adams: I'm glad you mentioned that. That's like one of the, I think that's one of the more useful tools out there that is actually relatively, like, relatively easy to kind of write into contracts or to put into a policy for a team to be using or team to be adopting, for example.

Scott Chamberlin: Correct. Yep. No, a big fan, so.

Chris Adams: Well that's good news for Boris. Boris, if you're hearing this then yeah, thumbs up, and the rest of the team there, I only mentioned Boris 'cause he's in one of the team I know and he's in the Climateaction.tech Slack that a few of us tend

Scott Chamberlin: Yeah. Boris and I talked last week. Yeah. A big fan of his work and I think Sasha Luccioni, who I actually never met, but yeah, I think she's also the project lead on that one.

Chris Adams: Oh, Scott, we are coming up to the time and I didn't get a chance to talk about anything going on in France, and with things like Mistral sharing some of their data, some of their environmental impact figures and stuff like that because it's actually, it's kind of, I mean, literal, just two days ago we had Mistral, the French kind of competitor to open AI,

they, for the first time started sharing some environmental figures and quite a lot of detail. More so than a single kind of like mention from Sam Altman about power, about the energy used by AI query. We've, we actually got quite a lot of data about the carbon and the water usage and stuff like that.

But no energy though. But that's something we'll have to speak about another time. So hopefully maybe I'll get, be able to get you on and we can talk a little bit about that and talk about, I don't know the off-grid data centers of Crusoe and all the things like that. But until then though, Scott, I really, I'm, I've really enjoyed this deep dive with you and I do hope that the, our listeners have been able to keep up as we go progressively more detailed.

And, if you have stayed with us, listeners, what we'll do is we'll make sure that we've got plenty of show notes so that people who are curious about any of this stuff can have plenty to read over the weekend. Scott, this has been loads of fun. Thank you so much for coming on and I hope you have a lovely day in Evergreen Town.

Scott Chamberlin: Thanks Chris.

Chris Adams: Alright, take care of yourself, Scott. Thanks. 

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware. foundation. That's greensoftware. foundation in any browser. Thanks again, and see you in the next episode. 



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]]> In this episode of Environment Variables, host Chris Adams welcomes Scott Chamberlin, co-founder of Neuralwatt and ex-Microsoft Software Engineer, to discuss energy transparency in large language models (LLMs). They explore the challenges of measuring AI emissions, the importance of data center transparency, and projects that work to enable flexible, carbon-aware use of AI. Scott shares insights into the current state of LLM energy reporting, the complexities of benchmarking across vendors, and how collaborative efforts can help create shared metrics to guide responsible AI development.

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TRANSCRIPT BELOW:

Scott Chamberlin: Every AI factory is going to be power constrained in the future. And so what does compute look like if power is the number one limiting factor that you have to deal with? 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. We talk a lot about transparency on this podcast when talking about green software, because if you want to manage the environmental impact of software, it really helps if you can actually measure it.

And as we've covered on this podcast before, measurement can very quickly become quite the rabbit hole to go down, particularly in new domains such as generative AI. So I'm glad to have our guest, Scott Chamberlain today here to help us navigate as we plum these depths. Why am I glad in particular?

Well, in previous lives, Scott not only built the Microsoft Windows operating system power and carbon tracking tooling, getting deep into the weeds of measuring how devices consume electricity, but he was also key in helping Microsoft Azure work out their own internal carbon accounting standards. He then moved on to working at Intel to work on a few related projects, including work to expose these kinds of numbers in usable form to developers when people when making the chips that go in these servers. His new project Neuralwatt is bringing more transparency and control to AI language models.

And a few weeks back when I was asking on LinkedIn for pointers on how to understand the energy usage from LLMs I use, he shared a link to a very cool demo showing basically the thing I was asking for: real-time energy usage figures from Nvidia cards directly in the interface of a chat tool. The video's in the show notes if you're curious.

And it is really, cool. So Scott, thank you so much for joining us. Is there anything else that I missed that you'd like to add for the intro before we dive into any of this stuff?

Scott Chamberlin: No, that sounds good.

Chris Adams: Cool. Well, Scott, thank you very much once again for joining us. If you are new to this podcast, just a reminder, we'll try and share a link to every single project in the show notes.

So if there are things that are particularly interest, go to podcast.greensoftware.foundation and we'll do our best to make sure that we have links to any papers, projects, or demos like we said. Alright, Scott, I've done a bit of an intro about your background and everything like that, and you're calling me from a kind of pleasingly green room today.

So maybe I should ask you, can I ask where you're calling from today and a little bit about like the place?

Scott Chamberlin: So I live in the mountains just west of Denver, Colorado, in a small town called Evergreen. I moved here in the big reshuffles just after the pandemic, like a lot of people wanted to shift to a slightly different lifestyle. And so yeah, my kids are growing here, going to high school here, and yeah, super enjoy it.

It gives me quick ability to get outside right outside my door.

Chris Adams: Cool. All right. Thank you very much for that. So it's a green software podcast and you're calling from Evergreen as well, in a green room, right? Wow.

Scott Chamberlin: That's right. I have a, I actually have a funny story I want to share from the first time I was on this podcast. It was me and Henry Richardson from Watttime talking about carbon awareness. And I made some focus on how the future, I believe, everything's going to be carbon aware. And I used a specific example of my robot vacuum of like, it's certainly gonna be charging in a carbon aware way at some point in the future.

I shared the podcast with my dad and he listened to it and he comes back to me and says, "Scott, the most carbon reduced vacuum is a broom."

Chris Adams: Well, it, he's not wrong. I mean, it's a, it's manual but it does definitely solve the problem and it's definitely got lower embedded carbon, that's for sure, actually.

Scott Chamberlin: Yeah.

Chris Adams: Cool. So Scott, thank you very much for that. Now, I spoke a little bit about your kind of career working in ginormous trillion dollar or multi-billion dollar tech companies, but you are now working at a startup Neuralwatt, but you mentioned before, like during, in our prep call, you said that actually after leaving a couple of the big corporate jobs, you spent a bit of time working on like, building your own version of like what a cloud it might be.

And I, we kind of ended up calling it like, what I called it Scott Cloud, like the most carbon aware, battery backed up, like really, kind of green software, cloud possible and like pretty much applying everything you learned in your various roles when you were basically paid to become an expert in this.

Can you talk a little bit about, okay, first of all, if it's, if I should be calling it something other than Scott Cloud and like are there any particular takeaways you did from that? Because that's had like quite an interesting project and that's probably what I think half of the people who listened to this podcast, if they had essentially a bunch of time to build this, they'd probably build something similar.

So yeah. Talk. I mean, why did you build that and, yeah, what are the, were there any things you learned that you'd like to share from there?

Scott Chamberlin: Sure. So, I think it's important to know that I had spent basically every year from about 2019 through about 2022, trying to work to add features to existing systems to make them more, have less environmental impact, lower CO2, both embodied as well as runtime carbon.

And I think it's, I came to realize that adding these systems on to existing systems is always going to come with a significant amount of compromises or significant amount of challenges because, I mean, I think it's just a core principle of carbon awareness is that there is going to be some trade off with how the system was already designed.

And a lot of times it's fairly challenging to navigate those trade offs. I tend to approach them fairly algorithmically, doing optimization on them, but I had always in the back of my mind thought about what would a system look like if the most important principle that we were designing the system from was to minimize emissions? Like if that was the number one thing, and then say performance came second, reliability came second, security has to come first before everything. There's not a lot of tradeoffs you have to make with carbon awareness and security. So I started thinking, I'm like, "what does a data center architecture look like if this is the most important thing?"

So of course, starts with the lowest, it's not the lowest, it's the highest performance-per-watt hardware you can get your hands on. And so really serving the landscape of really what that looked like. Architecting all the, everything we know about carbon awareness into the platform so that developers don't necessarily have to put it into their code, but get to take advantage of it in a fairly transparent and automatic way. And so you end up having things like location shifting as a fundamental principle of how your platform looks to a developer. So, as the idea was, we'd have a data center in France and a data center in the Pacific Northwest of the United States, where you have fairly non-correlated solar and wind values, but you also have very green base loads, so you're not trying to overcome your base load from the beginning.

But that time shifting was basically transparent to the platform. I mean, not time shifting, I'm sorry. Location shifting was transparent to the platform. And then time shifting was implemented for the appropriate parts. but it was all done with just standard open source software, in a way that we minimized carbon while taking a little bit of a hit on performance a little bit of a hit on latency, but in a way the developer could continue to focus on performance and latency, but got all the benefits of carbon reduction at the same time.

Chris Adams: Ah, okay. So when you said system, you weren't talking about like just maybe like an orchestrator, like Kubernetes that just spins up virtual machines. You're talking about going quite a bit deeper down into that then, like looking at hardware itself?

Scott Chamberlin: I started the hardware itself. 'Cause you have to have batteries, you have to have ability to store renewable energy when it's available. You have to have low power chips. You have to have low powered networking. You have to have redundancy. 

And there's always these challenges when you talk about shifting in carbon awareness of, I guess the word is, leaving your resource, your capital resources idle.

So you have to take costs into account with that. And so the goal, but the other challenge that I wanted to do was the goal was have this all location based, very basic carbon accounting, and have as close to theoretically possible minimizing the carbon, as you can. Because it's not possible to get to zero without market based mechanics in when you're dealing with actual hardware.

So get as close to net zero as possible from a location based very, basic emissions accounting. So that was kind of the principle. And so, on that journey, we got pretty far to the point of ready to productize it, but then we decided to really pivot around energy and AI, which is where I'm at now.

But, so I don't have a lot of numbers of what that actual like net, close to the zero theoretically, baseline is. But I'm pretty close. It's like drastically smaller than what we are using in, say, Hyperscale or public cloud today. 

Chris Adams: Oh, I see. Okay. So you basically, so rather than retrofitting a bunch of like green ideas onto, I guess Hyperscale big box out outta town style data centers, which already have a bunch of assumptions already made into them, you, it was almost like a clean sheet of paper, basically. You're working with that and that's the thing you spend a bunch of time into. And it sounds like if you were making some of this stuff transparent, it was almost like it wasn't really a developer's job to figure out, know what it was like shifting a piece of code to run in, say, Oregon versus France, for example, that would, that, the system would take care of that stuff.

You would just say, I just want you to run this in the cleanest possible fashion and don't, and as long as you respect my requirements about security or where the data's allowed to go, and it would take care of the rest. Basically that was the idea behind some of that, right? 

Scott Chamberlin: That's the goal because in the many years I've been spending on this, like there's a great set of passionate developers that want to like minimize the emissions of the code, but it's a small percent, and I think the real change happens is if you make it part of the platform that you get a majority of the benefit, maybe, 80th percentile of the benefit, by making it automatic in a way.

Chris Adams: The default?

Yeah. 

Scott Chamberlin: My software behaves as expected, but I get all the benefits of carbon reduction automatically. 'Cause developers already have so much to care about. And again, like, it's not every developer actually is able to make the trade offs between performance and CO2 awareness appropriately.

Right. It's really hard and we haven't made it easy for people. So that was the goal. Like how do you actually like enable the system to do that for you while the developer can focus on the demands, the principles that they're used to focusing on, making their software fast, making their software secure, making it reliable, making it have good user experience, that kind of stuff. 

Chris Adams: Ah, that's interesting though. That's almost like, so like the kind of green aspect is almost like a implementation detail that doesn't necessarily need to be exposed to the developers somewhat in a way that when people talk about, say, designing systems for end users to use, there's a whole discussion about whether you, whether it's fair to expect someone to feel terrible for using Zoom and using Netflix, when really like, it makes more sense to actually do the work yourself as a designer or as a developer to design the system so by default is green. So rather than trying to get people to change their behavior massively, you're essentially going with the fact that people are kind of frail, busy, distracted people, and you're working at that level almost.

Scott Chamberlin: Yeah, I think that's the exact right term. It is green by default. And that phrase, when I started working on this in Windows, 

so you know, like you referred to earlier, like I created all the carbon aware features in Windows and there was a debate early on like how do we enable these? Like should the carbon awareness feature, should it be a user experience?

I mean, should the user be able to opt in, opt out, that kind of stuff? And it was actually my boss, I was talking to this, he's like, "if you're doing this, it has to be the default," right? And so, you're never going to make the impact on any system if somebody, at the scale we really need to make this impact on, if people have to opt in. It has to be the default. And then sure, they can opt out if there's certain reasons that they want a different behavior. But green by default has to be the main way we make impact. 

Chris Adams: That's actually quite an interesting, like framing because particularly when you talk about carbon aware and at devices themselves, this is something that we've seen with like a, I guess there is a, there, there's a default and then there's maybe like you, the thing you said before about it's really important to leave people in control so they can override that, feels like quite an important thing.

'Cause I remember when Apple rolled out the whole kind of carbon away charging for their phones, for example. Some people are like, "oh, ah, this is really cool. Things have, are slightly greener by default based on what Apple have showed me." But there are some other people who absolutely hated this because the user experience from their point of view is basically, I've got a phone, I need to charge it up, and I plugged it into my wall.

And then overnight it's been a really, high carbon grid period. So my phone hasn't been charged up and I woke up and now I've go to work and I've got no phone charger. And it just feels like this is exactly the thing. Like if you don't provide the, like a sensible kind of get out clause, then that can lead to a really, awful experience as well.

So there is like quite a lot of thought that needs to guess go into that kind of default, I suppose.

Scott Chamberlin: Definitely. Like the user experience of all of these things have to ultimately satisfy the expectations and needs of the users, right. You're, it is 

another like learning experience we had, it was a deep, it was really a thought experiment, right? When we were working on some of the, and Windows is actually, we were working on the ability to change the timer for how fast the device goes to sleep.

Because there's a drastic difference even in between an active mode, and the sleep state that, it's basically when the device will turn on if you touch the mouse, screen's off, it goes into low power state. And so one of the changes we made in Windows was to lower that value from the defaults.

And it's fairly complex about how these defaults get set. Basically, they're set by the OEMs and different power profiles. But we wanted to lower the default that all software was provided. And we did some analysis of what the ideal default would be. But the question in the user experience point of view was "if we set this too low, will there be too many people turning it to, basically, entirely off, rather than what the old default was, which was like 10 minutes?" So let's use these values. Theoretically, I can't remember what the exact values are, but old default, 10 minutes, new default three minutes for going from active to sleep.

If people were, if three minutes was not the right value and we got maybe 20% of the people entirely turning it off, is the carbon impact worse for the overall, fleet of Windows devices by those 20% people turning off 'cause we got a bad user experience by changing the default? So we had to do all these analyses, and have this ability to really look for unintended consequences of changing these.

And that's why the user experience is really critical when you're dealing with some of these things.

Chris Adams: Ah, that's, okay, that's quite useful nuance to actually take into account 'cause there is, there's a whole discussion about kind of setting defaults for green, but then there's also some of the other things. And I actually, now that you said that I realize I'm actually just, 'cause I am one of these terrible people who does that because I've, like,

I mean I'm using a Mac. Right. And, you see when people are using a laptop and it starts to dim and they start like touching the touch pat thing to kinda make it brighten again. And you see people do that a few times. There's an application called Caffeine on a Mac, and that basically stops it going to sleep, right. And so that's great. I mean, but It's also then introduces the idea of like, am Is my a DD bad adult brain gonna remember to switch that back off again? Like, this are the things that come up. So this is actually something that I have direct experience, so that is very much hitting true with me, actually.

Okay. So that was the thing you did with, I'm calling it Scott Cloud, but I assume there was another name that we had for that, but that's, that work eventually became something that Neuralwatt. That's like you went from there and move into this stuff, right?

Scott Chamberlin: Right. So, Scott Cloud or Carbon, Net Zero Cloud, was basically a science experiment. And I wanted to deploy it for the purposes of just really seeing, you learn so much when things are in production and you have real users, but before I did it, I started talking to a lot of people I trusted in my network.

And one of my old colleagues from Microsoft and a good friend of mine, he really dug into it and started pushing me on like some serious questions like, "well, what does this really impact in terms of energy?" Like it was a CO2 optimization exercise, was that project. And he's like, "well what's the impact on energy?

What's the impact on AI?" And actually to, Asim Hussain, he is, he's asked the same question. He's like, "you can't release anything today," and this is, let's rewind, like a year ago, he's like, "you can't release anything today that doesn't have some story about AI," right? And this was just a basic just compute platform with nothing specific about AI.

So both of those comments really struck home. I was like, okay, I gotta like figure out this AI stuff we got. And I've gotta answer the energy question, it's wasn't hard 'cause it was already being measured as part of the platform, but I just was focused on CO2. And what it turned out was that there were some really interesting implications once we started to apply some of the optimization techniques to the GPU and how the GPU was being run from energy point of view, that ended up being in, that we, when we looked into it and it ended up being like potentially more impactful in the short term than the overall platform. And so, that colleague Chad Gibson, really convinced me in our discussions to really spin that piece out of the platform as a basis of the startup that we went and decided to build, which we call Neuralwatt now.

So yeah, what Neuralwatt really is, like the legacy of that, all that work, but the pieces that we could really take out of it that were focused on GPU energy optimization, within the context of AI, growth and energy demands, because those are becoming really critical challenges, not just for just businesses, but there are critical challenges that are underlying all of our, the work against green software, underlying all of the work, and around trying to reduce emissions of compute as a whole.

Right? And we're just really looking at a new paradigm with the exponential increase in energy use of compute and what behaviors that's driving in terms of getting new generators online, as well as what is the user experience behaviors when LLMs are built into everything, LLMs or other AIs are built into everything?

And so I felt that was really important to get focused on as quickly as possible. And that's where we really, really jumped off, with Neuralwatt on.

Chris Adams: Oh, I see. 

Okay. So the, basically there is a chunk of like, usage and there's the ability to kind of make an improvement in the existing set of like, like a fleet of servers and everything. Like that's already could have deployed around the world. But you see this thing which is growing really fast.

And if we look at things like the International Energy Agency's own report, AI and Energy, they basically say over the next five years looks like it's gonna be a rough, their various projections are saying it's probably gonna be the same energy use as all data centers. So it makes more sense to try and blunt some of that shift as early as possible.

Or like that's where you felt like you had more chance for leverage essentially. 

Scott Chamberlin: More chance for leverage, more interest in really having an impact. Because, I mean, we were in really in a period of flat growth in terms of energy for data centers prior to the AI boom because the increase in use in data centers was basically equaled out by the improvement in energy efficiency of the systems themselves.

And there's a lot of factors that went into why that was really balancing, relatively balancing out, but the deployment of the GPUs and the deployment of massively parallel compute and utilization of those from the point of view of AI both training and inference, really changed that equation entirely. Right. And so basically from 2019 on, we've basically seen going from relatively flat growth in data centers to very steep ramp in terms of energy growth.

Chris Adams: Okay. Alright. Now we're gonna come back to Neuralwatt for a little bit later. Partly because the demo you shared was pretty actually quite cool actually, and I still haven't had anything that provides that kind of live information. But one thing that I did learn when I was asking about this, and this is probably speaks to your time when you're working in a number of larger companies, is that there is a bit of a art to get large companies who are largely driven by like, say, profits for the next quarter to actually invest in kind of transparency or sustainability measures. And one thing that I do know that when you were working at Microsoft, one thing I saw actually, and this is one thing I was surprised by when I was asked, I was asking on LinkedIn, like, okay, well if I'm using various AI tools, what's out there that can expose numbers to me?

And there was actually some work by a guy, Will Alpine, providing some metrics on existing AI for an existing kind of AI pipeline. that's one of the only tools I've seen that does expose the numbers or provide the numbers from the actual, the cloud provider themselves. And as I understood it, that wasn't a thing that was almost like a passion project that was funded by some internal kind of carbon fund or something.

Could you maybe talk a little bit about that and how that, and what it's like getting, I guess, large organizations to fund some ideas like that because I found that really interesting to see that, and I, and there was, and as I understand it, the way that there was actually a kind of pool of resources for employees to do that kind of work was actually quite novel.

And not something I've seen in that many, places before.

Scott Chamberlin: Yeah, no, I think that was great work and Will is, want to, I'm a big fan of Will's work and I had the fortune to collaborate with him at that period of both of our careers when really it was, I don't think carbon work is easy to get done anywhere, in my experience, but that, I think Microsoft had a little bit of forethought in terms of designing the carbon tax. And yeah, we did have the ability to really vet a mission vet projects that could have a material impact against Microsoft's net zero goals and get those funded by the carbon tax that was implemented internally.

And so the mechanism was, every, as Microsoft built the capability to audit and report on their carbon, they would assign a dollar value to that from teams and then that money went from those teams budget into a central budget that was then reallocated for carbon reduction goals.

And yeah, I think Will was really at the forefront of identifying that these AI and, we all just really said ML back then, but now we all just say AI, but this GPU energy use was a big driver of the growth and so he really did a ton of work to figure out what that looked like at scale, figure out the mechanics of really exposing it within the Hyperscale cloud environment, taking, essentially like NVIDIA's also done a great job in terms of keeping energy values in.

their APIs and exposed through their chips and through their drivers, so that you can use it fairly easy on GPU. I would say it's more challenging on CPUs to do so, or the rest of the system, but, so he like did a great job in collaboration with those interfaces to get that exposed into the Azure, I think it's the ML studio is what it's called.

So that it has been there for many years, this ability to see and audit your energy values, if you're using the Azure platform. 

Yeah, those super good work.

Chris Adams: Yeah, so this was the thing. I forget the name of it and I'm a bit embarrassed to actually forget it. But, let, I'm just gonna play back to what I think you're saying. 'Cause when I was reading about this is something that I hadn't seen in that many other organizations. So like there's an internal carbon levy, which is basically for every ton that gets emitted, there was like a kind of a dollar amount allocated to that. And that went to like a kind of internal, let's call it a carbon war chest, right? So like there's a bunch of money that you could use. And then any member of staff was basically then able to say, I think we should use some of this to deliver this thing because we think it's gonna provide some savings or it's gonna help us hit our whatever kind of sustainability targets we actually have.

And one of the things that came outta that was essentially, actual meaningful energy report energy figures, if you're using these tools, and this is something that no, the other clouds, you're definitely not gonna get from Amazon right now. Google will show you the carbon but won't show you the energy.

And if you're using chat GPT, you definitely can't see this stuff. But it sounds like the APIs do exist. So it's just a, it has been largely a case of staff being prepared, they're being kind of will inside the system. And people being able to kind of win those, some of those fights to get people to allocate time and money to actually make this thing that's available for people, right? 

Scott Chamberlin: The Nvidia APIs definitely exist. I think the challenge is the methodology and the standards, right? So, within a cloud there's a lot of like complexity around how cycles and compute is getting assigned to users and how do you fairly and accurately count for that? GPUs happen to be a little bit simpler 'cause we tend to allocate a single chip to a single user at a single time.

Whereas in like CPUs, there's a lot of like hyper threading, most clouds are moving to over subscription or even just single hardware threads are 10 are starting to get shared between multiple users. And how do we allocate the, first the energy, all this starts with energy, how to allocate first the energy, and then the CO2 based on a location.

And then, the big complexity in terms of the perception that these clouds want to have around net zero. They're, they want to, everyone wants to say they're net zero for a market-based mechanic. And what's the prevailing viewpoint within the, what is allowed with the GHG protocol or what is the perception that the marketing team wants to have?

Is a lot of the challenges. it tends to, at least in the GPU energy, there's not like huge technical challenges, but there's a lot of like marketing and 

accounting and methodology challenges to overcome.

Chris Adams: So that's interesting. Well, so I did an interview with Kate Goldenring who was working at Fermyon at the time. We'll share a link to that for people and I will also share some links to both the internal carbon levy and how essentially large organizations have funded this kind of like climate kind of green software stuff internally.

'Cause I think other people working inside their companies will kind of want, will find that useful. But I'm just gonna play back to you a little bit about what you said there and then we'll talk a little bit about the, demo you shared with me. So it does seem like, so GPUs like, the thing that's used for AI accelerators, they can provide the numbers.

And that is actually something that's technically possible a lot of the time. And it sounds like that might be kind of tech technically slightly less complex at one level than way the way people sell kind of cloud computing. 'cause when we did the interview with Kate Goldenring, and we'll share the link to that, she basically told, she could have explained to me that, okay, let's say there is a server and it's got maybe, say 32 little processes like, cores inside this, what tends to happen, because not everyone is using all 32 cores at all the same time, you can pretty much get away with selling maybe 40 or 50 cores because not everyone's using all the same tool, all the cores at the same time. And that allows you to basically, essentially sell more compute.

So end up having, you make slightly more money and you end up having a much more kinda like profitable service. And that's been one of the kind of offers of cloud. And also from the perspective of people who are actually customers that is providing a degree of efficiency. So if you have, like, if you don't need to build another server because that one server is able to serve more customers, then there's a kinda hardware efficiency argument.

But it sounds like you're saying that with GPUs, you don't have that kind of over a subscription thing, so you could get the numbers, but there's a whole bunch of other things that might make it a bit more complicated elsewhere, simply because it's a new domain and we are finding out there are new things that happen with GpUs, for example.

Scott Chamberlin: Yeah. So, yeah, that's exactly what I was trying to say. And I think we are seeing emerging GPU over subscription, GPU sharing. So at the end of that will probably change at some point and at scale. It's certainly the technology is there. Like I think NVIDIA's acquisition of run.ai, enables some of this GPU sharing and that, they acquired that company of like six months ago and It's now open source and so people can take advantage of that.

But yes, I think that the core principle is like, from a embodied admissions point of view and in a, green software point of view, it's relatively a good practice to drive up the utilization of these embodied missions you've already like purchased and deployed. There are a lot, some performance implications around doing the sharing that how, it gives back user experience, but today the really, the state of the art is GPU, is that it's mostly just singly allocated and fully utilized when it's utilized or it's not fully utilized, but it's utilized for a single customer, at a time. But that is certainly changing.

Chris Adams: Oh, okay. So I basically, if I'm using a tool, then I'm not sharing it with anyone else in the same way that we typically we'd be doing with cloud and that, okay, that probably helps me understand that cool demo you shared with me then. So maybe we'll just talk a little bit about that. 'cause this was actually pretty, pretty neat when I actually asked that when you showed like, here's a video of literally the thing you wished existed, that was kind of handy.

Right? So, basically if you, we will share the link to the video, but the key thing that Scott shared with me was that using tools like say a chat GPT thing or anthropic where I'm asking questions and I'll see kind of tokens come out in us when I'm asking a question. It we were, we basically saw charts of realtime energy usage and it changing depending on what I was actually doing.

And, maybe you could talk a little bit about actually what's actually going on there and how you came to that. Because it sounds like Neuralwatt wasn't just about trying to provide some transparency. There's actually some other things you can do. So not only do you see it, but you can manage some of the energy use in the middle of a, for like an LLM session, for example, right? 

Scott Chamberlin: So yeah, at the first stage, the question is really just what is, can we measure what's happening today and what does it really look like in terms of how you typically deploy, say, a chat interface or inference system? So, like I was mentioning, we have ability fairly easily because NVIDIA does great work in this space to read those values on the GPU specifically, again, 

there's system implications for what's happening on the CPU what's happening on the network, the discs.

They tend to be outstripped by far because these GPUs use so much energy. 

But so, the first step in really that demo is really just to show what the behavior is like because what we ultimately do within the Neuralwatt code is we take over all of the energy management, all of the system, and We train our own models to basically shift the behavior of servers from one that is focused on maximizing performance for the available energy to balancing the performance for the energy in a energy efficiency mode, essentially. So we are training models that shift the behavior of energy of the computer for energy efficiency.

And so that's why we want to visualize multiple things. We want to visualize what the user experience trade off is. Again, going back to the user experience. You have to have great user experience if you're gonna be doing these things. And we want to visualize the potential gains and the potential value add for our customers in making this shift.

Because, I think we talk about, Jensen Huang made a quote at GTC that we love is that, we are a power constrained industry. 

Every AI factory is going to be power constrained in the future. And so what does compute look like if power is the number one limiting factor that you have to deal with?

So that's why we believe, we really want to enable service to operate differently than what they've done in the past. And we want there to be some, essentially think about, as, like energy awareness, right? That's the word I come back to. Like we want behavior of servers to be energy aware because of these power constraints.

Chris Adams: Ah, okay. Alright. you said a couple of things that I, that kind of, I just want to run by you to check. So, with the, there's this thing, there's all these new awarenesses, there's like carbon aware, then there's grid aware, then there's energy aware. This is clearly like an area where people were trying to figure out what to call things.

But the Neuralwatt, the neural, the thing that you folks are doing was basically okay, yes, you have access to the power and you can make that available, so I'm using something, but I'm just gonna try and run this by you and I might be right and you, I might need you to correct me on this, but it sounds a little bit like the thing that you are allowing to do is almost throttling the power that gets allocated to a given chip. 'Cause if you use, like things like Linux or certain systems they have, like they can introduce limits on the power that is allocated to a particular chip. But if you do that, that can have a unintended effect of making things run a little bit too slowly, for example.

But there, there's a bit of head, there's a bit of headroom there. But if you are able to go from giving absolute power, like, take as much power as you want to, having a kind of finite amount allocated, then you can basically still have a kind of a good, useful experience, but you can reduce it to the amount of power that's actually be consumed. It sounds like you're doing something a little bit like that, but with Neuralwatt thing. So rather than giving it, carte blanche to take all the power, you are kind of asking it to work within a kind of power envelope. That means that you're not having to use quite so much power to do the same kind of work.

Is that it?

Scott Chamberlin: Yeah. So if you go back to the history of like, before we had GPUs everywhere, the CPUs have fairly, let's call 'em like moderate level sophistication of terms of power management. They have sleep states, they have performance states, and there's components that run on the OS that are called, basically CPU governors that govern how a CPU behaves relative to various constraints.

And so, when you allocate a, let's say a Linux VM in the cloud, I don't know why this is, but a lot of 'em get default allocated with a, I'm the name of, it's slipping in my mind, but there's about five default CPU governors in the default Linux Distros, and they get out allocated with the power save one, actually.

And so what it does, it actually limits the top frequencies that you can get to, but it essentially is balancing power and performance is kind of the default that you get allocated. You can check these things, you can change it to a performance mode, which basically is gonna use all of the capability of the processor at a much higher energy use.

And, but on the GPU it's a lot less sophisticated, right? There's, GPUs don't tend to support any sleep states other than just power off and on. And they do have different performance states, but they're not as sophisticated as the CPU has historically been. And so essentially we are inserting ourselves into the OS Neuralwatt and managing it in a more sophisticated manner around exactly how you're describing.

We're making the trade off and we're learning the trade off really through modeling. We're learning the trade off to maintain great user experience, but get power gains, power savings, with our technology, and doing this across the system. So, yes, I think your description essentially, very good. And, we're just essentially adding a level sophistication into the OS, than what exists today.

Chris Adams: Okay. So basically, rather than being able to pull infinite power is, has, like, it's an upper limit by how much it can pull, but you'd probably want to kind of, the reason you're doing some of the training is you're based on how people use this, you'd like the upper limit, the kind of, the upper limit available to what's actually being needed so that you've, you're still giving enough room, but you're not, you're delivering some kind of savings.

Scott Chamberlin: Yeah, and it's important to understand that there's, it's fairly complex, which is why we train models to do this rather than do it, 

Chris Adams: Like sit at one level and just one and done. Yeah.

Scott Chamberlin: Because think about like a LLM, right? So there's essentially two large phases in inference for an LLM. And one of the first phase is really compute heavy, and then the second phase is more memory heavy. And so we can use different, power performance trade-offs in those phases. And understanding what those phases are and what the transition looks like from a reservable state is part of what we do. And then the GPU is just one part of the larger system, right?

It's engaged in the CPU. A lot of these LLMs are engaged in the network. And so how do we balance all the, tradeoffs so to maintain the great user experience for the best amount of power efficiency? That's essentially like what we're, our fitness function is when we're essentially training.

Chris Adams: Ah. I think I understand that now. And, and what you said about the, those two phases, presumably that's like one of, one of it is like taking a model, loading it to something a bit like memory. And then there's a second part which might be accessing, doing the lookups against that memory. Because you need to have the thing, the lookup process when you're seeing the text come out that is quite memory intensive rather than CPU intensive.

So if you're able to change how the power is used to reflect that, then you can deliver some kind of savings inside that. And if you scale that up a data center level that's like, like 10%, 20, I mean, maybe even, yeah. Do you have an idea of like what kind

Scott Chamberlin: We tend to shoot for at least 20% improvements in what I would say performance per unit of energy. So tokens per Joule is the metric I tend to come back to fairly often. But again, how exactly you measure energy on these things, what is the right metric, I think is, I think you need to use a bunch of 'em.

But, I like tokens per Joule 'cause it's fairly simple and it's fairly, it's easy to normalize. But like, it's, it gets super interesting in this conversation about like, inference time, compute and thinking LLMs and stuff like that. 'Cause they're generating tons and tons of tokens and not all of 'em are exposed to, essentially improve their output.

And so people use all their metrics, but they're harder to normalize. So, yeah, long, long story short, I tend to come back to tokens for Joule is my favorite, but,

Chris Adams: So what, so it sounds like the thing that you're working on doing is basically through kind of judicious use of like power envelopes that more accurately match what is actually being required by a GPU or anything like that, you're able to deliver some savings that way. That's essentially, that's one of the things, and like you said before when we were talking about kind of Scott Cloud, that's transparent to the user.

I don't have to be thinking about my prompt or something like that. This is happening in the background, so I don't really, my experience isn't changed, but I am basically receipt of that, 20% of power is basically not being turned into carbon dioxide in the sky, for example, but it's basically the same other than that though.

Scott Chamberlin: That's the goal, right? Essentially we've informed our work continually on number one, user experience has to be great, number two, developer experience has to be great, which means the developer shouldn't have to care about it. So, yeah, it's a single container download, it runs in the background.

It does all the governance in a fairly transparent way. But you know, all throughout as well, like, we actually have CO2 optimization mode as well, so we can do all of this. Fall mode is really energy, but we actually can flip a switch and we get an extra degree of variability where if we're optimizing on CO2, average or marginal emissions. so, we can vary those behaviors of the system relative to the intensity of the carbon in the grid as well. So

Chris Adams: Okay. Or possibly, if not the grid, then the 29 data, 29 gas turbines that are powering that data center today, for example.

Scott Chamberlin: I think that's an emerging problem. And I actually would love to talk to somebody that has a data center that is having a microgrid with a gas turbine, because I actually do believe there's additional optimization available for these little microgrids, that are being deployed alongside these data centers.

If you were to do plummet all the way through in this energy, again, go back to energy awareness, right. Like if your servers knew how your microgrids were behaving relative to the macro grid that they were connected to, like, there's so many interesting optimizations available and, people are looking at this from the point of view of the site infrastructure, but like the reality is all of the load is generated by the compute on the server.

Right. And that's what we're really trying to bring it all the way through to where load originates and the behavior where, while maintain that user experience. So

Chris Adams: Okay. So you said something interesting there, I think, about this part, like the fact that a, you mentioned before that GPU usage is a little bit less sophisticated right now. You said it's either all on and all off. And when you've got something which is like, the power, the multiple thousands of homes worth of power, that can be all on and all off very, quickly.

That's surely gotta have to have some kind of implications, within the data center, but also any poor people connected to the data center. Right? Because, if you are basically making the equivalent to tens of thousands of people disappear from the grid, then reappear from the grid like inside in less than a second, there's gotta be some like a knock on effect for that.

Like, you spoke about like gas turbines. It's like, is there, do you reckon we're gonna see people finding something in the middle to act like a kind of shock absorber for these changes that kind of go through to the grid? Because if you're operating a grid, that feels like the kind of thing that's gonna really mess with you being able to provide like a consistent, kind quality of power to everyone else.

If you've got the biggest use of energy also swinging up and down the most as well, surely.

Scott Chamberlin: Yeah, and it's certainly like a, I don't know if existential problem is the right word, but it's certainly a emerging, very challenging problem, 

Chris Adams: Mm-hmm. 

Scott Chamberlin: within the space of data centers is the essentially like seeking up of some of these behaviors among the GPUs to cause correlated spikes and drops in power and it's not, it has implications within your data center infrastructure and, to the point where we hear from customers that they're no longer deploying some of their UPSs or their battery backups within the GPU clusters because they don't have the electronics to handle the loads shifting so dramatically, to the point where we're also getting emerging challenges in the grid in terms of how these loads ramp up or down and affect, say, I'm not gonna get into where I'm not an expert in terms of the generational, aspects of generation on the grid and maintaining frequency, but it has implications for that as well. But so, we, in the software we can certainly smooth those things out, but there's also, I mean, there's weird behaviors happening right now in terms of trying to manage this.

My favorite, and I don't know if you've heard of this too, Chris, is PyTorch has a mode now where they basically burn just empty cycles to keep the power from dropping down dramatically when, I think it's when weights are sinking, in PyTorch, I'm not exactly sure when it was implemented.

Because i've only read about it, but you know, when you maybe need to sink weights across your network and so some GPUs have to stop, what they've implemented is some busy work so that the power doesn't drop dramatically and cause this really spiky behavior. 

Chris Adams: Ah. 

Scott Chamberlin: So I think what 

Chris Adams: you're referring to, Yeah. So this the PyTorch_no_powerplant_blowup=1 they had, right? Yeah. This, I remember reading about this in semi analysis. It just blew my mind. The idea that you have to essentially, keep it running because the spike would be so damaging to the rest of the grid that they have to kind of simulate some power, so it doesn't, so they don't have that that change propagate through to the rest of the grid, basically.

Scott Chamberlin: Correct. And so, that's one of the, we look at problems like that, there's that problem in terms of the thinking of the way the problem if you train, start a training run

where all the GPUs basically start at the same time and create a surge. And, so, we help with some of those situations in our software.

But yes, I think that some of the behaviors that are getting implemented, like the, no_powerplant_blowup=1 , they're fairly, I would say they're probably not great from a green software point of view because anytime we are, we're, doing busy work, that's an opportunity to reduce energy, reduce CO2 and there probably are ways of just managing that in a bit with a bit more sophistication depending on the amount of, the scale that you're working at, that is, probably may have been more appropriate than that.

 So this is definitely 

Chris Adams: still needs 

Scott Chamberlin: to be looked at a little bit. 

Chris Adams: So like, I mean before like in the pre AI days, there was this notion of like a thundering herd problem where everything tries to use the same connection or do the same at the same time. It sounds like this kind PyTorch_no_powerplant_blowup=1 is essentially like the kind of AI equivalent to like seeing that problem coming and then realizing it's a much greater magnitude and then figuring out, okay, we need to find a elegant solution to this in the long run.

But right now we're just gonna use this thing for now. Because it turns out that having incredibly spiky power usage kind of propagating outside the data center wrecks all kinds of havoc basically. And we probably don't want to do that if we want to keep being connected to the grid.

Scott Chamberlin: Yeah. But at it's really a, spiky behavior at scale is really problematic. Yes.

Chris Adams: Okay, cool. Dude, I'm so sorry. We're totally down this kind of like, this AI energy spikiness rabbit hole, but I guess it is what happens

when 

Scott Chamberlin: it's certainly a, it's certainly customers are really interested in this because it's, it, I mean if we were to like bubble up one level, like there's this core challenge in the AI space where the energy people don't necessarily talk the same language as the software people.

And we, I think that's one place where maybe Hyperscale has a little bit more advantage 'cause it has emerged from software companies, but Hyperscale is not the only game in town and especially when we're going to neo clouds and stuff like that. And so, I think one of our like side goals is really how do we actually enable people talking energy and infrastructure to have the same conversations and create requirements and coordinate with the software people running the loads within the data centers? I think that's the only way to really solve this holistically. So,

Chris Adams: I think you're right. I mean, this is, to bring this to some of the kind of world of green software, I suppose, the Green Software Foundation did a merger with, I think they're called it, SSIA, the Sustainable Servers and Infrastructure Alliance. I think it's something like that. We had them on a couple of episodes a while ago, one where there was a whole discussion about, okay, how do, setting out some hardware standards to have this thing kind of crossing this barrier.

Because, like you said, it does it, as we've learned on this podcast, some changes you might make at AI level can have all these quite significant implications. Not just thinking about like the air quality and climate related issues of having masses and masses of on-premise gas turbines. But there's a whole thing about power quality, which is not something that you've had to think about in terms of relating to other people, but that's something that's clearly needs to be on the agenda as we go forward.

Just like, like responsible developers. I should, before we just kind of go further down there, I should just check, we just, we're just coming up to time. we've spent all this time talking about

 this and like we've mentioned a couple of projects. Are there any other things that aren't related to, like spiky AI power that you are looking at and you find, Hey, I wish, I'm on this podcast, I wish more people knew about this project here or that project there.

Like, are there any things that you are, you've, read about the news or any people's work that you're really ins impressed by and you wish more people knew about right now?

Scott Chamberlin: Yeah, I mean, I think a lot of people probably on this podcast probably know about AI Energy Score. Like I think that's a promising project. Like I really believe that we need to have the ability to understand both the energy and the CO2 implications of some of these models we're using and the ability to compare them and compare the trade-offs.

I do think that, the level of sophistication needs to get a bit higher because it's, right now it's super easy to trade off model size and energy. Like, I can go, single GPU and, but I'm trading off capabilities for that. So how do we, I think on one of my blog posts, it was someone's ideas.

Like you really have to normalize against the capabilities and the energy at the same time for making your decisions about what the right model is for your use cases relative to the energy available to say the CO2 goals you have. So, but yeah, I think eventually they'll get there in that project.

So I think that's a super promising project. 

Chris Adams: We will share a link to that. So we definitely got some of that stuff for the AI Energy Score, 'cause it's entirely open source and you can run it for open models, you can run it for private models and if you are someone with a budget you can require customers to, or you can require suppliers to publish the results to the leaderboard, which would be incredibly useful because this whole thing was about energy transparency and like.

Scott Chamberlin: Yeah,

Chris Adams: I'm glad you mentioned that. That's like one of the, I think that's one of the more useful tools out there that is actually relatively, like, relatively easy to kind of write into contracts or to put into a policy for a team to be using or team to be adopting, for example.

Scott Chamberlin: Correct. Yep. No, a big fan, so.

Chris Adams: Well that's good news for Boris. Boris, if you're hearing this then yeah, thumbs up, and the rest of the team there, I only mentioned Boris 'cause he's in one of the team I know and he's in the Climateaction.tech Slack that a few of us tend

Scott Chamberlin: Yeah. Boris and I talked last week. Yeah. A big fan of his work and I think Sasha Luccioni, who I actually never met, but yeah, I think she's also the project lead on that one.

Chris Adams: Oh, Scott, we are coming up to the time and I didn't get a chance to talk about anything going on in France, and with things like Mistral sharing some of their data, some of their environmental impact figures and stuff like that because it's actually, it's kind of, I mean, literal, just two days ago we had Mistral, the French kind of competitor to open AI,

they, for the first time started sharing some environmental figures and quite a lot of detail. More so than a single kind of like mention from Sam Altman about power, about the energy used by AI query. We've, we actually got quite a lot of data about the carbon and the water usage and stuff like that.

But no energy though. But that's something we'll have to speak about another time. So hopefully maybe I'll get, be able to get you on and we can talk a little bit about that and talk about, I don't know the off-grid data centers of Crusoe and all the things like that. But until then though, Scott, I really, I'm, I've really enjoyed this deep dive with you and I do hope that the, our listeners have been able to keep up as we go progressively more detailed.

And, if you have stayed with us, listeners, what we'll do is we'll make sure that we've got plenty of show notes so that people who are curious about any of this stuff can have plenty to read over the weekend. Scott, this has been loads of fun. Thank you so much for coming on and I hope you have a lovely day in Evergreen Town.

Scott Chamberlin: Thanks Chris.

Chris Adams: Alright, take care of yourself, Scott. Thanks. 

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware. foundation. That's greensoftware. foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Real Efficiency at Scale with Sean Varley Real Efficiency at Scale with Sean Varley Thu, 24 Jul 2025 08:00:00 GMT 47:30 8056w261 false https://podcasts.castplus.fm/e/68rrqxr8-real-efficiency-at-scale-with-sean-varley 68dc7d157be17a7f01356b1b 68dc7cef46a2532cdd8d1c23 Anne Currie is joined by Sean Varley, Chief Evangelist and VP of Business Development at Ampere Computing, a leader in building energy-efficient, cloud-native processors. They unpack the energy demands of AI, why power caps and utilization matter more than raw compute, and how to rethink metrics like performance-per-rack for a greener digital future. Sean also discusses Ampere’s role in the AI Platform Alliance, the company’s partnership with Rakuten, and how infrastructure choices impact the climate trajectory of AI. 115 Anne Currie is joined by Sean Varley, Chief Evangelist and VP of Business Development at Ampere Computing, a leader in building energy-efficient, cloud-native processors. They unpack the energy demands of AI, why power caps and utilization matter more than raw compute, and how to rethink metrics like performance-per-rack for a greener digital future. Sean also discusses Ampere’s role in the AI Platform Alliance, the company’s partnership with Rakuten, and how infrastructure choices impact the climate trajectory of AI.

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TRANSCRIPT BELOW:

Sean Varley: Because at the end of the day, if you want to be more sustainable, then just use less electricity. That's the whole point, right. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello and welcome to the World of Environment Variables, where we bring you the latest news and updates from the world of sustainable software. So I'm your guest host today. It's not, you're not hearing the usual dulcet tones of Chris Adams. My name is Anne Currie. And today we'll be diving into a pressing and timely topic, how to scale AI infrastructure sustainably in a world where energy constraints are becoming a hard limit. And that means that we are gonna be, have to be a little bit more clever and a little bit more careful when we choose the chips we run on. So it's tempting to believe that innovation alone will lead us towards greener compute, but in reality, real sustainability gains happen when efficiency becomes a business imperative when performance per watt, cost and carbon footprint are all measured and all have weight. So, that's where companies like Ampere come in, with cloud native energy efficient approaches to chip design. They're rethinking how we power the AI boom, not just faster but smarter. It's a strategy that aligns directly with Green Software Foundation's mission to reduce carbon emissions from the software lifecycle, particularly in the cloud. So in this episode, we'll explore what this looks like at scale and what we can learn from Ampere's approach to real world efficiency. So what did it take? What does it take to make an AI ready infrastructure that's both powerful, effective, and sustainable? Let's find out. And today we have with us Sean Varley from Ampere.

So Sean, welcome to the show. Can you tell us a little bit about yourself?

Sean Varley: Yeah, absolutely Anne, and thanks first for having me on the podcast. I'm a big fan, so, I'm looking forward to this conversation. So I'm the chief evangelist of Ampere Computing. And, I, now what that means is that we run a lot of the ecosystem building and all of the partnership kind of, works that go on to support our silicon products in the marketplace.

And also, build a lot of awareness right around some of these concepts you introduced. You know, all of the, you know, kind of building out that awareness around sustainability and power efficiency and how that also really kinda works, within different workload contexts and workload context change over time.

So all of those sorts of things are kind of in scope, for the evangelism role.

Anne Currie: That's, that is fantastic. So I'll just introduce myself a little bit as well. My name is Anne Currie. If you haven't heard the podcast before, I am one of the authors of O'Reilly's new book, Building Green Software, which I, as I always say, everybody who's listening to this podcast should read Building Green Software.

That was, that is entirely why we wrote the book. I'm also the CEO of the training and Green Consulting Company as Strategically Green. So, hit me up on LinkedIn if you want to talk a little bit about training consultancy, but back to the, back to the podcast. Oh, and I need to remember that everything we'll be talking about today, there will be links about it in the show notes.

So you don't need to worry about writing down URLs or anything. Just look at the show notes before. So, now, I'm actually gonna start off the question by harking, start off the podcast by harking back to somebody that we had on the podcast a couple of months ago. A chap called, Charles Humble. And his, the assertion that he was making was that we all need to wake up to the fact that there isn't just one chip anymore, there isn't a default chip anymore that everybody uses and is kind of good enough for the best in all circumstances to use. when you are, setting up infrastructure, or in the cloud for example, and you have the dropdown that picks witch chip you're going use, the defaults might be Intel, for example. That is no longer a no-brainer, that you just go with the default. There are lots and lots of options, to the extent that, I mean, Ampere is a new chip company that decided to go into the market. So one of the questions that I have is why? You know, what gap did you see that it was worth coming in to fill?

Because 10 years ago we would've said there was no real gap, wouldn't we?

Sean Varley: That's right. Yeah. Actually it was a much more homogenous ecosystem back in those days. You know, and I, full disclosure, I came from Intel. I did a lot of time there. But about seven years, six years ago, I chose to come to Ampere. and part of this was the evolution of the market, right?

The cloud market came in and changed a lot of different things, because there's kind of classically, especially in server computing, there's sort of the enterprise and the cloud and the cloud of course has had a lot of years to grow now. And the way that the cloud has evolved was to, really kind of, you know, push all of the computing

to the top of its performance, the peak performance that you could get out of it. But there, you know, nobody really paid attention to power. Going back, you know, 10, 15, 20 years, nobody cared. And those were in the early days of Moore's law. And, part of what happened with Moore's Law is as frequencies, you know, grew then so did performance, you know, linearly.

And I think that sort of trained into the industry a lot of complacency. And that complacency then became more ossified into the, you know, the way that people architected and what they paid attention to, metrics that they paid attention to when they built chips. But going back about seven, eight years, we actually saw that there was a major opportunity to get equal or better performance for about half the power. And that's kind of what forms some of our interest in building a company like Ampere. Now, of course, Ampere, since its inception has been about sustainable computing and, me being personally sort of in interested in sustainability and green technology and those sorts of things

just outside of the, my profession, you know, I, was super happy to come to a company like Ampere that had that in its core. 

Anne Currie: And that's very interesting. So really and Ampere, your chip is a, is an X86 chip, so it's not competing against ARM is more competing against Intel and AMD.

Sean Varley: It's actually, it is an ARM chip. It's a, it's based on the ARM instruction set. And, yeah, so it's kind of an interesting dynamic, right? There was, there's been a number of different compute architectures that have been put into the marketplace. and the X86 instruction set classically by Intel and a MD who followed them, have dominated the marketplace, right?

And, well at least they've dominated the server marketplace. Now, ARM has traditionally been in mobile handsets, embedded computing, things like this. But part of where the, that architecture was built and its roots were grown up in more power-conscious markets, you know, because anything running on a battery you want to have be pretty power miserly

Anne Currie: Yeah.

Sean Varley: to use the word. So yeah, the ARM instruction set and the ARM architecture did offer us some opportunities to get a lift when we first, when we were a young company, but it doesn't necessarily have that much of a bearing on overall what we can do for sustainability, because there's many things that we can do for sustainability and the instruction set of the architecture is only one of them.

And it's a much smaller one. I, it is probably way too detailed to get into on this podcast, but it is one factor and so yes, we are ARM instruction set based and about four years back, we actually started creating our own course, on the instruction set. And that's sort of been an evolution for us because we wanted to maintain this focus on sustainability, low power consumption, and of course, along with that, high performance. 

Anne Currie: Oh, that's interesting. So as you say, the instruction set is only one part of what you're attempting, of what you're doing to be more efficient, to be, to use less power to per operation. What else are you, what else are you doing?

Sean Varley: Oh, many things. Yeah. So the part of this that kind gets away from the instruction set is how you architect and how you present the compute to the user, which may get further into kind of some of your background and interest around software because, part of what we've done is architect a chip or a set of family of chips that now that are very, well, they start off with area efficiency in the core.

And how we do a lot of that is we focus, on cache, cache configuration. So we, you, we use a lot more of what we call L2 cache, which is right next to the cores that helps us get performance. We've, kind of steered away from the X86 industry, which is much more of a larger L3 cache, which is a much bigger piece and area, part of the area of the chip.

And so that's one of the things that we've done. We've, but we've also kind of just decided that many of the features of the X86 architecture are not necessary for high performance or efficiency in the cloud. And part of this is because software has evolved. So what are those things? Turbo, for example. Turbo is a feature that kind of moves the frequency of the actual cores around, depending on how much thermal headroom the chip has. And so if you have a small amount of cores, the frequency could be really high. But if you have a lot amount of cores doing things, then you, then it pulls the frequency back down low because you've only got so much thermal budget in the chip. So we got, we said, oh, we're just gonna run all of our cores at the same frequency.

And we've designed ourselves at a point, in the, you know, voltage frequency curve that allows us that thermal headroom. Now, that's just one other concept, but, so many things have really kind of, you know, created this capability for us to focus on performance per watt and all of those things are contributors to how you get more efficient.

Anne Currie: Now that's, that is very interesting. So why, yeah, it's, what was your original motivation? Was it for the cloud? What did you, were you designing with the cloud in mind or were you designing more with the devices in mind?

Sean Varley: Yeah, we absolutely, we're in, 

are, you know, designing for cloud, because, 

cloud is such a big mover in how things evolve, right? I mean, if you're looking at markets, there's always market movers, market makers and the way that you can best accomplish getting something done. So if our goal is to create a more sustainable computing infrastructure, and now in the age of ai, that's even become more important, but, if our goal is that, then we need to go after the influencers, right? The people that will actually, you know, move this, the needle. And so the cloud was really important and we've, had a kind of this, you know, overall focus on that market, but it's not,

our technology is not limited to it. Our technology is, you know, by far and away much more power efficient anywhere from all the way out at the edge and devices and automotive and networks all the way into the cloud. But the cloud also gave us a lot of the paradigms that we have also been attached to.

So when we talk about cloud native computing, we're really kind of hearkening to that software model that was built out of the cloud. The software model built out of the cloud is something that they call serverless, in the older days. Or now it's, you know, microservices and some of these sorts of concepts.

And so as software has grown, so have we, you know, kind of put together a hardware architecture that meets that software where it is, because what that software is about is lots of processes, you know, working together to formulate a big service. And so those little processes are very latency sensitive.

They need to have predictability, and that's what we provide is our architectures, lots of cores that all run at the same kind of pace, and so you get high degree of predictability out of that architecture, which then makes the software and the entire service more efficient.

Anne Currie: So that's, that is very interesting. And I hadn't realized that. So obviously things like serverless going on in clouds, that is a, the software that's actually running on the chip is software that was written by usually the cloud provider. You know, the, clouds wrote that software.

So it, you are isolating from, it is, one of the interesting things about high performance software is that it's hard, really hard to write. In fact, in Building Green Software, I always talk about people about don't start there, it's really hard. You need specialist skills. You need to know the difference between L2 caches and L3 caches.

And you need to know how to use them. And the vast majority of engineers do not have those skills. And it will never achieve, will never acquire those skills. But the cloud providers where they are managing, providing managed services that you are using, like, you're just writing a code snippet that's running in Lambda or whatever. You are not writing the code that makes that snippet run. You're not writing the code that talks to the chip. Really super specialist engineers at AWS or Azure or whatever are writing that code.

So is that the, is that the move that you were anticipating?

Sean Varley: Absolutely. I mean, that's a big part of it, right? And as you just articulated a lot of the platform as a service kind of code, right, so that managed service that's coming out of a hyperscaler is, you know, built to be cloud native. It's built to be very microservice based.

And it has a lot of what we call SLAs in the industry, right? Service level agreements, which mean that you need to have a lot of, different functions complete, on time for the rest of the code to work as it was designed. And as you said, it is a much more complex way to do things, but the overall software industry has started to make it a lot easier to do this, right. And things like containers, you know, which are inherently much more efficient. you know, sort of, you know, entities, yeah, like, footprints, images is what I was really kind of going for there. They're, they are, you know, already you've cut out a lot of the fat, right, in the software. You've gotten down to a function. You mentioned Lambda, for example. A function is the most, you know, sort of nuclear piece of code that you could potentially write, I suppose, to do something. And so all of these functions working together, they need these types of execution architectures to really thrive and yes, you're right, that developers, you know, they have come a long way in having these serviceable components in the industry. You know, Docker sort of changed the world about, what is it, 10 years ago now, maybe longer. And all of a sudden people could go and grab these little units of, what they call endpoints in kind of, you know, kinda software lingo, you know? And so if I wanna get something done, I can go grab this container that will do it. And those containers and the number of containers that you can run on a cloud native architecture like Ampere's is vastly better than what you can find in most X86 architectures.

Why? Because these things run on cores. Right. And we have a lot of them.

Anne Currie: Yeah, so that is very interesting, the, so I also. Everybody who's listening to the podcast must also in like my other book on this very subjects, which is called the Cloud Native Attitude. And it was about why Docker is so important, why containers are so important.

Because they wrapped up, they allowed you to wrap up programs and then move those programs around so that's, it basically put a little handle that made you be able to move stuff around and started and stop it and orchestrate it. And what that meant was

Sean Varley: I love that analogy, by the way, the handle, and you just pick it up and move it anywhere you want it, right.

Anne Currie: Yeah, because really that was what, 

that was all that Docker really did. It wrapped something that was, a fairly standard Linux concept that had been around quite a long time. And it put a nice little API on it, which was effectively a handle, which let other tools move it around.

And then you've got orchestrators like Kubernetes, but you also got lots of other orchestrators too.

But what that meant in the cloud native world was that you could have services that were written by super experts or open source. So it had lots of experts from all over the place, writing them and tuning them and improving them and get, letting Moore's law and write, well, not Moore's Law, Wright's Law, which the law systems get better if you use them. Yet it gave people a chance to go in and improve things. But have those be the people who are improving things, be specialists and let that specialist code was incredibly hard to write, be shared with others. So you're kind of amortizing the incredibly difficult work. So fundamentally, what you are saying, and I think this is, you know, I, you could not be singing more from my hymn sheet on this, is that it's really hard to write code that interfaces well and uses CPUs well so that they're highly efficient and you get code efficiency and you get operational efficiency really hard to do. But, if you can do it, if you can find a way that it doesn't require every single person to write that code, which is really hard, but you can share it and leverage it through open source implementations or cloud implementations written by the cloud providers, then suddenly your CPUs can do all kinds of stuff that they couldn't have done previously.

Is that what you're saying?

Sean Varley: Absolutely, and I would've, I was gonna put tack on one little thing to your line was it's really hard to do this by yourself, right? 

And this is where the open source communities and all of these sorts of things that have really kind of revolutionized, especially the cloud, coming back to that topic that we were talking about.

Because the cloud has really been, I think evolved on the back of open source software, right? And that radically changed how software was written. But now coming back to your package and your handle, you can go get a function that was written in and probably optimize by somebody who spent the time to go look at how it ran in a specific architecture.

And now with things like Docker and GitHub and all these other tool chains where you can go out and grab containers that are already binary compiled for that instruction set that we were talking about earlier, this makes things a lot more accessible to a lot more people. And in some ways, you have to trust that, you know, this code was written to get the most out of this architecture, but sometimes there's labeling, right?

This was written for that, or, you know, a classic example in code is that certain types of algorithms get inline assembly done to make them the most efficient that they can be. And all of that usually was done in the service of performance, right? But one of the cool things about trying to do things in service of performance is that you can actually usually get better power efficiency out of that if you use the right methodologies. Now, if the performance came solely from something that was frequency scaled, that's not gonna be good for power necessarily. But if it's going to be done in what we call a scale out mechanism where you get your performance by scheduling things on, not just one core, but many cores,

and they can all work together in service of that one function, then that can actually create a real opportunity for power efficiency. 

Anne Currie: Yeah, so that maps back to something that in Building Green Software we talk about, which is utilization. So, you know, a machine is. And a machine use needs to be really well utilized because if it's not well utilized, it still uses pretty much the same power, but it's not doing anything if it's not actually doing anything. It's not doing anything useful with it. It's just a waste.

Sean Varley: I'm so glad you brought this up.

Anne Currie: Well go for it. Go for it. You know, you are the expert in this area.

Sean Varley: Oh, no. Yeah, I think you're, exactly right. You hit it on the, the nail on the head, and the part of the problem in the world today is that you have a lot of machines out there that are underutilized, and that low utilization of these machines contributes a lot to power inefficiency. Now I'm gonna come back to some other things that maybe go back to the, where we were talking about in certain terms of processor architecture, but is still super relevant to code and efficiency. So the one thing going back to everybody only had one choice on the menu, which was Intel at the time,

was that architecture instilled some biases or some habits, pick your sort of word here, but, people defaulted to a certain type of behavior. Now, one of the things that it trained into everyone out there in the world, especially code writers and infrastructure managers, was that you didn't ever get over about 50% utilization of the processor because what happened is if you did then at, after 50% all of the SLAs I was talking about earlier, those, that service level agreement where things are behaving nicely, went out the window, right? Nobody could then get predictable performance out of their code because why?

Hyperthreading. So Hyperthreading is where you share a core with two execution threads. That sharing at once you got went over 50%, then all of a sudden you are heavily dependent on the hyperthreading to get any more performance. And what that does is it just messes up all the predictability of the rest of the processes operating on that machine.

So the net result was train people 50% or below. Now our processors, if you're running at 50% or below, that means you're only using half of our complete capacity, right? So we've had to go out and train people, "no, run this thing at 80 or 90% utilization because that's where you hit this sweet spot," right?

That's where you're going to save 30, 40, 50% of the power required to do something because that's how we architected the chip. So these are the kinds of biases and habits and sort of rules of thumb that we all end up having to kind of combat. 

Anne Currie: Yeah, and it's interesting. I mean, that's say as, you say that completely maps back to a world in which we just weren't thinking about power, you know, we just didn't care about the level of waste. So, I, quite often en enterprise, enterprise engineers, architects are very used these days to the idea of lean, and agile.

It's about reduction of waste. And the biggest waste there is, underutilized machines. And we don't tend to think about it. And as you say, in this part, because we were trained now to thinking about it. 

Sean Varley: And also people were, didn't really care there, you know, back in the day, you know, going back again, 10, 15, 20 years ago, people didn't really care that much about how much power was consumed in their computing tasks because it wasn't top of mind for people, right. And frankly, we consumed a lot less of it, primarily because we had a lot of less infrastructure in service in, you know, worldwide I'm talking about, but also because, you know, back in, you know, in older chip architectures and older silicon process technology, it consumed less power. Now as we've gotten into modern process technology, that whole thing has changed. And now you've got chips that can burn hundreds and hundreds of watts by themselves, not to mention the GPUs, which can burn thousands of watts. And that's just a wholesale shift in, you know, kind of the trajectory of power consumption for our industry.

Anne Currie: So you've brought up AI and GPUs there, and obviously, and even more AI focused chips that are even potentially more power hungry. How does Ampere help? 'Cause Ampere is a CPU, not a GPU or a TPU, how does it

fit into this story?

Sean Varley: It fits in a number of different ways. So, maybe a couple of definitions for people. CPU is a general purpose processor, right? If we, it runs everything, and in, you know, kind of everyday parlance, it's an omnivore. It can do a lot of different things and it can, you know, do a lot of theso pretty well, but what you have is an industry that is evolving into more specialized computing. That's what A GPU is. But there are many other examples, accelerators and others types of, you know, kind of, not homogenous type computing, but heterogeneous computing, where you've got different specializations. GPU is just one of those.

And, but in AI, what we've found is, that the GPU architecture, of course, has driven that overall workload, you know, to a point where the power consumption of that type of a workload, because there's a lot of computational horsepower required to do, AI models

and, so that has driven, you know, the industry up into the right in terms of power consumption. And that has, you know, there's a bias now in the industry about, well, if you're gonna do AI, it's gonna just take a ton of power to do it. The answer to that is, "maybe..." right? Because what you've got is, maybe a little bit of a lack of education about the whole pantheon of AI, you know, kind of execution environments and models and things like that, and frameworks and all sorts of things.

All of these things matter because a CPU can do a really good job of doing the inference operation, for AI and it can do an excellent job of doing it efficiently. 'Cause coming back to your utilization, you know, kind of argument we were talking about earlier. Now, in GPUs, the utilization is even far more important because as you said, it sits there and burns a lot of power no matter what.

So if you're not using it, then you definitely don't want that thing just kind of, you know, running the meter. And so utilization has become a huge topic in GpU, you know, kinda circles and so, but CPUs kind of have a ton of technology in them for low power when not utilized.

You know, that's been a famous, you know, kind of set of capabilities. But also AI is not one thing. And so AI is the combination of specialized things that are being run in models and then a lot of generalized stuff that can be run and is run on CPUs. So where we come in, Ampere's concept for all that is what we call AI compute.

So AI compute is the ability to do a lot of the general purpose stuff and quite a bit of that AI specific stuff on CPUs, and you have a much more kind of flexible platform for doing either.

Anne Currie: So it's interesting. Do you, now I'm going show my own ignorance here 'cause I've just thought of this and therefore I'm gonna go horribly roll with it. There are kind of a, there are kind of platforms to help people be more hardware agnostic when it comes to stuff like, Triton, is it, and,

are there things that, do you fit in with anything like that,

or is it just, does everybody have to kind of decipher themselves whether they're gonna be, which bit of hardware they're gonna be using?

Sean Varley: Oh man. We could do a whole podcast on this. Okay.

Yeah. Let me try to like, break this down at least in a couple of simple terms. So, yes, I mean, there's two, first of all, there's two main operations in AI. There's training and there's inference. Now training is very high batch, high consumption, high utilization of a lot of compute.

So we will think of this as maybe racks full of GPUs because it's also high precision and it's a big, it's a kind of a very uniform operation, right? once you set it, you kind of forget it and you let it run for famously weeks or months, right? And it turns out a model, but once the model's turned out, it can be run on a lot of different frameworks.

Right. And so this is where, you know, that platform of choice part comes back in because inference is the operation where you're gonna get some result, some decision, some output out of a model. And that's gonna be the, by far and away the vast majority of AI operations of the future, right?

We've been, 

we're still training a lot of models, don't get me wrong. But in the future is gonna be a lot of inference and that particular operation doesn't require as high a precision. It doesn't require a lot of the same characteristics there that are required in training. Now that can be run a lot of different places on these open source frameworks.

And also what you're starting to see is now specializations in certain model genres. A genre, I would say is like a llama genre, you know, from meta, you know, they've built all of their own, much more efficient, you know, kind of frameworks in their CPP, their C++ implementation of the llama frameworks.

So you got specialization going on there. All that stuff can run on CPUs and GPUs and accelerators and lots of other types of things. Now it becomes more of a choice. What do I want to focus on when I do this AI operation? Do I really want to focus on something that's going to, you know, get me the fastest result, you know, ever?

Or can I maybe let that sort of thing run for a while and then give me results as they come? And a lot of this sort of decision making, use case based decision making will dictate a lot of the power efficiency of the actual AI operation.

Anne Currie: That is interesting. Thank you very much for that. So Ampere, you see, so Ampere is basically in that second thing, you are one of the options for inference.

Sean Varley: That's right, yeah. And we actually, we, our sort of whole thought process around this is, that we want to provide a very utilitarian resource, right? Maybe it's the right word. Because the utilitarianism of it is not that it's like low performance or anything like that, it's still high performance.

It's just that you're not going to necessarily need, you know, all of the resources of the most expensive or the most, kind of, parameter-laden model. So, 'cause models come in, a lot of parameters. We hear this term, right? You know, up to trillions of parameters, down to millions of parameters.

And somewhere in the middle is kind of that sweet spot right now, right? Somewhere in the 10 to 30 million per, or billion, sorry, billion parameter range and that sort of thing requires optimization and distillation. So we are building a resource that will be that sort of utility belt for AI of the future, where you need something that runs, you know, a like a llama 8 billion type of model, which is gonna be a workhorse of a lot of the AI operations that are done in GenAI, for example, that will run really well and it will also run with a lot less power than what it might have been required if you were to run it on a GPU. So there's gonna be a lot of choices in there will need to be, you know, folks that specialize in doing AI for a lot less you know, kind of power and cost.

Anne Currie: Something that Renee mentioned on stage when we were so, the CEO of Ampere and I were on stage at the same, in a panel a few months ago, which is how comes we're talking today, and one of the things she said that very much interested me was that Ampere chips could, didn't have to be water cooled, they could be air cooled. Is that, true? Because obviously that's something that comes up a lot in the water use and AI's terrible water use. What's, the story on that?

Sean Varley: Yes. That is actually one of our design objectives, right? If you put in a design objective, sustainability is one of your design objectives. That is what we do, right? So part of what we've done is we've said, look, our chips run at a certain kind of ceiling from a power perspective, and we can get a lot of performance out of that power envelope.

But that power envelope's gonna stay in the range where you can air cool the chip. This provides a lot of versatility. Because if you're talking about sort of the modern data center dynamic, which is, oh, I've got a lot of Brownfield, you know, older data centers that, now are they gonna become obsolete?

And then in the age of AI, because they can't actually run liquid cooling and stuff like that. No. We have infrastructure that goes into those types of data centers and also will get you a lot of computational horsepower for AI compute inside a power envelope that was more reasonable or already provisioned for that data center.

Right? We're talking about racks that run 15 kilowatts, 22 kilowatts. Somewhere in that 10 to 25 kilowatt range is sort of a sweet spot in those types of data centers. But now what you hear these days is that racks are starting to go to 60 kilowatts, a hundred kilowatts even higher. Recently, you know, Nvidia had been pushing the industry even higher than that.

Those things require a lot of specialization, and one of the specializations that are required is direct liquid cooling, what they call DLC. And that requires a whole different refit for the data center. It's also, of course, the reason why it's there is to dissipate a lot of heat.

Right. And that requires a lot of. Water.

Anne Currie: Which is fascinating because it, the water use implications of AI data centers comes up a lot at the moment and perfectly reasonably so. It is yet, it is not sustainable at the moment to put the, to put data centers in places where, and it's a shame because, places where there is a lot of solar power, for example, there's also often and not a lot of water. Right. Yeah.

If you can turn solar, the sun into air conditioning, that's so much better than taking away all their lovely clean water that they really needed to live on. 

Sean Varley: Yes. 

Anne Currie: So that's, I mean, is that the kind of thing that's, that you are envisaging, that it doesn't have to, you know, it works better in places where there's sunshine.

Sean Varley: Absolutely. And we create technology that can very efficiently implement a lot of these types of AI enabled or traditional, you know, kind of compute. And they could be anywhere. They could be, you know, at an edge data center in a much smaller, you know, environment where there's, you know, only a dozen racks.

But it's also equally comfortable in something where there's thousands of racks, 

because at the end of the day, if you want to be more sustainable, then just use less electricity. That's the whole point, right. 

And you know, we can get into a lot of these other schemes. you know, for trying to offset carbon emissions and all these sorts of things and, all those schemes,

i'm not saying they're, bad or anything like that, but at the end of the day, our whole mission is to just use less power for these types of operations. And it comes back to many of the concepts we've talked about, right? You know, utilize your in infrastructure. Use code efficient, you know, practices, which comes back to like containers and there's even much more refined you know, code practices now for, doing really efficient coding. And then, you know, utilize a power efficient hardware platform, right? Or the most power efficient platform for whatever job you're trying to do. And certain things can be done to advertise, you know, how much, you know, electricity you're consuming to get something done, right? And there's, that's a whole sort of, you know, next generation of code I think is just that power aware, you know, kind of capacity for what you're gonna run at any given moment.

Anne Currie: Well, that's fantastic. I, we've talked for quite a long time and that was very information dense. It was high utilization of time to information there. I think we had a quite a high rate there of information passed. So, is there, so that was incredibly interesting and I really enjoyed it and I hope that, the listeners enjoyed it. All the, if there's anything that we talked about, we'll try and make sure that it's in the show notes below. Make sure that you read Building Green Software and the Cloud Native Attitude, because that would, that's a lot of what we talked about here today. and is there anything else, is there anything you wanna finish with, Sean?

Sean Varley: Well, I just, I really enjoyed our discussion, Anne, thank you very much for having me. I think these technologies that are very important, and these concepts are very important, you know, there's a lot of misinformation out there in the world as we know, it's not just in, not just confined to politics,

Anne Currie: Yep.

Sean Varley: but yeah, there, you know, there's a lot of education I think that needs to go on in these types of environments that will help all of us to create something that is much greener and much more efficient. And by the way, it's good practice because almost every time you do something that's green, you're gonna end up saving money too.

Anne Currie: Oh, absolutely. Yes, totally. If you're not doing it because you're, well, you can do it because you're a good person, which is good,

but also do it 'cause you're a sensible person who doesn't have a

Sean Varley: That's great. Yeah. Successful businesses will be green, shall be green! Let's, there needs be a rule of thumb there.

Anne Currie: Yeah. Yeah. So it is interesting. If you've enjoyed this podcast, listen as well to the podcast that I did with Charles Humble a few weeks ago, that we, again, he touched on, it's an interesting one, is there's a lot of disinformation out there, misinformation out there, but a lot of that is because the situation has changed.

So things that were true 10 years ago are just not true today. So it's not deliberate misinformation, it's just that the situation has changed. You know, the context has changed. So if you, you might hear things and think, "but that didn't used to be true. So it can't be true." You can't make that judgment anymore. You know, it might be true now and it wasn't true then. But yeah, that's the world. We are moving quite quickly.

Sean Varley: Yeah, technology, it moves super fast. 

Anne Currie: Absolutely. I don't, I've been in, so I suspect that you and I have been in for, you know, 30 years past, but it's never moved as fast as it's moving now, is it really? 

Sean Varley: Oh, I agree. Yeah. AI has just put a whole like, you know, afterburner on the whole thing. Yeah.

Anne Currie: Yeah, it's just astonishing. But yeah. Yeah. So the world, yes, all the rules have changed and we need to change with it. So thank you very much indeed. And thank you very much for listening and I hope that you all enjoyed the podcast and I will speak to you again soon. So goodbye from me and goodbye from Sean.

Sean Varley: Thank you very much. Bye-bye.

Anne Currie: Bye-bye. 

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]]> Anne Currie is joined by Sean Varley, Chief Evangelist and VP of Business Development at Ampere Computing, a leader in building energy-efficient, cloud-native processors. They unpack the energy demands of AI, why power caps and utilization matter more than raw compute, and how to rethink metrics like performance-per-rack for a greener digital future. Sean also discusses Ampere’s role in the AI Platform Alliance, the company’s partnership with Rakuten, and how infrastructure choices impact the climate trajectory of AI.

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TRANSCRIPT BELOW:

Sean Varley: Because at the end of the day, if you want to be more sustainable, then just use less electricity. That's the whole point, right. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello and welcome to the World of Environment Variables, where we bring you the latest news and updates from the world of sustainable software. So I'm your guest host today. It's not, you're not hearing the usual dulcet tones of Chris Adams. My name is Anne Currie. And today we'll be diving into a pressing and timely topic, how to scale AI infrastructure sustainably in a world where energy constraints are becoming a hard limit. And that means that we are gonna be, have to be a little bit more clever and a little bit more careful when we choose the chips we run on. So it's tempting to believe that innovation alone will lead us towards greener compute, but in reality, real sustainability gains happen when efficiency becomes a business imperative when performance per watt, cost and carbon footprint are all measured and all have weight. So, that's where companies like Ampere come in, with cloud native energy efficient approaches to chip design. They're rethinking how we power the AI boom, not just faster but smarter. It's a strategy that aligns directly with Green Software Foundation's mission to reduce carbon emissions from the software lifecycle, particularly in the cloud. So in this episode, we'll explore what this looks like at scale and what we can learn from Ampere's approach to real world efficiency. So what did it take? What does it take to make an AI ready infrastructure that's both powerful, effective, and sustainable? Let's find out. And today we have with us Sean Varley from Ampere.

So Sean, welcome to the show. Can you tell us a little bit about yourself?

Sean Varley: Yeah, absolutely Anne, and thanks first for having me on the podcast. I'm a big fan, so, I'm looking forward to this conversation. So I'm the chief evangelist of Ampere Computing. And, I, now what that means is that we run a lot of the ecosystem building and all of the partnership kind of, works that go on to support our silicon products in the marketplace.

And also, build a lot of awareness right around some of these concepts you introduced. You know, all of the, you know, kind of building out that awareness around sustainability and power efficiency and how that also really kinda works, within different workload contexts and workload context change over time.

So all of those sorts of things are kind of in scope, for the evangelism role.

Anne Currie: That's, that is fantastic. So I'll just introduce myself a little bit as well. My name is Anne Currie. If you haven't heard the podcast before, I am one of the authors of O'Reilly's new book, Building Green Software, which I, as I always say, everybody who's listening to this podcast should read Building Green Software.

That was, that is entirely why we wrote the book. I'm also the CEO of the training and Green Consulting Company as Strategically Green. So, hit me up on LinkedIn if you want to talk a little bit about training consultancy, but back to the, back to the podcast. Oh, and I need to remember that everything we'll be talking about today, there will be links about it in the show notes.

So you don't need to worry about writing down URLs or anything. Just look at the show notes before. So, now, I'm actually gonna start off the question by harking, start off the podcast by harking back to somebody that we had on the podcast a couple of months ago. A chap called, Charles Humble. And his, the assertion that he was making was that we all need to wake up to the fact that there isn't just one chip anymore, there isn't a default chip anymore that everybody uses and is kind of good enough for the best in all circumstances to use. when you are, setting up infrastructure, or in the cloud for example, and you have the dropdown that picks witch chip you're going use, the defaults might be Intel, for example. That is no longer a no-brainer, that you just go with the default. There are lots and lots of options, to the extent that, I mean, Ampere is a new chip company that decided to go into the market. So one of the questions that I have is why? You know, what gap did you see that it was worth coming in to fill?

Because 10 years ago we would've said there was no real gap, wouldn't we?

Sean Varley: That's right. Yeah. Actually it was a much more homogenous ecosystem back in those days. You know, and I, full disclosure, I came from Intel. I did a lot of time there. But about seven years, six years ago, I chose to come to Ampere. and part of this was the evolution of the market, right?

The cloud market came in and changed a lot of different things, because there's kind of classically, especially in server computing, there's sort of the enterprise and the cloud and the cloud of course has had a lot of years to grow now. And the way that the cloud has evolved was to, really kind of, you know, push all of the computing

to the top of its performance, the peak performance that you could get out of it. But there, you know, nobody really paid attention to power. Going back, you know, 10, 15, 20 years, nobody cared. And those were in the early days of Moore's law. And, part of what happened with Moore's Law is as frequencies, you know, grew then so did performance, you know, linearly.

And I think that sort of trained into the industry a lot of complacency. And that complacency then became more ossified into the, you know, the way that people architected and what they paid attention to, metrics that they paid attention to when they built chips. But going back about seven, eight years, we actually saw that there was a major opportunity to get equal or better performance for about half the power. And that's kind of what forms some of our interest in building a company like Ampere. Now, of course, Ampere, since its inception has been about sustainable computing and, me being personally sort of in interested in sustainability and green technology and those sorts of things

just outside of the, my profession, you know, I, was super happy to come to a company like Ampere that had that in its core. 

Anne Currie: And that's very interesting. So really and Ampere, your chip is a, is an X86 chip, so it's not competing against ARM is more competing against Intel and AMD.

Sean Varley: It's actually, it is an ARM chip. It's a, it's based on the ARM instruction set. And, yeah, so it's kind of an interesting dynamic, right? There was, there's been a number of different compute architectures that have been put into the marketplace. and the X86 instruction set classically by Intel and a MD who followed them, have dominated the marketplace, right?

And, well at least they've dominated the server marketplace. Now, ARM has traditionally been in mobile handsets, embedded computing, things like this. But part of where the, that architecture was built and its roots were grown up in more power-conscious markets, you know, because anything running on a battery you want to have be pretty power miserly

Anne Currie: Yeah.

Sean Varley: to use the word. So yeah, the ARM instruction set and the ARM architecture did offer us some opportunities to get a lift when we first, when we were a young company, but it doesn't necessarily have that much of a bearing on overall what we can do for sustainability, because there's many things that we can do for sustainability and the instruction set of the architecture is only one of them.

And it's a much smaller one. I, it is probably way too detailed to get into on this podcast, but it is one factor and so yes, we are ARM instruction set based and about four years back, we actually started creating our own course, on the instruction set. And that's sort of been an evolution for us because we wanted to maintain this focus on sustainability, low power consumption, and of course, along with that, high performance. 

Anne Currie: Oh, that's interesting. So as you say, the instruction set is only one part of what you're attempting, of what you're doing to be more efficient, to be, to use less power to per operation. What else are you, what else are you doing?

Sean Varley: Oh, many things. Yeah. So the part of this that kind gets away from the instruction set is how you architect and how you present the compute to the user, which may get further into kind of some of your background and interest around software because, part of what we've done is architect a chip or a set of family of chips that now that are very, well, they start off with area efficiency in the core.

And how we do a lot of that is we focus, on cache, cache configuration. So we, you, we use a lot more of what we call L2 cache, which is right next to the cores that helps us get performance. We've, kind of steered away from the X86 industry, which is much more of a larger L3 cache, which is a much bigger piece and area, part of the area of the chip.

And so that's one of the things that we've done. We've, but we've also kind of just decided that many of the features of the X86 architecture are not necessary for high performance or efficiency in the cloud. And part of this is because software has evolved. So what are those things? Turbo, for example. Turbo is a feature that kind of moves the frequency of the actual cores around, depending on how much thermal headroom the chip has. And so if you have a small amount of cores, the frequency could be really high. But if you have a lot amount of cores doing things, then you, then it pulls the frequency back down low because you've only got so much thermal budget in the chip. So we got, we said, oh, we're just gonna run all of our cores at the same frequency.

And we've designed ourselves at a point, in the, you know, voltage frequency curve that allows us that thermal headroom. Now, that's just one other concept, but, so many things have really kind of, you know, created this capability for us to focus on performance per watt and all of those things are contributors to how you get more efficient.

Anne Currie: Now that's, that is very interesting. So why, yeah, it's, what was your original motivation? Was it for the cloud? What did you, were you designing with the cloud in mind or were you designing more with the devices in mind?

Sean Varley: Yeah, we absolutely, we're in, 

are, you know, designing for cloud, because, 

cloud is such a big mover in how things evolve, right? I mean, if you're looking at markets, there's always market movers, market makers and the way that you can best accomplish getting something done. So if our goal is to create a more sustainable computing infrastructure, and now in the age of ai, that's even become more important, but, if our goal is that, then we need to go after the influencers, right? The people that will actually, you know, move this, the needle. And so the cloud was really important and we've, had a kind of this, you know, overall focus on that market, but it's not,

our technology is not limited to it. Our technology is, you know, by far and away much more power efficient anywhere from all the way out at the edge and devices and automotive and networks all the way into the cloud. But the cloud also gave us a lot of the paradigms that we have also been attached to.

So when we talk about cloud native computing, we're really kind of hearkening to that software model that was built out of the cloud. The software model built out of the cloud is something that they call serverless, in the older days. Or now it's, you know, microservices and some of these sorts of concepts.

And so as software has grown, so have we, you know, kind of put together a hardware architecture that meets that software where it is, because what that software is about is lots of processes, you know, working together to formulate a big service. And so those little processes are very latency sensitive.

They need to have predictability, and that's what we provide is our architectures, lots of cores that all run at the same kind of pace, and so you get high degree of predictability out of that architecture, which then makes the software and the entire service more efficient.

Anne Currie: So that's, that is very interesting. And I hadn't realized that. So obviously things like serverless going on in clouds, that is a, the software that's actually running on the chip is software that was written by usually the cloud provider. You know, the, clouds wrote that software.

So it, you are isolating from, it is, one of the interesting things about high performance software is that it's hard, really hard to write. In fact, in Building Green Software, I always talk about people about don't start there, it's really hard. You need specialist skills. You need to know the difference between L2 caches and L3 caches.

And you need to know how to use them. And the vast majority of engineers do not have those skills. And it will never achieve, will never acquire those skills. But the cloud providers where they are managing, providing managed services that you are using, like, you're just writing a code snippet that's running in Lambda or whatever. You are not writing the code that makes that snippet run. You're not writing the code that talks to the chip. Really super specialist engineers at AWS or Azure or whatever are writing that code.

So is that the, is that the move that you were anticipating?

Sean Varley: Absolutely. I mean, that's a big part of it, right? And as you just articulated a lot of the platform as a service kind of code, right, so that managed service that's coming out of a hyperscaler is, you know, built to be cloud native. It's built to be very microservice based.

And it has a lot of what we call SLAs in the industry, right? Service level agreements, which mean that you need to have a lot of, different functions complete, on time for the rest of the code to work as it was designed. And as you said, it is a much more complex way to do things, but the overall software industry has started to make it a lot easier to do this, right. And things like containers, you know, which are inherently much more efficient. you know, sort of, you know, entities, yeah, like, footprints, images is what I was really kind of going for there. They're, they are, you know, already you've cut out a lot of the fat, right, in the software. You've gotten down to a function. You mentioned Lambda, for example. A function is the most, you know, sort of nuclear piece of code that you could potentially write, I suppose, to do something. And so all of these functions working together, they need these types of execution architectures to really thrive and yes, you're right, that developers, you know, they have come a long way in having these serviceable components in the industry. You know, Docker sort of changed the world about, what is it, 10 years ago now, maybe longer. And all of a sudden people could go and grab these little units of, what they call endpoints in kind of, you know, kinda software lingo, you know? And so if I wanna get something done, I can go grab this container that will do it. And those containers and the number of containers that you can run on a cloud native architecture like Ampere's is vastly better than what you can find in most X86 architectures.

Why? Because these things run on cores. Right. And we have a lot of them.

Anne Currie: Yeah, so that is very interesting, the, so I also. Everybody who's listening to the podcast must also in like my other book on this very subjects, which is called the Cloud Native Attitude. And it was about why Docker is so important, why containers are so important.

Because they wrapped up, they allowed you to wrap up programs and then move those programs around so that's, it basically put a little handle that made you be able to move stuff around and started and stop it and orchestrate it. And what that meant was

Sean Varley: I love that analogy, by the way, the handle, and you just pick it up and move it anywhere you want it, right.

Anne Currie: Yeah, because really that was what, 

that was all that Docker really did. It wrapped something that was, a fairly standard Linux concept that had been around quite a long time. And it put a nice little API on it, which was effectively a handle, which let other tools move it around.

And then you've got orchestrators like Kubernetes, but you also got lots of other orchestrators too.

But what that meant in the cloud native world was that you could have services that were written by super experts or open source. So it had lots of experts from all over the place, writing them and tuning them and improving them and get, letting Moore's law and write, well, not Moore's Law, Wright's Law, which the law systems get better if you use them. Yet it gave people a chance to go in and improve things. But have those be the people who are improving things, be specialists and let that specialist code was incredibly hard to write, be shared with others. So you're kind of amortizing the incredibly difficult work. So fundamentally, what you are saying, and I think this is, you know, I, you could not be singing more from my hymn sheet on this, is that it's really hard to write code that interfaces well and uses CPUs well so that they're highly efficient and you get code efficiency and you get operational efficiency really hard to do. But, if you can do it, if you can find a way that it doesn't require every single person to write that code, which is really hard, but you can share it and leverage it through open source implementations or cloud implementations written by the cloud providers, then suddenly your CPUs can do all kinds of stuff that they couldn't have done previously.

Is that what you're saying?

Sean Varley: Absolutely, and I would've, I was gonna put tack on one little thing to your line was it's really hard to do this by yourself, right? 

And this is where the open source communities and all of these sorts of things that have really kind of revolutionized, especially the cloud, coming back to that topic that we were talking about.

Because the cloud has really been, I think evolved on the back of open source software, right? And that radically changed how software was written. But now coming back to your package and your handle, you can go get a function that was written in and probably optimize by somebody who spent the time to go look at how it ran in a specific architecture.

And now with things like Docker and GitHub and all these other tool chains where you can go out and grab containers that are already binary compiled for that instruction set that we were talking about earlier, this makes things a lot more accessible to a lot more people. And in some ways, you have to trust that, you know, this code was written to get the most out of this architecture, but sometimes there's labeling, right?

This was written for that, or, you know, a classic example in code is that certain types of algorithms get inline assembly done to make them the most efficient that they can be. And all of that usually was done in the service of performance, right? But one of the cool things about trying to do things in service of performance is that you can actually usually get better power efficiency out of that if you use the right methodologies. Now, if the performance came solely from something that was frequency scaled, that's not gonna be good for power necessarily. But if it's going to be done in what we call a scale out mechanism where you get your performance by scheduling things on, not just one core, but many cores,

and they can all work together in service of that one function, then that can actually create a real opportunity for power efficiency. 

Anne Currie: Yeah, so that maps back to something that in Building Green Software we talk about, which is utilization. So, you know, a machine is. And a machine use needs to be really well utilized because if it's not well utilized, it still uses pretty much the same power, but it's not doing anything if it's not actually doing anything. It's not doing anything useful with it. It's just a waste.

Sean Varley: I'm so glad you brought this up.

Anne Currie: Well go for it. Go for it. You know, you are the expert in this area.

Sean Varley: Oh, no. Yeah, I think you're, exactly right. You hit it on the, the nail on the head, and the part of the problem in the world today is that you have a lot of machines out there that are underutilized, and that low utilization of these machines contributes a lot to power inefficiency. Now I'm gonna come back to some other things that maybe go back to the, where we were talking about in certain terms of processor architecture, but is still super relevant to code and efficiency. So the one thing going back to everybody only had one choice on the menu, which was Intel at the time,

was that architecture instilled some biases or some habits, pick your sort of word here, but, people defaulted to a certain type of behavior. Now, one of the things that it trained into everyone out there in the world, especially code writers and infrastructure managers, was that you didn't ever get over about 50% utilization of the processor because what happened is if you did then at, after 50% all of the SLAs I was talking about earlier, those, that service level agreement where things are behaving nicely, went out the window, right? Nobody could then get predictable performance out of their code because why?

Hyperthreading. So Hyperthreading is where you share a core with two execution threads. That sharing at once you got went over 50%, then all of a sudden you are heavily dependent on the hyperthreading to get any more performance. And what that does is it just messes up all the predictability of the rest of the processes operating on that machine.

So the net result was train people 50% or below. Now our processors, if you're running at 50% or below, that means you're only using half of our complete capacity, right? So we've had to go out and train people, "no, run this thing at 80 or 90% utilization because that's where you hit this sweet spot," right?

That's where you're going to save 30, 40, 50% of the power required to do something because that's how we architected the chip. So these are the kinds of biases and habits and sort of rules of thumb that we all end up having to kind of combat. 

Anne Currie: Yeah, and it's interesting. I mean, that's say as, you say that completely maps back to a world in which we just weren't thinking about power, you know, we just didn't care about the level of waste. So, I, quite often en enterprise, enterprise engineers, architects are very used these days to the idea of lean, and agile.

It's about reduction of waste. And the biggest waste there is, underutilized machines. And we don't tend to think about it. And as you say, in this part, because we were trained now to thinking about it. 

Sean Varley: And also people were, didn't really care there, you know, back in the day, you know, going back again, 10, 15, 20 years ago, people didn't really care that much about how much power was consumed in their computing tasks because it wasn't top of mind for people, right. And frankly, we consumed a lot less of it, primarily because we had a lot of less infrastructure in service in, you know, worldwide I'm talking about, but also because, you know, back in, you know, in older chip architectures and older silicon process technology, it consumed less power. Now as we've gotten into modern process technology, that whole thing has changed. And now you've got chips that can burn hundreds and hundreds of watts by themselves, not to mention the GPUs, which can burn thousands of watts. And that's just a wholesale shift in, you know, kind of the trajectory of power consumption for our industry.

Anne Currie: So you've brought up AI and GPUs there, and obviously, and even more AI focused chips that are even potentially more power hungry. How does Ampere help? 'Cause Ampere is a CPU, not a GPU or a TPU, how does it

fit into this story?

Sean Varley: It fits in a number of different ways. So, maybe a couple of definitions for people. CPU is a general purpose processor, right? If we, it runs everything, and in, you know, kind of everyday parlance, it's an omnivore. It can do a lot of different things and it can, you know, do a lot of theso pretty well, but what you have is an industry that is evolving into more specialized computing. That's what A GPU is. But there are many other examples, accelerators and others types of, you know, kind of, not homogenous type computing, but heterogeneous computing, where you've got different specializations. GPU is just one of those.

And, but in AI, what we've found is, that the GPU architecture, of course, has driven that overall workload, you know, to a point where the power consumption of that type of a workload, because there's a lot of computational horsepower required to do, AI models

and, so that has driven, you know, the industry up into the right in terms of power consumption. And that has, you know, there's a bias now in the industry about, well, if you're gonna do AI, it's gonna just take a ton of power to do it. The answer to that is, "maybe..." right? Because what you've got is, maybe a little bit of a lack of education about the whole pantheon of AI, you know, kind of execution environments and models and things like that, and frameworks and all sorts of things.

All of these things matter because a CPU can do a really good job of doing the inference operation, for AI and it can do an excellent job of doing it efficiently. 'Cause coming back to your utilization, you know, kind of argument we were talking about earlier. Now, in GPUs, the utilization is even far more important because as you said, it sits there and burns a lot of power no matter what.

So if you're not using it, then you definitely don't want that thing just kind of, you know, running the meter. And so utilization has become a huge topic in GpU, you know, kinda circles and so, but CPUs kind of have a ton of technology in them for low power when not utilized.

You know, that's been a famous, you know, kind of set of capabilities. But also AI is not one thing. And so AI is the combination of specialized things that are being run in models and then a lot of generalized stuff that can be run and is run on CPUs. So where we come in, Ampere's concept for all that is what we call AI compute.

So AI compute is the ability to do a lot of the general purpose stuff and quite a bit of that AI specific stuff on CPUs, and you have a much more kind of flexible platform for doing either.

Anne Currie: So it's interesting. Do you, now I'm going show my own ignorance here 'cause I've just thought of this and therefore I'm gonna go horribly roll with it. There are kind of a, there are kind of platforms to help people be more hardware agnostic when it comes to stuff like, Triton, is it, and,

are there things that, do you fit in with anything like that,

or is it just, does everybody have to kind of decipher themselves whether they're gonna be, which bit of hardware they're gonna be using?

Sean Varley: Oh man. We could do a whole podcast on this. Okay.

Yeah. Let me try to like, break this down at least in a couple of simple terms. So, yes, I mean, there's two, first of all, there's two main operations in AI. There's training and there's inference. Now training is very high batch, high consumption, high utilization of a lot of compute.

So we will think of this as maybe racks full of GPUs because it's also high precision and it's a big, it's a kind of a very uniform operation, right? once you set it, you kind of forget it and you let it run for famously weeks or months, right? And it turns out a model, but once the model's turned out, it can be run on a lot of different frameworks.

Right. And so this is where, you know, that platform of choice part comes back in because inference is the operation where you're gonna get some result, some decision, some output out of a model. And that's gonna be the, by far and away the vast majority of AI operations of the future, right?

We've been, 

we're still training a lot of models, don't get me wrong. But in the future is gonna be a lot of inference and that particular operation doesn't require as high a precision. It doesn't require a lot of the same characteristics there that are required in training. Now that can be run a lot of different places on these open source frameworks.

And also what you're starting to see is now specializations in certain model genres. A genre, I would say is like a llama genre, you know, from meta, you know, they've built all of their own, much more efficient, you know, kind of frameworks in their CPP, their C++ implementation of the llama frameworks.

So you got specialization going on there. All that stuff can run on CPUs and GPUs and accelerators and lots of other types of things. Now it becomes more of a choice. What do I want to focus on when I do this AI operation? Do I really want to focus on something that's going to, you know, get me the fastest result, you know, ever?

Or can I maybe let that sort of thing run for a while and then give me results as they come? And a lot of this sort of decision making, use case based decision making will dictate a lot of the power efficiency of the actual AI operation.

Anne Currie: That is interesting. Thank you very much for that. So Ampere, you see, so Ampere is basically in that second thing, you are one of the options for inference.

Sean Varley: That's right, yeah. And we actually, we, our sort of whole thought process around this is, that we want to provide a very utilitarian resource, right? Maybe it's the right word. Because the utilitarianism of it is not that it's like low performance or anything like that, it's still high performance.

It's just that you're not going to necessarily need, you know, all of the resources of the most expensive or the most, kind of, parameter-laden model. So, 'cause models come in, a lot of parameters. We hear this term, right? You know, up to trillions of parameters, down to millions of parameters.

And somewhere in the middle is kind of that sweet spot right now, right? Somewhere in the 10 to 30 million per, or billion, sorry, billion parameter range and that sort of thing requires optimization and distillation. So we are building a resource that will be that sort of utility belt for AI of the future, where you need something that runs, you know, a like a llama 8 billion type of model, which is gonna be a workhorse of a lot of the AI operations that are done in GenAI, for example, that will run really well and it will also run with a lot less power than what it might have been required if you were to run it on a GPU. So there's gonna be a lot of choices in there will need to be, you know, folks that specialize in doing AI for a lot less you know, kind of power and cost.

Anne Currie: Something that Renee mentioned on stage when we were so, the CEO of Ampere and I were on stage at the same, in a panel a few months ago, which is how comes we're talking today, and one of the things she said that very much interested me was that Ampere chips could, didn't have to be water cooled, they could be air cooled. Is that, true? Because obviously that's something that comes up a lot in the water use and AI's terrible water use. What's, the story on that?

Sean Varley: Yes. That is actually one of our design objectives, right? If you put in a design objective, sustainability is one of your design objectives. That is what we do, right? So part of what we've done is we've said, look, our chips run at a certain kind of ceiling from a power perspective, and we can get a lot of performance out of that power envelope.

But that power envelope's gonna stay in the range where you can air cool the chip. This provides a lot of versatility. Because if you're talking about sort of the modern data center dynamic, which is, oh, I've got a lot of Brownfield, you know, older data centers that, now are they gonna become obsolete?

And then in the age of AI, because they can't actually run liquid cooling and stuff like that. No. We have infrastructure that goes into those types of data centers and also will get you a lot of computational horsepower for AI compute inside a power envelope that was more reasonable or already provisioned for that data center.

Right? We're talking about racks that run 15 kilowatts, 22 kilowatts. Somewhere in that 10 to 25 kilowatt range is sort of a sweet spot in those types of data centers. But now what you hear these days is that racks are starting to go to 60 kilowatts, a hundred kilowatts even higher. Recently, you know, Nvidia had been pushing the industry even higher than that.

Those things require a lot of specialization, and one of the specializations that are required is direct liquid cooling, what they call DLC. And that requires a whole different refit for the data center. It's also, of course, the reason why it's there is to dissipate a lot of heat.

Right. And that requires a lot of. Water.

Anne Currie: Which is fascinating because it, the water use implications of AI data centers comes up a lot at the moment and perfectly reasonably so. It is yet, it is not sustainable at the moment to put the, to put data centers in places where, and it's a shame because, places where there is a lot of solar power, for example, there's also often and not a lot of water. Right. Yeah.

If you can turn solar, the sun into air conditioning, that's so much better than taking away all their lovely clean water that they really needed to live on. 

Sean Varley: Yes. 

Anne Currie: So that's, I mean, is that the kind of thing that's, that you are envisaging, that it doesn't have to, you know, it works better in places where there's sunshine.

Sean Varley: Absolutely. And we create technology that can very efficiently implement a lot of these types of AI enabled or traditional, you know, kind of compute. And they could be anywhere. They could be, you know, at an edge data center in a much smaller, you know, environment where there's, you know, only a dozen racks.

But it's also equally comfortable in something where there's thousands of racks, 

because at the end of the day, if you want to be more sustainable, then just use less electricity. That's the whole point, right. 

And you know, we can get into a lot of these other schemes. you know, for trying to offset carbon emissions and all these sorts of things and, all those schemes,

i'm not saying they're, bad or anything like that, but at the end of the day, our whole mission is to just use less power for these types of operations. And it comes back to many of the concepts we've talked about, right? You know, utilize your in infrastructure. Use code efficient, you know, practices, which comes back to like containers and there's even much more refined you know, code practices now for, doing really efficient coding. And then, you know, utilize a power efficient hardware platform, right? Or the most power efficient platform for whatever job you're trying to do. And certain things can be done to advertise, you know, how much, you know, electricity you're consuming to get something done, right? And there's, that's a whole sort of, you know, next generation of code I think is just that power aware, you know, kind of capacity for what you're gonna run at any given moment.

Anne Currie: Well, that's fantastic. I, we've talked for quite a long time and that was very information dense. It was high utilization of time to information there. I think we had a quite a high rate there of information passed. So, is there, so that was incredibly interesting and I really enjoyed it and I hope that, the listeners enjoyed it. All the, if there's anything that we talked about, we'll try and make sure that it's in the show notes below. Make sure that you read Building Green Software and the Cloud Native Attitude, because that would, that's a lot of what we talked about here today. and is there anything else, is there anything you wanna finish with, Sean?

Sean Varley: Well, I just, I really enjoyed our discussion, Anne, thank you very much for having me. I think these technologies that are very important, and these concepts are very important, you know, there's a lot of misinformation out there in the world as we know, it's not just in, not just confined to politics,

Anne Currie: Yep.

Sean Varley: but yeah, there, you know, there's a lot of education I think that needs to go on in these types of environments that will help all of us to create something that is much greener and much more efficient. And by the way, it's good practice because almost every time you do something that's green, you're gonna end up saving money too.

Anne Currie: Oh, absolutely. Yes, totally. If you're not doing it because you're, well, you can do it because you're a good person, which is good,

but also do it 'cause you're a sensible person who doesn't have a

Sean Varley: That's great. Yeah. Successful businesses will be green, shall be green! Let's, there needs be a rule of thumb there.

Anne Currie: Yeah. Yeah. So it is interesting. If you've enjoyed this podcast, listen as well to the podcast that I did with Charles Humble a few weeks ago, that we, again, he touched on, it's an interesting one, is there's a lot of disinformation out there, misinformation out there, but a lot of that is because the situation has changed.

So things that were true 10 years ago are just not true today. So it's not deliberate misinformation, it's just that the situation has changed. You know, the context has changed. So if you, you might hear things and think, "but that didn't used to be true. So it can't be true." You can't make that judgment anymore. You know, it might be true now and it wasn't true then. But yeah, that's the world. We are moving quite quickly.

Sean Varley: Yeah, technology, it moves super fast. 

Anne Currie: Absolutely. I don't, I've been in, so I suspect that you and I have been in for, you know, 30 years past, but it's never moved as fast as it's moving now, is it really? 

Sean Varley: Oh, I agree. Yeah. AI has just put a whole like, you know, afterburner on the whole thing. Yeah.

Anne Currie: Yeah, it's just astonishing. But yeah. Yeah. So the world, yes, all the rules have changed and we need to change with it. So thank you very much indeed. And thank you very much for listening and I hope that you all enjoyed the podcast and I will speak to you again soon. So goodbye from me and goodbye from Sean.

Sean Varley: Thank you very much. Bye-bye.

Anne Currie: Bye-bye. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

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]]> Real Time Cloud with Adrian Cockcroft Real Time Cloud with Adrian Cockcroft Sat, 12 Jul 2025 07:30:00 GMT 1:18:37 v17vyy40 false https://podcasts.castplus.fm/e/4892yy0n-real-time-cloud-with-adrian-cockcroft 68dc7d1046a2532cdd8d2674 68dc7cef46a2532cdd8d1c23 Chris Adams is joined by Adrian Cockcroft, former VP of Cloud Architecture Strategy at AWS, a pioneer of microservices at Netflix, and contributor to the Green Software Foundation’s Real Time Cloud project. They explore the evolution of cloud sustainability—from monoliths to microservices to serverless—and what it really takes to track carbon emissions in real time. Adrian explains why GPUs offer rare transparency in energy data, how the Real Time Cloud dataset works, and what’s holding cloud providers back from full carbon disclosure. Plus, he shares his latest obsession: building a generative AI-powered house automation system using agent swarms. 114 Chris Adams is joined by Adrian Cockcroft, former VP of Cloud Architecture Strategy at AWS, a pioneer of microservices at Netflix, and contributor to the Green Software Foundation’s Real Time Cloud project. They explore the evolution of cloud sustainability—from monoliths to microservices to serverless—and what it really takes to track carbon emissions in real time. Adrian explains why GPUs offer rare transparency in energy data, how the Real Time Cloud dataset works, and what’s holding cloud providers back from full carbon disclosure. Plus, he shares his latest obsession: building a generative AI-powered house automation system using agent swarms.

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TRANSCRIPT BELOW:

Adrian Cockcroft: We figured out it wasn't really possible to get real time energy statistics out of cloud providers because the numbers just didn't exist.

It turns out the only place you can get real time numbers is on things that are not virtualized.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. If you have worked in cloud computing for any length of time, then even if you do not know the name yet yourself, it's very likely that the way you design systems will have been influenced by my guest today, Adrian Cockcroft.

When at Netflix, Adrian led the move to the cloud there helping, popularize many of the patterns we use when deploying applications ourselves to the cloud. And his name then became synonymous with serverless throughout the 2010s when he joined AWS first leading on open source engagement, and then as a VP focused on what we might refer to now as cloud sustainability.

After leaving AWS, Adrian's kept his fingers in many pies, one of which is the Green Software Foundation's real time cloud project, an initiative to bring transparency and consistency to cloud emissions reporting. With the first dataset release from that project out the door, it seemed a good idea to invite him onto the show to see what's up.

Adrian, thank you so much for joining us today. Can I give you a bit of time to tell us about yourself and what you are, what's what you're keeping? What's keeping you busy these days? I.

Adrian Cockcroft: Yeah, it's great to see you and thanks also for your contributions to the project. We've had a lot of discussions over the last few years as we've worked on that together. well, I'm sort of semi-retired. I stopped my big corporate job at Amazon in 2022. and yeah, I spend my time worrying about my family.

I've got old parents that live in the uk, so I spend a lot of time with them. And, fixing stuff around the house and generally goofing around and doing things I feel like doing rather than stuff that's driven by some corporate agenda. So I'm enjoying that freedom. And, let's see the, yeah, I spend time on the, Green Software Foundation project.

I go to a few conferences and give a few talks and I try to keep up with, you know, what's happening in technology by playing around with whatever the latest tools are and things like that. And that's been my career over the years. I've generally been an early adopter through my entire career. as you mentioned, we were early adopters in cloud.

Back when people said This isn't gonna work and you'll be back in the data center soon. People forgot that was the initial reaction to what we said. it's a little bit like that now with people saying, all this AI stuff doesn't work and we're gonna be giving up and whatever. And it's like, well, I'm making bits of it work well enough to be interesting.

We can talk a bit about that later. and then I know you probably see behind me various musical instruments and things like that, so that's kind of, I, collect musical instruments that I don't have time to really learn how to play and mess around and make bad noises that make me happy. But luckily no one else has to listen to them particularly.

So that's kind of my, that and messing around with cars and things, that's sort of the entertainment for me.

Chris Adams: That sounds like quite a fun, state of stem semi-retirement, I have to say actually. So before we dive into the details of cloud, I have to ask, where are you calling from today Because you have an English accent and like, I have an English accent, but I'm calling from Berlin and I'm guessing you're not in England, so maybe you could do that.

'cause I follow you on social media and I see all these kind of cryptic and interesting posts about cars and stuff and it's usually sunnier than where I am as well. So there's gotta be a story there. What's going on there, Adrian?

Adrian Cockcroft: Well, I lived in England long enough to decide I didn't want to be rained on all the time. which is why I never moved to Seattle when, you know, I didn't move to California to move to America to go live in somewhere with the same weather as England. So that was one reason I never moved to Seattle when I was working for Amazon.

So used to live in the Bay Area in Los Gatos, near Netflix. about five years ago we moved down near Monterey, about an hour or two south of the Bay Area. I. Depending on traffic. we are within earshot of a race track called Laguna Seka that most people know. I can kind of see it outta my window.

I can see a few dots on the horizon on the, you know, moving and that's, there's a few cars you can just about hear them on if they're loud cars. and this is where they have in every August, this thing called Monterey Car Week with the Pebble Beach concourse and historic races. And we used to go to that every year and we like the kind of messing around with cars and going to the track occasionally culture.

So we moved down here and that's been, it's been fun. It's, you know, I don't have to commute anywhere. We have a nice place. The house prices are a lot cheaper down here than they are in the Bay Area itself. So we live in, technically we live in Salinas. lots of good vegetables around here. That's where a lot of the growers are.

and it's, we live actually out in the countryside, sort of. Just in the hills near, near there. So we have a nice place, have plenty of room for messing around and a big house, which requires lots of messing around with. And we can talk a bit about one of the projects I have later on to try and automate some of that.

Chris Adams: Yeah, that's quite a hint. Alright, well that does explain all the kind of cars and coffee stuff when I, like say 30 verse and Okay. If you're near a racetrack, that would explain some of the cars as well. Alright. Thank you

Adrian Cockcroft: Well, actually there's cars and coffee events just about everywhere in the world. If you, like looking at old cars and hanging out with car people, there's one probably every Saturday morning somewhere within 10 miles away. Pretty much anyone. Anyway, the other things, on that front that's sort of more related to Green Software Foundation is we've had a whole bunch of electric cars over the years.

I have one of the original Tesla Roadster cars that was made in 2010. I've had it since 2013. it actually has a sticker on the back saying, I bought this before Elon went nuts. so I'm keeping that. we used to have a Tesla model three and we replaced it recently with a Polestar three, which is quite a nice car with very bad software initially.

But they did a software update recently that basically fixed just about every bug and we, it's actually fun driving a car where you don't worry if it's about to do something strange and need a software reset, which was the state it was in when we first got it in April. But the difference, a bug fix can make whether they actually went and just fixed everything that was currently going wrong with it and went, transformed the car into something That's just actually a fun thing to drive now.

Chris Adams: So it was a bit like turning it off and turning it off and on again. And then you've got like a working car,

Adrian Cockcroft: Yeah. Well, yeah, we got really used to pushing the reset button. You hold the volume control down for 30 seconds and that resets the software and we would be doing that most days that we drove it

Chris Adams: Oh my God. I didn't realize that was a real thing that people did. Wow.

Adrian Cockcroft: Yeah. It's one of these things where a product can be transformed from something buggy and annoying to, oh, we just fixed all the software now.

It actually works properly. And, you know, it's, interesting to see. So, so it went from bad, really bad to actually pretty good with one software release. Yeah.

Chris Adams: guess that's the, wonders of software I suppose. Wow. Alright then, and I guess that gives us a nice segue to talk about, I guess some back to some of the cloud and serverless stuff then. So. Before you were helping out in some of the Green Software Foundation projects. I remember reading a post from you called the evolution from Monoliths to microservices to functions.

And I think for a lot of people it actually really joined the dots between how we think about sustainability and how things like scale to zero designs, might kind of what role they play when we design cloud services. And in that post, you laid out a few things, which I found quite interesting. You spoke about the idea that like, okay, most of the time when we build services, they may be being used maybe 40 hours a week and there's 168 hours a week.

So like 75% of the time it's doing nothing. And just like waiting there. Yet we've still spent all this time and money building all this stuff and, post. I remember you writing a little bit about saying, this actually aligns incentives in a way that we haven't seen before. And I think this idea of actually like changing the programming model that actually incentivizes the correct behavior.

I think that's really, that, that was really profound for me. And I figure like, now that I've got a chance to have you on the call on this podcast, I wanted to ask you what drove you to write that in the first place? And for folks who haven't actually read it, maybe, you could just talk a little bit about the argument that you were making and then why you wanted to actually write that as well.

Adrian Cockcroft: Yeah, that's actually one of the highest traffic blog posts that I ever wrote. There was a lot of, reads of that. The context then, so it was soon after I joined AWS, so it was probably 25. Early 2017, something like that. I joined AWS in 2016. I'd spent a few years basically involved in kind of, helping promote microservices as an architecture.

And, I was also interested in serverless and AWS Lambda as, an architecture. And I wanted to connect the dots. And it's a kind of, when I write things, some of the things I write, the approach I take is along the lines of his, this is how to think about a thing, right? These are the, it, I have a systems thinking approach generally, and so what I do is I try to expose the systems that I'm thinking about and the incentives and feedback loops and reasons why things are the way they are, rather than being prescriptive and saying, just do this, and this.

I. And the world will be great, or whatever the, you know, the more typical instructive things. So I tend to try and explain why things are the way they are and, sort of work in that. So that's, it's, an example of that type of writing for me. And we were, at the time, people were talking a lot about the monolith and microservices transition and what it meant and how to do it and things like that.

And I was trying to explain what we'd done at Netflix. And then I was thinking that there was a, the next generation of that transition was to serverless. And the, post was basically to just try and connect those dots, that was the overall goal of it. And then it is quite a long post. It's one of these things when you work with somebody, you know, PR people or whatever, and they say, you, you should write short blog posts and you should, you know, da Well this, and they shouldn't be so technical. So this is one of the longest and most technical posts I wrote, and it actually has the highest traffic. So, you know, ignore the PR people. It turns out if you put real content in something, it will get traffic. and, that's, the value you can, provide by trying to explain an idea.

So I think that's generally what that was about. This idea that. it was, I mean, the microservices idea was, is a tactic for implementing a for solving a problem. It isn't an end in itself. Right. And that's one of the distinctions I was trying to make. It's like if you have a large team working on a code base, they'll keep getting in each other's way.

And if you're trying to ship code and the code has a hundred people's contributions in it, one person has a bug, then that stops the shipment of the other 99 people. So there's this blocking effect of, of bugs in, in, in the whole thing. And then it also, you've got it destabilizes the entire thing.

You're shipping completely new code when you ship a new monolith was when you have say a hundred microservices with one person working on each. They can ship independently. And yeah, you have some interaction things you have to debug, but 99 of those services didn't change when you pushed your code. So it's easy to isolate where the problem is and roll it back.

So there's a bunch of things that make it easier. And then we thought, well, you've got the microservice, which does a thing. But it contains a bunch of functions. If you blow that up into individual functions, then you don't actually need all those functions all the time. And some code paths are very busy through the code.

They may be do it a hundred times, you know, every request goes through this part of the code, but may one times in a hundred or a thousand it does something else. So what you can do is break those into separate functions and different lambda functions. And you've got, so the code parts that don't get executed very often just aren't running.

The code gets called and then it stops and it's doesn't get called again, for a long time. Whereas the busy ones tend to stay in memory and get called a lot. Right. So that way you're actually, the memory footprint is more tuned to, and the execution footprint is tuned to what's actually going on.

So that was, the second thing. And then the third thing was that a lot of applications, particularly corporate in access, you mentioned they're only used during work hours. And those are the perfect ones to build serverless. They're internal. They are, they only exist for as long as anybody is actually trying to use them.

And they aren't just sitting their idle most of the time just because you need to have a wiki or something, or you need to have a thing that people check in with in the morning. Like anything that salespeople at the end of the quarter or the end of the month, those sorts of things make things super busy and it's idle the rest of the time, so you need very high concurrency for short periods of time.

Anything like that is, is sort of the area where I think serverless is particularly good. And later on I did another, series of talks where I basically said serverless first, not serverless only, but start trying to build something with serverless because you'll build it super quickly. And, one of the books I should reference is by, David Anderson.

is it called the Value Flywheel Effect or something like that will give a link in the show notes. And I helped. Talked, I, talked to him, helped him get, find the publisher for that book. And I wrote, did I write, I think I wrote a foreword for it, or at least put some nice words on the cover.

and that book talks about people developing app, entire applications in a few days. And then you get to tune it and optimize it. And maybe you take some part of it where you say, really, I need a container here. Something like that. but, you can rapidly build it with the tag I used to say was in the time it takes to, have meetings about how you're going to configure Kubernetes, you could have finished or building your entire application serverless, right?

And, you just get these internal discussions about exactly what version of Kubernetes to use and how to set it up and all this stuff. And it's like, I could have finished building the whole thing with the amount of effort you just put into trying to figure out how to configure something. So that's the sort of, a slightly flippant view I have on that.

And, anyway, the, other thing is just, and effectively the carbon footprint of a serverless application is minimal. But you do have to think about the additional systems that are running there all the time when you are not running. And a little bit of a, sort of a future segue, but AWS just changed them, their own accounting model to include those support services so that, when you look at the carbon footprint of a Lambda app that isn't running, you actually have a carbon footprint because the Lambda service needs to be there ready.

So you actually get a share of the shared service attributed to each customer that's using the, using it, right? So it's a little, it's a little bit deeper and it's kind of an interesting change in the model to be explicit that's what they're doing.

Chris Adams: Ah, I see. Okay. So on one level, some of this post was about like the, I guess the unit of code or the unit of change can become smaller by using this, but there's also a kind of corresponding thing on the hardware level. Like, you know, typically you might be, I remember when I was reading this, there was like, okay, I'm shipping a monolithic piece of code and I've got a physical server to begin with.

It's like the kind of. That was like how we were starting at maybe, I dunno, 10, 20 years ago. And then over time it's becoming smaller and smaller and that has made it a bit easier to build things kind of quickly. And, but one of the, flip side that we have seen is that, if you just look at say the Lambda function, then that's not a really accurate representation of all the stuff that's actually there.

You can't pretend that there is an infrastructure that has to be there. And it sounds like the accounting has now starting to reflect that the fact that yeah, you, someone needs to pay for the capacity in the same way that someone has to pay for the electricity grid, even though you're not, even when you're not using the grid for example, there is still a cost to make that capacity available for you to use.

Basically that's, what it seems to be a reference to.

Adrian Cockcroft: Yeah. And just going back to the car analogy.

People own cars. People lease cars. People rent cars, right? And you can, if you rent a car for a day, you can say, well, my carbon footprint of renting the car is one day's worth of car ownership, right? Except that in order for you to rent a car for the day, there has to be a fleet of cars sitting around idle That's ready for you to rent one. So really you want to take into account the overhead of your car rental company managing a fleet, and it's maybe got whatever, 70% utilization of the fleet. So 30% of the cars are sitting around waiting for somebody. So you basically have to uplevel your, I just need a car for a day to add an extra overhead of running that service, right?

So it's, it kind of follows that same thing, you know? And if you basically rent a car for every single day and you have a car every day of the year, but it's a rental car, that's an expensive way to own a car, right? I mean, even at a monthly rate, it's still more expensive than buying a car or leasing a car because you're paying for some overhead.

But it's kind of those sorts of models. So it's a bit like owning a car, maybe leasing a car, and, doing a rental car with sort of the monolith microservices. Serverless sort of analogy, if you like. cost model's a little different because, you're giving stuff back when you don't want it anymore.

is sort of the cloud analogy, right? The regular cloud service. I can just deep, I can scale things down.

Chris Adams: mm going back to something else you mentioned, I was talking to a CIO once and he was very annoyed 'cause he said that he'd only just found out that he could turn off all his test infrastructure at the weekends and overnight. and it was like they, he'd been running this stuff for two years and this, he finally realized and, he'd just, like, three quarters of his cost had just gone away from his test environment. And, he, was happy that had happened, but he was annoyed that it, took him two years for him to somebody to mention to him that this was possible and for him to tell them to do it.

Adrian Cockcroft: Right. So there's. Yeah. Any, tests, anything that's driven off people should absolutely be, you know, shut down. There are ways to just freeze a bunch of a, bunch of cloud instances can just be shut down and frozen and come back again later.

Chris Adams: so this is something I might come back to actually, because one of the things that in somewhat on, in some ways, if you look at, say maybe cloud computing, each individual server is probably quite a bit more efficient than maybe a corresponding, server you might buy from Dell or something like that from a few years ago because it's in a very optimized state.

But because it's so easy to turn on, this is one of the cha challenges that we can consistently have. So it's almost like a, and also in many ways. It's kind of in the interest of the people running very effect, very efficient servers to run, but have to basically have people paying for this capacity, which they're not using.

'cause it makes it easier to then like resell that. Like this is, I guess maybe this is one of the things that the shifts to serverless is supposed to address, or in theory, you know, it does align things somewhat, better and more. More in terms of like reducing usage when you're not actually using it, for example, rather than leaving things running like you're saying actually.

Adrian Cockcroft: Yeah, you don't have to remember to turn it off With serverless, it's off by default and it comes on and it's sort of a hundred percent utilized while you're running and then it turns off again. So in that sense, it is much more like you have a rental car that returns itself after 15 minutes or whatever.

Whatever your timeout

is or when you're done with it. It's more, maybe it's more like a taxi, right? That kind of going, one level beyond rental car, you have taxi, right? Which is you just use it to get there and you're done. So serverless is maybe more like a taxi service, right? And then, right. And then a daily rental is more like a.

Like an EC2 instance or something like that. And there's all these different things. So there we're used to dealing with these things and you wouldn't, you know, you wouldn't have a taxi sitting outside your house 24 hours a day just waiting for you to want to go somewhere, right? People say, well, serverless is expensive.

if you used it in that very stupid way, right?

Chris Adams: wouldn't, you'd, either lease a car or you'd buy a car if

Adrian Cockcroft: Yeah. If you, if it's being used continuously, if you've got traffic, enough traffic that the thing is a hundred percent active, sure you should put it in a container and just have a thing there rather than, waking it up every time, you know, having it woken up all the time

Chris Adams: Ah. I never really thought to make the comparison to cars, to be honest. 'cause I, I wrote a, piece a while back called A demand curve for compute, which compares these two, like, I just like energy for example. Like if you do something all the time, then you have something running all the time, it's a bit like maybe a nuclear power station, like it's expensive to buy, but per unit it makes a load of sense.

And then you work your way up from there basically. So, at the other end, like serverless, there are things like peak plants, which are only on for a little bit of time and they're really expensive for that short period of time. But because they're only on, 'cause they, can charge so much, you'll need to have them running maybe five to 15% of the year.

And that's how they, and that's how people design grids. And like, this idea of demand curves seems like, it's quite applicable to how we think about computing and how we might use different kinds of computing to solve different kinds of problems. For example.

Adrian Cockcroft: Yeah. Well that brings up another current topic. What's actually happening now is the peaker plants are running flat out running AI data centers capacity load, and the peaking is moving to battery, which is now getting to the point where batteries are sufficiently cheap and high capacity, that the peaker capacity is being driven by batteries which respond much more quickly to load.

And, some of the instabilities we've seen in the grids can be fixed by having enough battery capacity to handle, You know, a cloudy day or whatever, you know, the sort of the effects that you get from sudden surges in power demand or supply, right? And once you get enough battery capacity, that problem is soluble that the problem historically as the batteries have been too expensive, but they're getting cheaper very quickly.

So there've been a few, there's a few cost curves that I've seen recently showing that it's actually the cheapest thing to do for power now is, solar and batteries just put that in. And the batteries that they're now getting, originally they were saying you can get a few hours worth of battery cost effectively.

I think they're now up to like six to eight hours is cost effective. And we're getting close to the sort of 12 to 18 hours, which is means that you can go through the night in the winter on batteries. and it's cost effective to deploy batteries to do that. It's something about the economics that means that you have.

A certain amount of capacity, you still need some base load. geothermal isn't particularly interesting for that. I think as one of the cleaner technologies, a company called Vos building a station that, Google are using for some of their energy, I've spent some time looking at alternative energy.

But yeah, those peak of plants, they were sitting there mostly idle, and then all this extra demand suddenly appeared that wasn't in the plan for these big AI data centers and they're hoovering up all that capacity. So people are desperately trying to figure out how to add additional capacity, to take that on.

Chris Adams: We will come to that a little bit later in a bit more detail actually. So, but thank you. So maybe we can talk a little bit about, actually some of this stuff about. Essentially observability and being able to track some of this stuff because one thing that I've seen you present before is this idea of like carbon being just another metric.

And I think, what we'll do is we'll share a link in the show notes to a YouTube video. I called Monitoring Carbon. I think you presented this at Monitorama I Portland in 2022. And the argument that I understood it covers various other, it, it does talk a little bit about like the state of the art in 2022, but one of the key things you were kind of saying was basically as developers, we're gonna have to learn to track carbon because it's just gonna be another thing we have to track.

Just like, space left on a disc requests and things like that. So maybe you could talk a little bit about that and some of the re and just tell me if you think that's still the direction that we're going in. Basically, I.

Adrian Cockcroft: Yeah, so that was the first talk I gave after I left AWS I'd already given, agreed to present there. and then I left AWS I think just a few weeks before that event. so it was kind of an interesting thing. Hey, I, by the way, I quit my job and sort of retired now and, but this is the thing I was working on.

So I was, the last job I had a WSI was a VP in the sustainability organization, which is an Amazon wide organization, but I was focused on the AWS part of the, that problem in particular, the how to get. all of AWS sort of on the same page every, there was lots of individual things popping up. so we and lots of people writing their little presentations about what they thought AWS was doing.

And so we basically created a master PR approved, you know, press, press relations approved, deck that everyone agreed was like what we could say and should say, and it was high quality deck and got everyone to use the same, get on the same, be saying the same thing externally. Now, part of the problem there was that the various constraints we had at Amazon, we couldn't really talk about a lot of the things we were doing for all kinds of reasons.

So the story of Amazon, I think is better than most people think, but the, way it's told is really poor and it's very difficult to get, get things out of Amazon to actually, I. cover what they've been up to. So, so that was what I was working on. And along the way I thought, you know, we need to monitor.

ARM is a monitoring, observability conference I've been to many times and I have a long history in monitoring tools in particular. I thought, yeah, we should, I, should be trying to get everybody to add carbon as some kind of metric. And the problem is, then where do you get that metric from? And that wasn't very obvious at the time.

And I think there's sort of two things that have happened since 2022. One is that we actually haven't made much progress in terms of getting carbon as a metric in, most areas. There's a co with a couple of exceptions that we'll get to, but we haven't made as much progress as I hoped we would. And then the other one is that the sort of standards bodies and.

government regulations that were on the horizon then have mostly been stalled or slowed down, or delayed, whatever. so the requirement to do it from the business has generally come back, has reduced. Right. So, which is disappointing. 'cause now we're seeing even more climate change impacts and, you know, the globe doesn't care whether you're,

what your, corporate profitability or what you're trying to do or you know, what the reasons why you aren't doing it.

But, so we're just gonna get more and more cost from dealing with various types of climate disasters and we're seeing those happen all around us all the time. So, I think in some sense it's got to get much worse before people pay attention. And we're, you know, there's a big sort of battle going on to try and just make it, keep it focused and certainly Europe is doing a much better job of.

Right now. but even, the European regulations are a little watered down. And that's, I mean, I know that you are all over that's really your specialist area, you know, far more than I do about what's going on in, in that area.

Chris Adams: But yeah.

Adrian Cockcroft: It's a big topic, but I think in 2022, I thought that we would be having more regulations sooner, and that would be pushing more activity.

And then I wanted to basically, by talking about this, at that event, I wanted to get some of the tools, vendors to basically I would, for me to talk to them about how to do this. I ended up doing a little bit of advisory work for a few people, as a result, but not really that substantial. So that's kind of where I was then.

And then over the next year or so, I did some more talks, saying it's basically I just tried to figure out what was available from the different cloud providers. Did a talk about that, and then, wrote a. A-P-R-F-A-Q or a, proposal for a project for DSF saying, well, we should fix this. And it would be really nice if we did actually have a, this is what people would like to see, and then went and tried to see what we could get done.

Chris Adams: Okay, so that's, that, that's useful sort of kind bring, us up to this point here. And like, one thing I've appre appreciated about being on the Real Time Cloud project is that it's very easy, to basically call for transparency bec and there are absolutely reasons why you, why a company might not want to share their stuff, which are kind of considered like, I don't know, wrong reasons I suppose, or kind of like greedy reasons.

So, I used to work at a company called A that stood for avoid mass Extinction engine. And one thing we did was I. we were, we raised something in the region of 20 million US, dollars to find out all the ways you can't sell or carbon API in the early 2010s. And, you know, pivoting like a turntable, it's kind of a bit embarrassing at times.

Right? And one of the things that we, one of the potential routes that people went down was basically, we are gonna do this stuff and we are gonna work with large buyers to basically get people in their supply chain to share. Their emissions information, with the idea being that this would then be able to kind of highlight what they refer to as, supply chain engagement.

So that sounds great. Like we'll lend you some money so you can buy cheaper, you can buy more efficient fridges and do stuff like that. But there was another flip side to this, where when you're working with large enough companies or large enough buyers, one of the things they would basically say is they could use this information to then say, well, who are the people who are the least efficient?

And like, who am I gonna hit with my cost cutting stick first? Basically like who is, and this is one of, and for this reason, I can totally understand why organizations might not want to expose some of their cost structure. But at the same time, there is actually a kind of imperative coming from, well, like you said, the planet and from the science and everything like that.

And like, this is one thing that I feel like this is one of the drive, this is one of the thing that's been a real blocker right now. Because companies are basically saying we can't share this information 'cause we are going to end up revealing in how many times we maybe sell the same server, for example, like the, and these are kind of, you can see why people might, might not want to release that or, disclose that information.

'cause it can be sited, considered commercially sensitive. But there is also the imperative elsewhere. And like I wanted to ask you like. Faced with that, how do we navigate that? Or are there any things that you think we can be pushing for this for? Because I think this disclosure conundrum is a really difficult one to actually,, to get around basically.

And I, figured like you are on the call, you've been on both sides. Maybe you have some perspectives or some viewpoints on this that might be better. Shed some light here rather than it just being this, you are transparent. No, we're not gonna destroy our business kind of thing, because there's gotta be something, there's gotta be a third way or a more, useful way to talk about this.

Adrian Cockcroft: Yeah. And I think, I mean, there are three primary cloud providers that we've been working with or attempting to work with. And they're all different, right? And just Google generally have been the most transparent. they produce data that's easy to find, that's basically in a useful format. And they came out with their, their annual sustainability report recently, and there's a table of data in it, which is pretty much what we've been adopting as this is useful data.

Right? So that's one. but still they don't disclose some things because they don't have the right to disclose it. For example, if you want to know the power usage effectiveness, the PUE, they don't have it for all of their data centers. When you dig into that, you find that some of their regions are hosted in data centers they don't own,

right?

So somewhere in the world there's a big colo facility owned by Equinix or somebody, right? And they are, they needed to drop a small region in that area. So they leased some capacity in another data center. Now, the PUE for that data center is not the they, because they're not the only tenant. It's actually hard to calculate, but also the owner doesn't necessarily want to disclose the PUE, right?

So there's a one, the number isn't really obtainable. You could come up with a number, but they have to, you know, as a third party that they'd have to get to approve it. So that's a valid reason for not supplying a number. It's very annoying because you have p OE for some data centers and not others, and that applies to all the cloud providers.

so that's a valid, yeah, it's annoying, but valid reason for not providing a number. Right. So that's one level. And Google are pretty good at providing all the numbers, and they've been engaged with the project. They've had a few people turn up at the, on the meetings. they've fixed a few things where something wasn't quite right.

there was some missing data or something that didn't make sense and they just went fixed it. And there was also a mapping we needed from there. They're the Google data centers, which support things like Gmail and whatever, Google search to the Google Cloud data centers, which is a subset of it. But that we, they actually went and figured out their mapping for us and gave us a little table so we could look up the PUE for the data center and basically say, okay, this cloud region is in that data center.

They've worked well with it. So that's kind of what I'd like to see from the other cloud providers. It show, it's like, I like to see existence proofs. Well, they did it. Why can't you do that? Right. So that's what I'd expect to see from everybody. Microsoft were involved in setting up the GSF and were very enthusiastic for a while.

Particularly when Asif was there and driving it and, since he's moved on and, is now working directly for the GSF, I think the leadership at Microsoft is off worrying about the next shiny object, which is ai, whatever. Right? There's less su less support for sustainability and, we've found it hard to get, engagement from the Microsoft, Ah,

to get data out of them.

they have a report, they issued their new report for the year and they had total numbers for carbon, but they didn't release their individual regions updates, you know, so they released overall carbon data for 2024, but we haven't got any updated, nothing that I can find anyway on the individual regions, which is what we've been producing as our data set.

Chris Adams: Ah, okay. So basically as the moon and the moonshot has got further away, as they say, it's also got harder to see. Basically we still have this issuer then that this, it's less clear and we have less transparency from that. That's a bit depressed. That's a bit depressing. When early on they were basically very, they were real one.

They were. I was really glad to have them inside that because that they, they shared this stuff before Google shared it, so we actually had, okay, great. We've got two of the big three starting to disclose this stuff. Maybe we might be able to use this to kind of find against concessions from the largest provider to share this.

Because if you are a consumer of cloud, then you have some legal obligations that you still need to kind of, kind of meet, and this is not making it easy. And for the most part, it feels like if you don't have this, then you end up having to reach for a third party, for example, where you, like, you might use something like green pixie, for example, and like, that's totally okay to use something like that, but you happen to go via a third party where you know, you're, that, that's secondary data at best.

Basically it feels like there's something that you should be able to have with your, supplier, for example.

Adrian Cockcroft: Yeah. Just to clarify, I think there's several different types of, Sustainability data or sustainability related data that you get from a cloud provider. One of them is, well, I'm a customer and I have my account and I pay so much money to it, and how much carbon is associated with the, the things I've used, right?

And that is they all provide something along those lines to greater or lesser degree.

Chris Adams: Mm.

Adrian Cockcroft: but you can get, an estimate for the carbon footprint of an account, right? typically delayed by several months, two to three months, and it's a fairly, and it's pretty high level. So, and it gets, there's more detail available on, Google and Microsoft, and there's fairly high level data from AWS, but that's, one source.

The other source that we're interested in is, let's say I. I'm trying to decide where should I put a workload? And it could be I have flexibility, I can put it pretty much anywhere in the world or I can choose between different cloud providers in a particular country. what's the, and I want to know what the carbon footprint of that would be.

Right? So to do that, you need to be able to compare regions, and that's the data set that we've produced and standardized so that it lists every cloud region for the three main cloud providers. And for each of them we've got whatever information we can get about that region. And back in 2022, we have a fairly complete data set and 2023, it's missing.

Microsoft provide less data than in 2022. And in 2024 data, currently we have Google data, we have Microsoft have released their report, but haven't given us any new data. And AWS are probably releasing their data in the next, Few days, last year, it was on July the ninth, and I just checked this morning and it hasn't been released yet, so it's probably coming next week.

It's sometime in July. Right. So, we're hoping to see, well, we'll see what information we get from AWS and I'll, I, every year I write a blog post where I, they said, okay, the three reports are out. This is what happened. This is the trend year on year, and I'm working on an update of that blog post.

So probably by the time this, this podcast airs, I'm hoping that pod, that blog post will

Chris Adams: out there.

Adrian Cockcroft: I should have got it. I, you know, I've written as much as I can right now, but I'm waiting for the AWS ones, so. So we've sort of discussed Google have been pretty good, I guess, corporate citizens, disclosing whatever they can and engaging with the project.

Microsoft's sort of early enthusiasm. In their latest report, they actually mentioned the GSF and they mentioned they founded it and they mentioned that they support the real time cloud project, but they're not actually providing us any data and we're still trying to find the right people at Microsoft to escalate this to, to figure out, well, so gimme the data.

Right? and then AWS then they have, some different issues going on. they, the way that they run their systems, one of the things they found is that if they disclose something about how they work, people will start leveraging it. Right. You get this sort of gamifying thing. If there's an interface or, a disclosed piece of information, people will, optimize around it and start building on it.

You see, there's a lot in eBay. One of the reasons eBay's interface hasn't changed much over the years is that there are sellers that optimize around some weird feature of eBay and build a business around it. And every time eBay plans to change that, they're like, some sellers gonna lose their business, right?

So, if you over expose the details of how you work, there's sort of an arbitrage opportunity where somebody will build something on that and if you change it data, they get upset. So that's a one of the reasons that AWS doesn't like saying how it works,

right? Because it would cause people to optimize,

Chris Adams: yeah. Private

Adrian Cockcroft: optimize for the wrong things.

And, one example is that there's an Archive capability, tape Archive capability. That AWS has, and you can, and if you're thinking about I have lots of data sitting on desk, I should move it to tape. 'cause that is a much lower carbon footprint. And it is, except if you're in a tiny region that AWS has just set up, they haven't actually really got tapes there, the same services there, they're actually just storing it to disc until they have enough volume there, for them to put in a tape unit and transfer that to tape.

Like they want the same interface, but the implementation is different. Now, if they exposed which regions the, this is actually going to dis, it would say, well, this is a high carbon region, so I shouldn't store my data in there. Which means it would not get enough volume to actually install the tape.

Right? So you get the sort of negative feedback loop that's actually counterproductive. Right. So, so, so there's this, there's that sort of a, an example of. It's one of the reasons that they don't want to tell you how much carbon every different service is because it could cause you to optimize for things that are gonna cause you to do the opposite of what's the right thing to do Ultimately.

Chris Adams: okay. So that's one of the argument we see used for not disclosing how an organ, like. Per, like, per like service level and per region level things. 'cause one thing that when you use, say Amazon's carbon calculator, you'll get a display which broadly incentivizes to do, incentivizes you, you use to change basically nothing.

Right? like that's one thing we actually see. But, and that's different to say Google and Microsoft. We do provide service level stuff and region level stuff. So one of the reasons they're trying to hide some of that information is basically it's making it harder for us to kind of basically provide that service, for example, or there's all these second order effects that they're trying to basically avoid.

That's one of the arguments people are using,

Adrian Cockcroft: That's the argument that they have, and it's something that's pervasive. It's not just related to carbon. This is something that they've seen across lots of services is that people will, people will depend on an implementation. And they changed the implementation frequently. Like we're on, I dunno what the eighth or the ninth version of S3 total rewrite from scratch.

I dunno. When I was there, I think they were up to the seventh or eighth version and I knew somebody that was working on the team that was building the next version. Right. And this is tens of exabytes of storage that is migrated to a completely new underlying architecture every few years. If you depend upon the way it used to work, then you end up being suboptimal.

So there's some truth in that, however, and this is the example we were pointing at when I was at AWS, is that Microsoft and Google are releasing this data and we haven't, there's no evidence of bad things

Chris Adams: Yeah. The sky hasn't fallen when they

Adrian Cockcroft: Yeah. So, so I think that it, would be just fine too. And they are gradually increasing the resolution.

So what they had when. When they first released the, the account level information when I was there, and we'd managed to get this thing out in 2022, I guess 20 21, 20 22 was the, you had regions being continents, right? You just said Europe, Asia, and Americas.

And you had S3, E, c two, and other,

Chris Adams: yeah.

Adrian Cockcroft: and you had it to the nearest a hundred tons or something, or nearest a hundred kilograms.

Yeah, a hundred 10th of a ton. So most, so a bunch of people in Europe just got zero for everything and went, well, this is stupid. But actually, yeah, because of the way they, the, model works, they were generate, generating lots of energy to offset the carbon. It probably is zero for at least scope two.

scope, scope two, for the market based model.

Chris Adams: where you, count the, green energy you've used to kind of offset the, actual kind of, yeah. Figure. Alright.

Adrian Cockcroft: Yeah. So what they've done in the last couple of years, they finally got a team working on it. There's a manager called Alexis Bateman that I used to work with in the sustainability team that's now managing this, and she's cranking stuff out and they finally started releasing stuff. So the very latest release from AWS now has per region down to per region.

It has location based, just got added to the market based. So we actually have that finally.

Chris Adams: okay. Yeah.

Adrian Cockcroft: So this happened a few weeks ago. and the, and they've added, I think they have cloud. CloudFront because it's a global, CDN, it doesn't really live in a region. So they've separated CloudFront out and they also changed this model, as I mentioned earlier, so that the carbon model now includes supporting services that are required for you to use the thing.

So your, Lambda functions, even if they're not running, you've still got a carbon footprint because you need to have the lambda control planes there, ready to run you. So you pay for a share of that. And then the question is, how do you calculate these shares? And it's probably, you know, dollar based or something like that.

Some kind of usage based thing,

Chris Adams: Okay. Alright. So that's, yeah, I think I've, I read the, I hadn't realized about the location based, information being out there as well.

Actually,

Adrian Cockcroft: the location and the model with a new thing and they've now got this sort of, every few months they're getting a new thing out. They have def, they've clearly said they're going to do scope three. I know they're trying to do scope three where they real scope three thing rather than a financial allocation scope three.

So we could talk about that if you want, how much you wanna get into the weeds, of this stuff. But anyway,

So what we ended up with in the real Time cloud project was we figured out it wasn't really possible to get real time energy statistics out of cloud providers because the numbers just didn't exist.

It turns out the only place you can get real time numbers is on things that are not virtualized. And the thing that people don't generally virtualize is the GPUs. Yeah. So if you're using an Nvidia GPU, you can get a number out of it, which is the energy consumption of that GPU. So if anyone working on AI based workloads, you can get the dominant energy usage cap calculation is available to you, sources available.

But the CPUs, because the way virtualization works, you can't provide the information unless you're using, what they call a bare metal instance in the cloud, which you get access to the whole thing. So that's we gave up a bit on having like real time energy data and also the CNCF came up with a project called Kepler, which does good estimates and it does a workload analysis for people running on Kubernetes.

So it just, we just did a big, like point over at that. Just use, Kepler. If you want workload level energy estimates, use Kepler. and then. If we want to, and we focused instead on trying to gather and normalize the data, the metadata available on a region so that you could make region level decisions about where you want to deploy things and understand why certain regions were probably more efficient than others in terms of PUE and water usage and, carbon and the carbon free energy percentage that the carbon that the cloud provider had, meaning how much local generation did they have in that region.

So that was the table of data that we've produced and standardized, and we've put a 1.0 standard on it. And the current activity there is to rewrite the doc to be, basically, standards compliant so that we can create an ISO standard or propose an ISO standard around it. And the other thing we're doing is talking to the finops Foundation who come at this from the point of view of.

standardizing the way billing is done across cloud providers and they have all the cloud providers as members and all working on billing and they're trying to extend that billing to include the carbon aspects

of what's produced. working. so, we've done an interview with someone from Focus already who is basically talking about, they are almost. You, like you mentioned before, the idea that, okay, Microsoft and Google have shared this kind of per service level information and the sky hasn't fallen.

Chris Adams: They've created something a bit like that to kind of almost list these diff different kind of services. What, if I understand it, the GSF, you know, the, real time cloud thing might be like a carbon extension for some of that, because that doesn't necessarily, the, right now the focus stuff doesn't have that much detail about what carbon is or what, the kind of subtleties might be related to the kind of other, the kind of non-cash non, yeah, the, non-cash things you might wanna associate with, the way you, purchase cloud for example.

Adrian Cockcroft: Yeah, so focus is the name of the standard they've produced. Really all the cloud providers have signed up to it. If you go to an AWS's billing page, it talks about focus and has a focus, a conformant, schema. So the idea was all the cloud providers would have the same schema for their billing. Great obvious thing to do, but all the cloud providers have joined up to do that, which is fine.

Now Focus does, has some proposals for sustainability data, but they are just proposals for maybe the next version. They had a working group that looked at it and the problem they run into. One of the things is we've deeply looked into that in our group. We know why you can't do that. So what you'd really like is a billing record that says you just used, you know, 10 hours of this instance type.

And this is the carbon footprint of it. And the problem is you, that number cannot be calculated. and that's what you'd like to have. And intuitively you'd like to just no matter how much carbon it is, the problem is the carbon is not known at that time. You can generate the bill 'cause you know, you've used 10 hours of the thing, but you can't know the energy consumption and the carbon number, the carbon intensity, those two numbers are not known for a while.

So you typically get the data a month or two later. Whereas like, yeah, but you have to go back to your billing data. So you could put a guess in there. And things like the cloud carbon footprint tool and other tools that are out there will just generate guesses for you. but they are guesses. And then when you go and get the real data from your car cloud provider, the numbers will definitely be different, sometimes radically different.

so the question is, do you want to have an early guess or do you want to have a real number and what are you doing with that number? And if what you're doing is rolling it up into an audit report for your CFO to go and buy some carbon credits at the end of the year, that's what the monthly, reports are for.

Right? If you're a developer trying to tune a workload that is useless information to you, you need real, that's what the real Time cloud group was really trying to do is like if you're a developer trying to make a decision about what you should be doing. You know, calculating an SCI number or, understanding which cloud provider and which region has what impact.

That's the information you need to make a decision in real time about something. So the real time aspect is not about like in my milliseconds, I need to know the carbon or whatever. It's like I need to know now. I need to make a decision now.

Chris Adams: to make a forward looking decision

Adrian Cockcroft: Yeah. It's like I need to make a decision now, so what information do I have now?

Which is why we take the historical, metadata they have for the regions and we project it into the current year with, so just trending and filling in the gaps to say, this is our best guess for where you'd be if you needed to make a decision this year, on it. And we've got some little code that automatically generates the Nafus, estimate.

Chris Adams: so that's, at least useful. So people have an idea about what you might be using these two different kinds of data for. I guess maybe the thing, if we could just unpack one last thing before we move on to one of the questions is that one of the reasons you have this delay is basically because, is it, 'cause companies aren't, don't get the billing data themselves and they need to go then go out and buy credits.

Like this is for the market based calculations. So this, what you've said here is basically about carbon based on a market based figure. But if we had something like, maybe if we were to separate that out and looking, look at something like location based figures for electricity, which is like representing the kind of what's happening physically on the grid.

You plausibly could look at some of this stuff. Is that the, I mean, is that the way you see it? Really? Because I feel that we are now at this point where there's a figure for the grid, but that's not necessarily gonna be the, only figure you look at these days, for example, because as, because it's, we increasingly seeing people having different kinds of generation in the facility.

If you've got batteries, you might be, you might have charged batteries up when the energy's green, for example, or clean and then using it at a certain time. that's there's another layer that we need to, that you might need to take into account. Right.

Adrian Cockcroft: Yeah, so there's a couple of different reasons why the data is delayed. you know, you're in Germany, I'm sure with Germanic efficiency, you know exactly when you are going to get the information from your energy provider,

Chris Adams: They fax it to us. Yep. Yeah.

Adrian Cockcroft: and it will be nice and precise and there'll be high quality on it. now if you're operating a region in a developing nation.

not so much, right? There's bits of paper moving around. Probably. There's, random things happening. You dunno quite know when. So if you are trying to produce a service that is a global summary across all regions, you have to, you are limited to the slowest region that you operate in, right? you take this sort of distribution of how quickly you find out about the carbon intensity and the power usage of what's going on in your country and in the energy supply for your region.

And, you know, it's, whoever is slowest will de determine it, right? And AWS operates in regions in India, and Indonesia and places like that where, I don't know, maybe, there are efficient, maybe they aren't. But there, they, there are more global regions in more different countries on AWS. than in particularly in Asia than Azure and Google have, but fundamentally, it's gonna take you a few months to gather your billing and carbon data accurate to the point where it's not gonna change.

So then on top of that, you can then say, I'm gonna buy some credits to offset that. And there's two different ways of doing credits. You can buy green energy, procure your energy from a supplier that says, okay, I'm this energy that we already generated, you can buy the credits for it later. And so you can basically pre post allocate it, and you can do that within the rules for up to a year afterwards.

So at the end of the year, it comes to December, end of December, okay, how much energy we did we use, how much wasn't offset. I can buy energy credits from my energy suppliers to offset that. And the first thing you do is try and do it in region so that the energy is happening in the same grid. That you, your consumption was, and then you get to Singapore and go, okay, we all give up on Singapore.

There isn't enough local energy that's green, so we're going to buy energy somewhere, anywhere we can, green energy somewhere else and do a global offset on it. Google's been doing that since 2017, I think, or whenever they, said they were a hundred percent green back in the day, long time ago.

AWS since 2023, a hundred percent offset. but what, that's the mechanism they use and it's documented in their disclosure that they do it on a region by region basis and then they use global offsetting just for the, to mop up whatever's left over at the end. Right. So that's, and, then. A s does less of this, but is starting to do more, which is, carbon offsetting where you go and, you know, pay for a forest to not be cut down or you pay for built, grow some trees or you sequester some carbon.

And that is a little bit on the end that people are investing in to try and develop those markets. but most of it is, buying green energy. Like for the house here, I have an option to just subscribe to a different cloud, a different energy provider. It's called Central Coast Community Energy. And, Yeah, I pay them at slightly higher, you know, an extra cent or so per kilowatt hour. And I have a hundred percent green energy. And by market method, my, I'm completely green here, right? So that's fine. But it's the same thing going on. So, because what I'm paying for is the green energy. I'm not paying for carbon.

I'm probably is emitting carbon at night, certainly, but I'm generating more during the day 'cause I've got some solar panels here. Right. So that it, it's that mechanism that's being developed basically.

Chris Adams: Okay. Thank you for that. Alright, Adrian, I realize we we're coming up to time, so I, did have a bunch of questions about, what's making it harder to track, this stuff, like, because we are, we're now moving to work to the world of grid responsive data centers, for example, like various data.

We've been doing some stuff like that, and we're seeing cases of like, I don't know, in Memphis, the Colossus data center running primarily on gas turbines, right now, which is playing, which, massively complicates some of this. But we did actually say that there was some stuff going on in the house, and I do wanna kind of come back to that if we could, because that feels like it's, we won't have time to explore to do those, the, those subjects.

Justice basically. So we were talking, at the beginning of this podcast that in addition to doing this stuff here with the Green Software Foundation, you've been exploring and playing around with some of the, tools and some of the technology and like finding out if there's a, there, for example, and, When I looked up this project, when before doing some research for this podcast, I heard, I, I read about this thing called the House Consciousness System. So maybe you could talk a little bit about that because, you've been working as a technologist for, you know, at least 40 years now, and I see you've messing around with things like generative AI and AI for this and doing things that I am not expecting people to do.

So maybe you could talk a little bit about this, that HCS or the, or whatever, this project is, because I, found it quite interesting that you were to see your take on this, basically.

Adrian Cockcroft: Yeah. So the history of this going back a while is that a while ago, I mean, every now and again, I, do some programming and I wanted to do some programming a year or two ago in the R language, which is a statistical language, which I use occasionally. I keep forgetting the syntax of. So I thought, well, maybe I can ask the AI to remind me of the syntax.

And the AI just started writing code that worked. So I went, oh, this is cool. I can just tell it what I want and it'll write the code. And this is very early days when people. Most people weren't doing that kind of thing, so that was fine. and then, more recently I wanted to write some code in Python.

I'm not ever, never really wrote in code in Python. I can sort of read it, but I can't write it. So I started telling it what I wanted it to do and it wrote the code for me and it could get it working pretty well and it worked. So I have code, I was using it to generate code there. and this is mostly just 'cause I'm not, I don't have patience and time to be like a full on developer, but it, these are the things that it's good at.

So one of the things that I tend to focus on, if as a new tool around, I figure out what can it do and what can't it do. Think of cloud, the early days of cloud. We built Netflix on an extremely rudimentary set of cloud services. And it was like just about possible to make it work given the services we had.

And most people today would look at that and say, we wouldn't even start trying to build anything on that, right? But we made it work, and we made it work reliably. And, that became like a template and it caused other people to try and figure out how to do it. Now there's a lot more capability there now, so we're sort of in that early stages day, thing where a bunch of people say, well, this'll never work.

there are people figuring out how to make it work. What happened? where shall I go next? Let's talk about the idea. So years ago, I mean, I have lots of iot devices around my house. I like buying random, automated, and then none of them talk to, or some of them talk to each other, but I have too much random stuff and I have a, like an iPad with lots of icons on it, and I have to know which one does what.

Right? and it's annoying. And if I'm not home and my wife's trying to do something, she can't figure out which one. And she knows some of them, but like, she doesn't, know how, this stuff works. other visitors to the house don't know how to do things. And that's just, and a lot of people are in that environment.

And I was thinking about this a few years ago when I was at Amazon and I was talking to the Alexa team because they have, house automation kind of stuff. So why don't you build in something that is just like a more general thing that knows what's going on in the house. That would, and it's sort of like a, central consciousness because the re thing about consciousness, it's an observability system.

I regard consciousness as human observability. And part of the definition of consciousness for me is that you have to understand what unconscious means, right? If your definition of consciousness doesn't include unconscious, then you're not, you haven't picked the right thing. So it's the thing that goes away when you're asleep,

right?

'cause you're unconscious when you're asleep. So anything that goes away when you're asleep is consciousness to me. Right. And there are, and this isn't the standard definition. People have big arguments about it, but that's a, good working definition for me. 'cause what it means, it's the thing you can talk to, to discover its state, right.

So if somebody is conscious, you can ask them questions and find out what's going on with them. So in that sense, what I want is want my house to have a memory of all the things that have happened to it. I want it to look at the weather and remind me there's a storm coming and have you dealt, you know, ti it up outside so things don't blow away and all the stuff, right?

but right now the iot devices live in the moment. Let see, your temperature is 73 degrees and they sort of have a schedule for changing stuff, but they don't really have a memory and they aren't talking to each other. And so I had this idea that, Hey, why doesn't Alexa team build something like this?

And they, I sent, I found somebody in that team and they never built it and weren't interested in it, right? So I had this idea of. Kicking around. And then a few weeks ago I saw that Rueven Cohen, R-U-E-V-E-N, Cohen Coen, C-O-H-E-N, on LinkedIn, is just posting and posting about his agent's swarm work.

And he's just, like, he's building amazing stuff and said this, does this really work. So I wanted to play around with it and I decided I needed a new idea that I needed to try and build. That was a fairly aggressive thing. So I wrote up a rough idea of what this house consciousness would be.

And then I got together with Reuven. He showed me how to start an agent swarm, to just get with the CLO and Philanthropics Claude Code service to just go build it. And it wrote about 150,000 lines of code in a day or so.

Chris Adams: So these, so when you talk about a swarm of age, a swarm of agents, it's basically kind of like a model in a loop that's writing some code and there's multi, there's lots of them working together. That's what a swarm of agents in this case. Yeah.

Adrian Cockcroft: Yeah, but they aren't all writing code. So what you've got is sort of a, the latest version it, they call, he calls it a hive. So they're sort of a queen bee that who is just managing the hive. And it's basically like there's an AI acting as a light, as a development manager,

a dev manager. And the dev manager picks these specializations they want, so they start a selection of agents and they've got a QA agent, a DevOps agent for a deployment, a spec reading agent, a researching agent.

And they basically specialize. And what happens is if you're doing AI coding. The context. If you use one agent to do, if you just use one AI and you tell it all the things you want to do, it sort of gets confused. 'cause you've asked it to do lots of things at once. What you do here is by giving them each one track, mind specializations and a, and an ability to communicate, you get dramatically better results out of it.

So that's the aha moment if you like. But what it means is that to manage this swarm of agents, you need basically product manager and line manager skills, not developer

skills, right? You need a bit of developer skill to read the code and see if it works. But I don't, I'm currently writing, I switched from writing in Python.

The first thing I tried building, which was more just like a, can it build anything at all? And it built a thing and it ran, but didn't really work. 'cause I didn't, really specify what I wanted well enough. I'm now building an i an iPhone app in Swift, which I absolutely cannot write a single line of Swift.

I have no idea. And it's writing the code. I'm telling it to do code reviews and, Run and tests and things. So it's actually coding and testing and building itself and build a UI design and a plan. And so I'm doing that anyway. So I'm basically, I've now got a little obsessed by building myself this thing.

And you basically need a Max plan to do this, which is sort of about a hundred dollars a month AI plan. And once I finish building this, I'll sort of wind that back down to the usual $20 a month kind of level. and yeah, I mean, from my point of view, you can use AI to do a bunch of bad things, you know, generate fake news and stuff and adverts and things, but I'm actually using it to develop something that I always wanted to have, that isn't, there's no real business model for this thing other than I want it to exist.

I don't need a business model. I can spend a hun few hundred dollars, which is sort of go out to dinner. You spend a hundred dollars, right? That on building something, getting it to build something, which I'm sharing on GitHub. you can go and have a look at the repo, the, original Python repo is, it's sort of, it's there, it runs, but it's not, doesn't really do what I think.

It doesn't really work, right? 'cause I hadn't thought it through. I'm now doing front back, front end backwards. So I'm doing as much functionality as I can in this iOS app, and then I'll build the service to go behind it. I'll revisit that when I get to it. So that's kind of what I'm doing. I'm just happily use using this new tool to do something that will make me happy and potentially be useful for other people if they feel like it, but I don't care whether anyone else uses it.

So that's sort of my approach to figuring out new tools and finding where they work and where they don't work.

Chris Adams: Okay, so let me, if I can, I just wanna paraphrase some of that. So you've, so when people, like, I am not using a all that, that, that much at the moment, but I am dabbling and I'm using, I've been messing around with Claude and stuff like that to ask questions or, okay. I'm in Europe, so we use Misra 'cause it's the, French equivalent for example.

But one thing you said that was significant was that a, rather than me using one thing in like serial, it's happening concurrently. So there's lots of different things all burning

Adrian Cockcroft: They typically run like five to eight of these agents in parallel, and they're coordinating and communicating. They make to-do lists and they, do different specializations and they, it's basically like managing a team of developers.

Chris Adams: Ah, and that might be why you have people talking about, say, like, what the hell, you know, vibe, coders, dunno what they're doing here. 'cause in many cases. it's a new set of skills. It's not necessarily just, can you read, I mean, yeah. It helps to be able to read Python in the same way that if you are reading the output from a, chatbot, you want to, you know, you'll probably tweak it to make it sound like a human rather than, a, an ai model.

But there's also a bunch of other skills that you need to do, like spec writing and all these other things that you might, that typically might live in a product manager rather than a developer, for example, or someone who in different roles.

Adrian Cockcroft: it's much more product management than anything else. You have to have a clear idea of what you want. And figure out what the user flows through it are, and you know what functionality you want, how you want it arranged. And then it will build whatever you tell it to build and it will add on things you didn't ask for.

So you do it. So the other thing is to do it very incrementally and check every time to see what it did build. And you ask for this and it does like two or three times as much, and you go like, I want to keep these things. That was a good idea. No, I don't want that. And if you're working with a team of engineers, you say, I want them to, build a thing, they'll come back with extra stuff that they thought they, you might want.

Right? So there's actually, it's normal. I mean, this is how you manage a team of engineers to go build a new thing, right? So in that sense, it feels anybody that's managed a development team, this actually feels very familiar. If you're a developer on one of those teams, it doesn't feel very familiar. So I think that there's this sort of weird.

Thing where we sort of brought it up a level into management, but you still kind of need specialist experts. Like I'm stuck in a whole bunch of Apple stuff to do with iOS. That is nothing to do with the ai, it's just stuff that Apple makes difficult

Chris Adams: That would explain what you've been posting about what? How? Okay. That, that, that. Adds a context. 'cause I've seen you posting like, how do I know what to ask from Apple now? And I was like, why is he asking that? But okay. This is putting two and two together for me now. S Adrian. Thank you. So there was one last thing I was just gonna ask before we kind of wrap up.

Right. you mentioned that you're doing this all locally on your own computer? Or like, is it, are you running in like an, in an environment in the cloud, like a code space with GitHub? Or maybe, yeah. Where is that happening?

Adrian Cockcroft: Yeah. So when you have an agent swarm, it can do anything. You basically let it free run, so you need to put it in a box so that it doesn't delete your computer or do random things or whatever. Right. So. You go to GitHub and they have a free, it's free for up to some amount, which is plenty for me.

So far, having been doing this for a few weeks, you create a code space, which is basically an instance I guess running on Azure, which is like a little container. It shuts down if you don't use it for a few minutes or a few, you know, when it's idle. But basically it opens it and the only thing it can really do is run against the repo.

You opened it on, so the AI can sit there and it can do anything it wants in a copy of that repo and then it can push to the back, to the repo. I tell it usually when it's, when you finish this work, just push it back to the main repo and GitHub because when I'm working on iOS, I have to pull it back out into X code on my machine to build it.

So it's a safe box. It's a safe box. And I wouldn't run an agent swarm today on my own machine. Right. You could, but you're, it's sort of dangerous if it gets a bit carried

Chris Adams: That's what I was wondering about. That did seem like the idea of having one rogue machine work on my machine is on my own personal laptop feels a bit weird because I don't dunno where to post it in the, but if I've got a whole bunch of them, that times terrifying. Adrian.

Adrian Cockcroft: the option on Claude is minus, minus dangerous dangerously enable all permissions or something like that.

Chris Adams: Okay. All right.

Adrian Cockcroft: so that is the, mode we're running Claude in, and on, I think on our Google Gemini it's called YOLO mode.

Chris Adams: Yeah.

Adrian Cockcroft: you just, right, right. So, but when you're running it in that mode and a swarm other, and you can't just sit there and say, yes, it's okay to do this.

Yes, it's okay to delete that file, whatever, because it's tidying up, it's moving things around. It's writing code fragments and running them and deleting them again and stuff like that. It's doing all these things, but you don't want it to just do like an R minus rf.

Chris Adams: to just like hose your

Adrian Cockcroft: I once had somebody call, what does con stat CWD mean?

Oh, it means you're in a directory that doesn't exist. Now I'm at my home directory. What did you just type? Well, I was cleaning up some dot files, so I did RR minus R star,

Chris Adams: Oh dear.

Adrian Cockcroft: dot. He recursively deleted his parent directory.

Chris Adams: Oh geez. He just like wiped his entire machine.

Adrian Cockcroft: Yeah. The end of the day tidying up before he went home and said, well, you just lost your day's work.

Luckily we have a backup. I was the guy that ran the backups. This is before I, when, I was,

Chris Adams: Okay. I'm glad you mentioned this, final story here, because it sounds like you're using a code space almost. Whereas typically you might use it for convenience. You're almost using it for safety. Like I want to minimize the blast radius of these agents running am mock inside my system. And also I guess like conveniently, because it's, I mean, surely this should be something what you can work out the environmental footprint of this because if it's a billable tool and like if GitHub knows to bill me for all those minutes, they should be able to tell me the carbon footprint of this as well, right?

Like, I mean, yeah, there's could be some complexity with like using Azure, but like I should have something indicative at

Adrian Cockcroft: There's a little, console that tells you how many hours you've used, and if you use more than a certain amount, they start billing you for it. But you get some number of hours per month of CPU hours per month, which is enough for me. So far I haven't hit that limit and I've been playing around for a few weeks fairly intensively.

so that's, yeah, but the Claude itself, so no. So one thing last, one last thing. So we've got the three main cloud providers. We know roughly what they're doing. we now have Oracle that I'm trying to find somebody to talk to, to tell me how, what their carbon footprint is. Core weave is probably up and coming as a new big one.

And then you have all the stuff that anthropic or open AI or whatever they're all running, where are they running? So we've now got some very large sources of carbon. That we need to get accounting data for. And as far as I can tell, they are not publishing that data currently. So that's currently, I'd say that's the next phase.

It's like how do we, can measure an individual GPU pretty easily, but the, GPU services we're using are not being allocated. We're not getting data from. So that's probably, where I should wrap up. That's where we are now, we need to find out how much I need to know how much carbon I'm actually generating by telling Claude to build this thing for my house.

I dunno. No clue.

Chris Adams: I think that feels like a very useful rallying cry for anyone who does want to see if it's possible to instrument any of these tools, and they're listening to this podcast. Adrian, thank you so much for giving us the time to chat with us. I really enjoyed noting out with you and going really, deep into the weeds.

If people are curious about any of these projects that you've mentioned, we're gonna share the show notes, but where would you direct people's attention? Like is there a URL or a website that you'd point people to?

Adrian Cockcroft: Well, there's the Green Software Foundation's Realtime Cloud, website, GitHub site, and my own Adrian.co. You don't have to spell Cockcroft, you just have to get the first two letters, and on GitHub, and that's where you can follow along my random rants and things like that. and I have a Medium account as well @adrianco.

And where I have my blog, if you want to chat to a, an AI copy of me. There's a service called Soopra. We'll put a link to that. Soopra.ai, and there's an, I think it's Soopra/cockcroft is my, I uploaded all my blog posts into a persona so you can go ask it, and it comes up with sometimes reasonable answers to questions about microservices and Netflix and things like that.

Chris Adams: I wanna ask you all these questions about do you ever use that to fob people off or email you? But I think I'll have to pass that for another day.

Adrian Cockcroft: mostly when somebody sends me a list of questions for a podcast, like if I have time, I feed them into it. I didn't have time

Chris Adams: Okay. Alright. Alright, Adrian, thank you so much for this and I hope you have a lovely weekend. All right, take care mate.

Adrian Cockcroft: All right. Cheers. Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.


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]]> Chris Adams is joined by Adrian Cockcroft, former VP of Cloud Architecture Strategy at AWS, a pioneer of microservices at Netflix, and contributor to the Green Software Foundation’s Real Time Cloud project. They explore the evolution of cloud sustainability—from monoliths to microservices to serverless—and what it really takes to track carbon emissions in real time. Adrian explains why GPUs offer rare transparency in energy data, how the Real Time Cloud dataset works, and what’s holding cloud providers back from full carbon disclosure. Plus, he shares his latest obsession: building a generative AI-powered house automation system using agent swarms.

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TRANSCRIPT BELOW:

Adrian Cockcroft: We figured out it wasn't really possible to get real time energy statistics out of cloud providers because the numbers just didn't exist.

It turns out the only place you can get real time numbers is on things that are not virtualized.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. If you have worked in cloud computing for any length of time, then even if you do not know the name yet yourself, it's very likely that the way you design systems will have been influenced by my guest today, Adrian Cockcroft.

When at Netflix, Adrian led the move to the cloud there helping, popularize many of the patterns we use when deploying applications ourselves to the cloud. And his name then became synonymous with serverless throughout the 2010s when he joined AWS first leading on open source engagement, and then as a VP focused on what we might refer to now as cloud sustainability.

After leaving AWS, Adrian's kept his fingers in many pies, one of which is the Green Software Foundation's real time cloud project, an initiative to bring transparency and consistency to cloud emissions reporting. With the first dataset release from that project out the door, it seemed a good idea to invite him onto the show to see what's up.

Adrian, thank you so much for joining us today. Can I give you a bit of time to tell us about yourself and what you are, what's what you're keeping? What's keeping you busy these days? I.

Adrian Cockcroft: Yeah, it's great to see you and thanks also for your contributions to the project. We've had a lot of discussions over the last few years as we've worked on that together. well, I'm sort of semi-retired. I stopped my big corporate job at Amazon in 2022. and yeah, I spend my time worrying about my family.

I've got old parents that live in the uk, so I spend a lot of time with them. And, fixing stuff around the house and generally goofing around and doing things I feel like doing rather than stuff that's driven by some corporate agenda. So I'm enjoying that freedom. And, let's see the, yeah, I spend time on the, Green Software Foundation project.

I go to a few conferences and give a few talks and I try to keep up with, you know, what's happening in technology by playing around with whatever the latest tools are and things like that. And that's been my career over the years. I've generally been an early adopter through my entire career. as you mentioned, we were early adopters in cloud.

Back when people said This isn't gonna work and you'll be back in the data center soon. People forgot that was the initial reaction to what we said. it's a little bit like that now with people saying, all this AI stuff doesn't work and we're gonna be giving up and whatever. And it's like, well, I'm making bits of it work well enough to be interesting.

We can talk a bit about that later. and then I know you probably see behind me various musical instruments and things like that, so that's kind of, I, collect musical instruments that I don't have time to really learn how to play and mess around and make bad noises that make me happy. But luckily no one else has to listen to them particularly.

So that's kind of my, that and messing around with cars and things, that's sort of the entertainment for me.

Chris Adams: That sounds like quite a fun, state of stem semi-retirement, I have to say actually. So before we dive into the details of cloud, I have to ask, where are you calling from today Because you have an English accent and like, I have an English accent, but I'm calling from Berlin and I'm guessing you're not in England, so maybe you could do that.

'cause I follow you on social media and I see all these kind of cryptic and interesting posts about cars and stuff and it's usually sunnier than where I am as well. So there's gotta be a story there. What's going on there, Adrian?

Adrian Cockcroft: Well, I lived in England long enough to decide I didn't want to be rained on all the time. which is why I never moved to Seattle when, you know, I didn't move to California to move to America to go live in somewhere with the same weather as England. So that was one reason I never moved to Seattle when I was working for Amazon.

So used to live in the Bay Area in Los Gatos, near Netflix. about five years ago we moved down near Monterey, about an hour or two south of the Bay Area. I. Depending on traffic. we are within earshot of a race track called Laguna Seka that most people know. I can kind of see it outta my window.

I can see a few dots on the horizon on the, you know, moving and that's, there's a few cars you can just about hear them on if they're loud cars. and this is where they have in every August, this thing called Monterey Car Week with the Pebble Beach concourse and historic races. And we used to go to that every year and we like the kind of messing around with cars and going to the track occasionally culture.

So we moved down here and that's been, it's been fun. It's, you know, I don't have to commute anywhere. We have a nice place. The house prices are a lot cheaper down here than they are in the Bay Area itself. So we live in, technically we live in Salinas. lots of good vegetables around here. That's where a lot of the growers are.

and it's, we live actually out in the countryside, sort of. Just in the hills near, near there. So we have a nice place, have plenty of room for messing around and a big house, which requires lots of messing around with. And we can talk a bit about one of the projects I have later on to try and automate some of that.

Chris Adams: Yeah, that's quite a hint. Alright, well that does explain all the kind of cars and coffee stuff when I, like say 30 verse and Okay. If you're near a racetrack, that would explain some of the cars as well. Alright. Thank you

Adrian Cockcroft: Well, actually there's cars and coffee events just about everywhere in the world. If you, like looking at old cars and hanging out with car people, there's one probably every Saturday morning somewhere within 10 miles away. Pretty much anyone. Anyway, the other things, on that front that's sort of more related to Green Software Foundation is we've had a whole bunch of electric cars over the years.

I have one of the original Tesla Roadster cars that was made in 2010. I've had it since 2013. it actually has a sticker on the back saying, I bought this before Elon went nuts. so I'm keeping that. we used to have a Tesla model three and we replaced it recently with a Polestar three, which is quite a nice car with very bad software initially.

But they did a software update recently that basically fixed just about every bug and we, it's actually fun driving a car where you don't worry if it's about to do something strange and need a software reset, which was the state it was in when we first got it in April. But the difference, a bug fix can make whether they actually went and just fixed everything that was currently going wrong with it and went, transformed the car into something That's just actually a fun thing to drive now.

Chris Adams: So it was a bit like turning it off and turning it off and on again. And then you've got like a working car,

Adrian Cockcroft: Yeah. Well, yeah, we got really used to pushing the reset button. You hold the volume control down for 30 seconds and that resets the software and we would be doing that most days that we drove it

Chris Adams: Oh my God. I didn't realize that was a real thing that people did. Wow.

Adrian Cockcroft: Yeah. It's one of these things where a product can be transformed from something buggy and annoying to, oh, we just fixed all the software now.

It actually works properly. And, you know, it's, interesting to see. So, so it went from bad, really bad to actually pretty good with one software release. Yeah.

Chris Adams: guess that's the, wonders of software I suppose. Wow. Alright then, and I guess that gives us a nice segue to talk about, I guess some back to some of the cloud and serverless stuff then. So. Before you were helping out in some of the Green Software Foundation projects. I remember reading a post from you called the evolution from Monoliths to microservices to functions.

And I think for a lot of people it actually really joined the dots between how we think about sustainability and how things like scale to zero designs, might kind of what role they play when we design cloud services. And in that post, you laid out a few things, which I found quite interesting. You spoke about the idea that like, okay, most of the time when we build services, they may be being used maybe 40 hours a week and there's 168 hours a week.

So like 75% of the time it's doing nothing. And just like waiting there. Yet we've still spent all this time and money building all this stuff and, post. I remember you writing a little bit about saying, this actually aligns incentives in a way that we haven't seen before. And I think this idea of actually like changing the programming model that actually incentivizes the correct behavior.

I think that's really, that, that was really profound for me. And I figure like, now that I've got a chance to have you on the call on this podcast, I wanted to ask you what drove you to write that in the first place? And for folks who haven't actually read it, maybe, you could just talk a little bit about the argument that you were making and then why you wanted to actually write that as well.

Adrian Cockcroft: Yeah, that's actually one of the highest traffic blog posts that I ever wrote. There was a lot of, reads of that. The context then, so it was soon after I joined AWS, so it was probably 25. Early 2017, something like that. I joined AWS in 2016. I'd spent a few years basically involved in kind of, helping promote microservices as an architecture.

And, I was also interested in serverless and AWS Lambda as, an architecture. And I wanted to connect the dots. And it's a kind of, when I write things, some of the things I write, the approach I take is along the lines of his, this is how to think about a thing, right? These are the, it, I have a systems thinking approach generally, and so what I do is I try to expose the systems that I'm thinking about and the incentives and feedback loops and reasons why things are the way they are, rather than being prescriptive and saying, just do this, and this.

I. And the world will be great, or whatever the, you know, the more typical instructive things. So I tend to try and explain why things are the way they are and, sort of work in that. So that's, it's, an example of that type of writing for me. And we were, at the time, people were talking a lot about the monolith and microservices transition and what it meant and how to do it and things like that.

And I was trying to explain what we'd done at Netflix. And then I was thinking that there was a, the next generation of that transition was to serverless. And the, post was basically to just try and connect those dots, that was the overall goal of it. And then it is quite a long post. It's one of these things when you work with somebody, you know, PR people or whatever, and they say, you, you should write short blog posts and you should, you know, da Well this, and they shouldn't be so technical. So this is one of the longest and most technical posts I wrote, and it actually has the highest traffic. So, you know, ignore the PR people. It turns out if you put real content in something, it will get traffic. and, that's, the value you can, provide by trying to explain an idea.

So I think that's generally what that was about. This idea that. it was, I mean, the microservices idea was, is a tactic for implementing a for solving a problem. It isn't an end in itself. Right. And that's one of the distinctions I was trying to make. It's like if you have a large team working on a code base, they'll keep getting in each other's way.

And if you're trying to ship code and the code has a hundred people's contributions in it, one person has a bug, then that stops the shipment of the other 99 people. So there's this blocking effect of, of bugs in, in, in the whole thing. And then it also, you've got it destabilizes the entire thing.

You're shipping completely new code when you ship a new monolith was when you have say a hundred microservices with one person working on each. They can ship independently. And yeah, you have some interaction things you have to debug, but 99 of those services didn't change when you pushed your code. So it's easy to isolate where the problem is and roll it back.

So there's a bunch of things that make it easier. And then we thought, well, you've got the microservice, which does a thing. But it contains a bunch of functions. If you blow that up into individual functions, then you don't actually need all those functions all the time. And some code paths are very busy through the code.

They may be do it a hundred times, you know, every request goes through this part of the code, but may one times in a hundred or a thousand it does something else. So what you can do is break those into separate functions and different lambda functions. And you've got, so the code parts that don't get executed very often just aren't running.

The code gets called and then it stops and it's doesn't get called again, for a long time. Whereas the busy ones tend to stay in memory and get called a lot. Right. So that way you're actually, the memory footprint is more tuned to, and the execution footprint is tuned to what's actually going on.

So that was, the second thing. And then the third thing was that a lot of applications, particularly corporate in access, you mentioned they're only used during work hours. And those are the perfect ones to build serverless. They're internal. They are, they only exist for as long as anybody is actually trying to use them.

And they aren't just sitting their idle most of the time just because you need to have a wiki or something, or you need to have a thing that people check in with in the morning. Like anything that salespeople at the end of the quarter or the end of the month, those sorts of things make things super busy and it's idle the rest of the time, so you need very high concurrency for short periods of time.

Anything like that is, is sort of the area where I think serverless is particularly good. And later on I did another, series of talks where I basically said serverless first, not serverless only, but start trying to build something with serverless because you'll build it super quickly. And, one of the books I should reference is by, David Anderson.

is it called the Value Flywheel Effect or something like that will give a link in the show notes. And I helped. Talked, I, talked to him, helped him get, find the publisher for that book. And I wrote, did I write, I think I wrote a foreword for it, or at least put some nice words on the cover.

and that book talks about people developing app, entire applications in a few days. And then you get to tune it and optimize it. And maybe you take some part of it where you say, really, I need a container here. Something like that. but, you can rapidly build it with the tag I used to say was in the time it takes to, have meetings about how you're going to configure Kubernetes, you could have finished or building your entire application serverless, right?

And, you just get these internal discussions about exactly what version of Kubernetes to use and how to set it up and all this stuff. And it's like, I could have finished building the whole thing with the amount of effort you just put into trying to figure out how to configure something. So that's the sort of, a slightly flippant view I have on that.

And, anyway, the, other thing is just, and effectively the carbon footprint of a serverless application is minimal. But you do have to think about the additional systems that are running there all the time when you are not running. And a little bit of a, sort of a future segue, but AWS just changed them, their own accounting model to include those support services so that, when you look at the carbon footprint of a Lambda app that isn't running, you actually have a carbon footprint because the Lambda service needs to be there ready.

So you actually get a share of the shared service attributed to each customer that's using the, using it, right? So it's a little, it's a little bit deeper and it's kind of an interesting change in the model to be explicit that's what they're doing.

Chris Adams: Ah, I see. Okay. So on one level, some of this post was about like the, I guess the unit of code or the unit of change can become smaller by using this, but there's also a kind of corresponding thing on the hardware level. Like, you know, typically you might be, I remember when I was reading this, there was like, okay, I'm shipping a monolithic piece of code and I've got a physical server to begin with.

It's like the kind of. That was like how we were starting at maybe, I dunno, 10, 20 years ago. And then over time it's becoming smaller and smaller and that has made it a bit easier to build things kind of quickly. And, but one of the, flip side that we have seen is that, if you just look at say the Lambda function, then that's not a really accurate representation of all the stuff that's actually there.

You can't pretend that there is an infrastructure that has to be there. And it sounds like the accounting has now starting to reflect that the fact that yeah, you, someone needs to pay for the capacity in the same way that someone has to pay for the electricity grid, even though you're not, even when you're not using the grid for example, there is still a cost to make that capacity available for you to use.

Basically that's, what it seems to be a reference to.

Adrian Cockcroft: Yeah. And just going back to the car analogy.

People own cars. People lease cars. People rent cars, right? And you can, if you rent a car for a day, you can say, well, my carbon footprint of renting the car is one day's worth of car ownership, right? Except that in order for you to rent a car for the day, there has to be a fleet of cars sitting around idle That's ready for you to rent one. So really you want to take into account the overhead of your car rental company managing a fleet, and it's maybe got whatever, 70% utilization of the fleet. So 30% of the cars are sitting around waiting for somebody. So you basically have to uplevel your, I just need a car for a day to add an extra overhead of running that service, right?

So it's, it kind of follows that same thing, you know? And if you basically rent a car for every single day and you have a car every day of the year, but it's a rental car, that's an expensive way to own a car, right? I mean, even at a monthly rate, it's still more expensive than buying a car or leasing a car because you're paying for some overhead.

But it's kind of those sorts of models. So it's a bit like owning a car, maybe leasing a car, and, doing a rental car with sort of the monolith microservices. Serverless sort of analogy, if you like. cost model's a little different because, you're giving stuff back when you don't want it anymore.

is sort of the cloud analogy, right? The regular cloud service. I can just deep, I can scale things down.

Chris Adams: mm going back to something else you mentioned, I was talking to a CIO once and he was very annoyed 'cause he said that he'd only just found out that he could turn off all his test infrastructure at the weekends and overnight. and it was like they, he'd been running this stuff for two years and this, he finally realized and, he'd just, like, three quarters of his cost had just gone away from his test environment. And, he, was happy that had happened, but he was annoyed that it, took him two years for him to somebody to mention to him that this was possible and for him to tell them to do it.

Adrian Cockcroft: Right. So there's. Yeah. Any, tests, anything that's driven off people should absolutely be, you know, shut down. There are ways to just freeze a bunch of a, bunch of cloud instances can just be shut down and frozen and come back again later.

Chris Adams: so this is something I might come back to actually, because one of the things that in somewhat on, in some ways, if you look at, say maybe cloud computing, each individual server is probably quite a bit more efficient than maybe a corresponding, server you might buy from Dell or something like that from a few years ago because it's in a very optimized state.

But because it's so easy to turn on, this is one of the cha challenges that we can consistently have. So it's almost like a, and also in many ways. It's kind of in the interest of the people running very effect, very efficient servers to run, but have to basically have people paying for this capacity, which they're not using.

'cause it makes it easier to then like resell that. Like this is, I guess maybe this is one of the things that the shifts to serverless is supposed to address, or in theory, you know, it does align things somewhat, better and more. More in terms of like reducing usage when you're not actually using it, for example, rather than leaving things running like you're saying actually.

Adrian Cockcroft: Yeah, you don't have to remember to turn it off With serverless, it's off by default and it comes on and it's sort of a hundred percent utilized while you're running and then it turns off again. So in that sense, it is much more like you have a rental car that returns itself after 15 minutes or whatever.

Whatever your timeout

is or when you're done with it. It's more, maybe it's more like a taxi, right? That kind of going, one level beyond rental car, you have taxi, right? Which is you just use it to get there and you're done. So serverless is maybe more like a taxi service, right? And then, right. And then a daily rental is more like a.

Like an EC2 instance or something like that. And there's all these different things. So there we're used to dealing with these things and you wouldn't, you know, you wouldn't have a taxi sitting outside your house 24 hours a day just waiting for you to want to go somewhere, right? People say, well, serverless is expensive.

if you used it in that very stupid way, right?

Chris Adams: wouldn't, you'd, either lease a car or you'd buy a car if

Adrian Cockcroft: Yeah. If you, if it's being used continuously, if you've got traffic, enough traffic that the thing is a hundred percent active, sure you should put it in a container and just have a thing there rather than, waking it up every time, you know, having it woken up all the time

Chris Adams: Ah. I never really thought to make the comparison to cars, to be honest. 'cause I, I wrote a, piece a while back called A demand curve for compute, which compares these two, like, I just like energy for example. Like if you do something all the time, then you have something running all the time, it's a bit like maybe a nuclear power station, like it's expensive to buy, but per unit it makes a load of sense.

And then you work your way up from there basically. So, at the other end, like serverless, there are things like peak plants, which are only on for a little bit of time and they're really expensive for that short period of time. But because they're only on, 'cause they, can charge so much, you'll need to have them running maybe five to 15% of the year.

And that's how they, and that's how people design grids. And like, this idea of demand curves seems like, it's quite applicable to how we think about computing and how we might use different kinds of computing to solve different kinds of problems. For example.

Adrian Cockcroft: Yeah. Well that brings up another current topic. What's actually happening now is the peaker plants are running flat out running AI data centers capacity load, and the peaking is moving to battery, which is now getting to the point where batteries are sufficiently cheap and high capacity, that the peaker capacity is being driven by batteries which respond much more quickly to load.

And, some of the instabilities we've seen in the grids can be fixed by having enough battery capacity to handle, You know, a cloudy day or whatever, you know, the sort of the effects that you get from sudden surges in power demand or supply, right? And once you get enough battery capacity, that problem is soluble that the problem historically as the batteries have been too expensive, but they're getting cheaper very quickly.

So there've been a few, there's a few cost curves that I've seen recently showing that it's actually the cheapest thing to do for power now is, solar and batteries just put that in. And the batteries that they're now getting, originally they were saying you can get a few hours worth of battery cost effectively.

I think they're now up to like six to eight hours is cost effective. And we're getting close to the sort of 12 to 18 hours, which is means that you can go through the night in the winter on batteries. and it's cost effective to deploy batteries to do that. It's something about the economics that means that you have.

A certain amount of capacity, you still need some base load. geothermal isn't particularly interesting for that. I think as one of the cleaner technologies, a company called Vos building a station that, Google are using for some of their energy, I've spent some time looking at alternative energy.

But yeah, those peak of plants, they were sitting there mostly idle, and then all this extra demand suddenly appeared that wasn't in the plan for these big AI data centers and they're hoovering up all that capacity. So people are desperately trying to figure out how to add additional capacity, to take that on.

Chris Adams: We will come to that a little bit later in a bit more detail actually. So, but thank you. So maybe we can talk a little bit about, actually some of this stuff about. Essentially observability and being able to track some of this stuff because one thing that I've seen you present before is this idea of like carbon being just another metric.

And I think, what we'll do is we'll share a link in the show notes to a YouTube video. I called Monitoring Carbon. I think you presented this at Monitorama I Portland in 2022. And the argument that I understood it covers various other, it, it does talk a little bit about like the state of the art in 2022, but one of the key things you were kind of saying was basically as developers, we're gonna have to learn to track carbon because it's just gonna be another thing we have to track.

Just like, space left on a disc requests and things like that. So maybe you could talk a little bit about that and some of the re and just tell me if you think that's still the direction that we're going in. Basically, I.

Adrian Cockcroft: Yeah, so that was the first talk I gave after I left AWS I'd already given, agreed to present there. and then I left AWS I think just a few weeks before that event. so it was kind of an interesting thing. Hey, I, by the way, I quit my job and sort of retired now and, but this is the thing I was working on.

So I was, the last job I had a WSI was a VP in the sustainability organization, which is an Amazon wide organization, but I was focused on the AWS part of the, that problem in particular, the how to get. all of AWS sort of on the same page every, there was lots of individual things popping up. so we and lots of people writing their little presentations about what they thought AWS was doing.

And so we basically created a master PR approved, you know, press, press relations approved, deck that everyone agreed was like what we could say and should say, and it was high quality deck and got everyone to use the same, get on the same, be saying the same thing externally. Now, part of the problem there was that the various constraints we had at Amazon, we couldn't really talk about a lot of the things we were doing for all kinds of reasons.

So the story of Amazon, I think is better than most people think, but the, way it's told is really poor and it's very difficult to get, get things out of Amazon to actually, I. cover what they've been up to. So, so that was what I was working on. And along the way I thought, you know, we need to monitor.

ARM is a monitoring, observability conference I've been to many times and I have a long history in monitoring tools in particular. I thought, yeah, we should, I, should be trying to get everybody to add carbon as some kind of metric. And the problem is, then where do you get that metric from? And that wasn't very obvious at the time.

And I think there's sort of two things that have happened since 2022. One is that we actually haven't made much progress in terms of getting carbon as a metric in, most areas. There's a co with a couple of exceptions that we'll get to, but we haven't made as much progress as I hoped we would. And then the other one is that the sort of standards bodies and.

government regulations that were on the horizon then have mostly been stalled or slowed down, or delayed, whatever. so the requirement to do it from the business has generally come back, has reduced. Right. So, which is disappointing. 'cause now we're seeing even more climate change impacts and, you know, the globe doesn't care whether you're,

what your, corporate profitability or what you're trying to do or you know, what the reasons why you aren't doing it.

But, so we're just gonna get more and more cost from dealing with various types of climate disasters and we're seeing those happen all around us all the time. So, I think in some sense it's got to get much worse before people pay attention. And we're, you know, there's a big sort of battle going on to try and just make it, keep it focused and certainly Europe is doing a much better job of.

Right now. but even, the European regulations are a little watered down. And that's, I mean, I know that you are all over that's really your specialist area, you know, far more than I do about what's going on in, in that area.

Chris Adams: But yeah.

Adrian Cockcroft: It's a big topic, but I think in 2022, I thought that we would be having more regulations sooner, and that would be pushing more activity.

And then I wanted to basically, by talking about this, at that event, I wanted to get some of the tools, vendors to basically I would, for me to talk to them about how to do this. I ended up doing a little bit of advisory work for a few people, as a result, but not really that substantial. So that's kind of where I was then.

And then over the next year or so, I did some more talks, saying it's basically I just tried to figure out what was available from the different cloud providers. Did a talk about that, and then, wrote a. A-P-R-F-A-Q or a, proposal for a project for DSF saying, well, we should fix this. And it would be really nice if we did actually have a, this is what people would like to see, and then went and tried to see what we could get done.

Chris Adams: Okay, so that's, that, that's useful sort of kind bring, us up to this point here. And like, one thing I've appre appreciated about being on the Real Time Cloud project is that it's very easy, to basically call for transparency bec and there are absolutely reasons why you, why a company might not want to share their stuff, which are kind of considered like, I don't know, wrong reasons I suppose, or kind of like greedy reasons.

So, I used to work at a company called A that stood for avoid mass Extinction engine. And one thing we did was I. we were, we raised something in the region of 20 million US, dollars to find out all the ways you can't sell or carbon API in the early 2010s. And, you know, pivoting like a turntable, it's kind of a bit embarrassing at times.

Right? And one of the things that we, one of the potential routes that people went down was basically, we are gonna do this stuff and we are gonna work with large buyers to basically get people in their supply chain to share. Their emissions information, with the idea being that this would then be able to kind of highlight what they refer to as, supply chain engagement.

So that sounds great. Like we'll lend you some money so you can buy cheaper, you can buy more efficient fridges and do stuff like that. But there was another flip side to this, where when you're working with large enough companies or large enough buyers, one of the things they would basically say is they could use this information to then say, well, who are the people who are the least efficient?

And like, who am I gonna hit with my cost cutting stick first? Basically like who is, and this is one of, and for this reason, I can totally understand why organizations might not want to expose some of their cost structure. But at the same time, there is actually a kind of imperative coming from, well, like you said, the planet and from the science and everything like that.

And like, this is one thing that I feel like this is one of the drive, this is one of the thing that's been a real blocker right now. Because companies are basically saying we can't share this information 'cause we are going to end up revealing in how many times we maybe sell the same server, for example, like the, and these are kind of, you can see why people might, might not want to release that or, disclose that information.

'cause it can be sited, considered commercially sensitive. But there is also the imperative elsewhere. And like I wanted to ask you like. Faced with that, how do we navigate that? Or are there any things that you think we can be pushing for this for? Because I think this disclosure conundrum is a really difficult one to actually,, to get around basically.

And I, figured like you are on the call, you've been on both sides. Maybe you have some perspectives or some viewpoints on this that might be better. Shed some light here rather than it just being this, you are transparent. No, we're not gonna destroy our business kind of thing, because there's gotta be something, there's gotta be a third way or a more, useful way to talk about this.

Adrian Cockcroft: Yeah. And I think, I mean, there are three primary cloud providers that we've been working with or attempting to work with. And they're all different, right? And just Google generally have been the most transparent. they produce data that's easy to find, that's basically in a useful format. And they came out with their, their annual sustainability report recently, and there's a table of data in it, which is pretty much what we've been adopting as this is useful data.

Right? So that's one. but still they don't disclose some things because they don't have the right to disclose it. For example, if you want to know the power usage effectiveness, the PUE, they don't have it for all of their data centers. When you dig into that, you find that some of their regions are hosted in data centers they don't own,

right?

So somewhere in the world there's a big colo facility owned by Equinix or somebody, right? And they are, they needed to drop a small region in that area. So they leased some capacity in another data center. Now, the PUE for that data center is not the they, because they're not the only tenant. It's actually hard to calculate, but also the owner doesn't necessarily want to disclose the PUE, right?

So there's a one, the number isn't really obtainable. You could come up with a number, but they have to, you know, as a third party that they'd have to get to approve it. So that's a valid reason for not supplying a number. It's very annoying because you have p OE for some data centers and not others, and that applies to all the cloud providers.

so that's a valid, yeah, it's annoying, but valid reason for not providing a number. Right. So that's one level. And Google are pretty good at providing all the numbers, and they've been engaged with the project. They've had a few people turn up at the, on the meetings. they've fixed a few things where something wasn't quite right.

there was some missing data or something that didn't make sense and they just went fixed it. And there was also a mapping we needed from there. They're the Google data centers, which support things like Gmail and whatever, Google search to the Google Cloud data centers, which is a subset of it. But that we, they actually went and figured out their mapping for us and gave us a little table so we could look up the PUE for the data center and basically say, okay, this cloud region is in that data center.

They've worked well with it. So that's kind of what I'd like to see from the other cloud providers. It show, it's like, I like to see existence proofs. Well, they did it. Why can't you do that? Right. So that's what I'd expect to see from everybody. Microsoft were involved in setting up the GSF and were very enthusiastic for a while.

Particularly when Asif was there and driving it and, since he's moved on and, is now working directly for the GSF, I think the leadership at Microsoft is off worrying about the next shiny object, which is ai, whatever. Right? There's less su less support for sustainability and, we've found it hard to get, engagement from the Microsoft, Ah,

to get data out of them.

they have a report, they issued their new report for the year and they had total numbers for carbon, but they didn't release their individual regions updates, you know, so they released overall carbon data for 2024, but we haven't got any updated, nothing that I can find anyway on the individual regions, which is what we've been producing as our data set.

Chris Adams: Ah, okay. So basically as the moon and the moonshot has got further away, as they say, it's also got harder to see. Basically we still have this issuer then that this, it's less clear and we have less transparency from that. That's a bit depressed. That's a bit depressing. When early on they were basically very, they were real one.

They were. I was really glad to have them inside that because that they, they shared this stuff before Google shared it, so we actually had, okay, great. We've got two of the big three starting to disclose this stuff. Maybe we might be able to use this to kind of find against concessions from the largest provider to share this.

Because if you are a consumer of cloud, then you have some legal obligations that you still need to kind of, kind of meet, and this is not making it easy. And for the most part, it feels like if you don't have this, then you end up having to reach for a third party, for example, where you, like, you might use something like green pixie, for example, and like, that's totally okay to use something like that, but you happen to go via a third party where you know, you're, that, that's secondary data at best.

Basically it feels like there's something that you should be able to have with your, supplier, for example.

Adrian Cockcroft: Yeah. Just to clarify, I think there's several different types of, Sustainability data or sustainability related data that you get from a cloud provider. One of them is, well, I'm a customer and I have my account and I pay so much money to it, and how much carbon is associated with the, the things I've used, right?

And that is they all provide something along those lines to greater or lesser degree.

Chris Adams: Mm.

Adrian Cockcroft: but you can get, an estimate for the carbon footprint of an account, right? typically delayed by several months, two to three months, and it's a fairly, and it's pretty high level. So, and it gets, there's more detail available on, Google and Microsoft, and there's fairly high level data from AWS, but that's, one source.

The other source that we're interested in is, let's say I. I'm trying to decide where should I put a workload? And it could be I have flexibility, I can put it pretty much anywhere in the world or I can choose between different cloud providers in a particular country. what's the, and I want to know what the carbon footprint of that would be.

Right? So to do that, you need to be able to compare regions, and that's the data set that we've produced and standardized so that it lists every cloud region for the three main cloud providers. And for each of them we've got whatever information we can get about that region. And back in 2022, we have a fairly complete data set and 2023, it's missing.

Microsoft provide less data than in 2022. And in 2024 data, currently we have Google data, we have Microsoft have released their report, but haven't given us any new data. And AWS are probably releasing their data in the next, Few days, last year, it was on July the ninth, and I just checked this morning and it hasn't been released yet, so it's probably coming next week.

It's sometime in July. Right. So, we're hoping to see, well, we'll see what information we get from AWS and I'll, I, every year I write a blog post where I, they said, okay, the three reports are out. This is what happened. This is the trend year on year, and I'm working on an update of that blog post.

So probably by the time this, this podcast airs, I'm hoping that pod, that blog post will

Chris Adams: out there.

Adrian Cockcroft: I should have got it. I, you know, I've written as much as I can right now, but I'm waiting for the AWS ones, so. So we've sort of discussed Google have been pretty good, I guess, corporate citizens, disclosing whatever they can and engaging with the project.

Microsoft's sort of early enthusiasm. In their latest report, they actually mentioned the GSF and they mentioned they founded it and they mentioned that they support the real time cloud project, but they're not actually providing us any data and we're still trying to find the right people at Microsoft to escalate this to, to figure out, well, so gimme the data.

Right? and then AWS then they have, some different issues going on. they, the way that they run their systems, one of the things they found is that if they disclose something about how they work, people will start leveraging it. Right. You get this sort of gamifying thing. If there's an interface or, a disclosed piece of information, people will, optimize around it and start building on it.

You see, there's a lot in eBay. One of the reasons eBay's interface hasn't changed much over the years is that there are sellers that optimize around some weird feature of eBay and build a business around it. And every time eBay plans to change that, they're like, some sellers gonna lose their business, right?

So, if you over expose the details of how you work, there's sort of an arbitrage opportunity where somebody will build something on that and if you change it data, they get upset. So that's a one of the reasons that AWS doesn't like saying how it works,

right? Because it would cause people to optimize,

Chris Adams: yeah. Private

Adrian Cockcroft: optimize for the wrong things.

And, one example is that there's an Archive capability, tape Archive capability. That AWS has, and you can, and if you're thinking about I have lots of data sitting on desk, I should move it to tape. 'cause that is a much lower carbon footprint. And it is, except if you're in a tiny region that AWS has just set up, they haven't actually really got tapes there, the same services there, they're actually just storing it to disc until they have enough volume there, for them to put in a tape unit and transfer that to tape.

Like they want the same interface, but the implementation is different. Now, if they exposed which regions the, this is actually going to dis, it would say, well, this is a high carbon region, so I shouldn't store my data in there. Which means it would not get enough volume to actually install the tape.

Right? So you get the sort of negative feedback loop that's actually counterproductive. Right. So, so, so there's this, there's that sort of a, an example of. It's one of the reasons that they don't want to tell you how much carbon every different service is because it could cause you to optimize for things that are gonna cause you to do the opposite of what's the right thing to do Ultimately.

Chris Adams: okay. So that's one of the argument we see used for not disclosing how an organ, like. Per, like, per like service level and per region level things. 'cause one thing that when you use, say Amazon's carbon calculator, you'll get a display which broadly incentivizes to do, incentivizes you, you use to change basically nothing.

Right? like that's one thing we actually see. But, and that's different to say Google and Microsoft. We do provide service level stuff and region level stuff. So one of the reasons they're trying to hide some of that information is basically it's making it harder for us to kind of basically provide that service, for example, or there's all these second order effects that they're trying to basically avoid.

That's one of the arguments people are using,

Adrian Cockcroft: That's the argument that they have, and it's something that's pervasive. It's not just related to carbon. This is something that they've seen across lots of services is that people will, people will depend on an implementation. And they changed the implementation frequently. Like we're on, I dunno what the eighth or the ninth version of S3 total rewrite from scratch.

I dunno. When I was there, I think they were up to the seventh or eighth version and I knew somebody that was working on the team that was building the next version. Right. And this is tens of exabytes of storage that is migrated to a completely new underlying architecture every few years. If you depend upon the way it used to work, then you end up being suboptimal.

So there's some truth in that, however, and this is the example we were pointing at when I was at AWS, is that Microsoft and Google are releasing this data and we haven't, there's no evidence of bad things

Chris Adams: Yeah. The sky hasn't fallen when they

Adrian Cockcroft: Yeah. So, so I think that it, would be just fine too. And they are gradually increasing the resolution.

So what they had when. When they first released the, the account level information when I was there, and we'd managed to get this thing out in 2022, I guess 20 21, 20 22 was the, you had regions being continents, right? You just said Europe, Asia, and Americas.

And you had S3, E, c two, and other,

Chris Adams: yeah.

Adrian Cockcroft: and you had it to the nearest a hundred tons or something, or nearest a hundred kilograms.

Yeah, a hundred 10th of a ton. So most, so a bunch of people in Europe just got zero for everything and went, well, this is stupid. But actually, yeah, because of the way they, the, model works, they were generate, generating lots of energy to offset the carbon. It probably is zero for at least scope two.

scope, scope two, for the market based model.

Chris Adams: where you, count the, green energy you've used to kind of offset the, actual kind of, yeah. Figure. Alright.

Adrian Cockcroft: Yeah. So what they've done in the last couple of years, they finally got a team working on it. There's a manager called Alexis Bateman that I used to work with in the sustainability team that's now managing this, and she's cranking stuff out and they finally started releasing stuff. So the very latest release from AWS now has per region down to per region.

It has location based, just got added to the market based. So we actually have that finally.

Chris Adams: okay. Yeah.

Adrian Cockcroft: So this happened a few weeks ago. and the, and they've added, I think they have cloud. CloudFront because it's a global, CDN, it doesn't really live in a region. So they've separated CloudFront out and they also changed this model, as I mentioned earlier, so that the carbon model now includes supporting services that are required for you to use the thing.

So your, Lambda functions, even if they're not running, you've still got a carbon footprint because you need to have the lambda control planes there, ready to run you. So you pay for a share of that. And then the question is, how do you calculate these shares? And it's probably, you know, dollar based or something like that.

Some kind of usage based thing,

Chris Adams: Okay. Alright. So that's, yeah, I think I've, I read the, I hadn't realized about the location based, information being out there as well.

Actually,

Adrian Cockcroft: the location and the model with a new thing and they've now got this sort of, every few months they're getting a new thing out. They have def, they've clearly said they're going to do scope three. I know they're trying to do scope three where they real scope three thing rather than a financial allocation scope three.

So we could talk about that if you want, how much you wanna get into the weeds, of this stuff. But anyway,

So what we ended up with in the real Time cloud project was we figured out it wasn't really possible to get real time energy statistics out of cloud providers because the numbers just didn't exist.

It turns out the only place you can get real time numbers is on things that are not virtualized. And the thing that people don't generally virtualize is the GPUs. Yeah. So if you're using an Nvidia GPU, you can get a number out of it, which is the energy consumption of that GPU. So if anyone working on AI based workloads, you can get the dominant energy usage cap calculation is available to you, sources available.

But the CPUs, because the way virtualization works, you can't provide the information unless you're using, what they call a bare metal instance in the cloud, which you get access to the whole thing. So that's we gave up a bit on having like real time energy data and also the CNCF came up with a project called Kepler, which does good estimates and it does a workload analysis for people running on Kubernetes.

So it just, we just did a big, like point over at that. Just use, Kepler. If you want workload level energy estimates, use Kepler. and then. If we want to, and we focused instead on trying to gather and normalize the data, the metadata available on a region so that you could make region level decisions about where you want to deploy things and understand why certain regions were probably more efficient than others in terms of PUE and water usage and, carbon and the carbon free energy percentage that the carbon that the cloud provider had, meaning how much local generation did they have in that region.

So that was the table of data that we've produced and standardized, and we've put a 1.0 standard on it. And the current activity there is to rewrite the doc to be, basically, standards compliant so that we can create an ISO standard or propose an ISO standard around it. And the other thing we're doing is talking to the finops Foundation who come at this from the point of view of.

standardizing the way billing is done across cloud providers and they have all the cloud providers as members and all working on billing and they're trying to extend that billing to include the carbon aspects

of what's produced. working. so, we've done an interview with someone from Focus already who is basically talking about, they are almost. You, like you mentioned before, the idea that, okay, Microsoft and Google have shared this kind of per service level information and the sky hasn't fallen.

Chris Adams: They've created something a bit like that to kind of almost list these diff different kind of services. What, if I understand it, the GSF, you know, the, real time cloud thing might be like a carbon extension for some of that, because that doesn't necessarily, the, right now the focus stuff doesn't have that much detail about what carbon is or what, the kind of subtleties might be related to the kind of other, the kind of non-cash non, yeah, the, non-cash things you might wanna associate with, the way you, purchase cloud for example.

Adrian Cockcroft: Yeah, so focus is the name of the standard they've produced. Really all the cloud providers have signed up to it. If you go to an AWS's billing page, it talks about focus and has a focus, a conformant, schema. So the idea was all the cloud providers would have the same schema for their billing. Great obvious thing to do, but all the cloud providers have joined up to do that, which is fine.

Now Focus does, has some proposals for sustainability data, but they are just proposals for maybe the next version. They had a working group that looked at it and the problem they run into. One of the things is we've deeply looked into that in our group. We know why you can't do that. So what you'd really like is a billing record that says you just used, you know, 10 hours of this instance type.

And this is the carbon footprint of it. And the problem is you, that number cannot be calculated. and that's what you'd like to have. And intuitively you'd like to just no matter how much carbon it is, the problem is the carbon is not known at that time. You can generate the bill 'cause you know, you've used 10 hours of the thing, but you can't know the energy consumption and the carbon number, the carbon intensity, those two numbers are not known for a while.

So you typically get the data a month or two later. Whereas like, yeah, but you have to go back to your billing data. So you could put a guess in there. And things like the cloud carbon footprint tool and other tools that are out there will just generate guesses for you. but they are guesses. And then when you go and get the real data from your car cloud provider, the numbers will definitely be different, sometimes radically different.

so the question is, do you want to have an early guess or do you want to have a real number and what are you doing with that number? And if what you're doing is rolling it up into an audit report for your CFO to go and buy some carbon credits at the end of the year, that's what the monthly, reports are for.

Right? If you're a developer trying to tune a workload that is useless information to you, you need real, that's what the real Time cloud group was really trying to do is like if you're a developer trying to make a decision about what you should be doing. You know, calculating an SCI number or, understanding which cloud provider and which region has what impact.

That's the information you need to make a decision in real time about something. So the real time aspect is not about like in my milliseconds, I need to know the carbon or whatever. It's like I need to know now. I need to make a decision now.

Chris Adams: to make a forward looking decision

Adrian Cockcroft: Yeah. It's like I need to make a decision now, so what information do I have now?

Which is why we take the historical, metadata they have for the regions and we project it into the current year with, so just trending and filling in the gaps to say, this is our best guess for where you'd be if you needed to make a decision this year, on it. And we've got some little code that automatically generates the Nafus, estimate.

Chris Adams: so that's, at least useful. So people have an idea about what you might be using these two different kinds of data for. I guess maybe the thing, if we could just unpack one last thing before we move on to one of the questions is that one of the reasons you have this delay is basically because, is it, 'cause companies aren't, don't get the billing data themselves and they need to go then go out and buy credits.

Like this is for the market based calculations. So this, what you've said here is basically about carbon based on a market based figure. But if we had something like, maybe if we were to separate that out and looking, look at something like location based figures for electricity, which is like representing the kind of what's happening physically on the grid.

You plausibly could look at some of this stuff. Is that the, I mean, is that the way you see it? Really? Because I feel that we are now at this point where there's a figure for the grid, but that's not necessarily gonna be the, only figure you look at these days, for example, because as, because it's, we increasingly seeing people having different kinds of generation in the facility.

If you've got batteries, you might be, you might have charged batteries up when the energy's green, for example, or clean and then using it at a certain time. that's there's another layer that we need to, that you might need to take into account. Right.

Adrian Cockcroft: Yeah, so there's a couple of different reasons why the data is delayed. you know, you're in Germany, I'm sure with Germanic efficiency, you know exactly when you are going to get the information from your energy provider,

Chris Adams: They fax it to us. Yep. Yeah.

Adrian Cockcroft: and it will be nice and precise and there'll be high quality on it. now if you're operating a region in a developing nation.

not so much, right? There's bits of paper moving around. Probably. There's, random things happening. You dunno quite know when. So if you are trying to produce a service that is a global summary across all regions, you have to, you are limited to the slowest region that you operate in, right? you take this sort of distribution of how quickly you find out about the carbon intensity and the power usage of what's going on in your country and in the energy supply for your region.

And, you know, it's, whoever is slowest will de determine it, right? And AWS operates in regions in India, and Indonesia and places like that where, I don't know, maybe, there are efficient, maybe they aren't. But there, they, there are more global regions in more different countries on AWS. than in particularly in Asia than Azure and Google have, but fundamentally, it's gonna take you a few months to gather your billing and carbon data accurate to the point where it's not gonna change.

So then on top of that, you can then say, I'm gonna buy some credits to offset that. And there's two different ways of doing credits. You can buy green energy, procure your energy from a supplier that says, okay, I'm this energy that we already generated, you can buy the credits for it later. And so you can basically pre post allocate it, and you can do that within the rules for up to a year afterwards.

So at the end of the year, it comes to December, end of December, okay, how much energy we did we use, how much wasn't offset. I can buy energy credits from my energy suppliers to offset that. And the first thing you do is try and do it in region so that the energy is happening in the same grid. That you, your consumption was, and then you get to Singapore and go, okay, we all give up on Singapore.

There isn't enough local energy that's green, so we're going to buy energy somewhere, anywhere we can, green energy somewhere else and do a global offset on it. Google's been doing that since 2017, I think, or whenever they, said they were a hundred percent green back in the day, long time ago.

AWS since 2023, a hundred percent offset. but what, that's the mechanism they use and it's documented in their disclosure that they do it on a region by region basis and then they use global offsetting just for the, to mop up whatever's left over at the end. Right. So that's, and, then. A s does less of this, but is starting to do more, which is, carbon offsetting where you go and, you know, pay for a forest to not be cut down or you pay for built, grow some trees or you sequester some carbon.

And that is a little bit on the end that people are investing in to try and develop those markets. but most of it is, buying green energy. Like for the house here, I have an option to just subscribe to a different cloud, a different energy provider. It's called Central Coast Community Energy. And, Yeah, I pay them at slightly higher, you know, an extra cent or so per kilowatt hour. And I have a hundred percent green energy. And by market method, my, I'm completely green here, right? So that's fine. But it's the same thing going on. So, because what I'm paying for is the green energy. I'm not paying for carbon.

I'm probably is emitting carbon at night, certainly, but I'm generating more during the day 'cause I've got some solar panels here. Right. So that it, it's that mechanism that's being developed basically.

Chris Adams: Okay. Thank you for that. Alright, Adrian, I realize we we're coming up to time, so I, did have a bunch of questions about, what's making it harder to track, this stuff, like, because we are, we're now moving to work to the world of grid responsive data centers, for example, like various data.

We've been doing some stuff like that, and we're seeing cases of like, I don't know, in Memphis, the Colossus data center running primarily on gas turbines, right now, which is playing, which, massively complicates some of this. But we did actually say that there was some stuff going on in the house, and I do wanna kind of come back to that if we could, because that feels like it's, we won't have time to explore to do those, the, those subjects.

Justice basically. So we were talking, at the beginning of this podcast that in addition to doing this stuff here with the Green Software Foundation, you've been exploring and playing around with some of the, tools and some of the technology and like finding out if there's a, there, for example, and, When I looked up this project, when before doing some research for this podcast, I heard, I, I read about this thing called the House Consciousness System. So maybe you could talk a little bit about that because, you've been working as a technologist for, you know, at least 40 years now, and I see you've messing around with things like generative AI and AI for this and doing things that I am not expecting people to do.

So maybe you could talk a little bit about this, that HCS or the, or whatever, this project is, because I, found it quite interesting that you were to see your take on this, basically.

Adrian Cockcroft: Yeah. So the history of this going back a while is that a while ago, I mean, every now and again, I, do some programming and I wanted to do some programming a year or two ago in the R language, which is a statistical language, which I use occasionally. I keep forgetting the syntax of. So I thought, well, maybe I can ask the AI to remind me of the syntax.

And the AI just started writing code that worked. So I went, oh, this is cool. I can just tell it what I want and it'll write the code. And this is very early days when people. Most people weren't doing that kind of thing, so that was fine. and then, more recently I wanted to write some code in Python.

I'm not ever, never really wrote in code in Python. I can sort of read it, but I can't write it. So I started telling it what I wanted it to do and it wrote the code for me and it could get it working pretty well and it worked. So I have code, I was using it to generate code there. and this is mostly just 'cause I'm not, I don't have patience and time to be like a full on developer, but it, these are the things that it's good at.

So one of the things that I tend to focus on, if as a new tool around, I figure out what can it do and what can't it do. Think of cloud, the early days of cloud. We built Netflix on an extremely rudimentary set of cloud services. And it was like just about possible to make it work given the services we had.

And most people today would look at that and say, we wouldn't even start trying to build anything on that, right? But we made it work, and we made it work reliably. And, that became like a template and it caused other people to try and figure out how to do it. Now there's a lot more capability there now, so we're sort of in that early stages day, thing where a bunch of people say, well, this'll never work.

there are people figuring out how to make it work. What happened? where shall I go next? Let's talk about the idea. So years ago, I mean, I have lots of iot devices around my house. I like buying random, automated, and then none of them talk to, or some of them talk to each other, but I have too much random stuff and I have a, like an iPad with lots of icons on it, and I have to know which one does what.

Right? and it's annoying. And if I'm not home and my wife's trying to do something, she can't figure out which one. And she knows some of them, but like, she doesn't, know how, this stuff works. other visitors to the house don't know how to do things. And that's just, and a lot of people are in that environment.

And I was thinking about this a few years ago when I was at Amazon and I was talking to the Alexa team because they have, house automation kind of stuff. So why don't you build in something that is just like a more general thing that knows what's going on in the house. That would, and it's sort of like a, central consciousness because the re thing about consciousness, it's an observability system.

I regard consciousness as human observability. And part of the definition of consciousness for me is that you have to understand what unconscious means, right? If your definition of consciousness doesn't include unconscious, then you're not, you haven't picked the right thing. So it's the thing that goes away when you're asleep,

right?

'cause you're unconscious when you're asleep. So anything that goes away when you're asleep is consciousness to me. Right. And there are, and this isn't the standard definition. People have big arguments about it, but that's a, good working definition for me. 'cause what it means, it's the thing you can talk to, to discover its state, right.

So if somebody is conscious, you can ask them questions and find out what's going on with them. So in that sense, what I want is want my house to have a memory of all the things that have happened to it. I want it to look at the weather and remind me there's a storm coming and have you dealt, you know, ti it up outside so things don't blow away and all the stuff, right?

but right now the iot devices live in the moment. Let see, your temperature is 73 degrees and they sort of have a schedule for changing stuff, but they don't really have a memory and they aren't talking to each other. And so I had this idea that, Hey, why doesn't Alexa team build something like this?

And they, I sent, I found somebody in that team and they never built it and weren't interested in it, right? So I had this idea of. Kicking around. And then a few weeks ago I saw that Rueven Cohen, R-U-E-V-E-N, Cohen Coen, C-O-H-E-N, on LinkedIn, is just posting and posting about his agent's swarm work.

And he's just, like, he's building amazing stuff and said this, does this really work. So I wanted to play around with it and I decided I needed a new idea that I needed to try and build. That was a fairly aggressive thing. So I wrote up a rough idea of what this house consciousness would be.

And then I got together with Reuven. He showed me how to start an agent swarm, to just get with the CLO and Philanthropics Claude Code service to just go build it. And it wrote about 150,000 lines of code in a day or so.

Chris Adams: So these, so when you talk about a swarm of age, a swarm of agents, it's basically kind of like a model in a loop that's writing some code and there's multi, there's lots of them working together. That's what a swarm of agents in this case. Yeah.

Adrian Cockcroft: Yeah, but they aren't all writing code. So what you've got is sort of a, the latest version it, they call, he calls it a hive. So they're sort of a queen bee that who is just managing the hive. And it's basically like there's an AI acting as a light, as a development manager,

a dev manager. And the dev manager picks these specializations they want, so they start a selection of agents and they've got a QA agent, a DevOps agent for a deployment, a spec reading agent, a researching agent.

And they basically specialize. And what happens is if you're doing AI coding. The context. If you use one agent to do, if you just use one AI and you tell it all the things you want to do, it sort of gets confused. 'cause you've asked it to do lots of things at once. What you do here is by giving them each one track, mind specializations and a, and an ability to communicate, you get dramatically better results out of it.

So that's the aha moment if you like. But what it means is that to manage this swarm of agents, you need basically product manager and line manager skills, not developer

skills, right? You need a bit of developer skill to read the code and see if it works. But I don't, I'm currently writing, I switched from writing in Python.

The first thing I tried building, which was more just like a, can it build anything at all? And it built a thing and it ran, but didn't really work. 'cause I didn't, really specify what I wanted well enough. I'm now building an i an iPhone app in Swift, which I absolutely cannot write a single line of Swift.

I have no idea. And it's writing the code. I'm telling it to do code reviews and, Run and tests and things. So it's actually coding and testing and building itself and build a UI design and a plan. And so I'm doing that anyway. So I'm basically, I've now got a little obsessed by building myself this thing.

And you basically need a Max plan to do this, which is sort of about a hundred dollars a month AI plan. And once I finish building this, I'll sort of wind that back down to the usual $20 a month kind of level. and yeah, I mean, from my point of view, you can use AI to do a bunch of bad things, you know, generate fake news and stuff and adverts and things, but I'm actually using it to develop something that I always wanted to have, that isn't, there's no real business model for this thing other than I want it to exist.

I don't need a business model. I can spend a hun few hundred dollars, which is sort of go out to dinner. You spend a hundred dollars, right? That on building something, getting it to build something, which I'm sharing on GitHub. you can go and have a look at the repo, the, original Python repo is, it's sort of, it's there, it runs, but it's not, doesn't really do what I think.

It doesn't really work, right? 'cause I hadn't thought it through. I'm now doing front back, front end backwards. So I'm doing as much functionality as I can in this iOS app, and then I'll build the service to go behind it. I'll revisit that when I get to it. So that's kind of what I'm doing. I'm just happily use using this new tool to do something that will make me happy and potentially be useful for other people if they feel like it, but I don't care whether anyone else uses it.

So that's sort of my approach to figuring out new tools and finding where they work and where they don't work.

Chris Adams: Okay, so let me, if I can, I just wanna paraphrase some of that. So you've, so when people, like, I am not using a all that, that, that much at the moment, but I am dabbling and I'm using, I've been messing around with Claude and stuff like that to ask questions or, okay. I'm in Europe, so we use Misra 'cause it's the, French equivalent for example.

But one thing you said that was significant was that a, rather than me using one thing in like serial, it's happening concurrently. So there's lots of different things all burning

Adrian Cockcroft: They typically run like five to eight of these agents in parallel, and they're coordinating and communicating. They make to-do lists and they, do different specializations and they, it's basically like managing a team of developers.

Chris Adams: Ah, and that might be why you have people talking about, say, like, what the hell, you know, vibe, coders, dunno what they're doing here. 'cause in many cases. it's a new set of skills. It's not necessarily just, can you read, I mean, yeah. It helps to be able to read Python in the same way that if you are reading the output from a, chatbot, you want to, you know, you'll probably tweak it to make it sound like a human rather than, a, an ai model.

But there's also a bunch of other skills that you need to do, like spec writing and all these other things that you might, that typically might live in a product manager rather than a developer, for example, or someone who in different roles.

Adrian Cockcroft: it's much more product management than anything else. You have to have a clear idea of what you want. And figure out what the user flows through it are, and you know what functionality you want, how you want it arranged. And then it will build whatever you tell it to build and it will add on things you didn't ask for.

So you do it. So the other thing is to do it very incrementally and check every time to see what it did build. And you ask for this and it does like two or three times as much, and you go like, I want to keep these things. That was a good idea. No, I don't want that. And if you're working with a team of engineers, you say, I want them to, build a thing, they'll come back with extra stuff that they thought they, you might want.

Right? So there's actually, it's normal. I mean, this is how you manage a team of engineers to go build a new thing, right? So in that sense, it feels anybody that's managed a development team, this actually feels very familiar. If you're a developer on one of those teams, it doesn't feel very familiar. So I think that there's this sort of weird.

Thing where we sort of brought it up a level into management, but you still kind of need specialist experts. Like I'm stuck in a whole bunch of Apple stuff to do with iOS. That is nothing to do with the ai, it's just stuff that Apple makes difficult

Chris Adams: That would explain what you've been posting about what? How? Okay. That, that, that. Adds a context. 'cause I've seen you posting like, how do I know what to ask from Apple now? And I was like, why is he asking that? But okay. This is putting two and two together for me now. S Adrian. Thank you. So there was one last thing I was just gonna ask before we kind of wrap up.

Right. you mentioned that you're doing this all locally on your own computer? Or like, is it, are you running in like an, in an environment in the cloud, like a code space with GitHub? Or maybe, yeah. Where is that happening?

Adrian Cockcroft: Yeah. So when you have an agent swarm, it can do anything. You basically let it free run, so you need to put it in a box so that it doesn't delete your computer or do random things or whatever. Right. So. You go to GitHub and they have a free, it's free for up to some amount, which is plenty for me.

So far, having been doing this for a few weeks, you create a code space, which is basically an instance I guess running on Azure, which is like a little container. It shuts down if you don't use it for a few minutes or a few, you know, when it's idle. But basically it opens it and the only thing it can really do is run against the repo.

You opened it on, so the AI can sit there and it can do anything it wants in a copy of that repo and then it can push to the back, to the repo. I tell it usually when it's, when you finish this work, just push it back to the main repo and GitHub because when I'm working on iOS, I have to pull it back out into X code on my machine to build it.

So it's a safe box. It's a safe box. And I wouldn't run an agent swarm today on my own machine. Right. You could, but you're, it's sort of dangerous if it gets a bit carried

Chris Adams: That's what I was wondering about. That did seem like the idea of having one rogue machine work on my machine is on my own personal laptop feels a bit weird because I don't dunno where to post it in the, but if I've got a whole bunch of them, that times terrifying. Adrian.

Adrian Cockcroft: the option on Claude is minus, minus dangerous dangerously enable all permissions or something like that.

Chris Adams: Okay. All right.

Adrian Cockcroft: so that is the, mode we're running Claude in, and on, I think on our Google Gemini it's called YOLO mode.

Chris Adams: Yeah.

Adrian Cockcroft: you just, right, right. So, but when you're running it in that mode and a swarm other, and you can't just sit there and say, yes, it's okay to do this.

Yes, it's okay to delete that file, whatever, because it's tidying up, it's moving things around. It's writing code fragments and running them and deleting them again and stuff like that. It's doing all these things, but you don't want it to just do like an R minus rf.

Chris Adams: to just like hose your

Adrian Cockcroft: I once had somebody call, what does con stat CWD mean?

Oh, it means you're in a directory that doesn't exist. Now I'm at my home directory. What did you just type? Well, I was cleaning up some dot files, so I did RR minus R star,

Chris Adams: Oh dear.

Adrian Cockcroft: dot. He recursively deleted his parent directory.

Chris Adams: Oh geez. He just like wiped his entire machine.

Adrian Cockcroft: Yeah. The end of the day tidying up before he went home and said, well, you just lost your day's work.

Luckily we have a backup. I was the guy that ran the backups. This is before I, when, I was,

Chris Adams: Okay. I'm glad you mentioned this, final story here, because it sounds like you're using a code space almost. Whereas typically you might use it for convenience. You're almost using it for safety. Like I want to minimize the blast radius of these agents running am mock inside my system. And also I guess like conveniently, because it's, I mean, surely this should be something what you can work out the environmental footprint of this because if it's a billable tool and like if GitHub knows to bill me for all those minutes, they should be able to tell me the carbon footprint of this as well, right?

Like, I mean, yeah, there's could be some complexity with like using Azure, but like I should have something indicative at

Adrian Cockcroft: There's a little, console that tells you how many hours you've used, and if you use more than a certain amount, they start billing you for it. But you get some number of hours per month of CPU hours per month, which is enough for me. So far I haven't hit that limit and I've been playing around for a few weeks fairly intensively.

so that's, yeah, but the Claude itself, so no. So one thing last, one last thing. So we've got the three main cloud providers. We know roughly what they're doing. we now have Oracle that I'm trying to find somebody to talk to, to tell me how, what their carbon footprint is. Core weave is probably up and coming as a new big one.

And then you have all the stuff that anthropic or open AI or whatever they're all running, where are they running? So we've now got some very large sources of carbon. That we need to get accounting data for. And as far as I can tell, they are not publishing that data currently. So that's currently, I'd say that's the next phase.

It's like how do we, can measure an individual GPU pretty easily, but the, GPU services we're using are not being allocated. We're not getting data from. So that's probably, where I should wrap up. That's where we are now, we need to find out how much I need to know how much carbon I'm actually generating by telling Claude to build this thing for my house.

I dunno. No clue.

Chris Adams: I think that feels like a very useful rallying cry for anyone who does want to see if it's possible to instrument any of these tools, and they're listening to this podcast. Adrian, thank you so much for giving us the time to chat with us. I really enjoyed noting out with you and going really, deep into the weeds.

If people are curious about any of these projects that you've mentioned, we're gonna share the show notes, but where would you direct people's attention? Like is there a URL or a website that you'd point people to?

Adrian Cockcroft: Well, there's the Green Software Foundation's Realtime Cloud, website, GitHub site, and my own Adrian.co. You don't have to spell Cockcroft, you just have to get the first two letters, and on GitHub, and that's where you can follow along my random rants and things like that. and I have a Medium account as well @adrianco.

And where I have my blog, if you want to chat to a, an AI copy of me. There's a service called Soopra. We'll put a link to that. Soopra.ai, and there's an, I think it's Soopra/cockcroft is my, I uploaded all my blog posts into a persona so you can go ask it, and it comes up with sometimes reasonable answers to questions about microservices and Netflix and things like that.

Chris Adams: I wanna ask you all these questions about do you ever use that to fob people off or email you? But I think I'll have to pass that for another day.

Adrian Cockcroft: mostly when somebody sends me a list of questions for a podcast, like if I have time, I feed them into it. I didn't have time

Chris Adams: Okay. Alright. Alright, Adrian, thank you so much for this and I hope you have a lovely weekend. All right, take care mate.

Adrian Cockcroft: All right. Cheers. Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.


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]]> Backstage: Software Standards Working Group SCI Backstage: Software Standards Working Group SCI Thu, 03 Jul 2025 07:00:00 GMT 16:06 41pmzqn0 false https://podcasts.castplus.fm/e/08j0kyj8-backstage-software-standards-working-group 68dc7d2546a2532cdd8d2e2f 68dc7cef46a2532cdd8d1c23 In this Backstage episode of Environment Variables, podcast producer Chris Skipper highlights the Green Software Foundation’s Software Standards Working Group—chaired by Henry Richardson (WattTime) and Navveen Balani (Accenture). This group is central to shaping global benchmarks for sustainable software. Key initiatives discussed include the Software Carbon Intensity (SCI) Specification, its extensions for AI and the web, the Real-Time Energy and Carbon Standard for cloud providers, the SCI Guide, and the TOSS framework. Together, these tools aim to drive emissions reduction through interoperable, transparent, and globally applicable standards. 113 In this Backstage episode of Environment Variables, podcast producer Chris Skipper highlights the Green Software Foundation’s Software Standards Working Group—chaired by Henry Richardson (WattTime) and Navveen Balani (Accenture). This group is central to shaping global benchmarks for sustainable software. Key initiatives discussed include the Software Carbon Intensity (SCI) Specification, its extensions for AI and the web, the Real-Time Energy and Carbon Standard for cloud providers, the SCI Guide, and the TOSS framework. Together, these tools aim to drive emissions reduction through interoperable, transparent, and globally applicable standards. 

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TRANSCRIPT BELOW:

Chris Skipper: Welcome to Backstage, the behind the scenes series from Environment Variables, where we take a look at the Green Software Foundation's key initiatives and working groups. I'm the producer and host, Chris Skipper. Today we are shining a spotlight on the Green Software Foundation's Software Standards working group. This group plays a critical role in shaping the specifications and benchmarks that guide the development of green software.

Chaired by Henry Richardson, a senior analyst at what time, and Navveen Balani, managing Director and Chief Technologist for Technology Sustainable Innovation at Accenture, the group's mission is to build baseline specifications that can be used across the world, whether you're running systems in a cloud environment in Europe or on the ground in a developing country.

In other words, the Software Standards Working Group is all about creating interoperable, reliable standards, tools that allow us to measure, compare, and improve the sustainability of software in a meaningful way.

Some of the major projects they lead at the Green Software Foundation include the Software Carbon Intensity Specification, or SCI, which defines how to calculate the carbon emissions of software; the SCI for Artificial Intelligence, which extends this framework to cover the unique challenges of measuring emissions from AI workloads; the SCI for Web, which focuses on emissions from websites and front end systems;

the Realtime Energy and Carbon Standard for Cloud Providers, which aims to establish benchmarks for emissions data and cloud platforms;

the SCI Guide, which helps organizations navigate energy carbon intensity and embodied emissions methodologies,

and the Transforming Organizations for Sustainable Software, or TOSS framework, which offers a broader blueprint for integrating sustainability across business and development processes.

Together these initiatives support the foundation's broader mission to reduce the total change in global carbon emissions associated with software by prioritizing abatement over offsetting, and building trust through open, transparent, and inclusive standards. Now for some recent updates from the working group.

Earlier this year, the group made a big move by bringing the SCI for AI project directly into its core focus. As the world turns more and more to artificial intelligence, figuring out how to measure AI's energy use and emissions footprint is becoming a priority. That's why they've committed to developing a baseline SCI specification for AI over the next few months, drawing on insights from a recent Green AI committee workshop and collaborating closely with experts across the space.

There's also growing interest in extending the SCI framework beyond carbon. In a recent meeting, the group discussed the potential for creating a software water intensity metric, a way to track water usage associated with digital infrastructure, especially data centers. While that comes with some challenges, including limited data access from cloud providers, it reflects the working group's commitment to looking at sustainability from multiple environmental angles.

To help shape these priorities,

they've also launched a survey across the foundation, which collected feedback from members. Should the group focus more on Web and mobile technologies, which represent a huge slice of the developer ecosystem? Should they start exploring procurement and circularity? what about realtime cloud data or hardware software integration?

The survey aims to get clear answers and direct the group's resources more effectively. The group also saw new projects take shape, like the Immersion Cooling Specifications, designed to optimize cooling systems for data centers, and the Mycelium project, which is creating a standard data model to allow software and infrastructure to better talk to each other, enabling smarter energy aware decisions at runtime.

So that's a brief overview of the software standards working group. A powerhouse behind the standards and specs that are quietly transforming how the world builds software. Now let's explore more of the work that the Software Standards Working Group is doing with the software Carbon Intensity Specification, the SCI. A groundbreaking framework designed to help developers and organizations calculate, understand, and reduce the environmental impact of their software.

The SCI specification offers a standardized methodology for measuring carbon intensity, empowering the tech industry to make more informed decisions in designing and deploying greener software systems. For this part of the podcast, we aim some questions at Navveen Balani from Accenture, one of the co-chairs of the Software Standards working group.

Navveen rather graciously provided us with some sound bites as answers. 

 

Chris Skipper: My first question for Navveen was about the SCI specification and its unique methodology.

The SCI specification introduces a unique methodology for calculating carbon intensity using the factors of energy efficiency, hardware efficiency, and carbon awareness. Can you share more about how this methodology was developed and its potential to drive innovation in software development?

Navveen Balani: Thank you, Chris. The software carbon intensity specification was developed to provide a standardized, actionable way to measure the

environmental impact of software. What makes it unique is its focus on three core levels,

energy efficiency, hardware efficiency, and carbon awareness. Energy efficiency

looks at how much electricity a piece of software consumes to perform a task.

So writing optimized code, minimizing unnecessary processing, and improving performance, all contribute. Hardware efficiency considers how effectively the software uses the infrastructure it runs on,

getting more done with fewer resources and carbon awareness adds a critical layer by factoring in when and where software runs.

By understanding the carbon intensity of electricity grids, applications can shift workloads to cleaner energy regions or time windows. The methodology was shaped through deep collaboration within the Green Software Foundation involving practitioners, academics, and industry leaders from member organizations.

It was designed to be not only scientifically grounded, but also practical, measurable and adaptable across different environments. What truly sets SCI apart and drives innovation is its focus on reduction rather than offsets. The specification emphasizes direct actions that teams can take to lower emissions, like optimizing compute usage, improving code efficiency, or adopting carbon aware scheduling.

These aren't theoretical ideas. They're concrete, easy to implement practices that can be embedded into the existing development lifecycle. So SCI is more than just a carbon metric. It's a practical framework that empowers developers and organizations to build software that's efficient, high performing, and environmentally responsible by design.

Chris Skipper: The SCI encourages developers to use granular, real world data where possible. Are there any tools or technologies you'd recommend to developers and teams to better align with the SCI methodology and promote carbon aware software design?

Navveen Balani: Absolutely.

One of the most powerful aspects of the SCI specification is its encouragement to use real world, granular data to inform decisions, and there are already a number of tools available to help developers and teams put this into practice. A great example is the Impact Framework, which is designed to make the environmental impact of software easier to calculate and share.

What's powerful about it

is that it doesn't require complex setup or custom code. Developers simply define their system using a lightweight manifest file,

and the framework takes care of

the rest — calculating metrics like carbon emissions in a standardized, transparent way, this makes it easier for teams to align with the SCI methodology and Track how the software contributes to environmental impact over time. Then there's the carbon aware SDK, which enables applications to make smarter decisions about when and where to run based on the carbon intensity of the electricity grid. This kind of dynamic scheduling can make a significant difference,

especially at scale.

There's also a growing body of Green Software Patterns available to guide design decisions. The Green Software Foundation has published a collection of these patterns, offering developers practical approaches to reduce emissions by design. In addition, cloud providers like AWS, Microsoft Azure and Google Cloud are increasingly offering their own sustainability focused patterns and best practices, helping teams make cloud native applications more energy efficient and carbon aware. And for those looking to explore even more, the awesome Green Software Repository on GitHub is a fantastic curated list of tools, frameworks, and research. It's a great place to discover new ways to build software that's not only efficient, but also environmentally conscious.

So whether you're just starting or already deep into green software practices, there's a growing ecosystem of tools and resources to support the journey. And the SCI specification provides the foundation to tie it all together.

Chris Skipper: Looking ahead, what are the next steps for the software standards working group and the SCI specification? Are there plans to expand the scope or functionality of the specification to address emerging challenges in green software?

Navveen Balani: Looking ahead, the Software Standards working group is continuing to evolve the SCI specification to keep pace with the rapidly changing software landscape. And one of the most exciting developments is the work underway on SCI for AI. While the existing SCI specification provides a solid foundation for measuring software carbon intensity, AI introduces new complexities.

Especially when it comes to defining what constitutes the software boundary, identifying appropriate functional units and establishing meaningful measurements for different types of AI systems. This includes everything from classical machine learning models to generative AI and emerging AI agent-based workloads.

To address these challenges, the SCI for AI initiative was launched. It's a focused effort hosted through open workshops and collaborative working groups to adapt and extend the SCI methodology specifically for AI systems. The goal is to create a standardized, transparent way to measure the carbon intensity of AI workloads while remaining grounded in the same core principles of energy efficiency, hardware efficiency, and carbon awareness.

Beyond AI, there are also efforts to extend the SCI framework to other domains such as

SCI for Web,

which focuses on defining practical measurement boundaries and metrics for Web applications and user facing systems. The broader aim is to ensure that whether you're building an AI model, a backend service, or a web-based interface, there's a consistent and actionable way to assess and reduce its environmental impact. So the SCI specification is evolving not just in scope, but in its ability to address the unique challenges of emerging technologies. It's helping to create a more unified, measurable, and responsible approach to software sustainability across the board.

 

Chris Skipper: Thanks to Navveen for those insightful answers. Next, we have some events coming up in the next few weeks.

First starting today on July 3rd in Manheim, we have Developer Week 2025. Get sustainability-focused talks during one of the largest software developer conferences in Europe. Next we have GreenIO, Munich, which is a conference powered by Apidays, which is a conference happening on the third and 4th of July. Get the latest insights from thought leaders in tech sustainability and hands-on feedback from practitioners scaling Green IT.

In the UK in Brighton, we have Evolved 25, shaping tomorrow, which is happening on July the fourth. Explore how technology can drive progress and a more sustainable digital future.

Next up on July the eighth from 7:00 to 9:00 PM CEST In Amsterdam, we have Grid-aware Websites, a new dimension in Sustainable Web Development hosted by the Green Web Foundation, where Fershad Irani will talk about the Green Web Foundation's latest initiative, Grid Aware.

Then next week Wednesday, there's a completely virtual event, Master JobRunr v8, a live coding webinar, July the 9th, sign up via the link below.

Then also on Wednesday, on the 9th of July in Berlin, we have the Green Coding Meetup, Blauer Engel, for software/carbon aware computing, happening from 6:30 PM.

Then on Thursday, July the 10th from 6:00 PM to 8:00 PM CEST, we have Shaping Progress, Responsibility, AI, and Sustainability in Frankfurt.

Then finally on Tuesday, July the 15th, we have a hybrid event hosted by Green Software Development, Karlsruhe in Karlsruhe, Germany, which is entitled Green Data Center for Green Software, Green Software for Green Data Center.

Sign up via the link below.

So we've reached the end of this special backstage episode on the Software Standards Working Group and the SCI Project at the GSF. I hope you enjoyed the podcast. As always, all the resources and links mentioned in today's episode can be found in the show notes below. If you are a developer, engineer, policy lead, or sustainability advocate, and you want to contribute to these efforts, this group is always looking for new voices.

Check out the Green Software Foundation website to find out how to join the conversation. And to listen to more episodes about green software, please visit podcast.greensoftware.foundation and we'll see you on the next episode. Bye for now.



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]]> In this Backstage episode of Environment Variables, podcast producer Chris Skipper highlights the Green Software Foundation’s Software Standards Working Group—chaired by Henry Richardson (WattTime) and Navveen Balani (Accenture). This group is central to shaping global benchmarks for sustainable software. Key initiatives discussed include the Software Carbon Intensity (SCI) Specification, its extensions for AI and the web, the Real-Time Energy and Carbon Standard for cloud providers, the SCI Guide, and the TOSS framework. Together, these tools aim to drive emissions reduction through interoperable, transparent, and globally applicable standards. 

Learn more about our people:

Find out more about the GSF:

Resources:

Events:

If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Chris Skipper: Welcome to Backstage, the behind the scenes series from Environment Variables, where we take a look at the Green Software Foundation's key initiatives and working groups. I'm the producer and host, Chris Skipper. Today we are shining a spotlight on the Green Software Foundation's Software Standards working group. This group plays a critical role in shaping the specifications and benchmarks that guide the development of green software.

Chaired by Henry Richardson, a senior analyst at what time, and Navveen Balani, managing Director and Chief Technologist for Technology Sustainable Innovation at Accenture, the group's mission is to build baseline specifications that can be used across the world, whether you're running systems in a cloud environment in Europe or on the ground in a developing country.

In other words, the Software Standards Working Group is all about creating interoperable, reliable standards, tools that allow us to measure, compare, and improve the sustainability of software in a meaningful way.

Some of the major projects they lead at the Green Software Foundation include the Software Carbon Intensity Specification, or SCI, which defines how to calculate the carbon emissions of software; the SCI for Artificial Intelligence, which extends this framework to cover the unique challenges of measuring emissions from AI workloads; the SCI for Web, which focuses on emissions from websites and front end systems;

the Realtime Energy and Carbon Standard for Cloud Providers, which aims to establish benchmarks for emissions data and cloud platforms;

the SCI Guide, which helps organizations navigate energy carbon intensity and embodied emissions methodologies,

and the Transforming Organizations for Sustainable Software, or TOSS framework, which offers a broader blueprint for integrating sustainability across business and development processes.

Together these initiatives support the foundation's broader mission to reduce the total change in global carbon emissions associated with software by prioritizing abatement over offsetting, and building trust through open, transparent, and inclusive standards. Now for some recent updates from the working group.

Earlier this year, the group made a big move by bringing the SCI for AI project directly into its core focus. As the world turns more and more to artificial intelligence, figuring out how to measure AI's energy use and emissions footprint is becoming a priority. That's why they've committed to developing a baseline SCI specification for AI over the next few months, drawing on insights from a recent Green AI committee workshop and collaborating closely with experts across the space.

There's also growing interest in extending the SCI framework beyond carbon. In a recent meeting, the group discussed the potential for creating a software water intensity metric, a way to track water usage associated with digital infrastructure, especially data centers. While that comes with some challenges, including limited data access from cloud providers, it reflects the working group's commitment to looking at sustainability from multiple environmental angles.

To help shape these priorities,

they've also launched a survey across the foundation, which collected feedback from members. Should the group focus more on Web and mobile technologies, which represent a huge slice of the developer ecosystem? Should they start exploring procurement and circularity? what about realtime cloud data or hardware software integration?

The survey aims to get clear answers and direct the group's resources more effectively. The group also saw new projects take shape, like the Immersion Cooling Specifications, designed to optimize cooling systems for data centers, and the Mycelium project, which is creating a standard data model to allow software and infrastructure to better talk to each other, enabling smarter energy aware decisions at runtime.

So that's a brief overview of the software standards working group. A powerhouse behind the standards and specs that are quietly transforming how the world builds software. Now let's explore more of the work that the Software Standards Working Group is doing with the software Carbon Intensity Specification, the SCI. A groundbreaking framework designed to help developers and organizations calculate, understand, and reduce the environmental impact of their software.

The SCI specification offers a standardized methodology for measuring carbon intensity, empowering the tech industry to make more informed decisions in designing and deploying greener software systems. For this part of the podcast, we aim some questions at Navveen Balani from Accenture, one of the co-chairs of the Software Standards working group.

Navveen rather graciously provided us with some sound bites as answers. 

 

Chris Skipper: My first question for Navveen was about the SCI specification and its unique methodology.

The SCI specification introduces a unique methodology for calculating carbon intensity using the factors of energy efficiency, hardware efficiency, and carbon awareness. Can you share more about how this methodology was developed and its potential to drive innovation in software development?

Navveen Balani: Thank you, Chris. The software carbon intensity specification was developed to provide a standardized, actionable way to measure the

environmental impact of software. What makes it unique is its focus on three core levels,

energy efficiency, hardware efficiency, and carbon awareness. Energy efficiency

looks at how much electricity a piece of software consumes to perform a task.

So writing optimized code, minimizing unnecessary processing, and improving performance, all contribute. Hardware efficiency considers how effectively the software uses the infrastructure it runs on,

getting more done with fewer resources and carbon awareness adds a critical layer by factoring in when and where software runs.

By understanding the carbon intensity of electricity grids, applications can shift workloads to cleaner energy regions or time windows. The methodology was shaped through deep collaboration within the Green Software Foundation involving practitioners, academics, and industry leaders from member organizations.

It was designed to be not only scientifically grounded, but also practical, measurable and adaptable across different environments. What truly sets SCI apart and drives innovation is its focus on reduction rather than offsets. The specification emphasizes direct actions that teams can take to lower emissions, like optimizing compute usage, improving code efficiency, or adopting carbon aware scheduling.

These aren't theoretical ideas. They're concrete, easy to implement practices that can be embedded into the existing development lifecycle. So SCI is more than just a carbon metric. It's a practical framework that empowers developers and organizations to build software that's efficient, high performing, and environmentally responsible by design.

Chris Skipper: The SCI encourages developers to use granular, real world data where possible. Are there any tools or technologies you'd recommend to developers and teams to better align with the SCI methodology and promote carbon aware software design?

Navveen Balani: Absolutely.

One of the most powerful aspects of the SCI specification is its encouragement to use real world, granular data to inform decisions, and there are already a number of tools available to help developers and teams put this into practice. A great example is the Impact Framework, which is designed to make the environmental impact of software easier to calculate and share.

What's powerful about it

is that it doesn't require complex setup or custom code. Developers simply define their system using a lightweight manifest file,

and the framework takes care of

the rest — calculating metrics like carbon emissions in a standardized, transparent way, this makes it easier for teams to align with the SCI methodology and Track how the software contributes to environmental impact over time. Then there's the carbon aware SDK, which enables applications to make smarter decisions about when and where to run based on the carbon intensity of the electricity grid. This kind of dynamic scheduling can make a significant difference,

especially at scale.

There's also a growing body of Green Software Patterns available to guide design decisions. The Green Software Foundation has published a collection of these patterns, offering developers practical approaches to reduce emissions by design. In addition, cloud providers like AWS, Microsoft Azure and Google Cloud are increasingly offering their own sustainability focused patterns and best practices, helping teams make cloud native applications more energy efficient and carbon aware. And for those looking to explore even more, the awesome Green Software Repository on GitHub is a fantastic curated list of tools, frameworks, and research. It's a great place to discover new ways to build software that's not only efficient, but also environmentally conscious.

So whether you're just starting or already deep into green software practices, there's a growing ecosystem of tools and resources to support the journey. And the SCI specification provides the foundation to tie it all together.

Chris Skipper: Looking ahead, what are the next steps for the software standards working group and the SCI specification? Are there plans to expand the scope or functionality of the specification to address emerging challenges in green software?

Navveen Balani: Looking ahead, the Software Standards working group is continuing to evolve the SCI specification to keep pace with the rapidly changing software landscape. And one of the most exciting developments is the work underway on SCI for AI. While the existing SCI specification provides a solid foundation for measuring software carbon intensity, AI introduces new complexities.

Especially when it comes to defining what constitutes the software boundary, identifying appropriate functional units and establishing meaningful measurements for different types of AI systems. This includes everything from classical machine learning models to generative AI and emerging AI agent-based workloads.

To address these challenges, the SCI for AI initiative was launched. It's a focused effort hosted through open workshops and collaborative working groups to adapt and extend the SCI methodology specifically for AI systems. The goal is to create a standardized, transparent way to measure the carbon intensity of AI workloads while remaining grounded in the same core principles of energy efficiency, hardware efficiency, and carbon awareness.

Beyond AI, there are also efforts to extend the SCI framework to other domains such as

SCI for Web,

which focuses on defining practical measurement boundaries and metrics for Web applications and user facing systems. The broader aim is to ensure that whether you're building an AI model, a backend service, or a web-based interface, there's a consistent and actionable way to assess and reduce its environmental impact. So the SCI specification is evolving not just in scope, but in its ability to address the unique challenges of emerging technologies. It's helping to create a more unified, measurable, and responsible approach to software sustainability across the board.

 

Chris Skipper: Thanks to Navveen for those insightful answers. Next, we have some events coming up in the next few weeks.

First starting today on July 3rd in Manheim, we have Developer Week 2025. Get sustainability-focused talks during one of the largest software developer conferences in Europe. Next we have GreenIO, Munich, which is a conference powered by Apidays, which is a conference happening on the third and 4th of July. Get the latest insights from thought leaders in tech sustainability and hands-on feedback from practitioners scaling Green IT.

In the UK in Brighton, we have Evolved 25, shaping tomorrow, which is happening on July the fourth. Explore how technology can drive progress and a more sustainable digital future.

Next up on July the eighth from 7:00 to 9:00 PM CEST In Amsterdam, we have Grid-aware Websites, a new dimension in Sustainable Web Development hosted by the Green Web Foundation, where Fershad Irani will talk about the Green Web Foundation's latest initiative, Grid Aware.

Then next week Wednesday, there's a completely virtual event, Master JobRunr v8, a live coding webinar, July the 9th, sign up via the link below.

Then also on Wednesday, on the 9th of July in Berlin, we have the Green Coding Meetup, Blauer Engel, for software/carbon aware computing, happening from 6:30 PM.

Then on Thursday, July the 10th from 6:00 PM to 8:00 PM CEST, we have Shaping Progress, Responsibility, AI, and Sustainability in Frankfurt.

Then finally on Tuesday, July the 15th, we have a hybrid event hosted by Green Software Development, Karlsruhe in Karlsruhe, Germany, which is entitled Green Data Center for Green Software, Green Software for Green Data Center.

Sign up via the link below.

So we've reached the end of this special backstage episode on the Software Standards Working Group and the SCI Project at the GSF. I hope you enjoyed the podcast. As always, all the resources and links mentioned in today's episode can be found in the show notes below. If you are a developer, engineer, policy lead, or sustainability advocate, and you want to contribute to these efforts, this group is always looking for new voices.

Check out the Green Software Foundation website to find out how to join the conversation. And to listen to more episodes about green software, please visit podcast.greensoftware.foundation and we'll see you on the next episode. Bye for now.



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]]> Environment Variables Year Three Roundup Environment Variables Year Three Roundup Thu, 26 Jun 2025 08:00:00 GMT 25:20 703p3pn1 false https://podcasts.castplus.fm/e/xn14j458-environment-variables-year-three-roundup 68dc7d0c6d92c33f9c88f4e5 68dc7cef46a2532cdd8d1c23 It’s been three years of Environment Variables! What a landmark year for the Green Software Foundation. From launching behind-the-scenes Backstage episodes, to covering the explosive impact of AI on software emissions, to broadening our audience through beginner-friendly conversations; this retrospective showcases our mission to create a trusted ecosystem for sustainable software. Here’s to many more years of EV! 112 It’s been three years of Environment Variables! What a landmark year for the Green Software Foundation. From launching behind-the-scenes Backstage episodes, to covering the explosive impact of AI on software emissions, to broadening our audience through beginner-friendly conversations; this retrospective showcases our mission to create a trusted ecosystem for sustainable software. Here’s to many more years of EV!

Learn more about our people:

Find out more about the GSF:

Resources:

If you enjoyed this episode then please either:

TRANSCRIPT BELOW:
Chris Skipper: Welcome to Environment Variables from the Green Software Foundation. The podcast that brings you the latest in sustainable software development has now been running for three years.

So that's three years of the latest news in green software, talking about everything from AI energy through to the cloud, and its effect on our environment and how we as a software community can make things better for everybody else.

This past year Environment Variables has truly embodied the mission of the Green Software Foundation, and that's to create a trusted ecosystem of people, standards, tools, and best practices for creating and building green software. Now this episode's gonna feature some of the more key episodes that we did over the last year.

We're gonna be looking at a wide variety of topics and it's going to be hopefully a nice journey back through both the timeline of the podcast, but also the landscape of green software over the last year and how it has dramatically changed, not only due to the dramatic rise in use of AI amongst other things, but also just to the fantastic ideas that people have brought to the table in order to try and solve the problem of trying to decarbonize software. So without further ado, let's dive in to the first topic.

​

Chris Skipper: First, we brought about a new change in the way the podcast was structured. A new type of episode called Backstage.

Backstage is basically a behind the scenes look at the Green Software Foundation, internal projects and working groups. It's a space for our community to hear directly from project leaders to share the wins and their lessons learned and reinforce trust and transparency, which is one of the core tenets of the Green Software Foundation Manifesto.

Now, there were a bunch of great projects that were featured over the last year. We're gonna look at two specifically.

In our first backstage episode, we introduced the TOSS project. TOSS stands for Transforming Organizations for Sustainable Software, and it's led by the fantastic Pindy Bhullar. This project aims to embed sustainability into business strategy and operations through a four pillar framework.

. It's a perfect example of how the foundation operationalizes its mission to minimize emissions by supporting organizations on their sustainability journey.

Let's hear the snippet from Pendi explaining these four pillars.

Pindy Bhullar: Transforming organizations for sustainable software is the acronym for toss. Businesses will be able to utilize the toss framework as a guide to lay the groundwork for managing change and also improving software operations in the future, software practices within organizations can be integrated with sustainability in a cohesive and agile manner, rather than addressing green software practices in an isolated approach.

For a company to fully benefit from sustainable transformation of their software development processes, we need to review all aspects of technology. The Toss framework is designed to be embedded across multiple aspects of its business operations. Dividing the task framework along four pillars has allowed for simultaneous, top down and bottom up reinforcement of sustainable practices, as well as the integration of new tools, processes, and regulations that I merge over time.

The four pillars aim to foster a dynamic foundation for companies to understand where to act now, to adjust later and expand within organizational's sustainable software transformation. The four pillars are strategy, implementation. Operational compliance and regulations and within each of the pillars, we have designed a decision tree that will be constructed to guide organizations in transforming their software journey.

Chris Skipper: Some fantastic insights from pindi there, and I'm sure you can agree. The Toss project has an applicability outside of just software development. It's one of those projects that's really gonna grow exponentially in the next few years. Next up, we have green software patterns. Green software Patterns Project is an open source initiative designed to help software practitioners reduce emissions by applying vendor neutral best practices. Guests, Franziska Warncke and Liya Mathew; project leads for the initiative discussed how organizations like Aviva and MasterCard have successfully integrated these patterns to enhance software sustainability. They also explored the rigorous review process for new patterns, upcoming advancements, such as persona based approaches and how developers and researchers can contribute to the project.

That's one thing to remember about Backstage is actually highlights that there are so many projects going on at the GSF. We actually need more people to get involved. So if you are interested in getting involved, please Visit greensoftware.foundation to find out more. Let's hear now from Liya Mathew about the Green Software Patterns Project.

Liya Mathew: One of the core and most useful features of patterns is the ability to correlate the software carbon intensity specification. Think of it as a bridge that connects learning and measurement. When we look through existing catalog of patterns, one essential thing that stands out is their adaptability.

Many of these patterns not only aligned with sustainability, but also coincide with security and reliability best practices. The beauty of this approach is that we don't need to completely rewrite a software architecture. To make it more sustainable. Small actions like catching static data or providing a dark mode can make significant difference.

These are simple, yet effective steps that can lead us a long way towards sustainability. Also, we are nearing the graduation of patterns V one. This milestone marks a significant achievement and we are already looking ahead to the next exciting phase. Patterns we two. In patterns we two, we are focusing on persona based and behavioral patterns, which will bring even more tailored and impactful solutions to our community.

These new patterns will help address specific needs and behaviors, making our tools even more adaptable and effective.

Chris Skipper: Moving on. We also kept our regular episode format The Week in Green Software, also known affectionately as Twigs. So Twigs was originally hosted by Chris Adams and is now occasionally hosted by the Fabulous and Currie as well.

It offers quick actionable updates in the green software space with a rising sustainability news. With a rising tide of sustainability and AI developments, this format helps listeners stay current. I can tell you now that in the last year, the number of news topics has just exploded when it comes to anything to do with AI and the impact it's having on the environment.

And I think part of that is due to the work of the GSF and its community members. We used to have to really struggle to find news topics when this podcast first started back in 2022. But now in 2025, every week, I would say nearly every hour, there's a new topic coming out about how software is affecting the environment.

I. So The Week in Green Software is your one stop place for finding all that information dialed down into one place. And also you can sign up to the GSF newsletter as well via the link below, which will give you a rundown of all the week's latest new topics as well. So let's look at a couple episodes of twigs from the previous year.

The first one is an episode with the executive director of the GSF Asim Hussain. Asim really embodies the mission of the GSF in so many ways and is always passionate about the effect that software is having on the environment. In this episode, which was subtitled, Obscuring AI's Real Carbon Output , Asim joined Chris to unpack the complexities of AI's, carbon emissions, renewable energy credits, and regulatory developments.

This episode emphasized the need for better carbon accounting practices; work the foundation is helping to advance. Let's hear this little snippet from Asim now.

Asim Hussain: You can plant a tree, right? And then you planted the tree. That tree will grow and there's issue there. This drought tree will grow and it'll suck carbon from the atmosphere. And you can say that's a carbon credit at planting a tree. Or there's carbon avoidance offsets and there's many variant, and that's actually very good variance of carbon avoidance offsets.

But there is a variant of a carbon avoidance offset where I've got a tree and you pay me not to cut it down. And so where is the additionality? If I'm actually planting a tree, it's happening and planting a tree. I'm, I'm, I'm adding additional kind of capacity in, in carbon removal. And then the renewable energy markets is exactly the same.

You can have renewable energy, which if you buy means a renewable power plant is gonna get built and you can have renewable energy, which is just kind of sold. And if you buy it or you don't buy, there's no change. Nothing's gonna happen. There's no more new renewable plant's gonna get built. Only one of them has that additionality component.

And so therefore, only one of them should really be used in any kind of renewable energy claims. But both of them are allowed in terms of renewable energy claims.

Chris Skipper: One of the things I love about the way Asim talks about software in general is always, he uses idioms like that planting of a tree to explain a real complex, uh, topic and make it more palatable for a wider audience, which is something that we're gonna explore later on in this episode as well. But before we do that, let's move on to another episode of The Week in Green Software, which was subtitled Sustainable AI Progress.

I think you can see a theme that's been going on here. This was our hundredth episode, which was a massive milestone in its own, and the Fantastic Anne Currie hosted Holly Cummins to explore light switch ops, zombie servers, and sustainable cloud architecture. This conversation. Perfectly aligns with the foundation's mission to minimize emissions through smarter, more efficient systems, and having the really knowledgeable, brilliant.

Holly Cummins on to talk about light switch ops was just fantastic. , Let's listen to this next clip from her talking about light switch ops.

Holly Cummins: We have a great deal of confidence that it's reliable to turn a light off and on, and that it's low friction to do it. And so we need to get to that point with our computer systems and, and you can sort of, uh, roll with the analogy a bit more as well, which is in our houses, it tends to be quite a manual thing of turning the lights off and on.

You know, I, I, I, you know, I. Turn the light on when I need it. In institutional buildings, it's usually not a manual process to turn the lights off and on. Instead, what we end up is we end up with some kind of automation. So like often there's a motion sensor. So, you know, I used to have it that, um, if I would stay in our office late at night.

At some point if you sat too still because you were coating and deep in thought, the lights around you would go off and then you'd have to like wave your arms to make the lights go back on. And it's that, that, you know, it's this sort of idea of like, we can detect the traffic, we can detect the activity and not waste the energy.

And again, we can do. Exactly this with with our computer system so we can have it so that it's really easy to turn them off and on. And then we can go one step further and we can automate it and we can say, let's script to turn things off at 5:00 PM because we're only in one geo

Chris Skipper: So as you can see, there's always been this theme of Rise in AI, you know, and I think everybody who's involved in this, uh, community and even people outside of it are really kind of frightened and scared of the impact that AI is having on the environment. But one thing that the GSF brings is this anchoring, this hope that there is actually change for the better.

And there are people who are actively working against that, within the, within the software industry. And. There's, there's actually gonna be a lot of change coming in the next year, which will make things a lot more hopeful, uh, for the carbon output of the software industry. So between 2024 and 2025 AI's impact on the environment became one of the most discussed topics in our industry, and obviously on this podcast.

In 2023 alone data center, electricity consumption for AI workloads was estimated to grow by more than 20%. With foundation models like ChatGPT four, using hundreds of megawatt hours per training run,

obviously there are a lot of statistics out there that are quite frightening, but hopefully Environment Variables brings you some peace of mind. And with that, we wanted to expand our audience to a wider group of people that weren't just software developers to make things more palatable for your everyday computer user, for example. ,

So one of those episodes that we're gonna feature around that move to try and increase our audience growth is an episode called AI Energy Measurement for Beginners, where Charles Tripp and Dawn Nafus helped us break down how AI's energy use is measured and why it's often misunderstood.

Their beginner friendly approach supports one of the GFS key goals, which is making green practices more accessible And inclusive. Here is Charles talking about one of those points in this next snippet.

Charles Tripp: I think there's a, there's like a historical bias towards number of operations because in old computers without much caching or anything like this, right? Like, uh, I, I restore old computers and, um, like an old 3 86 or IBM xt, right? Like it's running, it has registers in the CPU and then it has main memory and it, and almost everything is basically how many operations I'm doing is going to.

Closely correlate with how fast the thing runs and probably how much energy it uses, because most of the energy consumption on those systems is, is just basically constant no matter what I'm doing. Right. Yeah. It's just, it doesn't like idle down the processor while it's not working. Right. There's a historical bias that's built up over time that like was focused on the, the, you know, and it's also at the programmer level.

Like I'm thinking about what is the, the computer doing? What do I have control over? Yeah. What's, what, yeah. One, am I able to, but it's only through, it's only through actually measuring it that you gain a clearer picture of like what is actually using energy. Um, and I think if you get that picture, then you'll gain, um, uh, uh, an understanding more of.

How can I make this software or the data center or anything in between, like job allocation, more energy efficient, but it's only through actually measuring that we can get that clear picture. Because if we guess, especially using kind of our biases from how we, how we learn to use computers, how we learn about how computers work, we're actually.

Very likely to get an incorrect understanding, incorrect picture of what the, what's driving the energy consumption. It's much less intuitive than people think.

Chris Skipper: thanks to Charles for breaking it down in really simple terms and for his contribution to the podcast. Another episode that tried to simplify the world of AI and the impact that it's having on the environment is called the economics of ai, which we did with Max Schultze.

He joined us to talk about the economics of cloud infrastructure and ai. He challenged the idea that AI must be resource intensive arguing instead for clearer data, stronger public policy, and greater transparency, all values that the GSF hold dear. Let's listen to that clip of Max talking about those principles.

Max Schulze: I think when as a developer you hear transparency and, okay, they have to report data. What you're thinking is, oh, they're gonna have an API where I can pull this information. Also, let's say from the inside of the data center now in Germany, it is also funny for everybody listening one way to fulfill that because the law was not specific.

Data centers now are hanging a piece of paper. I'm not kidding. On their fence with this information, right? So this is like them reporting this. And of course we as, I'm also a software engineer, so we as technical people, what we need is the data center to have an API that basically assigns the environmental impact of the entire data center to something.

And that something has always bothered me that we say, oh, it's the server or it's the, I don't know, the rack or the cluster, but ly. What does software consume? Software consumes basically three things. We call it compute, network, and storage, but in more philosophical terms, it's the ability to store, process and transfer data.

And that is the resource that software consumes. A software does not consume a data center or a server. It consumes these three things. Mm-hmm. And a server makes those things turns actually energy and a lot of raw materials into digital resources. Then the data center in turn provides the shell in which the server can do that function, right?

It, it's the factory building, it's the data center. The machine that makes the T-shirts is the server and the t-shirt is what people wear.

Chris Skipper: Again, it's those analogies that make things easier for people to understand the world of software and the impact it's having on the environment. Also, with that idea of reaching a broader audience, we try to also talk about the energy grid as well as software development as those two things are intrinsically linked. So one of the episodes that we wanna feature now is called How To Tell When Energy Is Green with Killian Daly.

Killian explained how EnergyTag is creating a standard for time and location-based energy tracking. Two topics that we've covered a lot on this podcast. This work enables companies to make verifiable clean energy claims, helping build trust across industries. Let's listen to this clip from Killian.

Killian Daly: Interestingly, uh, actually on the 14th of January, just before, uh, um, the inauguration of Donald Trump, uh, as US president, so the Biden administration issued an executive order, which hasn't yet been rescinded, um, basically on, uh, data centers, on federal lands. And, and in that they do require these three pillars.

Uh, so they, they do have a three pillar requirement on, uh, on electricity sourcing, which is very interesting, right? I think that's. Quite a good template. Uh, and I think, you know, we definitely need to think about like, okay, if you're gonna start building loads of data centers in Ireland, for example, Ireland, uh, 20%, 25% of electricity consumption in Ireland is, is from data centers.

That's way more than anywhere else in the world in relative terms. Yeah, there's a big conversation at the moment in Ireland about like, okay, well how do we make sure this is clean? How do we think about, um, uh, procurement requirements for building a new data center? That's a piece of legislation that's on being written at the moment.

And how do we also require these data centers to do reporting of their emissions once they're operational? So the Irish government, uh, is also putting together a reporting framework for data centers and the energy agency. So the. Sustainable Energy Authority of Ireland, SEAI, they published a report a couple of weeks ago saying, yeah, they do, you know what they need to do this hourly reporting based on, uh, contracts bought in Ireland.

So I think we're seeing already promising signs of, of legislation coming down the road in, um, you know, in other sectors outside of hydrogen. And I think data centers is, is, is probably an obvious one.

Chris Skipper: Fantastic clip there from Killian. It also highlights how the work that the GSF is having is having an impact on the political landscape as well in terms of public policy and the discussions that are having in the higher ups of governments.

Moving on. We wanna talk about our final episode that we wanna highlight in this episode from the last year, and that's the episode, How to Explain Software to Normal People with James Martin. We ended the year with this episode with James, who talked about strategies for communicating digital sustainability to non-technical audiences, which is something that we try to do here at Environment Variables too. From Frugal AI to policy advocacy, this episode reinforced the power of inclusive storytelling. Let's listen to this clip from James Martin.

James Martin: A few years ago, the, the French Environment Minister said people should stop, uh, trying to send so many, uh, funny, funny email attachments, you know? Oh, really? Like, like when you send a joke, a jokey video to all your colleagues, you should stop doing that because it's, it's not good for the planet. It honestly, the, uh, minister could say something that misguided, because that's not.

We, you and I know that's not where the impact is. Um, the, the impact is in the cloud. The impact is in, uh, hardware. So instead of, it's about the, the, the communication is repetition and the, the, the, I always start with digital is 4% of global emissions. 1% of that is, is data centers. 3% of that is hardware and software is sort of.

They're sort of all over the place. So that's the, the, the thing I, that's the figure I use the most to get things started. And I think the, the number one misconception that people need to get their heads around is the people tend to think that tech is, uh, immaterial. It's because of expressions like the cloud.

It just sounds. Like, is this floaty thing rather than massive industry? Ethereal. We need to make it, we need to make it more physical. If, uh, I can't remember who said that if, if data centers could fly, then it would, it would make our, our job a lot, a lot easier. Um, but no, that, that's why you need to always come back to the figures.

4% is double, uh, the emissions of planes. And yet, um. The airline industry gets tens of hundreds times more hassle than the tech industry in terms of, uh, trying to keep control of their, of their emissions. So what you need is a lot more, uh, tangible examples and you need people to, to explain this impact over time.

So you need to move away from bad examples like. Funny email attachments or The thing about, um, the keep hearing in AI is, uh, one, one chat GBT prompt is 10 times more energy than Google. That may or may not be true, but it's a bit, again, it's a bit of the, it's the wrong example because it doesn't, it doesn't focus on the bigger picture and it can Yeah, it kind of implies, yeah, and it can make people, if I just, if I just like reduce my, my, my usage of this, then I'm gonna have like 10 times the impact I'm gonna.

You know, that's all only too, that feels a bit kind of individual in a bit like individualizing the problem. Surely it does, and, and it's putting it on people's, it's putting the onus on the users, whereas it's, once again, it, it's not their fault. You need to see the bigger picture. And this is what I've, I've been repeating since I wrote that, uh, that white paper actually, you can't say you have a green IT approach if you're only focusing on data centers, hardware or software.

You've got to focus. Arnold all three, otherwise. Yeah, exactly. Holistically

Chris Skipper: With that, we've come to the end of this episode. Well, what a year it's been for Environment Variables, and we'll just take a look at some of the statistics.

Just to blow our own horn here a little bit. We've reached over 350,000 plays. Engagement and followers to the podcast have gone up by 30%, which indicates to us that Environment Variables really matters to the people that listen to it. And it's raising awareness to the need to decarbonize the software industry.

Looking ahead. We remain committed to the foundation's vision of changing the culture of software development.

So sustainability is as fundamental as security or performance. Year four, we'll bring new stories, new tools, new opportunities, new people hopefully and all in an effort to reduce emissions together. So thank you for being part of our mission, and here's to another year of action advocacy and green software innovation.

And now to play us out is the new and improved Environment Variables podcast theme.

 Hey everybody. Thanks for listening. This is Chris, the producer again, just reaching out to say thank you for being a part of this community and a bigger part of the GSF as a whole. If you wanna listen to more episodes of Environment Variables, please head to podcast.greensoftware.foundation to listen to more,

or click the link below to discover more about the Green Software Foundation and how to be part of the podcast as well.

And if you're listening to this on a platform, please click follow or subscribe to hear more episodes of Environment Variables.

We'll catch you on the next one. Bye for now.




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]]> It’s been three years of Environment Variables! What a landmark year for the Green Software Foundation. From launching behind-the-scenes Backstage episodes, to covering the explosive impact of AI on software emissions, to broadening our audience through beginner-friendly conversations; this retrospective showcases our mission to create a trusted ecosystem for sustainable software. Here’s to many more years of EV!

Learn more about our people:

Find out more about the GSF:

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TRANSCRIPT BELOW:
Chris Skipper: Welcome to Environment Variables from the Green Software Foundation. The podcast that brings you the latest in sustainable software development has now been running for three years.

So that's three years of the latest news in green software, talking about everything from AI energy through to the cloud, and its effect on our environment and how we as a software community can make things better for everybody else.

This past year Environment Variables has truly embodied the mission of the Green Software Foundation, and that's to create a trusted ecosystem of people, standards, tools, and best practices for creating and building green software. Now this episode's gonna feature some of the more key episodes that we did over the last year.

We're gonna be looking at a wide variety of topics and it's going to be hopefully a nice journey back through both the timeline of the podcast, but also the landscape of green software over the last year and how it has dramatically changed, not only due to the dramatic rise in use of AI amongst other things, but also just to the fantastic ideas that people have brought to the table in order to try and solve the problem of trying to decarbonize software. So without further ado, let's dive in to the first topic.

​

Chris Skipper: First, we brought about a new change in the way the podcast was structured. A new type of episode called Backstage.

Backstage is basically a behind the scenes look at the Green Software Foundation, internal projects and working groups. It's a space for our community to hear directly from project leaders to share the wins and their lessons learned and reinforce trust and transparency, which is one of the core tenets of the Green Software Foundation Manifesto.

Now, there were a bunch of great projects that were featured over the last year. We're gonna look at two specifically.

In our first backstage episode, we introduced the TOSS project. TOSS stands for Transforming Organizations for Sustainable Software, and it's led by the fantastic Pindy Bhullar. This project aims to embed sustainability into business strategy and operations through a four pillar framework.

. It's a perfect example of how the foundation operationalizes its mission to minimize emissions by supporting organizations on their sustainability journey.

Let's hear the snippet from Pendi explaining these four pillars.

Pindy Bhullar: Transforming organizations for sustainable software is the acronym for toss. Businesses will be able to utilize the toss framework as a guide to lay the groundwork for managing change and also improving software operations in the future, software practices within organizations can be integrated with sustainability in a cohesive and agile manner, rather than addressing green software practices in an isolated approach.

For a company to fully benefit from sustainable transformation of their software development processes, we need to review all aspects of technology. The Toss framework is designed to be embedded across multiple aspects of its business operations. Dividing the task framework along four pillars has allowed for simultaneous, top down and bottom up reinforcement of sustainable practices, as well as the integration of new tools, processes, and regulations that I merge over time.

The four pillars aim to foster a dynamic foundation for companies to understand where to act now, to adjust later and expand within organizational's sustainable software transformation. The four pillars are strategy, implementation. Operational compliance and regulations and within each of the pillars, we have designed a decision tree that will be constructed to guide organizations in transforming their software journey.

Chris Skipper: Some fantastic insights from pindi there, and I'm sure you can agree. The Toss project has an applicability outside of just software development. It's one of those projects that's really gonna grow exponentially in the next few years. Next up, we have green software patterns. Green software Patterns Project is an open source initiative designed to help software practitioners reduce emissions by applying vendor neutral best practices. Guests, Franziska Warncke and Liya Mathew; project leads for the initiative discussed how organizations like Aviva and MasterCard have successfully integrated these patterns to enhance software sustainability. They also explored the rigorous review process for new patterns, upcoming advancements, such as persona based approaches and how developers and researchers can contribute to the project.

That's one thing to remember about Backstage is actually highlights that there are so many projects going on at the GSF. We actually need more people to get involved. So if you are interested in getting involved, please Visit greensoftware.foundation to find out more. Let's hear now from Liya Mathew about the Green Software Patterns Project.

Liya Mathew: One of the core and most useful features of patterns is the ability to correlate the software carbon intensity specification. Think of it as a bridge that connects learning and measurement. When we look through existing catalog of patterns, one essential thing that stands out is their adaptability.

Many of these patterns not only aligned with sustainability, but also coincide with security and reliability best practices. The beauty of this approach is that we don't need to completely rewrite a software architecture. To make it more sustainable. Small actions like catching static data or providing a dark mode can make significant difference.

These are simple, yet effective steps that can lead us a long way towards sustainability. Also, we are nearing the graduation of patterns V one. This milestone marks a significant achievement and we are already looking ahead to the next exciting phase. Patterns we two. In patterns we two, we are focusing on persona based and behavioral patterns, which will bring even more tailored and impactful solutions to our community.

These new patterns will help address specific needs and behaviors, making our tools even more adaptable and effective.

Chris Skipper: Moving on. We also kept our regular episode format The Week in Green Software, also known affectionately as Twigs. So Twigs was originally hosted by Chris Adams and is now occasionally hosted by the Fabulous and Currie as well.

It offers quick actionable updates in the green software space with a rising sustainability news. With a rising tide of sustainability and AI developments, this format helps listeners stay current. I can tell you now that in the last year, the number of news topics has just exploded when it comes to anything to do with AI and the impact it's having on the environment.

And I think part of that is due to the work of the GSF and its community members. We used to have to really struggle to find news topics when this podcast first started back in 2022. But now in 2025, every week, I would say nearly every hour, there's a new topic coming out about how software is affecting the environment.

I. So The Week in Green Software is your one stop place for finding all that information dialed down into one place. And also you can sign up to the GSF newsletter as well via the link below, which will give you a rundown of all the week's latest new topics as well. So let's look at a couple episodes of twigs from the previous year.

The first one is an episode with the executive director of the GSF Asim Hussain. Asim really embodies the mission of the GSF in so many ways and is always passionate about the effect that software is having on the environment. In this episode, which was subtitled, Obscuring AI's Real Carbon Output , Asim joined Chris to unpack the complexities of AI's, carbon emissions, renewable energy credits, and regulatory developments.

This episode emphasized the need for better carbon accounting practices; work the foundation is helping to advance. Let's hear this little snippet from Asim now.

Asim Hussain: You can plant a tree, right? And then you planted the tree. That tree will grow and there's issue there. This drought tree will grow and it'll suck carbon from the atmosphere. And you can say that's a carbon credit at planting a tree. Or there's carbon avoidance offsets and there's many variant, and that's actually very good variance of carbon avoidance offsets.

But there is a variant of a carbon avoidance offset where I've got a tree and you pay me not to cut it down. And so where is the additionality? If I'm actually planting a tree, it's happening and planting a tree. I'm, I'm, I'm adding additional kind of capacity in, in carbon removal. And then the renewable energy markets is exactly the same.

You can have renewable energy, which if you buy means a renewable power plant is gonna get built and you can have renewable energy, which is just kind of sold. And if you buy it or you don't buy, there's no change. Nothing's gonna happen. There's no more new renewable plant's gonna get built. Only one of them has that additionality component.

And so therefore, only one of them should really be used in any kind of renewable energy claims. But both of them are allowed in terms of renewable energy claims.

Chris Skipper: One of the things I love about the way Asim talks about software in general is always, he uses idioms like that planting of a tree to explain a real complex, uh, topic and make it more palatable for a wider audience, which is something that we're gonna explore later on in this episode as well. But before we do that, let's move on to another episode of The Week in Green Software, which was subtitled Sustainable AI Progress.

I think you can see a theme that's been going on here. This was our hundredth episode, which was a massive milestone in its own, and the Fantastic Anne Currie hosted Holly Cummins to explore light switch ops, zombie servers, and sustainable cloud architecture. This conversation. Perfectly aligns with the foundation's mission to minimize emissions through smarter, more efficient systems, and having the really knowledgeable, brilliant.

Holly Cummins on to talk about light switch ops was just fantastic. , Let's listen to this next clip from her talking about light switch ops.

Holly Cummins: We have a great deal of confidence that it's reliable to turn a light off and on, and that it's low friction to do it. And so we need to get to that point with our computer systems and, and you can sort of, uh, roll with the analogy a bit more as well, which is in our houses, it tends to be quite a manual thing of turning the lights off and on.

You know, I, I, I, you know, I. Turn the light on when I need it. In institutional buildings, it's usually not a manual process to turn the lights off and on. Instead, what we end up is we end up with some kind of automation. So like often there's a motion sensor. So, you know, I used to have it that, um, if I would stay in our office late at night.

At some point if you sat too still because you were coating and deep in thought, the lights around you would go off and then you'd have to like wave your arms to make the lights go back on. And it's that, that, you know, it's this sort of idea of like, we can detect the traffic, we can detect the activity and not waste the energy.

And again, we can do. Exactly this with with our computer system so we can have it so that it's really easy to turn them off and on. And then we can go one step further and we can automate it and we can say, let's script to turn things off at 5:00 PM because we're only in one geo

Chris Skipper: So as you can see, there's always been this theme of Rise in AI, you know, and I think everybody who's involved in this, uh, community and even people outside of it are really kind of frightened and scared of the impact that AI is having on the environment. But one thing that the GSF brings is this anchoring, this hope that there is actually change for the better.

And there are people who are actively working against that, within the, within the software industry. And. There's, there's actually gonna be a lot of change coming in the next year, which will make things a lot more hopeful, uh, for the carbon output of the software industry. So between 2024 and 2025 AI's impact on the environment became one of the most discussed topics in our industry, and obviously on this podcast.

In 2023 alone data center, electricity consumption for AI workloads was estimated to grow by more than 20%. With foundation models like ChatGPT four, using hundreds of megawatt hours per training run,

obviously there are a lot of statistics out there that are quite frightening, but hopefully Environment Variables brings you some peace of mind. And with that, we wanted to expand our audience to a wider group of people that weren't just software developers to make things more palatable for your everyday computer user, for example. ,

So one of those episodes that we're gonna feature around that move to try and increase our audience growth is an episode called AI Energy Measurement for Beginners, where Charles Tripp and Dawn Nafus helped us break down how AI's energy use is measured and why it's often misunderstood.

Their beginner friendly approach supports one of the GFS key goals, which is making green practices more accessible And inclusive. Here is Charles talking about one of those points in this next snippet.

Charles Tripp: I think there's a, there's like a historical bias towards number of operations because in old computers without much caching or anything like this, right? Like, uh, I, I restore old computers and, um, like an old 3 86 or IBM xt, right? Like it's running, it has registers in the CPU and then it has main memory and it, and almost everything is basically how many operations I'm doing is going to.

Closely correlate with how fast the thing runs and probably how much energy it uses, because most of the energy consumption on those systems is, is just basically constant no matter what I'm doing. Right. Yeah. It's just, it doesn't like idle down the processor while it's not working. Right. There's a historical bias that's built up over time that like was focused on the, the, you know, and it's also at the programmer level.

Like I'm thinking about what is the, the computer doing? What do I have control over? Yeah. What's, what, yeah. One, am I able to, but it's only through, it's only through actually measuring it that you gain a clearer picture of like what is actually using energy. Um, and I think if you get that picture, then you'll gain, um, uh, uh, an understanding more of.

How can I make this software or the data center or anything in between, like job allocation, more energy efficient, but it's only through actually measuring that we can get that clear picture. Because if we guess, especially using kind of our biases from how we, how we learn to use computers, how we learn about how computers work, we're actually.

Very likely to get an incorrect understanding, incorrect picture of what the, what's driving the energy consumption. It's much less intuitive than people think.

Chris Skipper: thanks to Charles for breaking it down in really simple terms and for his contribution to the podcast. Another episode that tried to simplify the world of AI and the impact that it's having on the environment is called the economics of ai, which we did with Max Schultze.

He joined us to talk about the economics of cloud infrastructure and ai. He challenged the idea that AI must be resource intensive arguing instead for clearer data, stronger public policy, and greater transparency, all values that the GSF hold dear. Let's listen to that clip of Max talking about those principles.

Max Schulze: I think when as a developer you hear transparency and, okay, they have to report data. What you're thinking is, oh, they're gonna have an API where I can pull this information. Also, let's say from the inside of the data center now in Germany, it is also funny for everybody listening one way to fulfill that because the law was not specific.

Data centers now are hanging a piece of paper. I'm not kidding. On their fence with this information, right? So this is like them reporting this. And of course we as, I'm also a software engineer, so we as technical people, what we need is the data center to have an API that basically assigns the environmental impact of the entire data center to something.

And that something has always bothered me that we say, oh, it's the server or it's the, I don't know, the rack or the cluster, but ly. What does software consume? Software consumes basically three things. We call it compute, network, and storage, but in more philosophical terms, it's the ability to store, process and transfer data.

And that is the resource that software consumes. A software does not consume a data center or a server. It consumes these three things. Mm-hmm. And a server makes those things turns actually energy and a lot of raw materials into digital resources. Then the data center in turn provides the shell in which the server can do that function, right?

It, it's the factory building, it's the data center. The machine that makes the T-shirts is the server and the t-shirt is what people wear.

Chris Skipper: Again, it's those analogies that make things easier for people to understand the world of software and the impact it's having on the environment. Also, with that idea of reaching a broader audience, we try to also talk about the energy grid as well as software development as those two things are intrinsically linked. So one of the episodes that we wanna feature now is called How To Tell When Energy Is Green with Killian Daly.

Killian explained how EnergyTag is creating a standard for time and location-based energy tracking. Two topics that we've covered a lot on this podcast. This work enables companies to make verifiable clean energy claims, helping build trust across industries. Let's listen to this clip from Killian.

Killian Daly: Interestingly, uh, actually on the 14th of January, just before, uh, um, the inauguration of Donald Trump, uh, as US president, so the Biden administration issued an executive order, which hasn't yet been rescinded, um, basically on, uh, data centers, on federal lands. And, and in that they do require these three pillars.

Uh, so they, they do have a three pillar requirement on, uh, on electricity sourcing, which is very interesting, right? I think that's. Quite a good template. Uh, and I think, you know, we definitely need to think about like, okay, if you're gonna start building loads of data centers in Ireland, for example, Ireland, uh, 20%, 25% of electricity consumption in Ireland is, is from data centers.

That's way more than anywhere else in the world in relative terms. Yeah, there's a big conversation at the moment in Ireland about like, okay, well how do we make sure this is clean? How do we think about, um, uh, procurement requirements for building a new data center? That's a piece of legislation that's on being written at the moment.

And how do we also require these data centers to do reporting of their emissions once they're operational? So the Irish government, uh, is also putting together a reporting framework for data centers and the energy agency. So the. Sustainable Energy Authority of Ireland, SEAI, they published a report a couple of weeks ago saying, yeah, they do, you know what they need to do this hourly reporting based on, uh, contracts bought in Ireland.

So I think we're seeing already promising signs of, of legislation coming down the road in, um, you know, in other sectors outside of hydrogen. And I think data centers is, is, is probably an obvious one.

Chris Skipper: Fantastic clip there from Killian. It also highlights how the work that the GSF is having is having an impact on the political landscape as well in terms of public policy and the discussions that are having in the higher ups of governments.

Moving on. We wanna talk about our final episode that we wanna highlight in this episode from the last year, and that's the episode, How to Explain Software to Normal People with James Martin. We ended the year with this episode with James, who talked about strategies for communicating digital sustainability to non-technical audiences, which is something that we try to do here at Environment Variables too. From Frugal AI to policy advocacy, this episode reinforced the power of inclusive storytelling. Let's listen to this clip from James Martin.

James Martin: A few years ago, the, the French Environment Minister said people should stop, uh, trying to send so many, uh, funny, funny email attachments, you know? Oh, really? Like, like when you send a joke, a jokey video to all your colleagues, you should stop doing that because it's, it's not good for the planet. It honestly, the, uh, minister could say something that misguided, because that's not.

We, you and I know that's not where the impact is. Um, the, the impact is in the cloud. The impact is in, uh, hardware. So instead of, it's about the, the, the communication is repetition and the, the, the, I always start with digital is 4% of global emissions. 1% of that is, is data centers. 3% of that is hardware and software is sort of.

They're sort of all over the place. So that's the, the, the thing I, that's the figure I use the most to get things started. And I think the, the number one misconception that people need to get their heads around is the people tend to think that tech is, uh, immaterial. It's because of expressions like the cloud.

It just sounds. Like, is this floaty thing rather than massive industry? Ethereal. We need to make it, we need to make it more physical. If, uh, I can't remember who said that if, if data centers could fly, then it would, it would make our, our job a lot, a lot easier. Um, but no, that, that's why you need to always come back to the figures.

4% is double, uh, the emissions of planes. And yet, um. The airline industry gets tens of hundreds times more hassle than the tech industry in terms of, uh, trying to keep control of their, of their emissions. So what you need is a lot more, uh, tangible examples and you need people to, to explain this impact over time.

So you need to move away from bad examples like. Funny email attachments or The thing about, um, the keep hearing in AI is, uh, one, one chat GBT prompt is 10 times more energy than Google. That may or may not be true, but it's a bit, again, it's a bit of the, it's the wrong example because it doesn't, it doesn't focus on the bigger picture and it can Yeah, it kind of implies, yeah, and it can make people, if I just, if I just like reduce my, my, my usage of this, then I'm gonna have like 10 times the impact I'm gonna.

You know, that's all only too, that feels a bit kind of individual in a bit like individualizing the problem. Surely it does, and, and it's putting it on people's, it's putting the onus on the users, whereas it's, once again, it, it's not their fault. You need to see the bigger picture. And this is what I've, I've been repeating since I wrote that, uh, that white paper actually, you can't say you have a green IT approach if you're only focusing on data centers, hardware or software.

You've got to focus. Arnold all three, otherwise. Yeah, exactly. Holistically

Chris Skipper: With that, we've come to the end of this episode. Well, what a year it's been for Environment Variables, and we'll just take a look at some of the statistics.

Just to blow our own horn here a little bit. We've reached over 350,000 plays. Engagement and followers to the podcast have gone up by 30%, which indicates to us that Environment Variables really matters to the people that listen to it. And it's raising awareness to the need to decarbonize the software industry.

Looking ahead. We remain committed to the foundation's vision of changing the culture of software development.

So sustainability is as fundamental as security or performance. Year four, we'll bring new stories, new tools, new opportunities, new people hopefully and all in an effort to reduce emissions together. So thank you for being part of our mission, and here's to another year of action advocacy and green software innovation.

And now to play us out is the new and improved Environment Variables podcast theme.

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]]> Open Source Carbon Footprints Open Source Carbon Footprints Thu, 12 Jun 2025 07:00:00 GMT 42:35 71vlxp61 false https://podcasts.castplus.fm/e/pnm526kn-open-source-carbon-footprints 68dc7d02597bc7d53fb83346 68dc7cef46a2532cdd8d1c23 111 Chris Adams is joined by Thibaud Colas; product lead at Torchbox, president of the Django Software Foundation, and lead on Wagtail CMS. They explore the role of open source projects in tackling digital carbon emissions and discuss Wagtail's pioneering carbon footprint reporting, sustainable default settings, and grid-aware website features, all enabled through initiatives like Google Summer of Code. Thibaud shares how transparency, contributor motivation, and clear governance can drive impactful sustainability efforts in web development, and why measuring and reducing emissions in the Python ecosystem matters now more than ever.

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TRANSCRIPT BELOW:

Thibaud Colas: If you get your contributors to work on high value and high impact things, that's the best way to motivate them. So that's kind of the idea here is, formalize that we have a goal to reduce our footprint. And by virtue of this, we, you know, make it a more impactful thing for people to work on by having those numbers, by communicating this specific change to images, here is the potential for it to reduce emissions.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

 Hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. If you want the way we build software to be more sustainable and more inclusive, one way to improve the chances of this happening is to make it easier to build it that way,

so building greener software goes with the grain of the software framework you're using. And one way to do that is update the defaults that prioritize accessibility and sustainability in the framework itself. One of the people I've seen who really exemplifies this idea and this approach is my guest today, Thibaud Colas,

a lead developer at the software agency, Torchbox, the current president of the Django Software Foundation and the product lead at the popular Wagtail Content Management System, which is also built on top of Django. The Wagtail CMS powers sites like the NASA Jet Propulsion Labs website, the University of Pennsylvania's website, the Tate Gallery, and even the main NHS website in the UK.

So while it might not have the same coverage as WordPress, which covers more than a third of the internet, still powers a large number of, a number of large sites, and changes made in this framework can have a decent reach. So changes made here are worth discussing because the Wagtail CMS docs, in my view, are probably the most advanced talking about sustainability for any open source CMS right now.

And there's a clear link between sustainability and embodied admissions of the hardware that you actually, that people need to use to access your websites too. And with that in mind, you can see it's got some of the most developed accessibility features as well. But we're getting ahead of ourselves though, and Thibaud is in a much better place to talk about this than me.

So Thibaud, thank you so much for joining us. Can I give you the floor to introduce yourself for our listeners?

Thibaud Colas: Hi. It's my pleasure, Chris. Thank you for having me. I'm Thibaud, my pronouns are he/him. And, yeah, I'm the product lead, for the Wagtail CMS at Torchbox. Wagtail is an open source project and products, and Torchbox, we are the original creators of the project and main contributors. And, yeah, as product lead I helped shape the work of Torchbox on Wagtail and of other contributors as well. And, as president of the Jingo Software Foundation, I have similar responsibilities for the Django Project. Django being a big Web framework, one of the biggest on Python. Just to give you a sense of scale, Wagtail, that's on the order of 10 to 20,000 sites out there. And Django, we're talking half a million to a million projects.

Chris Adams: Cool. Thank you, Thibaud. And, Thibaud, where are you calling me from today? Because, I,

Thibaud Colas: I'm in Cambridge, UK. got started on Wagtail way back in New Zealand,

but travels took me back to Europe and UK. I'm from France originally.

Chris Adams: Oh, cool. Alright, thank you for that. So I'm Chris Adams. I am the co-chair of the Green Software Foundation Policy Working Group. I'm also one of the, we're also, we also have show notes for this.

So all the projects and links that we discuss will be available. So in your quest to basically develop better sustain sustainable software engineering skills, that will all be available for this. So we look up podcast.greensoftware.foundation to find that. Alright, Thibaud, we've got a bunch of questions to get through.

Shall we start?

Thibaud Colas: Yeah, sure.

Chris Adams: Okay. Alright. So one thing that I, that really came up on my radar a few years ago when I saw this, was that Wagtail was one of the few, one of the only projects I've seen so far that actually tried to put together a kind of carbon footprint inventory of all over the websites that it's responsible for.

And I remember the posts and we'll share a link to this explaining some of this and some figures for this. Like "we reckon that Wagtail was kind of responsible for around like more than 8,000 tons of CO2 per year from all the sites that we run." Could you maybe talk a little bit about, basically the approach you took for that and why you even did that.

'Cause there's probably a few discussions about decisions you had to make and trade offs you had to choose between model uses and coming up with numbers and all that. But maybe we go from the beginning about why you started that. Then we can dive into some of the details.

Thibaud Colas: Yeah. Yeah. Well, simply enough, you know, when you start to think about the impact of technology on what we build, as developers, at least we love to try and quantify that impact. You know, put some figures on there. And the carbon footprint of websites, well, when you think of the sites, there are lots of components.

There's things that happen in the browser, things that happen server side. And when I say server side these days, you know, the infrastructure is quite varied and somewhat opaque as well. So yeah, server side. So when it comes to Wagtail, with it being an open source project, people are, it's quite interoperable with all sorts of databases and file storage and web browsers obviously. So it becomes quite tricky to

actually put a number on the emissions of one site. And I guess that's where we started at Torchbox specifically trying to quantify the emissions of our clients for 50 to a hundred websites. And from there, you know, you realize that, it makes lots of sense to try and do it for the whole white tail ecosystem so that you can make hopefully decisions for the whole ecosystem based on sites out there. So yeah.

I think it was back in 2023 that we did this first, and there were definitely lots of ways back then to quantify sites' emissions. We didn't necessarily reinvent any, but we tried and understand, okay, when we have little knowledge of those thousands of websites out there, which methodologies should we be referring to when we try and put those figures together? So I say specifically methodologies because I think that's one of the potential pitfalls for developers starting in there. They assume that, somewhat like performance, you can

have quite finite reproducible numbers, but we're just not there yet with the carbon footprint of websites.

So I think it's really important that you combine. So in our case, you combine web sustainability guidelines, related methodologies, so it's called sustainable web design model, and that you also combine things that look more closely at the servers, you know, CPU and resource use,

and also other aspects in the browser.

Chris Adams: Okay, cool. And one thing that I actually quite appreciated when you did this or when, the, you know, the team you are part of did this, was that you, yeah you shared all these numbers, but you also shared the underlying spreadsheets and the working so that other folks who might be running projects themselves can use as either a starting point or even possibly challenge and propose maybe improvements as we learn more about this because we know that

it's a difficult field to kind of navigate right now, but it is getting a bit easier, and as we learn more things, we are able to kind of incorporate that into the way we kind of model and think about some of the interventions we might make to maybe reduce the environmental footprint or improve it basically?

Thibaud Colas: Yeah. Yeah, it's a, you might actually be aware of a project, Chris, the HTTP Archive's Web Almanac. They reviewed the whole of the Web on the other of 20 million websites every year, and they produced numbers based on this data set of websites. So that's kind of, I suppose what I tried to follow with this methodology as well, of sharing our results to the fullest extent so that other people can verify the numbers and potentially also put same numbers together

for their own sites, individual sites, or also site ecosystem. So, you know, Wagtail, it's a CMS among many other CMSs. There's lots of competitors in that

space and nothing would make me happier than seeing other CMSs do the same and hopefully reuse

what we've spent time putting together. And yeah, obviously when we do this once for, Wagtail, we can try and do it also for Django.

So there's also these benefits of, across the whole tech stack, having that kind of methodology more nailed down for people who make those decisions. You know, like product level decisions.

Chris Adams: Oh, okay, cool. And just like we have release cycles for presumably new websites or like new CMSs and everything like that, as we learn more, we might be able to improve the precision and the accuracy of some of this to refine the assumptions, right. And, you know, many eyes make bugs shallow. So Drupal folks, if you're hearing this, or WordPress folks, yeah.

Over to you basically.

Thibaud Colas: Exactly. And you know, definitely the methodologies evolve over time. So one of the recent ones I really like is how, with Firefox browser, you can measure the CPU usage to render a single page.

And just that is becoming so much more accessible these days that we could potentially do it on every release of the CMS.

Chris Adams: Cool. Well, let's come back to that because this is one thing that I found quite interesting about the, some of the work that you folks have been doing is not only were you starting to measure this, but you're looking at actually options you can take to maybe set new defaults or improve some of this stuff.

And, as I understand it, Wagtail, you've had some luck actually finding some funding and finding ways to basically cover the cost of people to essentially work on this stuff via things like the Google Summer of Code and things like that. Maybe you could talk a little bit about some of that, because as I understand it, you're in year three of successfully saying, "Hey, this is important.

Can someone fund it?" And then actually getting some funding to pay people to work on this stuff.

Thibaud Colas: Yeah. Yeah. Well, yeah. So, once you have those numbers in place as to, you know, how much emissions the sites out there produce, try and refine it down to a few specific aspects of the sites that, you know, you go through

the quick wins, you figure out what you have the most leverage over, and then you realize there's this and that concepts that are potentially quite fundable if you

know just how to frame it and who to talk to. And we, as Torchbox, we have quite a few clients that care about the footprint of their websites,

but it's definitely also a good avenue. The Google Summer of Code program you mentioned, it's about getting new people excited with open source as new contributors in the open source space.

It's entirely funded by Google. And essentially Google, they trust projects like Wagtail and Django to come up with those ideas that are, you know, impactful, and also sensible avenues for people to get up to speed with open source. And so, yeah, we, it's been three years now that we've done this with a sustainability focus where we try every year to have an idea in this space.

And I think it's quite interesting as an option because, few people that come to open source, you know, early in their

career are aware of sustainability. It's quite a good, opportunity for them to learn something very new, even if they might already know the technology like Django and Wagtail. And for us, it allows us to work on those concepts that, you know, we saw the data, we saw the promise. So I think, the first year we did 2023, we looked at image optimization.

It's actually quite a big thing for a CMS, in my opinion at least, that, you know, people wanna add lovely visuals to all of their pages and you know, maybe sometimes there is a case for fewer images if you want to lower the footprints. But it's definitely also a case where you have to have images, you want them to be

as low footprint as possible. So for that specific project, we were joined by two contributors, who helped us. One worked on the AVIF support in the CMS. AVIF being one of the newer image formats that promises to have a lower file size

than the alternative. And the other one helped us essentially make, the APIs we have in Wagtail to generate multiple images, make that more ergonomic. So you'd be able to generate, say, three different variations of an image and then only send to the user the one that fits the best for how the image is displayed

so that hopefully it's smaller.

So it's this responsive images concept.

Chris Adams: Oh, I see. So you're basically are. It may be that the server needs to generate some of these images 'cause you don't have control over who's accessing your website. But when someone's accessing something with maybe a small, like a touch device or something, rather than send this massive thing over the wire, you can send something which is appropriately smaller.

So it might take up less space inside the memory and the DOM and less over the wire as well, right.

Thibaud Colas: Exactly. You were talking about the grain of Wagtail. Wagtail has very few opinions as far as how you create the pages, but we definitely try and leverage the grain of HTML, so this responsive images pattern is quite well put together in HTML and Web standards and, yeah, really happy with the results.

Honestly, I think for the specific trial sites we rolled it out, it was on the other of 30% lower page weight and, for the Wagtail web at large, like every year we see the improvements in those, audits about how much usage there is of modern image format, how much usage there is of responsive images.

We see the figures improve. So, really cool.

Chris Adams: Cool. We should actually share links to some of these things as well, actually. 'Cause one of the wonderful things about working with an open source project is you can say, well, if you want this to be a norm, then is the PR you could copy basically, right.

Thibaud Colas: Yeah. And something like AVIF support, I'm sure we'll talk about it at some points. Definitely. You know, we couldn't create the AVIF decoders and so on ourselves, so we've been relying on the Python ecosystem at large. And yeah. Now those things are in a place where lots of projects have those decoders where available.

Chris Adams: Cool. Are there any things, are there any other like, so that's, that was year one and this is year three and I think I can probably share with you is that, so we're a nonprofit organization. We publish a library called CO2.js. We've added, we've managed to get some funding from Google once for the Google Summer of Docs, not Google Summer of Code, where they actually funded us to make some of this library a bit easier for other people to use. And we found that quite helpful because that's been one of the ways people come to this for the first time is they use a library called CO2.js. And that wasn't something we could prioritize. So it's, kind of nice.

It just, it would be nice if there's more organizations funding this kind of work rather than just like one Web giant. Like it's nice that Google is doing this, but if you too work in a large tech company and you wanna actually fund this stuff or make it easier for your engineers to do this, then,

yeah, it's right there, folks. Okay. So maybe I can ask about some of the other years that you have running, like is there anything else that you'd like, like to talk about or draw people's attention to for 

some of the other ones?

Thibaud Colas: Google Summer of Code is a three month program, but lots of those things, to be honest, they keep chugging along in the background

for quite a while and making improvements. So, year two of this, we worked on the starter project for Wagtail. So a starter website where, just like as you mentioned earlier, the defaults, trying to make sure that it's easier for people to get a site up and running that has all of the right things in place to be low impact.

So that time, a contributor, Aman Pandey, helped us with the designs

as well as the coding of these templates. And, just from the get go, the idea was let's measure the designs even before they touch a Web browser. Let's make sure that we understand all of the, you know, newer standards, like the Web specific guidelines that those designs have that baked in so that when you generate the

sites, you are guaranteed better results. so this template, this project template's still in progress, but the designs at least are super promising. And year three, so year three,

starting as of this week, just to be clear, is grid awareness. 

So grid awareness is a big term. Essentially it means looking at ways that, as the website loads in, your browser, it'll be optimized based on the carbon intensity of your computer and your local grid electricity. So what that means is if it would take, produce lots of emissions for the site to load in your browser, we try and make the website optimized for the emissions to lower. And yeah, so our contributor for this, Rohit, he's been around the Wagtail community for a bit and has this interest in sustainability.

And again, I think a great example of something that will tangibly help us reduce the impact of Wagtail websites out there and also make more developers aware of those patterns and, you know, the underlying need for those patterns.

Chris Adams: I am glad you mentioned the names actually. 'cause, on the initial year, I was working closely, I was working with Aman Pandey and I think one of your colleagues might be working with Paarth. So, hi Paarth. Hi Aman. I hope you're listening. It's really nice to actually see this. 'Cause these were people who are, like you said, early career didn't get that much exposure, but honestly compared to like the industry, they're relative experts now. And that might say more about the state of the industry is right now, but is, this was something I actually found it quite nice working with someone who was relatively young, who was actually really keen and honestly worryingly productive, did make me worry a bit about my own job going forward.

But yeah, this was one thing that was, really cool from that actually.

Thibaud Colas: Paarth and Aman are two of the mentors working with me on this Wagtail websites

project this year. So this is also the other goal of this Google Summer of Code program is retaining those people in the open source world and, yeah, definitely, you know, we are at a point now where we have more and more of those people coming to open source with that realization. There's way more room for this to happen on other projects like Wagtail, but, baby steps. 

Chris Adams: Oh wow. I didn't realize that you actually had, there was a kind of program to kind of build like, I guess like invest in, provide some of that leadership so people who prioritize this are able to kind of have a bit more of influence inside that project, for example.

Thibaud Colas: Yeah, exactly. Well, you know, open source, we have, we have very different incentives compared to the corporates and, yeah, for profit world. So we don't necessarily have, super clear ways to retain people, but definitely people who are interested and have the drive, like we try and retain them by having them move from contributors first time to repeat maintainers, mentors and so on.

Chris Adams: Oh, cool. All right, so that is a nice segue to allow us to talk a little bit about, I guess, taking ownership of carbon emissions and like the strategies that you have there. Because, one thing we should add into this list is that there's actually a roadmap for Wagtail specifically.

I think it's, is RFC 90 or is there a particular term for like a request for comments or something that you folks use to kind of talk about governance and talk about what you prioritize in this? 

Thibaud Colas: It's a bit of a running joke. In Python they have the PEP

proposals, Python Enhancement Proposal, and in Django they have the DEPs. People have been wondering if Wagtail should have the WEPs,

but right now we just have RFC, requests for comments.

And Yeah. ,It's just a super, like, simple way for us to invite.

It's really rather than, you know, create those governance, or technical architecture decision. Go documents, in, private chats, put them in public, and then invite feedback from others. So, you know, we've had this RFC for, couple years now, I believe. I got some good support from one of the experts out there on open source governance, Lauri Apple.

She coached me through, you know, trying to build up community momentum and also trying to find ways to make this reusable again beyond Wagtail and yeah, so this RFC, like, if you're deep in this space, it's nothing super special. 

It's about building awareness, finding opportunities for fundraising,

working on the right concepts, but I think it's quite unique for open source projects to have that kind of clear, like direction for those things. Open source projects don't even necessarily have a roadmap of any kind to start with. And one on specific topics like this I think it's really important. I think there's something Lauri says often, which is

if you get your contributors to work on high value and high impact things, that's the best way to motivate them. So that's kind of the idea here is formalize that we have a goal to reduce our footprint. And by virtue of this, we, you know, make it a more impactful thing for people to work on

by having those numbers, by communicating this specific change to images, here is the potential for it to reduce emissions.

Chris Adams: Oh, I see. Okay. So I've just followed the link to the RFC that you have here, and there's actually quite a lot of stuff here. So I can see a link to the free green software for practitioners course for people who don't know that much about it, I can see that Wagtail itself has a sustainability statement.

So like this, these are our priorities. So there's some immediate kind of explicit statement that this is something you care about. And then as I understand it, there's some references to other things. So there's the prior work, with the GSOC, Google Summer of Code. There's references to the W3C Web sustainability guidelines and a bunch of stuff like that.

And there's few other. We'll show a link to this because I think it's actually a really good example for other people to be aware of or see, like, this is what a relatively large mature project does, and this is what it looks like when they start prioritizing this. Because, yeah, there are some, there are some organizations that are doing this quite well.

I know that there is a .NET based CMS that I've totally forgotten the name, Umbraco CMS, also have some quite strong, have also quite advanced in this. And they're another good example of this, but there's kind of, when you talk about, okay, prioritizing this and responsibility, there's a whole question about, okay, well,

whose job is it or who's responsible for this? Because you are building a piece of software and like you might not get that much control over who adopts the software, for example, like I think when you shared this breakdown, we saw like a, I think you mentioned there was one Vietnamese website, Vinmec, that was like making up like a third of the reported emissions. 

Thibaud Colas: Put me in touch.

Chris Adams: Yeah.

Thibaud Colas: Yes. So this is a very, with the caveat that carbon accounting

isn't my expertise, you know, in the corporate world, we have the very clear, greenhouse gas GHG protocol, and scope one, scope two, scope three standards. And in that corporate world, I think, there's this, I think scope three, category 11, use of 

Chris Adams: use of products. 

Thibaud Colas: The use of, it's worse than that. It's use of sold products.

Chris Adams: That's it. Yeah. Sold. Yeah.

Thibaud Colas: So if you're not a corporation, we're not a corporation, wagtail, we, have about 20 contributors on the

core team. And if you don't sell your product, which standards are you meant to be using, then, to decide essentially which, which emissions we should be reporting on?

So the disfigure of the carbon footprint of Watta on the order of five to 10 thousand tons a year. That's assuming, you know, we take some ownership for this usage of Wagtail and of the websites built with it. And it's actually, I think, quite tricky to navigate in the open source world.

Understanding, which standards of reporting are, helpful because, you know, in some respects, people who shop for a website builder or CMS or any tech really kind of expect specific standards to be met. You know, you mentioned having a sustainability statement. No one's expecting that just yet in the open source world, but we definitely want things to move that way. And we have to, you know, make sure that when we create those figures they are somewhat comparable to other projects. So, yeah, I guess for Wagtail, you know, there's the fact that you don't control who uses

it and you don't control how they use it, either. So, if someone wants to, you know, make a site that's partly big

and it's partly popular in some country, maybe

Chris Adams: Yeah. 

Thibaud Colas: adult entertainment websites

that don't have any. 

Chris Adams: Does PornHub go on WordPress' ledger? Right? is it on their accounts? Yeah.

Thibaud Colas: Exactly. We have a few like this in the Wagtail and Django world and, you know, technology, you know, it's open source license. We have no interest in taking any kind of control or having a more contractual relationship with those projects, but we still need to navigate how to account for their use essentially. 

What actually got me started on this, Chris,

I think it's worth I mentioned, is the work of Mozilla

and Mozilla Foundation. They were the first ones I saw, I think back in 2020 reporting the use of Firefox browser

as part of the emissions of Mozilla. And it was, I think it was 98% of the emissions of Firefox were like, sorry, the emissions of Mozilla came from Firefox.

And it just got me thinking, you know, for Wagtail and Django, obviously it's a similar type of scale.

Chris Adams: Also with Firefox, the browser, like you don't necessarily pay Firefox to use it, but you may be paying via the fact that your atten, you know, you kind of pay in your attention. And the fact that when you click on a search, an ad in Google, one of the search services, Firefox is being paid that way.

So you're not actually making a direct monetary, like you're not giving them money directly, but there is payment taking place and changing hands. And this is one thing that is actually quite difficult to figure out. Like, okay, how do you represent that stuff? Because like you said, it's not sold per se, or you're not paying in money, but you may be be paying in something else, for example.

And, it's almost like you know this, I mean it's, I guess it's a good thing that you do see some of these protocols for reporting being updated because they're not necessarily a good fit for how lots of like new business models on the internet actually work, for example.

Thibaud Colas: Yeah. And it's really important for us to get in this space as, open source technologists, I believe. Because I mentioned procurement. Definitely the expectations are rising in Europe, in particular in the EU, on the carbon impact of technology. And I think it's quite a good opportunity for open source.

You know, we have very high transparency standards for us to meet those requirements, not necessarily to lower the emissions dramatically, but at least be transparent on the impact of the software.

Chris Adams: Yeah, I mean, this is actually, you touched on quite an interesting thing, which is both a link to some of the Mozilla work, but also, in the kind of AI world, which is kind of adjacent to us as like webby people. There's, I know that Mozilla provided a bit of funding to Code Carbon, which is an open source Python library for people to understand the environmental footprint of AI training, and I think these days some inference as well via the kind of Energy Score AI,

a project that they have with hugging face, for example. So the, you know, one of the reasons you have that is because, oh God, I'm gonna murder the name. There's a French supercomputer, Jean Paul. Jean. Oh, do you know the one I'm talking 

Thibaud Colas: No, I don't actually. 

Chris Adams: Okay. So maybe the thing I'll do is I'll give you a chance to respind to this

while I look it up, but I do know that one of the reasons we have any numbers at all for the, environmental footprint of AI was because there was a, you know, publicly funded, supported supercomputer with some work by, some people at hugging face, I forget, the Bloom model. 

Thibaud Colas: Oh, The Bloom 

Chris Adams: yeah. Yeah, exactly. That, we have these numbers and there was a paper produced to actually highlight this.

Because the supercomputer, the people who are running the super supercomputer are able to share some of these numbers where it's, where traditionally we've had a real challenge getting some of these numbers back. So that's one place where having some open examples, at least give us something like a proxy in the face of like not quite deafening silence from groups like Open AI and Anthropic and stuff like that, but we're not seeing that much in the way of numbers. And given that we're seeing this massive build out, it's definitely something we are, I'm very glad. It's useful to have like open source organization, open source projects, and some, other ways of funding this to actually at least create some data for us to have a kind of data informed discussion about some of this.

Thibaud Colas: A hundred percent. Yeah. This Bloom large language model is, I think really, it's really essential for us for, to see this research being done because then when, you know, people talk about adding AI in a CMS or in their Django projects, we can point them to understanding like, you know, what the potential increase in the carbon footprint of the project is, and yeah. You know, in the AI world, there's this whole debate about what open source means for AI models.

Definitely it's not, there's lots of gray areas there, but if you wanna reuse their research, it's much easier if there's just a underlying philosophy of open source and open data in those organizations.

Chris Adams: Jean Zay, that's the name of the supercomputer in France, which has this, there's actually ones in Boston as well. There is one over there. And the, in the US NREL, the National Renewable Energy Labs folks, they did, they've shared a bunch of information about this as well when they've got access to this, and this is actually providing a bit of kind of light to a discussion, which is mo mostly about heat so far it seems. So that's actually quite kind of useful. You've just made me realize that later on this year, this might be one of the angles that we might see people talking about the use of AI for actually drilling for oil and gas and other kind of stuff which is not great for climate because, NE, which is a nationally, it's a state owned.

NE is a state owned energy company in Italy. They are one of the few people who actually have a publicly owned supercomputer. And because Italy is one of the countries that signed the Paris Agreement, there's currently a whole law court case about essentially suing NE to say, well, if you are state owned and, this is, and you've signed this, why the hell are you actually now using AI to drill for oil and gas, for example? And this might be one of the ways that we actually see some numbers coming out of this. 'Cause since 2019, we know that there are companies which are doing things with this.

But for example, we know that say companies like Microsoft are involved in helping use these tools to kind of get oil and gas and fossil fuels out this out of the ground. But there's not much visible, there's not much out there right now since the press release has stopped in 2019, and it feels like it's a real gap we have when we talk about sustainability and technology, and particularly AI, I suppose.

Thibaud Colas: Yeah, that's really interesting for us to consider for Wagtail as well because, you know, we talk about the carbon footprint of the websites, but it's also important to consider what the website might be enabling or, you know, in positives and negatives. And, yeah, even beyond websites, when I've tried to, you know, take my work from Wagtail to Django and even the Python ecosystem at large, with Python, you have to reckon with the footprint of web services built with Python, but also of all the data science that supports this same, you know, oil and gas industry. So it's tricky.

Chris Adams: Yeah, I mean, we've just saw, like we're speaking on the 4th of June and we had there two days ago, and, we've seen like massive drone attack wiping out like a third of Russia's bomber fleet. Right. And that was basically like some Arduino drone pilot software was one of the key pieces that was used inside that.

And it's not necessarily like the open source developers, they didn't build it for that. But we are now seeing all this stuff show up and like we haven't figured out ways to kind of talk about where the responsibility lies or how you even think about some of this stuff. Because yeah, this takes the like,

we might have words like dual use for talking about these kind of technologies, but in a world of open source, it becomes much, much harder to figure out where some of these boundaries lie and how you actually, well, I guess, set some norms. I mean, maybe this is actually one thing. Yeah, I'll leave you some space then I wanna ask you a little bit about, you mentioned the wider Python ecosystem,

'cause I know that's something you've actually been having some conversations with as well.

Thibaud Colas: Yeah, well, connecting the dots, you know, it's also the usage, but also as contributors, you have to consider that maybe there are only so many people in the Wagtail or Django world

that are responsible for how the tech is put together. So maybe in some sense you do share some kind of responsibility personally for the tech you produced out there, even though you don't control how people will ue it. Which is, you know, a whole dimension of how you or how much you take ownership of that. And yeah, in the Python world more widely, you know, Python is the most popular language out there. Even if it might not be the most performance, even if there might be simpler languages that help you get more optimized, lower emissions software, people are gonna use Python in all sorts of ways.

And some of them, you agree with it, some you don't. I think that's one of the, you know, realities of open source contribution you have to be aware of.

Chris Adams: You've actually said something quite interesting about, okay, yeah, there's a limited number of people inside the Wagtail community and you've been able to have some success in like helping set some norms or helping help kind of set some directions there. And there's maybe a slightly larger group, which is like in the Django land, like when with you in the kind of acting as a president now, I know there's some interest that you have there. And there's groups that I've been involved with, right. But you also mentioned that there's a kind of wider Python ecosystem there, people talking about this, I mean, is this, if someone is actually looking, let's say they're coding on Python, they wanna find out who else is doing this. Like, is there someone you'd look, you'd point people to?

Or are there any conversations you're aware of going on right now?

 

Thibaud Colas: The Python ecosystem is big.

So one of the big challenges to get started with is just putting enough people together to have those discussions. 

I have tried on the Python discussion forum. I think it's, "who's working on sustainability in Python?" is the thread I put together. And I guess, I think, to me, what's important at this point is just getting tech people, you know, aware of the fact that we have this climate change challenge and that they can do something about it. And then, you know, realizing that open source has a role to play and as open source contributors we can very much move the needle. So in the Python world, you know, it's being so big and the uses being so different, there are lots of ways to help by working on the performance of Python itself, but there are also lots of ways to help outside that. Even something as simple, you know, as the Python Software Foundation trying to quantify their own organization footprint or the footprint of a conference like PyCon US, that can go quite a long way, I think.

Chris Adams: Cool. Thank you for that. Actually, I'm really glad you mentioned PyCon US because there were a number of, talks that I heard other people on other podcasts talking about it. They were really pleased to see. So there, there seemed to be some latent interest there. And what we could do is we'll share a link to some movie videos that were up there, because yeah, I was pleasantly surprised to see them when I saw PyCon's videos come up on YouTube because,

wow, it came up really fast. Like there is, you know, really nice things about like design pressure and how to think about like your code. But yeah, there's a few people saying who are totally new to, like, I've been, you know, the existing green software field, there are people who seem to be quite new to it talking about this.

So that's, that's encouraging.

Thibaud Colas: Yeah. And in some ways, you know, AI, the whole negative impacts of AI, the whole like problem and kind of forms for our whole industry, but with, you know, LLMs being so costly to train, so, you know, energy intensive to train in some ways it also helps people understand better the implications. Yeah, exactly.

And just build up awareness. So I think what you're referring to at PyCon US is, the work on

Chris Adams: Yes. 

Yeah. Thank you.

Thibaud Colas: Yeah, Machine Learning in Python, quantifying the energy impact of that and LM specifically. And, yeah, people like him, you know, he's involved with Google Summer of Code for Django, so

definitely in the position. Yeah. Yeah. And, I think it's just a, it's a matter of, for us as open source people of, nurturing, you know,

those areas of expertise. Making sure we have those people having the conversations and, yeah, also sharing them in a wider sphere of the industry at large.

Chris Adams: And I suppose, I mean, one of the other things is that pretty much all the people you've mentioned who are going through the Summer of Code stuff, these are people who are in one of the regions which you're seeing like 50 degrees Celsius heat waves and stuff like that. It, there's kind of like moral weight that comes from someone talking about, they say, "Hey, I'm experiencing this stuff and I'm in an area which is very much exposed to this" in a way that if you are in some way, you are somewhat insulated to, from a lot of these problems, it doesn't, it might not carry the same weight actually.

Wow. Thank you. I hadn't realized that.

Thibaud Colas: I really liked this parallel that one of my colleagues at Torchbox put together about our work in accessibility

and the war in Ukraine, talking about other big topics, where, you know, practically speaking, there is a war, it's horrendous, people are getting maims

and they don't necessarily have the same life after.

And if you invest in accessibility, means being be better able to support. people who go through the conflict with major harm and, yeah, I think it's quite important for us in open source, you know, when I, when Lauri talks about high impact contributions, to hark back to those values you might have about helping others and realize the connection, even though, you know, there are quite a few layers between me, a human contributor, and a Wagtail website, we can have that impact.

Chris Adams: Well, I guess that's the whole point of the web, right? The Web is, this is for everyone like London Olympics, Tim, I mean, Tim Berners-Lee, his like massive thing. "This is for everyone" being a kind of, okay, we're getting a bit teary and a bit, get a bit carried away ourselves and we're running short on time, so I should probably kind of wrap this up before we go too, far down that rabbit hole. Thank you so much for coming on for this. As we wrap up, are there any projects or things that you would like to draw people's attention to that you found particularly exciting of late before we wrap up?

Thibaud Colas: Definitely. I'd recommend people check out this Grid-Aware websites work that the Green Web Foundation puts together. 

Chris Adams: I did not tell him to say that. Okay. Yeah. Thank you. 

Thibaud Colas: He did not. But you know, it is actually really impactful in my mind to put together multiple CMS partners through this project and, on a personal basis, this type of project,

I was really skeptical of the benefits at the beginning, and it's really interesting to get your thought process starting on, yeah, like tangible ways to move the needle on new sites, but also existing ones. So specifically the work we're doing for this project, Google Summer of Code. I think we'll have results to show for it in about a month's time and hopefully it's reusable for other people.

Chris Adams: Yeah, there's actually, okay, now that you mentioned this, I've just gotta touch on it. There is actually a grid aware SDK, which is currently out there, and you can think of Grid Aware as being very aware, kind of like quite comparable to carbon aware, basically, but with a few extra different nuances.

The thing I should probably share is that this is actually work that has had a degree of funding from SIDN, which is a 

Dutch foundation that has been trying to figure out what to do in like greening the internet. So there are pockets of interest if you know who to speak to. And hopefully we should see more of these things kind of bearing fruit over the coming months.

Alright. I don't wanna spend too much time talking about that, because we're coming up to time. Thibaud, thank you so much for giving us your attention and time and sharing your learnings about, both in the word of Django, Python and in Wagtail. If people are curious about what you're up to, where should people look?

Thibaud Colas: I had, simply enough I'd love for them to join yet another thing you didn't ask me to mention, which is the Climateaction.tech Slack. this is my favorite place to, you know, have this tight-knit community of tech people working on this stuff. And just DM me there. And I'll be very happy to answer any questions about any of this or just get you started with your own projects. For me, specifically, otherwise in the Wagtail world, the Wagtail Newsletter is a good place to have this work come to you on a weekly basis. And, yeah, just LinkedIn otherwise.

Chris Adams: Brilliant. Thank you so much for this. I hope you have a lovely day and yeah. Hopefully we'll cross paths again soon. All right. Take care of yourself.

Thibaud Colas: Pleasure, Chris.

Chris Adams: Cheers. Okay, bye. 

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]]> Chris Adams is joined by Thibaud Colas; product lead at Torchbox, president of the Django Software Foundation, and lead on Wagtail CMS. They explore the role of open source projects in tackling digital carbon emissions and discuss Wagtail's pioneering carbon footprint reporting, sustainable default settings, and grid-aware website features, all enabled through initiatives like Google Summer of Code. Thibaud shares how transparency, contributor motivation, and clear governance can drive impactful sustainability efforts in web development, and why measuring and reducing emissions in the Python ecosystem matters now more than ever.

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TRANSCRIPT BELOW:

Thibaud Colas: If you get your contributors to work on high value and high impact things, that's the best way to motivate them. So that's kind of the idea here is, formalize that we have a goal to reduce our footprint. And by virtue of this, we, you know, make it a more impactful thing for people to work on by having those numbers, by communicating this specific change to images, here is the potential for it to reduce emissions.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

 Hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. If you want the way we build software to be more sustainable and more inclusive, one way to improve the chances of this happening is to make it easier to build it that way,

so building greener software goes with the grain of the software framework you're using. And one way to do that is update the defaults that prioritize accessibility and sustainability in the framework itself. One of the people I've seen who really exemplifies this idea and this approach is my guest today, Thibaud Colas,

a lead developer at the software agency, Torchbox, the current president of the Django Software Foundation and the product lead at the popular Wagtail Content Management System, which is also built on top of Django. The Wagtail CMS powers sites like the NASA Jet Propulsion Labs website, the University of Pennsylvania's website, the Tate Gallery, and even the main NHS website in the UK.

So while it might not have the same coverage as WordPress, which covers more than a third of the internet, still powers a large number of, a number of large sites, and changes made in this framework can have a decent reach. So changes made here are worth discussing because the Wagtail CMS docs, in my view, are probably the most advanced talking about sustainability for any open source CMS right now.

And there's a clear link between sustainability and embodied admissions of the hardware that you actually, that people need to use to access your websites too. And with that in mind, you can see it's got some of the most developed accessibility features as well. But we're getting ahead of ourselves though, and Thibaud is in a much better place to talk about this than me.

So Thibaud, thank you so much for joining us. Can I give you the floor to introduce yourself for our listeners?

Thibaud Colas: Hi. It's my pleasure, Chris. Thank you for having me. I'm Thibaud, my pronouns are he/him. And, yeah, I'm the product lead, for the Wagtail CMS at Torchbox. Wagtail is an open source project and products, and Torchbox, we are the original creators of the project and main contributors. And, yeah, as product lead I helped shape the work of Torchbox on Wagtail and of other contributors as well. And, as president of the Jingo Software Foundation, I have similar responsibilities for the Django Project. Django being a big Web framework, one of the biggest on Python. Just to give you a sense of scale, Wagtail, that's on the order of 10 to 20,000 sites out there. And Django, we're talking half a million to a million projects.

Chris Adams: Cool. Thank you, Thibaud. And, Thibaud, where are you calling me from today? Because, I,

Thibaud Colas: I'm in Cambridge, UK. got started on Wagtail way back in New Zealand,

but travels took me back to Europe and UK. I'm from France originally.

Chris Adams: Oh, cool. Alright, thank you for that. So I'm Chris Adams. I am the co-chair of the Green Software Foundation Policy Working Group. I'm also one of the, we're also, we also have show notes for this.

So all the projects and links that we discuss will be available. So in your quest to basically develop better sustain sustainable software engineering skills, that will all be available for this. So we look up podcast.greensoftware.foundation to find that. Alright, Thibaud, we've got a bunch of questions to get through.

Shall we start?

Thibaud Colas: Yeah, sure.

Chris Adams: Okay. Alright. So one thing that I, that really came up on my radar a few years ago when I saw this, was that Wagtail was one of the few, one of the only projects I've seen so far that actually tried to put together a kind of carbon footprint inventory of all over the websites that it's responsible for.

And I remember the posts and we'll share a link to this explaining some of this and some figures for this. Like "we reckon that Wagtail was kind of responsible for around like more than 8,000 tons of CO2 per year from all the sites that we run." Could you maybe talk a little bit about, basically the approach you took for that and why you even did that.

'Cause there's probably a few discussions about decisions you had to make and trade offs you had to choose between model uses and coming up with numbers and all that. But maybe we go from the beginning about why you started that. Then we can dive into some of the details.

Thibaud Colas: Yeah. Yeah. Well, simply enough, you know, when you start to think about the impact of technology on what we build, as developers, at least we love to try and quantify that impact. You know, put some figures on there. And the carbon footprint of websites, well, when you think of the sites, there are lots of components.

There's things that happen in the browser, things that happen server side. And when I say server side these days, you know, the infrastructure is quite varied and somewhat opaque as well. So yeah, server side. So when it comes to Wagtail, with it being an open source project, people are, it's quite interoperable with all sorts of databases and file storage and web browsers obviously. So it becomes quite tricky to

actually put a number on the emissions of one site. And I guess that's where we started at Torchbox specifically trying to quantify the emissions of our clients for 50 to a hundred websites. And from there, you know, you realize that, it makes lots of sense to try and do it for the whole white tail ecosystem so that you can make hopefully decisions for the whole ecosystem based on sites out there. So yeah.

I think it was back in 2023 that we did this first, and there were definitely lots of ways back then to quantify sites' emissions. We didn't necessarily reinvent any, but we tried and understand, okay, when we have little knowledge of those thousands of websites out there, which methodologies should we be referring to when we try and put those figures together? So I say specifically methodologies because I think that's one of the potential pitfalls for developers starting in there. They assume that, somewhat like performance, you can

have quite finite reproducible numbers, but we're just not there yet with the carbon footprint of websites.

So I think it's really important that you combine. So in our case, you combine web sustainability guidelines, related methodologies, so it's called sustainable web design model, and that you also combine things that look more closely at the servers, you know, CPU and resource use,

and also other aspects in the browser.

Chris Adams: Okay, cool. And one thing that I actually quite appreciated when you did this or when, the, you know, the team you are part of did this, was that you, yeah you shared all these numbers, but you also shared the underlying spreadsheets and the working so that other folks who might be running projects themselves can use as either a starting point or even possibly challenge and propose maybe improvements as we learn more about this because we know that

it's a difficult field to kind of navigate right now, but it is getting a bit easier, and as we learn more things, we are able to kind of incorporate that into the way we kind of model and think about some of the interventions we might make to maybe reduce the environmental footprint or improve it basically?

Thibaud Colas: Yeah. Yeah, it's a, you might actually be aware of a project, Chris, the HTTP Archive's Web Almanac. They reviewed the whole of the Web on the other of 20 million websites every year, and they produced numbers based on this data set of websites. So that's kind of, I suppose what I tried to follow with this methodology as well, of sharing our results to the fullest extent so that other people can verify the numbers and potentially also put same numbers together

for their own sites, individual sites, or also site ecosystem. So, you know, Wagtail, it's a CMS among many other CMSs. There's lots of competitors in that

space and nothing would make me happier than seeing other CMSs do the same and hopefully reuse

what we've spent time putting together. And yeah, obviously when we do this once for, Wagtail, we can try and do it also for Django.

So there's also these benefits of, across the whole tech stack, having that kind of methodology more nailed down for people who make those decisions. You know, like product level decisions.

Chris Adams: Oh, okay, cool. And just like we have release cycles for presumably new websites or like new CMSs and everything like that, as we learn more, we might be able to improve the precision and the accuracy of some of this to refine the assumptions, right. And, you know, many eyes make bugs shallow. So Drupal folks, if you're hearing this, or WordPress folks, yeah.

Over to you basically.

Thibaud Colas: Exactly. And you know, definitely the methodologies evolve over time. So one of the recent ones I really like is how, with Firefox browser, you can measure the CPU usage to render a single page.

And just that is becoming so much more accessible these days that we could potentially do it on every release of the CMS.

Chris Adams: Cool. Well, let's come back to that because this is one thing that I found quite interesting about the, some of the work that you folks have been doing is not only were you starting to measure this, but you're looking at actually options you can take to maybe set new defaults or improve some of this stuff.

And, as I understand it, Wagtail, you've had some luck actually finding some funding and finding ways to basically cover the cost of people to essentially work on this stuff via things like the Google Summer of Code and things like that. Maybe you could talk a little bit about some of that, because as I understand it, you're in year three of successfully saying, "Hey, this is important.

Can someone fund it?" And then actually getting some funding to pay people to work on this stuff.

Thibaud Colas: Yeah. Yeah. Well, yeah. So, once you have those numbers in place as to, you know, how much emissions the sites out there produce, try and refine it down to a few specific aspects of the sites that, you know, you go through

the quick wins, you figure out what you have the most leverage over, and then you realize there's this and that concepts that are potentially quite fundable if you

know just how to frame it and who to talk to. And we, as Torchbox, we have quite a few clients that care about the footprint of their websites,

but it's definitely also a good avenue. The Google Summer of Code program you mentioned, it's about getting new people excited with open source as new contributors in the open source space.

It's entirely funded by Google. And essentially Google, they trust projects like Wagtail and Django to come up with those ideas that are, you know, impactful, and also sensible avenues for people to get up to speed with open source. And so, yeah, we, it's been three years now that we've done this with a sustainability focus where we try every year to have an idea in this space.

And I think it's quite interesting as an option because, few people that come to open source, you know, early in their

career are aware of sustainability. It's quite a good, opportunity for them to learn something very new, even if they might already know the technology like Django and Wagtail. And for us, it allows us to work on those concepts that, you know, we saw the data, we saw the promise. So I think, the first year we did 2023, we looked at image optimization.

It's actually quite a big thing for a CMS, in my opinion at least, that, you know, people wanna add lovely visuals to all of their pages and you know, maybe sometimes there is a case for fewer images if you want to lower the footprints. But it's definitely also a case where you have to have images, you want them to be

as low footprint as possible. So for that specific project, we were joined by two contributors, who helped us. One worked on the AVIF support in the CMS. AVIF being one of the newer image formats that promises to have a lower file size

than the alternative. And the other one helped us essentially make, the APIs we have in Wagtail to generate multiple images, make that more ergonomic. So you'd be able to generate, say, three different variations of an image and then only send to the user the one that fits the best for how the image is displayed

so that hopefully it's smaller.

So it's this responsive images concept.

Chris Adams: Oh, I see. So you're basically are. It may be that the server needs to generate some of these images 'cause you don't have control over who's accessing your website. But when someone's accessing something with maybe a small, like a touch device or something, rather than send this massive thing over the wire, you can send something which is appropriately smaller.

So it might take up less space inside the memory and the DOM and less over the wire as well, right.

Thibaud Colas: Exactly. You were talking about the grain of Wagtail. Wagtail has very few opinions as far as how you create the pages, but we definitely try and leverage the grain of HTML, so this responsive images pattern is quite well put together in HTML and Web standards and, yeah, really happy with the results.

Honestly, I think for the specific trial sites we rolled it out, it was on the other of 30% lower page weight and, for the Wagtail web at large, like every year we see the improvements in those, audits about how much usage there is of modern image format, how much usage there is of responsive images.

We see the figures improve. So, really cool.

Chris Adams: Cool. We should actually share links to some of these things as well, actually. 'Cause one of the wonderful things about working with an open source project is you can say, well, if you want this to be a norm, then is the PR you could copy basically, right.

Thibaud Colas: Yeah. And something like AVIF support, I'm sure we'll talk about it at some points. Definitely. You know, we couldn't create the AVIF decoders and so on ourselves, so we've been relying on the Python ecosystem at large. And yeah. Now those things are in a place where lots of projects have those decoders where available.

Chris Adams: Cool. Are there any things, are there any other like, so that's, that was year one and this is year three and I think I can probably share with you is that, so we're a nonprofit organization. We publish a library called CO2.js. We've added, we've managed to get some funding from Google once for the Google Summer of Docs, not Google Summer of Code, where they actually funded us to make some of this library a bit easier for other people to use. And we found that quite helpful because that's been one of the ways people come to this for the first time is they use a library called CO2.js. And that wasn't something we could prioritize. So it's, kind of nice.

It just, it would be nice if there's more organizations funding this kind of work rather than just like one Web giant. Like it's nice that Google is doing this, but if you too work in a large tech company and you wanna actually fund this stuff or make it easier for your engineers to do this, then,

yeah, it's right there, folks. Okay. So maybe I can ask about some of the other years that you have running, like is there anything else that you'd like, like to talk about or draw people's attention to for 

some of the other ones?

Thibaud Colas: Google Summer of Code is a three month program, but lots of those things, to be honest, they keep chugging along in the background

for quite a while and making improvements. So, year two of this, we worked on the starter project for Wagtail. So a starter website where, just like as you mentioned earlier, the defaults, trying to make sure that it's easier for people to get a site up and running that has all of the right things in place to be low impact.

So that time, a contributor, Aman Pandey, helped us with the designs

as well as the coding of these templates. And, just from the get go, the idea was let's measure the designs even before they touch a Web browser. Let's make sure that we understand all of the, you know, newer standards, like the Web specific guidelines that those designs have that baked in so that when you generate the

sites, you are guaranteed better results. so this template, this project template's still in progress, but the designs at least are super promising. And year three, so year three,

starting as of this week, just to be clear, is grid awareness. 

So grid awareness is a big term. Essentially it means looking at ways that, as the website loads in, your browser, it'll be optimized based on the carbon intensity of your computer and your local grid electricity. So what that means is if it would take, produce lots of emissions for the site to load in your browser, we try and make the website optimized for the emissions to lower. And yeah, so our contributor for this, Rohit, he's been around the Wagtail community for a bit and has this interest in sustainability.

And again, I think a great example of something that will tangibly help us reduce the impact of Wagtail websites out there and also make more developers aware of those patterns and, you know, the underlying need for those patterns.

Chris Adams: I am glad you mentioned the names actually. 'cause, on the initial year, I was working closely, I was working with Aman Pandey and I think one of your colleagues might be working with Paarth. So, hi Paarth. Hi Aman. I hope you're listening. It's really nice to actually see this. 'Cause these were people who are, like you said, early career didn't get that much exposure, but honestly compared to like the industry, they're relative experts now. And that might say more about the state of the industry is right now, but is, this was something I actually found it quite nice working with someone who was relatively young, who was actually really keen and honestly worryingly productive, did make me worry a bit about my own job going forward.

But yeah, this was one thing that was, really cool from that actually.

Thibaud Colas: Paarth and Aman are two of the mentors working with me on this Wagtail websites

project this year. So this is also the other goal of this Google Summer of Code program is retaining those people in the open source world and, yeah, definitely, you know, we are at a point now where we have more and more of those people coming to open source with that realization. There's way more room for this to happen on other projects like Wagtail, but, baby steps. 

Chris Adams: Oh wow. I didn't realize that you actually had, there was a kind of program to kind of build like, I guess like invest in, provide some of that leadership so people who prioritize this are able to kind of have a bit more of influence inside that project, for example.

Thibaud Colas: Yeah, exactly. Well, you know, open source, we have, we have very different incentives compared to the corporates and, yeah, for profit world. So we don't necessarily have, super clear ways to retain people, but definitely people who are interested and have the drive, like we try and retain them by having them move from contributors first time to repeat maintainers, mentors and so on.

Chris Adams: Oh, cool. All right, so that is a nice segue to allow us to talk a little bit about, I guess, taking ownership of carbon emissions and like the strategies that you have there. Because, one thing we should add into this list is that there's actually a roadmap for Wagtail specifically.

I think it's, is RFC 90 or is there a particular term for like a request for comments or something that you folks use to kind of talk about governance and talk about what you prioritize in this? 

Thibaud Colas: It's a bit of a running joke. In Python they have the PEP

proposals, Python Enhancement Proposal, and in Django they have the DEPs. People have been wondering if Wagtail should have the WEPs,

but right now we just have RFC, requests for comments.

And Yeah. ,It's just a super, like, simple way for us to invite.

It's really rather than, you know, create those governance, or technical architecture decision. Go documents, in, private chats, put them in public, and then invite feedback from others. So, you know, we've had this RFC for, couple years now, I believe. I got some good support from one of the experts out there on open source governance, Lauri Apple.

She coached me through, you know, trying to build up community momentum and also trying to find ways to make this reusable again beyond Wagtail and yeah, so this RFC, like, if you're deep in this space, it's nothing super special. 

It's about building awareness, finding opportunities for fundraising,

working on the right concepts, but I think it's quite unique for open source projects to have that kind of clear, like direction for those things. Open source projects don't even necessarily have a roadmap of any kind to start with. And one on specific topics like this I think it's really important. I think there's something Lauri says often, which is

if you get your contributors to work on high value and high impact things, that's the best way to motivate them. So that's kind of the idea here is formalize that we have a goal to reduce our footprint. And by virtue of this, we, you know, make it a more impactful thing for people to work on

by having those numbers, by communicating this specific change to images, here is the potential for it to reduce emissions.

Chris Adams: Oh, I see. Okay. So I've just followed the link to the RFC that you have here, and there's actually quite a lot of stuff here. So I can see a link to the free green software for practitioners course for people who don't know that much about it, I can see that Wagtail itself has a sustainability statement.

So like this, these are our priorities. So there's some immediate kind of explicit statement that this is something you care about. And then as I understand it, there's some references to other things. So there's the prior work, with the GSOC, Google Summer of Code. There's references to the W3C Web sustainability guidelines and a bunch of stuff like that.

And there's few other. We'll show a link to this because I think it's actually a really good example for other people to be aware of or see, like, this is what a relatively large mature project does, and this is what it looks like when they start prioritizing this. Because, yeah, there are some, there are some organizations that are doing this quite well.

I know that there is a .NET based CMS that I've totally forgotten the name, Umbraco CMS, also have some quite strong, have also quite advanced in this. And they're another good example of this, but there's kind of, when you talk about, okay, prioritizing this and responsibility, there's a whole question about, okay, well,

whose job is it or who's responsible for this? Because you are building a piece of software and like you might not get that much control over who adopts the software, for example, like I think when you shared this breakdown, we saw like a, I think you mentioned there was one Vietnamese website, Vinmec, that was like making up like a third of the reported emissions. 

Thibaud Colas: Put me in touch.

Chris Adams: Yeah.

Thibaud Colas: Yes. So this is a very, with the caveat that carbon accounting

isn't my expertise, you know, in the corporate world, we have the very clear, greenhouse gas GHG protocol, and scope one, scope two, scope three standards. And in that corporate world, I think, there's this, I think scope three, category 11, use of 

Chris Adams: use of products. 

Thibaud Colas: The use of, it's worse than that. It's use of sold products.

Chris Adams: That's it. Yeah. Sold. Yeah.

Thibaud Colas: So if you're not a corporation, we're not a corporation, wagtail, we, have about 20 contributors on the

core team. And if you don't sell your product, which standards are you meant to be using, then, to decide essentially which, which emissions we should be reporting on?

So the disfigure of the carbon footprint of Watta on the order of five to 10 thousand tons a year. That's assuming, you know, we take some ownership for this usage of Wagtail and of the websites built with it. And it's actually, I think, quite tricky to navigate in the open source world.

Understanding, which standards of reporting are, helpful because, you know, in some respects, people who shop for a website builder or CMS or any tech really kind of expect specific standards to be met. You know, you mentioned having a sustainability statement. No one's expecting that just yet in the open source world, but we definitely want things to move that way. And we have to, you know, make sure that when we create those figures they are somewhat comparable to other projects. So, yeah, I guess for Wagtail, you know, there's the fact that you don't control who uses

it and you don't control how they use it, either. So, if someone wants to, you know, make a site that's partly big

and it's partly popular in some country, maybe

Chris Adams: Yeah. 

Thibaud Colas: adult entertainment websites

that don't have any. 

Chris Adams: Does PornHub go on WordPress' ledger? Right? is it on their accounts? Yeah.

Thibaud Colas: Exactly. We have a few like this in the Wagtail and Django world and, you know, technology, you know, it's open source license. We have no interest in taking any kind of control or having a more contractual relationship with those projects, but we still need to navigate how to account for their use essentially. 

What actually got me started on this, Chris,

I think it's worth I mentioned, is the work of Mozilla

and Mozilla Foundation. They were the first ones I saw, I think back in 2020 reporting the use of Firefox browser

as part of the emissions of Mozilla. And it was, I think it was 98% of the emissions of Firefox were like, sorry, the emissions of Mozilla came from Firefox.

And it just got me thinking, you know, for Wagtail and Django, obviously it's a similar type of scale.

Chris Adams: Also with Firefox, the browser, like you don't necessarily pay Firefox to use it, but you may be paying via the fact that your atten, you know, you kind of pay in your attention. And the fact that when you click on a search, an ad in Google, one of the search services, Firefox is being paid that way.

So you're not actually making a direct monetary, like you're not giving them money directly, but there is payment taking place and changing hands. And this is one thing that is actually quite difficult to figure out. Like, okay, how do you represent that stuff? Because like you said, it's not sold per se, or you're not paying in money, but you may be be paying in something else, for example.

And, it's almost like you know this, I mean it's, I guess it's a good thing that you do see some of these protocols for reporting being updated because they're not necessarily a good fit for how lots of like new business models on the internet actually work, for example.

Thibaud Colas: Yeah. And it's really important for us to get in this space as, open source technologists, I believe. Because I mentioned procurement. Definitely the expectations are rising in Europe, in particular in the EU, on the carbon impact of technology. And I think it's quite a good opportunity for open source.

You know, we have very high transparency standards for us to meet those requirements, not necessarily to lower the emissions dramatically, but at least be transparent on the impact of the software.

Chris Adams: Yeah, I mean, this is actually, you touched on quite an interesting thing, which is both a link to some of the Mozilla work, but also, in the kind of AI world, which is kind of adjacent to us as like webby people. There's, I know that Mozilla provided a bit of funding to Code Carbon, which is an open source Python library for people to understand the environmental footprint of AI training, and I think these days some inference as well via the kind of Energy Score AI,

a project that they have with hugging face, for example. So the, you know, one of the reasons you have that is because, oh God, I'm gonna murder the name. There's a French supercomputer, Jean Paul. Jean. Oh, do you know the one I'm talking 

Thibaud Colas: No, I don't actually. 

Chris Adams: Okay. So maybe the thing I'll do is I'll give you a chance to respind to this

while I look it up, but I do know that one of the reasons we have any numbers at all for the, environmental footprint of AI was because there was a, you know, publicly funded, supported supercomputer with some work by, some people at hugging face, I forget, the Bloom model. 

Thibaud Colas: Oh, The Bloom 

Chris Adams: yeah. Yeah, exactly. That, we have these numbers and there was a paper produced to actually highlight this.

Because the supercomputer, the people who are running the super supercomputer are able to share some of these numbers where it's, where traditionally we've had a real challenge getting some of these numbers back. So that's one place where having some open examples, at least give us something like a proxy in the face of like not quite deafening silence from groups like Open AI and Anthropic and stuff like that, but we're not seeing that much in the way of numbers. And given that we're seeing this massive build out, it's definitely something we are, I'm very glad. It's useful to have like open source organization, open source projects, and some, other ways of funding this to actually at least create some data for us to have a kind of data informed discussion about some of this.

Thibaud Colas: A hundred percent. Yeah. This Bloom large language model is, I think really, it's really essential for us for, to see this research being done because then when, you know, people talk about adding AI in a CMS or in their Django projects, we can point them to understanding like, you know, what the potential increase in the carbon footprint of the project is, and yeah. You know, in the AI world, there's this whole debate about what open source means for AI models.

Definitely it's not, there's lots of gray areas there, but if you wanna reuse their research, it's much easier if there's just a underlying philosophy of open source and open data in those organizations.

Chris Adams: Jean Zay, that's the name of the supercomputer in France, which has this, there's actually ones in Boston as well. There is one over there. And the, in the US NREL, the National Renewable Energy Labs folks, they did, they've shared a bunch of information about this as well when they've got access to this, and this is actually providing a bit of kind of light to a discussion, which is mo mostly about heat so far it seems. So that's actually quite kind of useful. You've just made me realize that later on this year, this might be one of the angles that we might see people talking about the use of AI for actually drilling for oil and gas and other kind of stuff which is not great for climate because, NE, which is a nationally, it's a state owned.

NE is a state owned energy company in Italy. They are one of the few people who actually have a publicly owned supercomputer. And because Italy is one of the countries that signed the Paris Agreement, there's currently a whole law court case about essentially suing NE to say, well, if you are state owned and, this is, and you've signed this, why the hell are you actually now using AI to drill for oil and gas, for example? And this might be one of the ways that we actually see some numbers coming out of this. 'Cause since 2019, we know that there are companies which are doing things with this.

But for example, we know that say companies like Microsoft are involved in helping use these tools to kind of get oil and gas and fossil fuels out this out of the ground. But there's not much visible, there's not much out there right now since the press release has stopped in 2019, and it feels like it's a real gap we have when we talk about sustainability and technology, and particularly AI, I suppose.

Thibaud Colas: Yeah, that's really interesting for us to consider for Wagtail as well because, you know, we talk about the carbon footprint of the websites, but it's also important to consider what the website might be enabling or, you know, in positives and negatives. And, yeah, even beyond websites, when I've tried to, you know, take my work from Wagtail to Django and even the Python ecosystem at large, with Python, you have to reckon with the footprint of web services built with Python, but also of all the data science that supports this same, you know, oil and gas industry. So it's tricky.

Chris Adams: Yeah, I mean, we've just saw, like we're speaking on the 4th of June and we had there two days ago, and, we've seen like massive drone attack wiping out like a third of Russia's bomber fleet. Right. And that was basically like some Arduino drone pilot software was one of the key pieces that was used inside that.

And it's not necessarily like the open source developers, they didn't build it for that. But we are now seeing all this stuff show up and like we haven't figured out ways to kind of talk about where the responsibility lies or how you even think about some of this stuff. Because yeah, this takes the like,

we might have words like dual use for talking about these kind of technologies, but in a world of open source, it becomes much, much harder to figure out where some of these boundaries lie and how you actually, well, I guess, set some norms. I mean, maybe this is actually one thing. Yeah, I'll leave you some space then I wanna ask you a little bit about, you mentioned the wider Python ecosystem,

'cause I know that's something you've actually been having some conversations with as well.

Thibaud Colas: Yeah, well, connecting the dots, you know, it's also the usage, but also as contributors, you have to consider that maybe there are only so many people in the Wagtail or Django world

that are responsible for how the tech is put together. So maybe in some sense you do share some kind of responsibility personally for the tech you produced out there, even though you don't control how people will ue it. Which is, you know, a whole dimension of how you or how much you take ownership of that. And yeah, in the Python world more widely, you know, Python is the most popular language out there. Even if it might not be the most performance, even if there might be simpler languages that help you get more optimized, lower emissions software, people are gonna use Python in all sorts of ways.

And some of them, you agree with it, some you don't. I think that's one of the, you know, realities of open source contribution you have to be aware of.

Chris Adams: You've actually said something quite interesting about, okay, yeah, there's a limited number of people inside the Wagtail community and you've been able to have some success in like helping set some norms or helping help kind of set some directions there. And there's maybe a slightly larger group, which is like in the Django land, like when with you in the kind of acting as a president now, I know there's some interest that you have there. And there's groups that I've been involved with, right. But you also mentioned that there's a kind of wider Python ecosystem there, people talking about this, I mean, is this, if someone is actually looking, let's say they're coding on Python, they wanna find out who else is doing this. Like, is there someone you'd look, you'd point people to?

Or are there any conversations you're aware of going on right now?

 

Thibaud Colas: The Python ecosystem is big.

So one of the big challenges to get started with is just putting enough people together to have those discussions. 

I have tried on the Python discussion forum. I think it's, "who's working on sustainability in Python?" is the thread I put together. And I guess, I think, to me, what's important at this point is just getting tech people, you know, aware of the fact that we have this climate change challenge and that they can do something about it. And then, you know, realizing that open source has a role to play and as open source contributors we can very much move the needle. So in the Python world, you know, it's being so big and the uses being so different, there are lots of ways to help by working on the performance of Python itself, but there are also lots of ways to help outside that. Even something as simple, you know, as the Python Software Foundation trying to quantify their own organization footprint or the footprint of a conference like PyCon US, that can go quite a long way, I think.

Chris Adams: Cool. Thank you for that. Actually, I'm really glad you mentioned PyCon US because there were a number of, talks that I heard other people on other podcasts talking about it. They were really pleased to see. So there, there seemed to be some latent interest there. And what we could do is we'll share a link to some movie videos that were up there, because yeah, I was pleasantly surprised to see them when I saw PyCon's videos come up on YouTube because,

wow, it came up really fast. Like there is, you know, really nice things about like design pressure and how to think about like your code. But yeah, there's a few people saying who are totally new to, like, I've been, you know, the existing green software field, there are people who seem to be quite new to it talking about this.

So that's, that's encouraging.

Thibaud Colas: Yeah. And in some ways, you know, AI, the whole negative impacts of AI, the whole like problem and kind of forms for our whole industry, but with, you know, LLMs being so costly to train, so, you know, energy intensive to train in some ways it also helps people understand better the implications. Yeah, exactly.

And just build up awareness. So I think what you're referring to at PyCon US is, the work on

Chris Adams: Yes. 

Yeah. Thank you.

Thibaud Colas: Yeah, Machine Learning in Python, quantifying the energy impact of that and LM specifically. And, yeah, people like him, you know, he's involved with Google Summer of Code for Django, so

definitely in the position. Yeah. Yeah. And, I think it's just a, it's a matter of, for us as open source people of, nurturing, you know,

those areas of expertise. Making sure we have those people having the conversations and, yeah, also sharing them in a wider sphere of the industry at large.

Chris Adams: And I suppose, I mean, one of the other things is that pretty much all the people you've mentioned who are going through the Summer of Code stuff, these are people who are in one of the regions which you're seeing like 50 degrees Celsius heat waves and stuff like that. It, there's kind of like moral weight that comes from someone talking about, they say, "Hey, I'm experiencing this stuff and I'm in an area which is very much exposed to this" in a way that if you are in some way, you are somewhat insulated to, from a lot of these problems, it doesn't, it might not carry the same weight actually.

Wow. Thank you. I hadn't realized that.

Thibaud Colas: I really liked this parallel that one of my colleagues at Torchbox put together about our work in accessibility

and the war in Ukraine, talking about other big topics, where, you know, practically speaking, there is a war, it's horrendous, people are getting maims

and they don't necessarily have the same life after.

And if you invest in accessibility, means being be better able to support. people who go through the conflict with major harm and, yeah, I think it's quite important for us in open source, you know, when I, when Lauri talks about high impact contributions, to hark back to those values you might have about helping others and realize the connection, even though, you know, there are quite a few layers between me, a human contributor, and a Wagtail website, we can have that impact.

Chris Adams: Well, I guess that's the whole point of the web, right? The Web is, this is for everyone like London Olympics, Tim, I mean, Tim Berners-Lee, his like massive thing. "This is for everyone" being a kind of, okay, we're getting a bit teary and a bit, get a bit carried away ourselves and we're running short on time, so I should probably kind of wrap this up before we go too, far down that rabbit hole. Thank you so much for coming on for this. As we wrap up, are there any projects or things that you would like to draw people's attention to that you found particularly exciting of late before we wrap up?

Thibaud Colas: Definitely. I'd recommend people check out this Grid-Aware websites work that the Green Web Foundation puts together. 

Chris Adams: I did not tell him to say that. Okay. Yeah. Thank you. 

Thibaud Colas: He did not. But you know, it is actually really impactful in my mind to put together multiple CMS partners through this project and, on a personal basis, this type of project,

I was really skeptical of the benefits at the beginning, and it's really interesting to get your thought process starting on, yeah, like tangible ways to move the needle on new sites, but also existing ones. So specifically the work we're doing for this project, Google Summer of Code. I think we'll have results to show for it in about a month's time and hopefully it's reusable for other people.

Chris Adams: Yeah, there's actually, okay, now that you mentioned this, I've just gotta touch on it. There is actually a grid aware SDK, which is currently out there, and you can think of Grid Aware as being very aware, kind of like quite comparable to carbon aware, basically, but with a few extra different nuances.

The thing I should probably share is that this is actually work that has had a degree of funding from SIDN, which is a 

Dutch foundation that has been trying to figure out what to do in like greening the internet. So there are pockets of interest if you know who to speak to. And hopefully we should see more of these things kind of bearing fruit over the coming months.

Alright. I don't wanna spend too much time talking about that, because we're coming up to time. Thibaud, thank you so much for giving us your attention and time and sharing your learnings about, both in the word of Django, Python and in Wagtail. If people are curious about what you're up to, where should people look?

Thibaud Colas: I had, simply enough I'd love for them to join yet another thing you didn't ask me to mention, which is the Climateaction.tech Slack. this is my favorite place to, you know, have this tight-knit community of tech people working on this stuff. And just DM me there. And I'll be very happy to answer any questions about any of this or just get you started with your own projects. For me, specifically, otherwise in the Wagtail world, the Wagtail Newsletter is a good place to have this work come to you on a weekly basis. And, yeah, just LinkedIn otherwise.

Chris Adams: Brilliant. Thank you so much for this. I hope you have a lovely day and yeah. Hopefully we'll cross paths again soon. All right. Take care of yourself.

Thibaud Colas: Pleasure, Chris.

Chris Adams: Cheers. Okay, bye. 

 Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Cloud Native Attitude Cloud Native Attitude Thu, 05 Jun 2025 07:00:00 GMT 52:07 x06l7830 false https://podcasts.castplus.fm/e/mn4x035n-cloud-native-attitude 68dc7d0809b1c365e49ab31d 68dc7cef46a2532cdd8d1c23 Environment Variables host Anne Currie welcomes Jamie Dobson, co-founder of Container Solutions and author of the upcoming book Visionaries, Rebels and Machines. Together, they explore the history and future of cloud computing through the lens of sustainability, efficiency, and resilience. Drawing on insights from their past work, including The Cloud Native Attitude and Building Green Software, they discuss how cloud-native principles can support the transition to renewable energy, the potential and pitfalls of AI, and why behavioral change, regulation, and smart incentives are key to a greener digital future. 110 Environment Variables host Anne Currie welcomes Jamie Dobson, co-founder of Container Solutions and author of the upcoming book Visionaries, Rebels and Machines. Together, they explore the history and future of cloud computing through the lens of sustainability, efficiency, and resilience. Drawing on insights from their past work, including The Cloud Native Attitude and Building Green Software, they discuss how cloud-native principles can support the transition to renewable energy, the potential and pitfalls of AI, and why behavioral change, regulation, and smart incentives are key to a greener digital future.

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TRANSCRIPT BELOW:


Jamie Dobson: We're loaded up all these data centers, we're increasing data sets, but ultimately no matter how much compute and data you throw at an artificial neural network, I think it would never fully replace what a human does. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello and welcome to Environment Variables Podcast, where we give you the latest news and updates from the world of sustainable software development. So this week I am your guest host Anne Currie. And you don't have the dulcet tones of Chris Adams, you're left with me this week. So we're gonna do something a little bit different this week.

I have got an old friend and colleague and co-author, Jamie Dobson in to talk about it. So Jamie is the co-founder and CEO of a company called Container Solutions. And he's the author of the soon to be released book; Visionaries, Rebels and Machines, which I've read, and that's what we'll be talking a lot about.

And he's also the, one of my co-authors of a book I wrote nearly 10 years ago called the Cloud Native Attitude, which is about the principles of moving into the cloud. And there's an awful lot in there about sustainability with that, there's a lot we need to talk about around that. And it was actually for me, the precursor to the book that I wrote which came out with O'Reilly last year, with co-authors Sarah Hsu and Sara Bergman, Building Green Software, which as I always say every week,

everybody listening to this podcast should read because you'll find it very interesting and it is couldn't be more germane. So today we're gonna talk about those three books, really, and the thematic links between them all, which are really about resource efficiency, building at scale without it costing a ridiculous amount of money or using a ridiculous amount of resources.

And also resilience, which is something we're gonna really have to focus on when it comes to running on renewable power. So, let me let Jamie introduce himself and maybe tell us a little bit about his new book, Visionaries, Rebels, I can never remember whether it's Rebels, visionaries and Machines.

It's Visionaries, Rebels and Machines. Go for it, Jamie.

Jamie Dobson: Visionaries, Rebels and Machines. That's correct. Hello Anne. Thanks for having me on the podcast. And hello to all your listeners. who tune in every week? Yeah. So my name is Jamie. I am indeed the co-founder of a company called Container Solutions. But it's no longer, I'm no longer, I should say, the chief exec,

'cause I handed that role over about a year ago, which is probably why, or, you know, it explains why I could find the time to finish writing this damn book. So Container Solutions is a company that specializes in cloud transformation, helping customers, you know, get off whatever infrastructure they're running on now and get onto, you know, efficient cloud infrastructure.

And if we do that right, then it's kind of green and sustainable infrastructure, but it's hard to get right, which I'm sure we're gonna discuss today. 

Anne Currie: Indeed. Yes. Yes. So, so you've got a book that's about to come out, which I have read, but it's not yet available in, the, in the stores, but it will be available on, in all good book bookstores, Visionaries, Rebels and Machines. And I, the reason why I asked you to come on is because I think there are a lot of ideas in there that would, that we need to be talking about and thinking about.

So, so tell us a little bit about Visionaries, Rebels and Machines, and then I'll tell you why I think it's interesting.

Jamie Dobson: Absolutely. Yeah. So, so Visionaries, Rebels and Machines, we have to start at a point in time. And that point in time is about four or five years ago. And I was asked the question, "what's the cloud?" It was, the person asking me, it was a junior colleague, new to Container Solutions. And, you know, I started to answer, or at least I opened my mouth,

and of course I can answer that question, but I can't answer it necessarily succinctly. So I was asked the question, I think probably around about June, so maybe about five years ago today actually. And over the summer period I was thinking, "God, how do you answer that question? What is the cloud?" And so I started to creep backwards in time.

Well, the cloud is, you know, there's a bunch of computers in a warehouse somewhere. But what's a computer? And then once I asked that question. Well, computers are things made up of transistor. Well, what's a transistor? And what I came to the conclusion over the summer, was the following:

The cloud can only really be understood in its own historical context. And so interestingly, once we got to the point of, you know, answering the question, what is the cloud? The arrow was already flying. You know, there was a, an arrow was shot round about the late Victorian time at Thomas Edison's Menlo Park facility in New Jersey, and that arrow flew all the way through the last century through the web, through cloud computing, and it continues to fly with the rise of artificial intelligence. And so the last part of the book is, okay, now we know what the cloud is and what it does, where might it take us next in regards to artificial neural networks and all of that stuff? So that was the book. The Visionaries and the Rebels of the people who built teams, built teams that were innovative. All of them had psychological safety even though the, that concept wasn't known at the time. And so, these historical figures are not just ancient history, like not just Thomas Edison, but also the Jeff Bezos's of the world, the Reed Hastings's, and the modern figures of cloud computing. The visionaries and the rebels can teach the rest of us what to do with our machines, including how to make 'em sustainable. 

Anne Currie: And that is the interesting thing there. So I enjoyed the book. It's, it is quite, it is a readable romp. And I very much connect with your, with your initial motivation of trying to explain something that sounds simple, but actually you realize, oh gosh, I'm gonna have to write an entire book to even get my own head around this rather than, you know, 'cause that was true for, well, when we wrote, it's actually a, Cloud Native Attitude, which was the book that we wrote together started off 10 years ago, was pretty much for the same, it was kicked off in the same way. We were, we were saying, well, what is cloud native? What, what are people doing it for, and why are they doing it this way? And quite often, and Building Green Software,

the O'Reilly book, which is really germane to this, to this podcast, was again, the same thing. It's what is, what does the future look like for systems to be sustainable? How do we align, and make, what is the future gonna look like? And, where, and that's always seated in the past. What has been successful?

How did we get here? 

Jamie Dobson: Absolutely. So you can't move into the future unless you understand your past. And I think the similarities between the Cloud Native Attitude and Visionaries and Rebels is the tone. So my book deals with horrible things, child poverty, exploitation of people, and the truth is that a reader will put up with that for maybe one paragraph.

So if you want to, if you want to teach computing and how it can enslave the human race or not, or how it can liberate them and touch all of these really difficult themes, you've got to do it in a pretty lighthearted manner. And the reason people are saying, "oh, it's a page turner. it's entertaining, it's a bit of a rump,"

it's because we focus on the characters and all the things that happens to them. And I think that started with a cloud-native attitude because unless you can speak quite lightheartedly, you so quickly get bogged down in concepts that even for people like us who work in computing and are passionate about computing, it's just extremely boring. And there are some fantastic books out there right now about artificial intelligence, but they're so dry that the message fails to land. And I think I was trying to avoid that. 

Anne Currie: And you know for, 'cause we wrote Cloud Native Attitude together. But it is, if these, books are ideally a form of leadership. When you write a book, you are either, you are kind of saying, look, this is what I want to happen in the future.

You're trying to lead people and explain and reason and inspire. But you have to inspire. If it's boring, you're not gonna lead anyone. No one wants to follow you to the boring location they want to follow you to the exciting location.

Jamie Dobson: No. Exactly. And I think the problem is computer people, most of us have been to university, so we're on the academic path. And what happens is you forget to tell stories. So everything becomes about what the research says, "research indicates." So it's all exposition and no narrative. And the problem that is people switch off very quickly, and the paradox is that you don't make your point because you've bored your reader to death.

Anne Currie: Yeah. And this is something that's, that comes up for me over and over again in the green software movement that we quite often, we tell the story of it's being, everything being very sad. And everybody goes, "well, I don't wanna be there in that sad world." And, but it's not a sad story. I mean, it is like climate change is a really sad story.

It's terrible. It's something we need to avoid. We're running away from something, but we're also running towards something. Because there's something amazing here, which is renewables are so cheap. If we can build systems that run on solar and wind, and a little bit of storage, but not, but much less storage than we currently expect,

then we have a world in which there's really loads more power. We can do so much more than we do now, and it's just a matter of choosing what we do with it. It is a, we are not just running away from something. We're running towards something, which is amazing. And, so yeah, we tried to keep that tone.

And Building Green Software is designed to be funny. You are. It's the only O'Reilly book. One of, one of my reviewers says it's the only O'Reilly book where you actually get, you laugh out loud whilst reading it. You could read it on the beach.

Jamie Dobson: This is exactly why we created a conference at Container Solutions called WTF. What The F is Cloud Native? And it's basically because if you cannot entertain, you'll never get your message across. I've got a question for you, Anne, this wonderful future that we're heading towards, I see it as well. But 

in the research for visionaries and rebels, there was a big chapter I had on Henry Ford, and in the end it didn't, quite make it into the book, but basically, once Edison had created electricity, then all of a sudden you had elevators for the first time. So the New York landscape did not become a thing till we had electricity because there was a limit on how big the buildings could be. And that exact moment Henry Ford came in with the motorcar, and he was so successful in getting it off the production line cheaply, the beautiful boulevards of New York, of American cities, New York, St Louis, and places like that ended because basically people said, "well, we don't need to be in the city.

We can drive to the suburbs." And a lot of historians were saying if Henry Ford had just gone a bit slower, we would've adapted to the motor car quicker and therefore the cities of today would look very different. And one of my concerns with green software is,

the speed of which we're moving with data centers and AI is so quick.

I wonder if we're having another motor car moment. the future's within grasp, but if we go too quick, might we screw it up on the way?

Anne Currie: So I think what you are circling around here is the idea of, it is something that comes up quite often, which is Jevons Paradox, which is the idea that, as you get better at using something, you use more of it, it becomes cheaper, because actually because there's untapped demand.

So where there's, where people are going, "gee, you know, I really want to live in a high rise city because then naturally everybody can live together and it will be vastly better for us and we'll prefer it. And therefore we take more elevators and we go up because we've got elevators."

And people really want cars. I mean, it's one of the things, I don't drive. but everybody loves to drive. There's no point in, tying green with like nobody driving because they love to drive. And there was untapped demand for it, and therefore it was met. And remember at the time there was really, but back then we didn't consider there to be any problem with using more petrol. We didn't consider there to be any problem with using fossil fuels. And everybody went, "yeah, hooray! Let's use more and more of it."

But it did massively improve our quality of life. So I think all green messages we have to say, well, we want the improvement in quality of life, but we also want a planet and we have to optimize both of those in parallel.

We can't say that you're trading off. And this, I know that people have a tendency to look down on efficiency improvements, but efficiency improvements are what has driven humanity up until now. And efficiency improvements are so much more powerful than we think. We just don't understand how much more efficient things can get.

Jamie Dobson: Yeah. 

Anne Currie: And therefore we go, oh, well, you know, we, if people have 10 times as many cars or whatever, probably not 10 times as many. Well, compared to back to Henry Ford's days, we've got a lot more cars. We've got a lot more mobility. There is a almost seemingly limitless, demand for cars. But there are plenty of other areas of life where efficiency has outstripped the demand.

So in terms of electricity use, household electricity use in the west in the past 20, 30 years, household electricity use, despite the fact that everybody has automated their houses we've got, everybody's got washing machines and dishwashers and tumble dryers and TVs, and electricity use has still gone down.

And the reason why it's gone down is because all of those devices appeared, but then became more and more efficient. And efficiency improvements really are extraordinarily powerful. Much more than people realize. And if we force people to put the work in, and it's not free, it requires an enormous amount of work, but if people are motivated and incentivized to make those efficiency improvements, we can do an awful lot.

We can get.

Jamie Dobson: My suspicion is the world will change. So not many people realize that the car was actually very good for the environment. All around London, my children ask me, what's that thing outside the house?" It's a scraper for your feet, for your boots. And that's because all the streets of London were caked two inch shit deep of horse manure.

And at the end of every single street, the way it was piled high. So the public health issues with horses was an absolute nightmare. Not to mention the fact that people used to get kicked in the head or pulled into ditches. Fatalities from horses was, you know, a weekly account in New York City. But so it changed. So once we got the electricity, we got the lifts, the horses went away.

 My suspicion is right now we cannot run a sustainable culture or city without radically changing things.

 So, for example, did you ever stop to wonder why is your power pack warm? You know, when you charge your phone or your laptop, why does it get warm? Do you know what the answer to that question is?

Anne Currie: No, I don't actually. That's a very good question.

Jamie Dobson: There you go. So who won? Who won the battle? Tesla or Edison. So.

Anne Currie: Tesla. 

Jamie Dobson: Tesla did win. So it's basically AC versus DC. What's the best system to have? Well, DC, direct current kills you if you touch it direct current by accident and the voltage is right, you die. But what you feel on the back of your charger is heat, which is a side effect of converting AC back to DC because computer devices don't work on AC because it, the current has to go round and round, like water, in a fountain because that's the only way transistorized things work. So now people are saying, well, actually, arguably we should have a DC grid because globally we are wasting so much electricity because of this excess heat that is produced when we go from AC back to DC. So, and I get the feeling, and do you remember when we were kids, if you put your washing on at three in the morning, you got cheaper electricity.

I cannot help but think it's not just about renewable energy, but it's also the way we consume energy to make that more effective.

Anne Currie: Yeah.

Jamie Dobson: And I think if that doesn't change, I basically think, when Edison arrived, society as we knew it absolutely changed. We had no refrigerators and that changed our behaviors.

Now, some people would say, well, you became a slave to the machine. I think that's a little bit too far, but we certainly went into some sort of analog digital relationship with the machines we work, all of which drive efficiencies. I think the next chapter for sustainable energy and computing will be a change in our habits, but I can't, I don't know exactly what they're gonna be.

Anne Currie: Oh, that's definitely a thing. It's something I've talked about on the podcast before. It's the mind shift from fossil fuels, which are kind of always on, you know, easy to dispatch, so easy to turn on, easy to turn off to something, to solar and wind, which is really expensive to store,

really cheap if you use it as it is generated. But grids were designed, in many ways this is the same kind of things that you talk about in your book. Grids were originally designed specifically to provide power that was easily dispatchable, you know, that it was fossil fuels.

And that means that the whole of the philosophy of the grid is about something called supply side response. And that is all that is basically saying, "do you know, users, you don't need to worry." Flick of a switch, the electricity will always be there and it's the responsibility of the dev, of the providers of the electricity, of the grids to make sure that the electricity is always there to meet your demand.

You never have to think about it. But for renewables it's generally agreed that what we're gonna have to do is move to something called demand side response, where users are incentivized to change their use to match when the sun is shining and the wind is blowing. As you say, when we were kids in the UK, we used to have something called economy seven.

You had seven hours a day, which was usually at night. where, because it was all, because back in then, I'm guessing, 'cause it was a coal fired power station. Coal fired power stations were not so easy to turn off and on again, which gas is. So we don't have it anymore. And it's, and, but in those days you say the coal fired power station was running during the night and nobody was using the power.

So we wanted to actually get people to try and use the power during the night. And we used effectively what are now called time of use tariffs to incentivize people to use spare power, which was during the night in the UK. 

Jamie Dobson: It sounds like a huge dislocation to life, but when I first came to London, the London Mayor or the authorities made an announcement that when something like this, "oh, air pollution's really bad today. Don't go out running, close your windows. Old people don't go out, don't do any exercise."

And I remember thinking "this can't be real. Is this some sort of prank?" But this is a thing in London. And I remember thinking, but at no point would the Mayor of London say, "okay, the air pollution's bad. You're not allowed to drive your car today," right? And it showed where the priorities lie. But it wasn't that difficult.

So everybody just shrugs their shoulders and says, "oh, well, okay, I just won't do any out outdoor activities today." So I think that demand side response is possible. I do wonder what happens though if, let's say, obviously the sun's shining, so that's the time you should run your data centers. What happens when the sun's not shining?

Are the cloud providers gonna be happy to have an asset sat there doing nothing when it's dark, for example, or when the wind's not blowing?

Anne Currie: Well, it's interesting. I think it depends how much, if, it's all about what is the level of difference in electricity cost between the time when the sun is shining or the wind's blowing, and the time when it isn't. I'm massively impressed by work that India is doing at the moment on this, on time of use tariffs because they have tons of, or and they know what they're looking forward, they already know they're one of the fastest growing. So India is one of the fastest growing countries in the world for rolling out solar power. Unsurprisingly, 'cause it's pretty sunny in India. So they're looking forward and they're thinking, well, hang on a minute.

You know, we are gonna have this amazing amount of solar power in the future, but we are going to have to change people's behaviors to make sure that they run on it, not the other thing. So, the way they're doing that is that the strategy that they're adopting for incentivizing people to change their behavior.

And as you say, actually people will change behavior. They just need a little bit of a push and some incentives and they will change their behavior. The incentive they're using is time of use tariffs. And India is pushing out all of the province, the states in India to introduce time of use tariffs which reflect the actual cost of electricity and push people towards times of the day when they're, when they'll be. And it's, it is a gradual process, but you can see that it will roll on and on and they're, looking at a tenfold difference that what they're saying is. That the difference should be tenfold between when your electricity is generated from the sun and when it isn't.

And a tenfold difference in price does justify a lot of behavioral change. You might as say, you might not want to turn off your, your data center during the night. But some people will go, well, hang on a minute. If it's literally, because for most data centers, the main cost is electricity. If there's a tenfold difference in electricity cost between, the day and the night, then they'll start to adapt and start to do less and start to turn things down.

Necessity is the mother of invention. If you don't, if you give a flat tariffs to everybody, they're not gonna make any changes. But if you start to actually incentivize response, demand side response, it will happen.

Jamie Dobson: Then of course then that comes back to regulation, doesn't it? Because I think of the things that Edison, well actually it was his colleague, Samuel Insull, realized if you're gonna, it makes no sense to run the grid unless it's some sort of public utility or a natural monopoly. And you can only really fairly run a natural monopoly if the price is a negotiated and set in public and all the industries regulated. So do you think the, that these tariffs, the time of use tariffs, will become part of the regulatory framework of the governments.

Anne Currie: Oh, yeah, I mean, it already is. I was saying it's, in India. It's a regulatory thing. It is part of the industrial strategy of India. There are.

Jamie Dobson: Then indirectly, then indirectly the cloud providers will be regulated because they'll be regulated through the supply of electricity.

Anne Currie: Indeed. Yeah. I mean it's interesting. there's a battle in, so some European countries, it's happening at the moment. I think, Spain already has time of use tariffs. There are other countries that have time of use tariffs and it changes behavior. And in the UK there is a battle at the moment, over, between suppliers about where the time of use tariffs are introduced.

So that battle is kind of being spearheaded by the CEO of in the UK it's Octopus Energy. Greg Jackson isn't it, I think is really saying, "look, this is what we need to do." Because, I mean, in the UK it is ridiculous that the government really doesn't want, they fear that everybody will be panicked and not be able to handle a time of use tariff.

And, but even though we used to have them not very long ago.

Jamie Dobson: It's ridiculous. People always panic about the public sentiment, but you just need to look at COVID, how flexible people can be when they understand the need for it. That's number one. And number two, when I was a kid, and that's only 40 years ago, we used to tend to lights off 'cause it was too expensive.

So we did have different behavior in the evening when we needed more electricity than in the daytime when we didn't. It's not that difficult to imagine. You know what? Do you know? What made me laugh is the average serving of meat, I think in the 1970s was 200 grams. And if you look at 200 grams, it's actually quite tiny.

It sits on your plate like a little slither of lamb. I was like, "oh my God, that's not enough. That's not enough food." But then you realize that is what we all used to eat, only 30 or 40 years ago. And so we've slowly been sort of, you know, everything's been supersized, including what we expect from the electricity companies. I think a gradual shifting back, you would, you'd barely notice it. And that's exactly how the government took salt out of our diet. That just slowly regulated how much salt could be in processed food until it had gone all together.

Anne Currie: Yeah, but I think you have to be careful about how you pitch this. Well, I think one of the issues with green is that it's pitched as, it's a reduction in meat and it's a reduction in, there's a reduction in that. I don't think it only has to be a sad story. It has to be a good story.

Something we're, a hill that we're, that we want to take because it's worth taking, not just something that we're, we are running away from that. I like the time of use tariff approach in India because it's saying, if you do this, you'll get electricity, which is a 10th the price, you know that it is something, it's a win.

It's not just like run away from the bad thing. It is run towards the good thing. And it with a minor, and you're not saying, "change your behavior because we're ordering you to do it" or because we're going to make electricity much more expensive. Although inevitably, electricity, fossil fuel, electricity will become more expensive because it is naturally more expensive these days.

Renewables have become so cheap. 

Jamie Dobson: Could cloud computing become a forcing function for cheaper electricity? Because the cloud providers need so much electricity, could this possibly accelerate the sort of the raise to green energy? 

Anne Currie: Well, it definitely can, and it has done in the past. I mean, it in, the early days, the well, so until maybe five years ago or so, the biggest non-governmental purchaser of renewable power in the world was Google. And they were buying renewable power, they were buying and, bankrolling renewable power for their data centers.

And they, so they're not the biggest, non-governmental purchaser of renewables anymore because it is now amazon to power their data centers because they got a long way behind and we all made a giant fuss about it and said, well, why aren't your data centers green? And so they put a whole load of money into renewables.

A lot of the reason why there's enormous amount of renewables these days and enormous amount of investment has gone into it, was because of the cloud vendors. Now, that is not because the cloud vendors are all secretly social justice warriors. I mean, they did it for their own benefit. But they did do it.

Jamie Dobson: That's another pattern that reoccurs is so, at the turn of the last century, so many entrepreneurs were sat on so much money that class unrest was really bubbling. So all of a sudden you got the subway in New York, subway in Paris, the municipal control of transportation, all kinds of stuff.

And then you're left thinking, "oh, was, were, they all do-gooders? Was that the reason they did that?"

Some of them may have been, but mainly they were trying to avoid class unrest. And so it's interesting that these, a good outcome can come on the back of self-interest, that is true, isn't it?

Anne Currie: Yeah, it is true. I it, and it's very hard to know what the unintended consequences, positive and negative of, all behaviors are. So, a lot of investment in early stuff becomes wasted later. So, you, like, you mentioned, subways, railways in the UK and worldwide.

Lots of early investment in railways resulted in loads of over provisioning of railways. And then as things got a bit more efficient and everybody goes, well actually you only need one train to go between London and Edinburgh and not 16 different trains on different lines. You get some kind of consolidation down and improvements in efficiency and that's how actually things become cost effective because actually overprovisioning is very cost ineffective.

Jamie Dobson: Well, that's true, but that is a very cheeky way to transfer money from rich people to poor people, because obviously what happened is, rich people invested in the railways, railways were over provisioned, those people never got a return. The rest of us were left with cheap railway infrastructure. Exactly the same happened with internet. Everyone's like, right, we gotta wrap the world up in optic fibers. Private companies came in, private investors came in, paid for all of that. Then we had way too many optic fiber cables, and now we've all got practically free internet access. So that occasionally it, it goes either way.

Anne Currie: Yeah, and I have to say, I, and I see the same thing with AI. So AI is interesting 'cause on the one hand I rail against how, and AI is unbelievably inefficient at the moment that there's an awful lot of talk about, oh, we'll have to build nuclear because we need it for AI and all that kind of stuff and we'll build all the nuclear and we'll build all the, you know, and hopefully, we'll we need to try and steer people towards doing with nuclear and doing it with solar and wind rather than, rather than fossil fuels. But at the end, it's going to be, there's so much wasted inefficient code in AI. AI is going to need a fraction of the power that we eventually build, we initially build to power the AI. I mean, because at the moment I'm talking to people who are doing measurements and differences between different AI models that do, you know, an equivalent amount of stuff.

The ones that are optimized, 10,000 times more efficient, 600,000 times more efficient. I've even heard a million times more efficient. There's so much waste in AI at the moment.

Jamie Dobson: Absolutely, and I think people don't, are not focused particularly on theoretical breakthroughs. So Jeffrey Hinton came up with the back, back propagation of errors in neural networks. I think it was about 1983. That's in the book by the way. And that was a breakthrough. That breakthrough, that theoretical breakthrough's got nothing to do with computing power or anything. It's a theoretical breakthrough. Right now we're desperate for something like that. So 

we're loaded up all these data centers, we're increasing data sets, but ultimately no matter how much compute and data you throw at an artificial neural network, I think it would never fully replace what a human does. 

So I think it's nice to know that as we lay, you know, we lay down this computing infrastructure and fingers crossed all of its powered by, you know, renewable energy, in the background, researchers will be chipping away at the next theoretical And I think they have to come with artificial intelligence because I think there will be limits to what you can do with generative AI.

And I think we're probably reaching them limits right now.

Anne Currie: Well, improving AI efficiency does not require massive theoretical breakthroughs. It just, it can be done using the same techniques that we've used for 30 years to improve the efficiency of software. It is just software. I mean, if you look at, DeepSeek, for example, DeepSeek did, have done, I think, so DeepSeek had to make their AI more efficient because the Biden administration said they can't have the fancy chips.

So they just went, "oh, we can't have the fancy chips, so we're just gonna make some software changes." And they did it like that, effectively. They're a tiny company and they increased the efficiency tenfold pretty much instantly. And they used three different methods, all of which, well, one of which is probably Max House and it's probably was probably most of the 10 x.

The others, there's still so much room for additional efficiency improvement with them. They did, they got rid of over provisioning. They moved from 32 bits of precision to eight bit precision 'cause they didn't need the 32 bit. That was a classic case of over provisioning. So they've removed the over provisioning and that's been known about for years.

That's not new. AI engineers have known that 32 bit is, over egging it. And they could run on 8 bit for years. So they didn't do anything new. They didn't have to do any new research. All they had to do was implement something that has, that was well known, but people just couldn't be assed doing. 

Jamie Dobson: Yeah, all of this noise will soon die down and people behind the scenes away from the attention grabbing headlights will continue to crack on with these things. And so my prediction is that everything's going to, everyone's gonna be pissed off in the next six to 12 months. "AI failed to deliver,"

but in the background, use cases will get pieced together.

People will find these optimizations, they'll make it cheaper. And I do reckon, ultimately, generative AI will sink into the background just in the same way that nobody really talks about the internet, right? It's the Web or it's mobile phone applications that do something sat on top of the computer network infrastructure. I think that's probably what's gonna happen.

Anne Currie: I suspect that generative AI is not going to entirely disappear just because, so I used to work, many years ago, I worked in the, in the fashion industry. I was, I worked for a company that was one of the first in pure play internet e-commerce companies. And because it was fashion, we used a lot of photography.

An awful lot of photography, and a lot of it, we had a whole team of editors. So, you know, I can see companies that work with photography, they have, a surprisingly large number of people in the world edit photographs. And so you know that there's a huge, demand for making that easier.

The downside is that you then, even now, all photo, all photographs that you see online represent people who do not exist. You know, they, it is like all models you see, it's probably not, that model kind of is kind of based on a person, but.

Jamie Dobson: lots of people, isn't it? So I think that generative AI stuff will remain, but I think it will become specific. So for example, I saw yesterday that the government are piecing together a number of different tools that's, let's call that the substrate, but on top of that, it's to give civil servants conversational interface about what was our policies,

can you summarize this for me, can you suggest a new policy, which is dangerous because anything, any decision based on past data, it's a reflection of and not necessarily a vision of what could be. So I think that's probably what's gonna happen, but I could be wrong and because the truth is none of us actually know.

It's all speculation at this point. 

Anne Currie: Yeah, so, so before we, well actually we've still got a bit of time, but before we go, I want to focus a little bit on what I see are the themes that run through the creation of the internet and the creation of modern technology in Visionaries, Rebels and Machines, and the Cloud Native Attitude, and Building Green Software.

And I think a lot of the themes there are, trying to de deliver your results, the thing that you want, the thing that's gonna improve your life, or the thing that people think is gonna improve their life on fewer resources with fewer resources, because that's the only way it scales. The cloud was all essentially all about how do we deliver our Google's, I mean, it was the cloud was, came outta Google. And it came outta Google, which was the first hyperscaler, and Google was saying, well, actually we really need to deliver our services at incredible scale, but we can't spend the, you know, there's a limit to how much money we can spend on doing it.

So we have to do it using operational efficiency and code efficiency so that we deliver this service on fewer resources and also resilience, you know, because things fail at scale and therefore we need to be resilient to failure. But that efficiency and that ability to respond to changing circumstances is exactly what we need to handle the energy transition.

Jamie Dobson: So I think the common theme that goes all the way from Thomas Edison to the teams building systems using AI now is that technologies change, but human nature doesn't. So, so the way those teams were managed has been absolutely consistent. I think one of the great contributions of Visionaries and Rebels is to show to people, you don't need to change the way you manage your techies because actually these, this is of success stories that lasted 150 years. Second theme is that once the foundations are laid, it's not the creators of a technology that dictate its destiny, but the users. So once we had a grid, boof, people started inventing applications. Exactly the same once the internet was there, people started inventing web applications. And once the cloud was there, we had Netflix, and then we had Starling Bank and all the things built on top of the substrate. So I think for sure what's gonna come next for sustainable computing will not necessarily be dictated by those building cloud infrastructure. The teams out there, the safe teams, the innovative teams taking risks. I think they will find the use cases. They will dictate what happens next.

Anne Currie: Well, so that's interesting 'cause that actually instantly reminds me of the approach that India, which we already talked about, that India are taking where you say, well look, we'll incentivize people to stick a whole load of renewable power into the local grid, into the grid. We've got the grid.

The grid just distributes the power and we introduce those incentivizing time of use tariffs, and we say, look, you know, there's really cheap energy at these times. Fill your boots. You decide what you're gonna do with it. And then just leaving the users of the grid, the users of those time of use tariffs to work out what's gonna happen.

Jamie Dobson: And I think people will look to India. I think everybody looks at other countries that are doing these experiments. So if it works out in India, then of course you could imagine that other countries might say, "oh, well, that's actually worked out over there. We can copy that as well." But ultimately they're building on existing infrastructure.

You know, they say, well, this is what we've got, what, you know, how can we, what does that interface between our users look like? And by making a change there, they will change user behavior somewhere else. 

Anne Currie: Yeah.

Jamie Dobson: It's hard to predict, though. It's hard to predict.

Anne Currie: it is hard to predict it. it's kind of, it's an interesting, something that comes up in grid discussions about this, quite often, is this whole kind of idea that, in some ways countries that are less developed than America and the UK are in a much better position for the energy transition because governments can go, we'll have time of use tariffs in every day.

We'll, it's not that far. For, you know, the people quite used to microgrids, they're quite used to things being fluctuating. They're not, they haven't got used to everything being available at the flick of a switch and a hundred percent reliable. Reliability, to a certain extent, breeds fragility. It breeds people who've forgotten how to handle change.

Jamie Dobson: Yeah. So of course there are places in the world that have got cell phone infrastructure, but they don't have any telecommunications infrastructure, because by the time they came around to installing it, cell phones were a thing, so they just completely skipped. That whole step in technology. We've still got phone boxes in the UK that we, nobody knows what to do with. They're on the street corners, growing moss, and that's a legacy, exactly like what I mentioned earlier, the mud scrapers outside of people's houses. These are a legacy of previous sort of infrastructure. Horses in the case of the scraper and then the telephone boxes in case in the case of cellphones. So I think that's true that india probably has got places that are either off grid or nowhere near as reliable as what we have, for example, in the UK. So then it makes sense that the government can be more experimental because the people are not gonna lose anything. There's nothing to lose.

There's only gains. 

Anne Currie: Indeed. Yes. And in fact, actually, I mean, it is interesting that time of use tariffs being introduced in the UK is now controversial because we have become strategic snowflakes. We can't. We can't, they fear that we can't change, although I think they're wrong. And in fact, time of use tariffs were totally fine 30 years ago.

And nobody died as a result of economy seven heating.

Jamie Dobson: There's an absolute relationship between the reliability of a system and how spoiled its users has become. So if you, when I first went to the Netherlands, the train would be two minutes late and people would literally slam their feet on the ground in anger, right? And swear in Dutch about the state of the NS. Coming from the UK it's like, "well, whatever."

Now, exactly the same happened when, when the video store came along. Most people were used to consuming media as and when, you know, they chose to. But with the video shop, they only had limited editions of new releases. The frustration that created in users of video stores is exactly what led to Netflix's creation. So the more reliable something is, the more complacent, and the higher the expectations its users have of the system. But I think COVID taught the UK government that we could be way more flexible than they fear we are. 

Anne Currie: Yeah. I agree. And, except actually I don't think they learned that lesson because they immediately forgot it again. 

Jamie Dobson: Apparently there's loads of lessons they didn't learn. 'Cause apparently we're less ready for a pandemic now than we were before COVID.

Anne Currie: Yeah. It is a, it is amazing how many lessons we didn't learn that, but, I think that takes us through a final thing that we should discuss, which I think comes out of what you've just said there about resilience, which is some, and it's something that is a modern thing that we talk about a little bit in the book, in all those three books, which is Chaos Engineering, which is the modern approach to resilience, which is that you get more, ironically, you get more resilient systems by building them on top of systems that you don't expect to be a hundred percent resilient.

The expectations of, of a hundred percent availability, supply side response builds, in the end, more fragile systems. 

Jamie Dobson: The fragility has to go somewhere. So the more resilient the system is, the more fragile the users are. And then the converse of that is true. The more a system fails, the more flexible its users become, and the more workarounds they have because they're not sure if it's gonna be ready. I do know one of the key lessons I took while whilst putting Visionaries and Rebels together could be distilled into one sentence. A system that doesn't fail during its development will fail catastrophically in production. And so what you're left with is electricity grid, the internet, the cloud computing, they're so amazingly, you know, resilient and reliable, they are literally are literally always there. You start to take, you do start to take them for granted. but the paradox is that if you want to create resilient systems, you've got to simulate, stimulate failure in order to learn how to deal with failure, therefore avoid it in the future. It's all a little bit circular really.

Anne Currie: Yeah. Yeah. So the irony is that exposing end users to the fluctuation in the availability and price of electricity for renewables, it sounds scary, but it will produce, in the end, a more resilient society. A more resilient system on a countrywide scale.

Jamie Dobson: And in your opinion, what's the relationship between this, these type of tariffs and demand side behavior and cloud computing? Where is the link there?

Anne Currie: Well, I mean, data centers are users of a grid. They are users that, they are prime users of electricity. If we make a tenfold difference, and I don't think it's gonna, it's gonna affect, it is gonna work for anything less than a tenfold difference in price, we will start to see behavioral change.

We will start to see data centers go, "do you know, is there a way that we can, we can reduce the number of machines that are running," because at that point the cost will start. So we need to get it to a point where the cost, the different time of use tariff costs make it worthwhile switching to operations to when the sun is shining the winds blowing.

But that is what we have to do, because we need the demand side response behavior. We need the change of response from users. So we have to make it worth their while.

Jamie Dobson: You're gonna use economic nudges to make data centers consume green energy, right? So that's the energy side of the equation. What do we do about water supply? So, I don't know if you realize, but lots of data have been refused planning permission

because they will drain fresh water from people's houses governments, quite, you know, are not ready to sort of take that on the chin.

So what are your thoughts on the water issue?

Anne Currie: Well, again, that's, that is a known issue. If we actually, at the moment, if they don't have to do it, they won't do it. So if it will, cooling using water is very cheap and easy. And therefore they do, that's what they do.

That is the default. But there are alternatives. I mean, if you look at more modern chips that are, I mean Intel, it's a bit of an old fashioned chip these days, it's very hot. The Nvidia chips are very hot, but there are chips that are coming out that are much more efficient, that're much cooler, that, and that are often designed to be air cooled, not water cooled.

So, if we move towards, so it is not unknown, the technology exists for chips that don't get so hot that they require water cooling. The future is chips that can be air cooled. And if they can be air cooled, they're cooled with aircon. And aircon can be fueled by solar power, because obviously, you know, it's when it's hot and it's sunny that you have the biggest problem with heating, it's when it's not sunny and warm,

it's less of an issue. So, the future here, the solution is better and more efficient chips hardware that can be air cooled. That that is for most hardware. I think that has to be, that is at least a big part of the solution.

Jamie Dobson: Does the future involve huge data centers that fall under government regulation? Because one of the reasons why the electricity grid became a natural monopoly, is 'cause it made no sense to put six sets of cables down. There wouldn't've been enough space in the street and actually the electricity providers couldn't get economies of scale and therefore could not pass on cheap electricity to its users and therefore electricity would never be become widespread.

So is there a similar argument for the cloud providers presently?

Anne Currie: I have to say I'm a huge believer that we just do it through pricing, that we want data centers to be closed. So in Scotland, and we throw away, we turn off wind, we pay wind farms to turn off. We spend billions and billions of pounds every year to paying wind farms to turn off because there is no user for that power within easy reach of that wind farm.

And we're only talking about Scotland. We're not talking about Siberia. 

Jamie Dobson: I think we could build a data center there.

Anne Currie: Why don't we build a data center there?

Jamie Dobson: They've got plenty of wind and water.

Anne Currie: And an extremely well educated workforce. And it's a bit cooler up there as well, so you don't need to do quite so much cooling anyway. So, but there's no incentive. So while there's no incentive,

people won't act. Once there is incentive and a really juicy incentive in place, you know, a 10 x difference in price, we will see behavioral change. Because we do. People, humans are very good at changing their behavior, but only if there's a good reason to do so.

Jamie Dobson: Yeah, absolutely. Yeah.

Anne Currie: And actually that kind of brings us to the end of our hour.

And we, I think we've had a really interesting discussion. I hope the readers of the listeners and potentially in the future readers have enjoyed the discussion. All the links for everything we talked about, all the books, all the comments, will be in the show notes below, so you can go and have a look.

And you have to, yeah, actually, you know, you can pre-order Jamie's book, Visionaries, Rebels and Machines on Amazon or any good bookshop, now. You can also buy the Cloud Native Attitude or Building Green Software, which you can also read for free if you have an O'Reilly subscription. And when I get round to it, I'm eventually going to create a commons Building Green Software and I kick me.

Everybody should be kicking me all the time to do that because it's just bit work that I need to do. Anyway, so Jamie, thank you so much for being on the podcast. I've really enjoyed our chat. Is there anything final you wanna say before we disappear off?

Jamie Dobson: Nothing final for me. The book launch, there'll be a launch party in London at some point. It's available on Kindle, but for now, I'm just happy to get you know, feedback and it's been great to talk to you today, Anne, and I really hope your listeners took something away from this.

Anne Currie: So I hope people enjoyed the conversation. It was a bit, a little bit of an author's book club, so a bit different to normal. But I hope you enjoyed it and let us know if you want to hear more of this kind of discussion. Thank you very much and, until we meet again, goodbye from me. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Environment Variables host Anne Currie welcomes Jamie Dobson, co-founder of Container Solutions and author of the upcoming book Visionaries, Rebels and Machines. Together, they explore the history and future of cloud computing through the lens of sustainability, efficiency, and resilience. Drawing on insights from their past work, including The Cloud Native Attitude and Building Green Software, they discuss how cloud-native principles can support the transition to renewable energy, the potential and pitfalls of AI, and why behavioral change, regulation, and smart incentives are key to a greener digital future.

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TRANSCRIPT BELOW:


Jamie Dobson: We're loaded up all these data centers, we're increasing data sets, but ultimately no matter how much compute and data you throw at an artificial neural network, I think it would never fully replace what a human does. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello and welcome to Environment Variables Podcast, where we give you the latest news and updates from the world of sustainable software development. So this week I am your guest host Anne Currie. And you don't have the dulcet tones of Chris Adams, you're left with me this week. So we're gonna do something a little bit different this week.

I have got an old friend and colleague and co-author, Jamie Dobson in to talk about it. So Jamie is the co-founder and CEO of a company called Container Solutions. And he's the author of the soon to be released book; Visionaries, Rebels and Machines, which I've read, and that's what we'll be talking a lot about.

And he's also the, one of my co-authors of a book I wrote nearly 10 years ago called the Cloud Native Attitude, which is about the principles of moving into the cloud. And there's an awful lot in there about sustainability with that, there's a lot we need to talk about around that. And it was actually for me, the precursor to the book that I wrote which came out with O'Reilly last year, with co-authors Sarah Hsu and Sara Bergman, Building Green Software, which as I always say every week,

everybody listening to this podcast should read because you'll find it very interesting and it is couldn't be more germane. So today we're gonna talk about those three books, really, and the thematic links between them all, which are really about resource efficiency, building at scale without it costing a ridiculous amount of money or using a ridiculous amount of resources.

And also resilience, which is something we're gonna really have to focus on when it comes to running on renewable power. So, let me let Jamie introduce himself and maybe tell us a little bit about his new book, Visionaries, Rebels, I can never remember whether it's Rebels, visionaries and Machines.

It's Visionaries, Rebels and Machines. Go for it, Jamie.

Jamie Dobson: Visionaries, Rebels and Machines. That's correct. Hello Anne. Thanks for having me on the podcast. And hello to all your listeners. who tune in every week? Yeah. So my name is Jamie. I am indeed the co-founder of a company called Container Solutions. But it's no longer, I'm no longer, I should say, the chief exec,

'cause I handed that role over about a year ago, which is probably why, or, you know, it explains why I could find the time to finish writing this damn book. So Container Solutions is a company that specializes in cloud transformation, helping customers, you know, get off whatever infrastructure they're running on now and get onto, you know, efficient cloud infrastructure.

And if we do that right, then it's kind of green and sustainable infrastructure, but it's hard to get right, which I'm sure we're gonna discuss today. 

Anne Currie: Indeed. Yes. Yes. So, so you've got a book that's about to come out, which I have read, but it's not yet available in, the, in the stores, but it will be available on, in all good book bookstores, Visionaries, Rebels and Machines. And I, the reason why I asked you to come on is because I think there are a lot of ideas in there that would, that we need to be talking about and thinking about.

So, so tell us a little bit about Visionaries, Rebels and Machines, and then I'll tell you why I think it's interesting.

Jamie Dobson: Absolutely. Yeah. So, so Visionaries, Rebels and Machines, we have to start at a point in time. And that point in time is about four or five years ago. And I was asked the question, "what's the cloud?" It was, the person asking me, it was a junior colleague, new to Container Solutions. And, you know, I started to answer, or at least I opened my mouth,

and of course I can answer that question, but I can't answer it necessarily succinctly. So I was asked the question, I think probably around about June, so maybe about five years ago today actually. And over the summer period I was thinking, "God, how do you answer that question? What is the cloud?" And so I started to creep backwards in time.

Well, the cloud is, you know, there's a bunch of computers in a warehouse somewhere. But what's a computer? And then once I asked that question. Well, computers are things made up of transistor. Well, what's a transistor? And what I came to the conclusion over the summer, was the following:

The cloud can only really be understood in its own historical context. And so interestingly, once we got to the point of, you know, answering the question, what is the cloud? The arrow was already flying. You know, there was a, an arrow was shot round about the late Victorian time at Thomas Edison's Menlo Park facility in New Jersey, and that arrow flew all the way through the last century through the web, through cloud computing, and it continues to fly with the rise of artificial intelligence. And so the last part of the book is, okay, now we know what the cloud is and what it does, where might it take us next in regards to artificial neural networks and all of that stuff? So that was the book. The Visionaries and the Rebels of the people who built teams, built teams that were innovative. All of them had psychological safety even though the, that concept wasn't known at the time. And so, these historical figures are not just ancient history, like not just Thomas Edison, but also the Jeff Bezos's of the world, the Reed Hastings's, and the modern figures of cloud computing. The visionaries and the rebels can teach the rest of us what to do with our machines, including how to make 'em sustainable. 

Anne Currie: And that is the interesting thing there. So I enjoyed the book. It's, it is quite, it is a readable romp. And I very much connect with your, with your initial motivation of trying to explain something that sounds simple, but actually you realize, oh gosh, I'm gonna have to write an entire book to even get my own head around this rather than, you know, 'cause that was true for, well, when we wrote, it's actually a, Cloud Native Attitude, which was the book that we wrote together started off 10 years ago, was pretty much for the same, it was kicked off in the same way. We were, we were saying, well, what is cloud native? What, what are people doing it for, and why are they doing it this way? And quite often, and Building Green Software,

the O'Reilly book, which is really germane to this, to this podcast, was again, the same thing. It's what is, what does the future look like for systems to be sustainable? How do we align, and make, what is the future gonna look like? And, where, and that's always seated in the past. What has been successful?

How did we get here? 

Jamie Dobson: Absolutely. So you can't move into the future unless you understand your past. And I think the similarities between the Cloud Native Attitude and Visionaries and Rebels is the tone. So my book deals with horrible things, child poverty, exploitation of people, and the truth is that a reader will put up with that for maybe one paragraph.

So if you want to, if you want to teach computing and how it can enslave the human race or not, or how it can liberate them and touch all of these really difficult themes, you've got to do it in a pretty lighthearted manner. And the reason people are saying, "oh, it's a page turner. it's entertaining, it's a bit of a rump,"

it's because we focus on the characters and all the things that happens to them. And I think that started with a cloud-native attitude because unless you can speak quite lightheartedly, you so quickly get bogged down in concepts that even for people like us who work in computing and are passionate about computing, it's just extremely boring. And there are some fantastic books out there right now about artificial intelligence, but they're so dry that the message fails to land. And I think I was trying to avoid that. 

Anne Currie: And you know for, 'cause we wrote Cloud Native Attitude together. But it is, if these, books are ideally a form of leadership. When you write a book, you are either, you are kind of saying, look, this is what I want to happen in the future.

You're trying to lead people and explain and reason and inspire. But you have to inspire. If it's boring, you're not gonna lead anyone. No one wants to follow you to the boring location they want to follow you to the exciting location.

Jamie Dobson: No. Exactly. And I think the problem is computer people, most of us have been to university, so we're on the academic path. And what happens is you forget to tell stories. So everything becomes about what the research says, "research indicates." So it's all exposition and no narrative. And the problem that is people switch off very quickly, and the paradox is that you don't make your point because you've bored your reader to death.

Anne Currie: Yeah. And this is something that's, that comes up for me over and over again in the green software movement that we quite often, we tell the story of it's being, everything being very sad. And everybody goes, "well, I don't wanna be there in that sad world." And, but it's not a sad story. I mean, it is like climate change is a really sad story.

It's terrible. It's something we need to avoid. We're running away from something, but we're also running towards something. Because there's something amazing here, which is renewables are so cheap. If we can build systems that run on solar and wind, and a little bit of storage, but not, but much less storage than we currently expect,

then we have a world in which there's really loads more power. We can do so much more than we do now, and it's just a matter of choosing what we do with it. It is a, we are not just running away from something. We're running towards something, which is amazing. And, so yeah, we tried to keep that tone.

And Building Green Software is designed to be funny. You are. It's the only O'Reilly book. One of, one of my reviewers says it's the only O'Reilly book where you actually get, you laugh out loud whilst reading it. You could read it on the beach.

Jamie Dobson: This is exactly why we created a conference at Container Solutions called WTF. What The F is Cloud Native? And it's basically because if you cannot entertain, you'll never get your message across. I've got a question for you, Anne, this wonderful future that we're heading towards, I see it as well. But 

in the research for visionaries and rebels, there was a big chapter I had on Henry Ford, and in the end it didn't, quite make it into the book, but basically, once Edison had created electricity, then all of a sudden you had elevators for the first time. So the New York landscape did not become a thing till we had electricity because there was a limit on how big the buildings could be. And that exact moment Henry Ford came in with the motorcar, and he was so successful in getting it off the production line cheaply, the beautiful boulevards of New York, of American cities, New York, St Louis, and places like that ended because basically people said, "well, we don't need to be in the city.

We can drive to the suburbs." And a lot of historians were saying if Henry Ford had just gone a bit slower, we would've adapted to the motor car quicker and therefore the cities of today would look very different. And one of my concerns with green software is,

the speed of which we're moving with data centers and AI is so quick.

I wonder if we're having another motor car moment. the future's within grasp, but if we go too quick, might we screw it up on the way?

Anne Currie: So I think what you are circling around here is the idea of, it is something that comes up quite often, which is Jevons Paradox, which is the idea that, as you get better at using something, you use more of it, it becomes cheaper, because actually because there's untapped demand.

So where there's, where people are going, "gee, you know, I really want to live in a high rise city because then naturally everybody can live together and it will be vastly better for us and we'll prefer it. And therefore we take more elevators and we go up because we've got elevators."

And people really want cars. I mean, it's one of the things, I don't drive. but everybody loves to drive. There's no point in, tying green with like nobody driving because they love to drive. And there was untapped demand for it, and therefore it was met. And remember at the time there was really, but back then we didn't consider there to be any problem with using more petrol. We didn't consider there to be any problem with using fossil fuels. And everybody went, "yeah, hooray! Let's use more and more of it."

But it did massively improve our quality of life. So I think all green messages we have to say, well, we want the improvement in quality of life, but we also want a planet and we have to optimize both of those in parallel.

We can't say that you're trading off. And this, I know that people have a tendency to look down on efficiency improvements, but efficiency improvements are what has driven humanity up until now. And efficiency improvements are so much more powerful than we think. We just don't understand how much more efficient things can get.

Jamie Dobson: Yeah. 

Anne Currie: And therefore we go, oh, well, you know, we, if people have 10 times as many cars or whatever, probably not 10 times as many. Well, compared to back to Henry Ford's days, we've got a lot more cars. We've got a lot more mobility. There is a almost seemingly limitless, demand for cars. But there are plenty of other areas of life where efficiency has outstripped the demand.

So in terms of electricity use, household electricity use in the west in the past 20, 30 years, household electricity use, despite the fact that everybody has automated their houses we've got, everybody's got washing machines and dishwashers and tumble dryers and TVs, and electricity use has still gone down.

And the reason why it's gone down is because all of those devices appeared, but then became more and more efficient. And efficiency improvements really are extraordinarily powerful. Much more than people realize. And if we force people to put the work in, and it's not free, it requires an enormous amount of work, but if people are motivated and incentivized to make those efficiency improvements, we can do an awful lot.

We can get.

Jamie Dobson: My suspicion is the world will change. So not many people realize that the car was actually very good for the environment. All around London, my children ask me, what's that thing outside the house?" It's a scraper for your feet, for your boots. And that's because all the streets of London were caked two inch shit deep of horse manure.

And at the end of every single street, the way it was piled high. So the public health issues with horses was an absolute nightmare. Not to mention the fact that people used to get kicked in the head or pulled into ditches. Fatalities from horses was, you know, a weekly account in New York City. But so it changed. So once we got the electricity, we got the lifts, the horses went away.

 My suspicion is right now we cannot run a sustainable culture or city without radically changing things.

 So, for example, did you ever stop to wonder why is your power pack warm? You know, when you charge your phone or your laptop, why does it get warm? Do you know what the answer to that question is?

Anne Currie: No, I don't actually. That's a very good question.

Jamie Dobson: There you go. So who won? Who won the battle? Tesla or Edison. So.

Anne Currie: Tesla. 

Jamie Dobson: Tesla did win. So it's basically AC versus DC. What's the best system to have? Well, DC, direct current kills you if you touch it direct current by accident and the voltage is right, you die. But what you feel on the back of your charger is heat, which is a side effect of converting AC back to DC because computer devices don't work on AC because it, the current has to go round and round, like water, in a fountain because that's the only way transistorized things work. So now people are saying, well, actually, arguably we should have a DC grid because globally we are wasting so much electricity because of this excess heat that is produced when we go from AC back to DC. So, and I get the feeling, and do you remember when we were kids, if you put your washing on at three in the morning, you got cheaper electricity.

I cannot help but think it's not just about renewable energy, but it's also the way we consume energy to make that more effective.

Anne Currie: Yeah.

Jamie Dobson: And I think if that doesn't change, I basically think, when Edison arrived, society as we knew it absolutely changed. We had no refrigerators and that changed our behaviors.

Now, some people would say, well, you became a slave to the machine. I think that's a little bit too far, but we certainly went into some sort of analog digital relationship with the machines we work, all of which drive efficiencies. I think the next chapter for sustainable energy and computing will be a change in our habits, but I can't, I don't know exactly what they're gonna be.

Anne Currie: Oh, that's definitely a thing. It's something I've talked about on the podcast before. It's the mind shift from fossil fuels, which are kind of always on, you know, easy to dispatch, so easy to turn on, easy to turn off to something, to solar and wind, which is really expensive to store,

really cheap if you use it as it is generated. But grids were designed, in many ways this is the same kind of things that you talk about in your book. Grids were originally designed specifically to provide power that was easily dispatchable, you know, that it was fossil fuels.

And that means that the whole of the philosophy of the grid is about something called supply side response. And that is all that is basically saying, "do you know, users, you don't need to worry." Flick of a switch, the electricity will always be there and it's the responsibility of the dev, of the providers of the electricity, of the grids to make sure that the electricity is always there to meet your demand.

You never have to think about it. But for renewables it's generally agreed that what we're gonna have to do is move to something called demand side response, where users are incentivized to change their use to match when the sun is shining and the wind is blowing. As you say, when we were kids in the UK, we used to have something called economy seven.

You had seven hours a day, which was usually at night. where, because it was all, because back in then, I'm guessing, 'cause it was a coal fired power station. Coal fired power stations were not so easy to turn off and on again, which gas is. So we don't have it anymore. And it's, and, but in those days you say the coal fired power station was running during the night and nobody was using the power.

So we wanted to actually get people to try and use the power during the night. And we used effectively what are now called time of use tariffs to incentivize people to use spare power, which was during the night in the UK. 

Jamie Dobson: It sounds like a huge dislocation to life, but when I first came to London, the London Mayor or the authorities made an announcement that when something like this, "oh, air pollution's really bad today. Don't go out running, close your windows. Old people don't go out, don't do any exercise."

And I remember thinking "this can't be real. Is this some sort of prank?" But this is a thing in London. And I remember thinking, but at no point would the Mayor of London say, "okay, the air pollution's bad. You're not allowed to drive your car today," right? And it showed where the priorities lie. But it wasn't that difficult.

So everybody just shrugs their shoulders and says, "oh, well, okay, I just won't do any out outdoor activities today." So I think that demand side response is possible. I do wonder what happens though if, let's say, obviously the sun's shining, so that's the time you should run your data centers. What happens when the sun's not shining?

Are the cloud providers gonna be happy to have an asset sat there doing nothing when it's dark, for example, or when the wind's not blowing?

Anne Currie: Well, it's interesting. I think it depends how much, if, it's all about what is the level of difference in electricity cost between the time when the sun is shining or the wind's blowing, and the time when it isn't. I'm massively impressed by work that India is doing at the moment on this, on time of use tariffs because they have tons of, or and they know what they're looking forward, they already know they're one of the fastest growing. So India is one of the fastest growing countries in the world for rolling out solar power. Unsurprisingly, 'cause it's pretty sunny in India. So they're looking forward and they're thinking, well, hang on a minute.

You know, we are gonna have this amazing amount of solar power in the future, but we are going to have to change people's behaviors to make sure that they run on it, not the other thing. So, the way they're doing that is that the strategy that they're adopting for incentivizing people to change their behavior.

And as you say, actually people will change behavior. They just need a little bit of a push and some incentives and they will change their behavior. The incentive they're using is time of use tariffs. And India is pushing out all of the province, the states in India to introduce time of use tariffs which reflect the actual cost of electricity and push people towards times of the day when they're, when they'll be. And it's, it is a gradual process, but you can see that it will roll on and on and they're, looking at a tenfold difference that what they're saying is. That the difference should be tenfold between when your electricity is generated from the sun and when it isn't.

And a tenfold difference in price does justify a lot of behavioral change. You might as say, you might not want to turn off your, your data center during the night. But some people will go, well, hang on a minute. If it's literally, because for most data centers, the main cost is electricity. If there's a tenfold difference in electricity cost between, the day and the night, then they'll start to adapt and start to do less and start to turn things down.

Necessity is the mother of invention. If you don't, if you give a flat tariffs to everybody, they're not gonna make any changes. But if you start to actually incentivize response, demand side response, it will happen.

Jamie Dobson: Then of course then that comes back to regulation, doesn't it? Because I think of the things that Edison, well actually it was his colleague, Samuel Insull, realized if you're gonna, it makes no sense to run the grid unless it's some sort of public utility or a natural monopoly. And you can only really fairly run a natural monopoly if the price is a negotiated and set in public and all the industries regulated. So do you think the, that these tariffs, the time of use tariffs, will become part of the regulatory framework of the governments.

Anne Currie: Oh, yeah, I mean, it already is. I was saying it's, in India. It's a regulatory thing. It is part of the industrial strategy of India. There are.

Jamie Dobson: Then indirectly, then indirectly the cloud providers will be regulated because they'll be regulated through the supply of electricity.

Anne Currie: Indeed. Yeah. I mean it's interesting. there's a battle in, so some European countries, it's happening at the moment. I think, Spain already has time of use tariffs. There are other countries that have time of use tariffs and it changes behavior. And in the UK there is a battle at the moment, over, between suppliers about where the time of use tariffs are introduced.

So that battle is kind of being spearheaded by the CEO of in the UK it's Octopus Energy. Greg Jackson isn't it, I think is really saying, "look, this is what we need to do." Because, I mean, in the UK it is ridiculous that the government really doesn't want, they fear that everybody will be panicked and not be able to handle a time of use tariff.

And, but even though we used to have them not very long ago.

Jamie Dobson: It's ridiculous. People always panic about the public sentiment, but you just need to look at COVID, how flexible people can be when they understand the need for it. That's number one. And number two, when I was a kid, and that's only 40 years ago, we used to tend to lights off 'cause it was too expensive.

So we did have different behavior in the evening when we needed more electricity than in the daytime when we didn't. It's not that difficult to imagine. You know what? Do you know? What made me laugh is the average serving of meat, I think in the 1970s was 200 grams. And if you look at 200 grams, it's actually quite tiny.

It sits on your plate like a little slither of lamb. I was like, "oh my God, that's not enough. That's not enough food." But then you realize that is what we all used to eat, only 30 or 40 years ago. And so we've slowly been sort of, you know, everything's been supersized, including what we expect from the electricity companies. I think a gradual shifting back, you would, you'd barely notice it. And that's exactly how the government took salt out of our diet. That just slowly regulated how much salt could be in processed food until it had gone all together.

Anne Currie: Yeah, but I think you have to be careful about how you pitch this. Well, I think one of the issues with green is that it's pitched as, it's a reduction in meat and it's a reduction in, there's a reduction in that. I don't think it only has to be a sad story. It has to be a good story.

Something we're, a hill that we're, that we want to take because it's worth taking, not just something that we're, we are running away from that. I like the time of use tariff approach in India because it's saying, if you do this, you'll get electricity, which is a 10th the price, you know that it is something, it's a win.

It's not just like run away from the bad thing. It is run towards the good thing. And it with a minor, and you're not saying, "change your behavior because we're ordering you to do it" or because we're going to make electricity much more expensive. Although inevitably, electricity, fossil fuel, electricity will become more expensive because it is naturally more expensive these days.

Renewables have become so cheap. 

Jamie Dobson: Could cloud computing become a forcing function for cheaper electricity? Because the cloud providers need so much electricity, could this possibly accelerate the sort of the raise to green energy? 

Anne Currie: Well, it definitely can, and it has done in the past. I mean, it in, the early days, the well, so until maybe five years ago or so, the biggest non-governmental purchaser of renewable power in the world was Google. And they were buying renewable power, they were buying and, bankrolling renewable power for their data centers.

And they, so they're not the biggest, non-governmental purchaser of renewables anymore because it is now amazon to power their data centers because they got a long way behind and we all made a giant fuss about it and said, well, why aren't your data centers green? And so they put a whole load of money into renewables.

A lot of the reason why there's enormous amount of renewables these days and enormous amount of investment has gone into it, was because of the cloud vendors. Now, that is not because the cloud vendors are all secretly social justice warriors. I mean, they did it for their own benefit. But they did do it.

Jamie Dobson: That's another pattern that reoccurs is so, at the turn of the last century, so many entrepreneurs were sat on so much money that class unrest was really bubbling. So all of a sudden you got the subway in New York, subway in Paris, the municipal control of transportation, all kinds of stuff.

And then you're left thinking, "oh, was, were, they all do-gooders? Was that the reason they did that?"

Some of them may have been, but mainly they were trying to avoid class unrest. And so it's interesting that these, a good outcome can come on the back of self-interest, that is true, isn't it?

Anne Currie: Yeah, it is true. I it, and it's very hard to know what the unintended consequences, positive and negative of, all behaviors are. So, a lot of investment in early stuff becomes wasted later. So, you, like, you mentioned, subways, railways in the UK and worldwide.

Lots of early investment in railways resulted in loads of over provisioning of railways. And then as things got a bit more efficient and everybody goes, well actually you only need one train to go between London and Edinburgh and not 16 different trains on different lines. You get some kind of consolidation down and improvements in efficiency and that's how actually things become cost effective because actually overprovisioning is very cost ineffective.

Jamie Dobson: Well, that's true, but that is a very cheeky way to transfer money from rich people to poor people, because obviously what happened is, rich people invested in the railways, railways were over provisioned, those people never got a return. The rest of us were left with cheap railway infrastructure. Exactly the same happened with internet. Everyone's like, right, we gotta wrap the world up in optic fibers. Private companies came in, private investors came in, paid for all of that. Then we had way too many optic fiber cables, and now we've all got practically free internet access. So that occasionally it, it goes either way.

Anne Currie: Yeah, and I have to say, I, and I see the same thing with AI. So AI is interesting 'cause on the one hand I rail against how, and AI is unbelievably inefficient at the moment that there's an awful lot of talk about, oh, we'll have to build nuclear because we need it for AI and all that kind of stuff and we'll build all the nuclear and we'll build all the, you know, and hopefully, we'll we need to try and steer people towards doing with nuclear and doing it with solar and wind rather than, rather than fossil fuels. But at the end, it's going to be, there's so much wasted inefficient code in AI. AI is going to need a fraction of the power that we eventually build, we initially build to power the AI. I mean, because at the moment I'm talking to people who are doing measurements and differences between different AI models that do, you know, an equivalent amount of stuff.

The ones that are optimized, 10,000 times more efficient, 600,000 times more efficient. I've even heard a million times more efficient. There's so much waste in AI at the moment.

Jamie Dobson: Absolutely, and I think people don't, are not focused particularly on theoretical breakthroughs. So Jeffrey Hinton came up with the back, back propagation of errors in neural networks. I think it was about 1983. That's in the book by the way. And that was a breakthrough. That breakthrough, that theoretical breakthrough's got nothing to do with computing power or anything. It's a theoretical breakthrough. Right now we're desperate for something like that. So 

we're loaded up all these data centers, we're increasing data sets, but ultimately no matter how much compute and data you throw at an artificial neural network, I think it would never fully replace what a human does. 

So I think it's nice to know that as we lay, you know, we lay down this computing infrastructure and fingers crossed all of its powered by, you know, renewable energy, in the background, researchers will be chipping away at the next theoretical And I think they have to come with artificial intelligence because I think there will be limits to what you can do with generative AI.

And I think we're probably reaching them limits right now.

Anne Currie: Well, improving AI efficiency does not require massive theoretical breakthroughs. It just, it can be done using the same techniques that we've used for 30 years to improve the efficiency of software. It is just software. I mean, if you look at, DeepSeek, for example, DeepSeek did, have done, I think, so DeepSeek had to make their AI more efficient because the Biden administration said they can't have the fancy chips.

So they just went, "oh, we can't have the fancy chips, so we're just gonna make some software changes." And they did it like that, effectively. They're a tiny company and they increased the efficiency tenfold pretty much instantly. And they used three different methods, all of which, well, one of which is probably Max House and it's probably was probably most of the 10 x.

The others, there's still so much room for additional efficiency improvement with them. They did, they got rid of over provisioning. They moved from 32 bits of precision to eight bit precision 'cause they didn't need the 32 bit. That was a classic case of over provisioning. So they've removed the over provisioning and that's been known about for years.

That's not new. AI engineers have known that 32 bit is, over egging it. And they could run on 8 bit for years. So they didn't do anything new. They didn't have to do any new research. All they had to do was implement something that has, that was well known, but people just couldn't be assed doing. 

Jamie Dobson: Yeah, all of this noise will soon die down and people behind the scenes away from the attention grabbing headlights will continue to crack on with these things. And so my prediction is that everything's going to, everyone's gonna be pissed off in the next six to 12 months. "AI failed to deliver,"

but in the background, use cases will get pieced together.

People will find these optimizations, they'll make it cheaper. And I do reckon, ultimately, generative AI will sink into the background just in the same way that nobody really talks about the internet, right? It's the Web or it's mobile phone applications that do something sat on top of the computer network infrastructure. I think that's probably what's gonna happen.

Anne Currie: I suspect that generative AI is not going to entirely disappear just because, so I used to work, many years ago, I worked in the, in the fashion industry. I was, I worked for a company that was one of the first in pure play internet e-commerce companies. And because it was fashion, we used a lot of photography.

An awful lot of photography, and a lot of it, we had a whole team of editors. So, you know, I can see companies that work with photography, they have, a surprisingly large number of people in the world edit photographs. And so you know that there's a huge, demand for making that easier.

The downside is that you then, even now, all photo, all photographs that you see online represent people who do not exist. You know, they, it is like all models you see, it's probably not, that model kind of is kind of based on a person, but.

Jamie Dobson: lots of people, isn't it? So I think that generative AI stuff will remain, but I think it will become specific. So for example, I saw yesterday that the government are piecing together a number of different tools that's, let's call that the substrate, but on top of that, it's to give civil servants conversational interface about what was our policies,

can you summarize this for me, can you suggest a new policy, which is dangerous because anything, any decision based on past data, it's a reflection of and not necessarily a vision of what could be. So I think that's probably what's gonna happen, but I could be wrong and because the truth is none of us actually know.

It's all speculation at this point. 

Anne Currie: Yeah, so, so before we, well actually we've still got a bit of time, but before we go, I want to focus a little bit on what I see are the themes that run through the creation of the internet and the creation of modern technology in Visionaries, Rebels and Machines, and the Cloud Native Attitude, and Building Green Software.

And I think a lot of the themes there are, trying to de deliver your results, the thing that you want, the thing that's gonna improve your life, or the thing that people think is gonna improve their life on fewer resources with fewer resources, because that's the only way it scales. The cloud was all essentially all about how do we deliver our Google's, I mean, it was the cloud was, came outta Google. And it came outta Google, which was the first hyperscaler, and Google was saying, well, actually we really need to deliver our services at incredible scale, but we can't spend the, you know, there's a limit to how much money we can spend on doing it.

So we have to do it using operational efficiency and code efficiency so that we deliver this service on fewer resources and also resilience, you know, because things fail at scale and therefore we need to be resilient to failure. But that efficiency and that ability to respond to changing circumstances is exactly what we need to handle the energy transition.

Jamie Dobson: So I think the common theme that goes all the way from Thomas Edison to the teams building systems using AI now is that technologies change, but human nature doesn't. So, so the way those teams were managed has been absolutely consistent. I think one of the great contributions of Visionaries and Rebels is to show to people, you don't need to change the way you manage your techies because actually these, this is of success stories that lasted 150 years. Second theme is that once the foundations are laid, it's not the creators of a technology that dictate its destiny, but the users. So once we had a grid, boof, people started inventing applications. Exactly the same once the internet was there, people started inventing web applications. And once the cloud was there, we had Netflix, and then we had Starling Bank and all the things built on top of the substrate. So I think for sure what's gonna come next for sustainable computing will not necessarily be dictated by those building cloud infrastructure. The teams out there, the safe teams, the innovative teams taking risks. I think they will find the use cases. They will dictate what happens next.

Anne Currie: Well, so that's interesting 'cause that actually instantly reminds me of the approach that India, which we already talked about, that India are taking where you say, well look, we'll incentivize people to stick a whole load of renewable power into the local grid, into the grid. We've got the grid.

The grid just distributes the power and we introduce those incentivizing time of use tariffs, and we say, look, you know, there's really cheap energy at these times. Fill your boots. You decide what you're gonna do with it. And then just leaving the users of the grid, the users of those time of use tariffs to work out what's gonna happen.

Jamie Dobson: And I think people will look to India. I think everybody looks at other countries that are doing these experiments. So if it works out in India, then of course you could imagine that other countries might say, "oh, well, that's actually worked out over there. We can copy that as well." But ultimately they're building on existing infrastructure.

You know, they say, well, this is what we've got, what, you know, how can we, what does that interface between our users look like? And by making a change there, they will change user behavior somewhere else. 

Anne Currie: Yeah.

Jamie Dobson: It's hard to predict, though. It's hard to predict.

Anne Currie: it is hard to predict it. it's kind of, it's an interesting, something that comes up in grid discussions about this, quite often, is this whole kind of idea that, in some ways countries that are less developed than America and the UK are in a much better position for the energy transition because governments can go, we'll have time of use tariffs in every day.

We'll, it's not that far. For, you know, the people quite used to microgrids, they're quite used to things being fluctuating. They're not, they haven't got used to everything being available at the flick of a switch and a hundred percent reliable. Reliability, to a certain extent, breeds fragility. It breeds people who've forgotten how to handle change.

Jamie Dobson: Yeah. So of course there are places in the world that have got cell phone infrastructure, but they don't have any telecommunications infrastructure, because by the time they came around to installing it, cell phones were a thing, so they just completely skipped. That whole step in technology. We've still got phone boxes in the UK that we, nobody knows what to do with. They're on the street corners, growing moss, and that's a legacy, exactly like what I mentioned earlier, the mud scrapers outside of people's houses. These are a legacy of previous sort of infrastructure. Horses in the case of the scraper and then the telephone boxes in case in the case of cellphones. So I think that's true that india probably has got places that are either off grid or nowhere near as reliable as what we have, for example, in the UK. So then it makes sense that the government can be more experimental because the people are not gonna lose anything. There's nothing to lose.

There's only gains. 

Anne Currie: Indeed. Yes. And in fact, actually, I mean, it is interesting that time of use tariffs being introduced in the UK is now controversial because we have become strategic snowflakes. We can't. We can't, they fear that we can't change, although I think they're wrong. And in fact, time of use tariffs were totally fine 30 years ago.

And nobody died as a result of economy seven heating.

Jamie Dobson: There's an absolute relationship between the reliability of a system and how spoiled its users has become. So if you, when I first went to the Netherlands, the train would be two minutes late and people would literally slam their feet on the ground in anger, right? And swear in Dutch about the state of the NS. Coming from the UK it's like, "well, whatever."

Now, exactly the same happened when, when the video store came along. Most people were used to consuming media as and when, you know, they chose to. But with the video shop, they only had limited editions of new releases. The frustration that created in users of video stores is exactly what led to Netflix's creation. So the more reliable something is, the more complacent, and the higher the expectations its users have of the system. But I think COVID taught the UK government that we could be way more flexible than they fear we are. 

Anne Currie: Yeah. I agree. And, except actually I don't think they learned that lesson because they immediately forgot it again. 

Jamie Dobson: Apparently there's loads of lessons they didn't learn. 'Cause apparently we're less ready for a pandemic now than we were before COVID.

Anne Currie: Yeah. It is a, it is amazing how many lessons we didn't learn that, but, I think that takes us through a final thing that we should discuss, which I think comes out of what you've just said there about resilience, which is some, and it's something that is a modern thing that we talk about a little bit in the book, in all those three books, which is Chaos Engineering, which is the modern approach to resilience, which is that you get more, ironically, you get more resilient systems by building them on top of systems that you don't expect to be a hundred percent resilient.

The expectations of, of a hundred percent availability, supply side response builds, in the end, more fragile systems. 

Jamie Dobson: The fragility has to go somewhere. So the more resilient the system is, the more fragile the users are. And then the converse of that is true. The more a system fails, the more flexible its users become, and the more workarounds they have because they're not sure if it's gonna be ready. I do know one of the key lessons I took while whilst putting Visionaries and Rebels together could be distilled into one sentence. A system that doesn't fail during its development will fail catastrophically in production. And so what you're left with is electricity grid, the internet, the cloud computing, they're so amazingly, you know, resilient and reliable, they are literally are literally always there. You start to take, you do start to take them for granted. but the paradox is that if you want to create resilient systems, you've got to simulate, stimulate failure in order to learn how to deal with failure, therefore avoid it in the future. It's all a little bit circular really.

Anne Currie: Yeah. Yeah. So the irony is that exposing end users to the fluctuation in the availability and price of electricity for renewables, it sounds scary, but it will produce, in the end, a more resilient society. A more resilient system on a countrywide scale.

Jamie Dobson: And in your opinion, what's the relationship between this, these type of tariffs and demand side behavior and cloud computing? Where is the link there?

Anne Currie: Well, I mean, data centers are users of a grid. They are users that, they are prime users of electricity. If we make a tenfold difference, and I don't think it's gonna, it's gonna affect, it is gonna work for anything less than a tenfold difference in price, we will start to see behavioral change.

We will start to see data centers go, "do you know, is there a way that we can, we can reduce the number of machines that are running," because at that point the cost will start. So we need to get it to a point where the cost, the different time of use tariff costs make it worthwhile switching to operations to when the sun is shining the winds blowing.

But that is what we have to do, because we need the demand side response behavior. We need the change of response from users. So we have to make it worth their while.

Jamie Dobson: You're gonna use economic nudges to make data centers consume green energy, right? So that's the energy side of the equation. What do we do about water supply? So, I don't know if you realize, but lots of data have been refused planning permission

because they will drain fresh water from people's houses governments, quite, you know, are not ready to sort of take that on the chin.

So what are your thoughts on the water issue?

Anne Currie: Well, again, that's, that is a known issue. If we actually, at the moment, if they don't have to do it, they won't do it. So if it will, cooling using water is very cheap and easy. And therefore they do, that's what they do.

That is the default. But there are alternatives. I mean, if you look at more modern chips that are, I mean Intel, it's a bit of an old fashioned chip these days, it's very hot. The Nvidia chips are very hot, but there are chips that are coming out that are much more efficient, that're much cooler, that, and that are often designed to be air cooled, not water cooled.

So, if we move towards, so it is not unknown, the technology exists for chips that don't get so hot that they require water cooling. The future is chips that can be air cooled. And if they can be air cooled, they're cooled with aircon. And aircon can be fueled by solar power, because obviously, you know, it's when it's hot and it's sunny that you have the biggest problem with heating, it's when it's not sunny and warm,

it's less of an issue. So, the future here, the solution is better and more efficient chips hardware that can be air cooled. That that is for most hardware. I think that has to be, that is at least a big part of the solution.

Jamie Dobson: Does the future involve huge data centers that fall under government regulation? Because one of the reasons why the electricity grid became a natural monopoly, is 'cause it made no sense to put six sets of cables down. There wouldn't've been enough space in the street and actually the electricity providers couldn't get economies of scale and therefore could not pass on cheap electricity to its users and therefore electricity would never be become widespread.

So is there a similar argument for the cloud providers presently?

Anne Currie: I have to say I'm a huge believer that we just do it through pricing, that we want data centers to be closed. So in Scotland, and we throw away, we turn off wind, we pay wind farms to turn off. We spend billions and billions of pounds every year to paying wind farms to turn off because there is no user for that power within easy reach of that wind farm.

And we're only talking about Scotland. We're not talking about Siberia. 

Jamie Dobson: I think we could build a data center there.

Anne Currie: Why don't we build a data center there?

Jamie Dobson: They've got plenty of wind and water.

Anne Currie: And an extremely well educated workforce. And it's a bit cooler up there as well, so you don't need to do quite so much cooling anyway. So, but there's no incentive. So while there's no incentive,

people won't act. Once there is incentive and a really juicy incentive in place, you know, a 10 x difference in price, we will see behavioral change. Because we do. People, humans are very good at changing their behavior, but only if there's a good reason to do so.

Jamie Dobson: Yeah, absolutely. Yeah.

Anne Currie: And actually that kind of brings us to the end of our hour.

And we, I think we've had a really interesting discussion. I hope the readers of the listeners and potentially in the future readers have enjoyed the discussion. All the links for everything we talked about, all the books, all the comments, will be in the show notes below, so you can go and have a look.

And you have to, yeah, actually, you know, you can pre-order Jamie's book, Visionaries, Rebels and Machines on Amazon or any good bookshop, now. You can also buy the Cloud Native Attitude or Building Green Software, which you can also read for free if you have an O'Reilly subscription. And when I get round to it, I'm eventually going to create a commons Building Green Software and I kick me.

Everybody should be kicking me all the time to do that because it's just bit work that I need to do. Anyway, so Jamie, thank you so much for being on the podcast. I've really enjoyed our chat. Is there anything final you wanna say before we disappear off?

Jamie Dobson: Nothing final for me. The book launch, there'll be a launch party in London at some point. It's available on Kindle, but for now, I'm just happy to get you know, feedback and it's been great to talk to you today, Anne, and I really hope your listeners took something away from this.

Anne Currie: So I hope people enjoyed the conversation. It was a bit, a little bit of an author's book club, so a bit different to normal. But I hope you enjoyed it and let us know if you want to hear more of this kind of discussion. Thank you very much and, until we meet again, goodbye from me. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> How to Explain Green Software to Normal People How to Explain Green Software to Normal People Thu, 29 May 2025 07:00:00 GMT 46:27 71vlx661 false https://podcasts.castplus.fm/e/pnm52kkn-how-to-explain-green-software-to-normal-people 68dc7d01597bc7d53fb832e5 68dc7cef46a2532cdd8d1c23 109 Host Chris Adams speaks with James Martin about how to communicate the environmental impact of software to a general audience. Drawing on his background in journalism and sustainability communications, James shares strategies for translating complex digital sustainability issues into accessible narratives, explains why AI's growing resource demands require scrutiny, and highlights France’s leadership in frugal AI policy and standards. From impact calculators to debunking greenwashing, this episode unpacks how informed storytelling can drive responsible tech choices.

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TRANSCRIPT BELOW:

James Martin: When I hear the term AI for Good, which we hear a lot of at the moment, I would say that I would challenge that and I would encourage people to challenge that too by saying "are sure this AI is for good? Are you sure this tech is for good? Are you sure that the good that it does, far outweighs the potential harm that it has?"

Because it's not always the case. A lot of the AI for good examples see at the moment are just, they can't be backed with scientific data at all.

And that comes back to another of my points. If you can't prove that it's for good, then it's not, and it's probably greenwashing.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Welcome to Environment Variables, where we bring you the latest news and updates from the Board of Sustainable Software Development. I'm your host, Chris Adams. Our guest today is James Martin, a content and communications expert who has spent years translating complex text strategies into compelling narratives that drive change.

From leading communications with a special focus on sustainability at Scaleway, to founding BetterTech.blog, James has been at the forefront of making green tech more actionable and accessible. He's spoken at major climate and tech events, most recently ChangeNOW. He's written a comprehensive white paper on green IT, and played a key role in Gen AI Impact a French NGO working to measure the impact of AI. And also he's a Green Software Foundation champion.

So, James, thank you so much for joining the podcast. Really lovely to see you again after we last crossed paths in, I guess Paris, I think. Maybe I've tried to introduce you a little bit, but I figure there's maybe some things you might wanna talk about as well. So, can I give you the floor to just like introduce yourself and, talk a little bit about yourself?

James Martin: Yeah, thanks very much, Chris. First and foremost, I just wanted to say I'm really happy to, be on this podcast with you because, this podcast is one of the things that really got me excited, and it started me off on my green IT adventure. So, thanks to you and Anne for putting all, putting out all these amazing episodes.

Basically what I'm speaking today in the name of BetterTech, which is my blog, which I founded 2018. So I've been a, I've been a journalist for most of my career. And, so for about 15 years I was writing for a French cultural magazine. I had a page in that two weeks. And I started off writing out, "here's a new iPhone, here's a new console." And after that I got a bit bored of just saying the same thing every time. So I was drawn towards more responsible topics, like how do you reduce your screen time, how do you protect your data?

And also, of course, what is the impact of technology on the planet? So that started in that, in that magazine, and then I got so into it, i founded my own blog on the topic.

And then that was pretty much when an opportunity came up, in 2020, 2021 to work at Scaleway. I thought that sounds really interesting because, that is a European cloud provider, so not American. And also they were already very, communicating a lot about the sustainable aspect of what they do. So, yeah, I was very happy to join them and lead their communications from 2021 with this huge focus sustainability.

Chris Adams: Ah.

James Martin: Yeah, that's how, that's basically where it started. At that time, Scaleway had its centers and one of them called DC five, which is one of the most sustainable Europe because it doesn't have air conditioning, so it uses a lot less energy. That's it. It has adiabatic cooling. So we focused a lot of communication efforts on that. But then after year or two, Scaleway decided to sell its data centers. I had to look at are the other ways I could talk about sustainability in the cloud? So from digging around into green IT, especially into some green Software Foundation resources,

I basically understood that not just data centers, it's hardware and software. So I also, with a bit of help from one of a pivotal meeting, was meeting Neil Fryer from who the Green Software Foundation at a conference. I got him to come and speak at Scaleway to people like me were sort of concerned about the impact of tech. And then that led to the white paper that you mentioned that I erase in 2023, which is basic. It's basically how engineers can reduce the impact of of technology. So, and then that led to speaking opportunities and then to realize that, yeah, I'm not a, I'm not a developer. I'm not an engineer. I may be the first non-developer on this podcast. So I can't build Green Tech, but I can explain how it works and I think that's an important thing to be able to do, if we want to convince as many people as possible of how important this is, then it needs to be communicated properly.

And, yeah, so that's what I've been doing ever since.

Chris Adams: Okay, thanks. Okay, so I'm, I appreciate that you're coming here as not as a non, as someone who's not like a full-time techie who's like using GitHub on the daily and everything like that, because I think that means you, you get a bit of a chance to like see how normal people see this who aren't conversant in like object storage or block storage or stuff like that.

So maybe we can talk a little bit about that then, because when people start to think about, say, the environmental footprint of digital services, right? It's often coming from a very low base. And it's like people might start thinking about like the carbon footprint of their emails, and that's like the thing they should be focusing on first.

And like if you do have a bit of domain knowledge, you'll often realize that actually that's probably not where you'd start if you have a kind of more, more developed understanding of the problem. Now you've spent some of your time being this translator between techies and like people who are not full, you know, who, who aren't writing code and building applications all day long, for example.

So maybe we could talk a little bit about like the misunderstandings people have when they come to this in the first place and how you might address some of this because this seems to be your day job and this might be something that could, that might help who are other techies realize how they might change the way they talk about this for other people to make a bit more accessible and intelligible.

James Martin: Yes. So, thank you for mentioning day job first and foremost, because, so Scaleway was my former day job and I have another day job working for another french scale app. But here I'm very much speaking in the name of my blog. It's because I care so much about topics that I continue to talk about them, to write about them on the side because it's just, I just think something that needs to be done. So this is why today with my BetterTech hat

Chris Adams: Hat on. Yeah.

James Martin: so yeah, just wanted to make that clear. The first thing that people do when people misunderstand stuff, the first thing I want to say is it's not their fault. Sometimes they are led down the wrong path. Like,  a few years ago, French environment Minister said people should stop trying to send so many funny email attachments.

Chris Adams: Oh, really? 

James Martin: Like when you send a joking video to all your colleagues, you should stop doing that because it's not good for the planet. It honestly, the minister could say something that misguided because that's not where we, you and I know, not where the impact is. The impact is in the cloud.
The impact is in hardware. So it is sort of, about the communication is repetition and I always start with, digital is 4% of global emissions, and 1% of that is data centers, 3% of that is hardware, and software is sort of all over the place. That's the thing I, the figure I use the most to get things started. I think the, there's number one misconception that people need to get their heads around is the people tend to think that tech is, immaterial. It's because of expressions like the cloud. It just sounds,

Chris Adams: Like this floaty thing rather than massive industrial concrete things. Yeah.

James Martin: Need to make it more physical. If, I can't remember who said that if data centers could fly, then it would make our job easier. But no, that's where you need to always come back to the figures. 4% is double the emissions of planes. And yet, the AIrline industry gets tens of hundreds times more hassle than the tech industry in terms of trying to keep control of their emissions. So what you need is a lot more examples, and you need people to explain this impact over time, so you need to move away from bad examples, like funny email attachments or the thing about, we keep hearing in AI is, one ChatGPT prompt is 10 times more energy than Google. That may or may not be true, but it's a bit, again, it's a bit of the, it's the wrong example, because it doesn't. It doesn't focus on the bigger picture. 

Chris Adams: Yeah. That kind of implies that if I just like reduce my usage of this, then I'm gonna have like 10 times the impact. I'm gonna, you know, that's all I need to, that feels a bit kind of individual, a bit like individualizing the problem, surely. Right?

James Martin: And it's putting it on people's, it's putting the onus on the users, whereas it's not their fault. You need to see the bigger picture. And this is what I've been repeating since I wrote that white paper actually, you can't say you have a green IT approach if you're only focusing on data centers, hardware or software. You've got to focus on, yeah, exactly. Holistically.

That said, you should also encourage people to have greener habits because that's, me stopping using ChatGPT just on my own won't have much impact, but it will if I can convince, if I can tell my family, if I can tell my friends, if I can talk about it in podcasts and conferences, then maybe the more people question their usage, then maybe the providers of that tech start providing more frugal examples. But

Chris Adams: Ah, I see. So that's like maybe almost like choice architecture, giving people like, you know, foregrounding some of the options. So, you know, making it easier to do, possibly the more sustainable thing, rather than making people at the right, at the end of the process do all the hard work. You, it sounds like you're suggesting that okay, as a professional, part of my role is to kind of put different choices in front of someone who's maybe using my service to make it easier for them to do more sustainable things, rather than like things which are much more environmentally destructive, for example.

James Martin: Yes. And I would add a final thing, which is sort of super important because people, there are a topics like electric cars for example, which people get really emotional and angry about, 'cause people are very attached to their cars and yet cars are the number one source of emissions in most Western countries. The way around the emotion is to use, I really focus on only using science-based facts. If it's from the IPCC, if it's from the IEA, if it's like really serious scientific studies then you can use it. If it's just someone speculating on LinkedIn, no. So I always make sure that data I use as fully backed by science, by a sort of by all the GHG protocols, looking at all three scopes all that sort of thing. Because otherwise you just can't, it could be greenwashing.

Chris Adams: Okay. Alright, so maybe this is actually a nice segue for the next question because when people talk about, say, well basically in footprint from here, one of the challenges people have is like, like having some numbers, having tr, having some figures for any of this stuff. For example, if I'm using maybe a chat bot, i-, it's very hard for me to understand what the footprint might be. So in the absence of that, you can kind of see how people end up with an idea saying, oh yeah, every query is the same as a, you know, bottle of water, for example. Simply because there is a kind of dearth of information. And this is something that I think that I remember when you presented at Green IO, a conference around kind of green IT, you were talking about how this is actually something that you've had quite a lot of firsthand experience with now, particularly when you're working at Scaleway because there's like new calculators published and stuff like that. I mean, we can talk about the AI thing in a bit more detail later and, but I wanted to ask you a little bit about the impact calculators that I saw you present before.

So are there any principles or any kind of approaches that you think are really helpful when you're helping people engage with a topic like this when they're trying to use a calculator to kind of modify or like improve the footprint as like a professional.

James Martin: Yes. Well, one of the things that sort of peaked my curiosity when we were looking into the topic at Scaleway is, what percentage of servers or instances are really used?

And I was inspired by that, by the work of Holly Cummins from, from Red Hat, who famously said that instances possibly represent around 25% of cloud activity. When I asked around, do cloud providers in general try and identify that, that zombie activity and to just to shut it down, the, from asking around various cloud providers, the consensus I seem to get was, well, no, because people are paying for those instances. So we are just gonna, we are just going to, why would we flag that sort of thing?

So that also shows this sort of, the sort of pushback that a, that an environmental calculator might get. Even though, I mean, you could argue that, the fact that there are zombie instances is potentially more the client's fault than the cloud provider's fault. But yeah, the, building a project like that is just to say that you're going up against of habits where people haven't really, if you want more resources, you can have them, even if you've got too many. It's a

Chris Adams: Yeah, I guess the incentives. 

James Martin: Yeah, the cloud has been a, pretty much a all you can eat service in general for since it was invented. So going sort of try and get to get people to use it more responsibly can be seen a bit as going against the grain, but the good news is, it was, got lots of really positive feedback from clients about it and, I don't know how it's doing now, but I'm sure it's doing some really useful work.

Chris Adams: So you said, so I just wanna check one thing, 'cause we, you, we said, this idea of zombie instances. My, my guess when you say that is, that's basically a running virtual machine or something like that, that's consuming resources, but it doesn't appear to be doing any obviously useful work. Is that what a zombie is in this context?

Right. Okay, cool. And, I can kind of see like why you might not want to kind of turn people's stuff off, along that, because if you are, I mean, if you are running a data center, you're kind of incentivized to keep things up and if you're selling stuff, you're kind of incentivized to kind of make sure there's always stuff available.

Right. But I do, I, kind of see your point, like if you, if you're not at least making this visible to people, then yeah, how are people able to make kind of maybe any responsible choices about, okay, is this really the right size, for example? And if like a chunk of your revenue is reliant on that, that's probably another reason that you might not wanna do some of that stuff, so. Oh, okay. Alright. So there's like a change of incentives that we may need to think about, but I know that one thing that I have seen people talking about in France a lot is actually not just looking at energy and, yeah, okay, France has quite a clean grid because there's lots of things like low carbon energy, like nuclear and stuff like that, but is there something else to that? Like why, is it just because the energy's clean, there's nothing else to do? Or is there a bigger thing that you need to be aware of if you are building a calculator or making some of these, figures available to people?

Is energy the full picture or is there more to it that we should be thinking about?

James Martin: No. Exactly. That was the, that was really the real unique point about Scaleway's calculator, is it wasn't just the carbon calculators and so not just energy and emissions, but also the impact of hardware and also the impact of water, how much water is your data center using? And was a really important part of the project. And I remember my colleagues telling me the most challenging part of the project was actually getting the hardware data off the manufacturers. 'Cause they don't necessarily declare it. 

Nvidia, for example, still gives no lifecycle analysis data on their GPUs. So, it's incredible. But, there it is. So basically, what Scaleway set out to do is the opposite of what AWS does, which is, AWS says, we've bought all this green energy, renewable energy, we've bought enough carbon credits to cover us for the next seven years. Therefore, your cloud is green.

Chris Adams: Nothing to do. No changes. Yeah.

James Martin: Yeah. Which is completely false because it's ignoring the scope three, which is the biggest share of emissions, the emissions. So all of that is ignored. I worked out from a report a while ago that nearly 65% of the tech sector's emissions are unaccounted for. It's a complete, in the dark. Then if you consider that only 11% of tech impacts our emissions, the rest is hardware,

then we're really, what the information that we've got so far is like, it's portion of the real impact. So that was why, it was such a big deal that Scaleway was setting out to, to cover much of the real impact as possible. Because

once you have as broad a picture of as possible of that impact, then you can make the right decisions. As you were saying, Chris, the, then you can choose, I'm going to go for data centers in France because as they say, as you, they, because they have this lower carbon intensity, I might try and use this type of product because it uses less energy. I'd say that is a, that is an added value provider can bring that should attract more clients, I'd have thought, with what with, you've got things like CSRD and all sorts of other 

Chris Adams: Yeah, it's literally written into the standards that you need to declare scope three for cloud and services and data centers now. So if getting that number is easier, then yeah, I can see why that would be helpful actually.

James Martin: Absolutely.

Chris Adams: All right. We'll share a link to that specific part of the European Sustainability reporting standards. 'Cause it kind of blew my mind when I saw it actually. Like I didn't realize it was really that explicit. And that's something that we have.  So you mentioned Nvidia and you mentioned there's a kind of like somewhat known environmental footprint associated with the actual hardware itself. And as I understand it, you mentioned GenAI Impact, which is an organization that's been doing some work to make. Some of these numbers a bit more visible to people when they're using some of that. Maybe, I could just ask you a little bit, and I know as I understand it, is GenAI impact, is it based primarily in France? Is that

James Martin: Yeah. So the sort of my origin story for that was, it was again, Green IO more hats off to Gael. So that was at Green IO Paris 2023. It ended with a, from, Théo Alves Da Costa, who is the co-president of Data for Good, is ONG, which has this like 6,000 data scientists, engineers who are all putting their skills to for good, basically as volunteers. And so he did it this presentation, which, notably drew on a white paper from Data for Good, which said that we didn't really know that much at the time, but that the impact of inference could be anything from 20 to 200 times more than the impact of training.

And he showed it with these bubbles, and you just, and I just looked at it and went, oh my God, this is beyond the, this goes way beyond any level of cloud impact that we've been used to before. So, yeah, that drew me to get interested in, I went to Data for Good's next meeting launched, GenAI Impact, which is the, project which ended up producing Ecologits.ai, which is a super handy calculator for.

Chris Adams: this is a tool to give you to like plugs into like if you're using any kind generative AI tools it as I understand it, like, 'cause we looked through it ourselves. Like if you're using maybe some Python code to call ChatGPT or Mistral or something, it will give you some of the numbers as you do it and it'll give you like the hardware, the water usage and stuff like that.

It gives you some figures, right?

James Martin: Exactly. And the way it does it is, pretty clever so it will mostly measure open source models, easy because you know what their parameters are all the data is open. And it will compare that with closed models. So it will be able to give you an estimation of the impact closed models like ChatGPT so you can use it to say, what is the impact of writing a tweet with, chat g PT versus what is the impact of doing it with llama or whatever? And, because big tech is so opaque, and this is one of my big, bug bears, it means that it gives us a sort of

Chris Adams: That's the best you've got to go on for like me. Yeah.

James Martin: very educated guess, and which is something that should, people to use frugal, AI. That's the idea.

Chris Adams: Okay. So I, this is one thing that I'm always amazed by when I go to France because there seems to be the, field seems to be further along quite a, definitely in Ger than Germany, for example. And like for example, France had the AI Action Summit this year. It's the only country in the world where the kind of government supported frugal AI channel.

You've mentioned this a few times and I'm, might give you a bit of space to actually tell people what frugal AI actually is. I mean, maybe we could talk, how does a conversation About AI spec, for example, how does it differ in France compared to maybe somewhere else in the world, like, that you've experienced because I, it does feel different to me, but I'm not quite sure why.

And I figure as someone who's in France, you've probably got a better idea about what's different and what's driving that.

James Martin: Yeah, it's, it really is a, it is the place to be. So let's say. If you've seen that the Paris just moved ahead of London as the sort of one of the best places for startups to be at the moment. And one of the reason for that is that very strong AI ecosystem. Everyone thinks of Mistral first and foremost, but are lots of others. But yeah, I just wanted to talk first, before I get into that, I wanted why do we need frugal AI? Because, it's not something that people think about on a daily basis, like I was saying before. you can, My wife the other day was, she's a teacher and she was preparing her, she was using ChatGPT to prepare help prepare her lesson. And I was like, no, don't use that. There are lots of, there are lots of other alternative, but to her it's just of course, there and to 800 million people who use every week. They do it because it's free and they do it because works really well. But, what they don't know is that because of tools like, like ChatGPT and we know that ChatGPT is amongst the highest impact of, model. Data center energy consumption is going to triple or maybe even quadruple by the end of the decade. And data center water consumption is going to quadruple by the end of the decade. And there are lots of very serious studies which all, they all came out at the end of last year. Most of them, they all concur that this is, or, all of these, if you put all of their graphs together, they are very, they're very similar and the scariest thing about them, in fact, is that they show that data center energy consumption has been pretty much flat for the past years because whilst cloud usage has been surging something like 500%, the data center operators like Scaleway and lots of other companies have been able to optimize that energy usage and keep it flat. The problem is that AI is, because this has all been based on CPUs, because AI uses GPUs, which use four times more energy and heat up 2.5 times more than CPUs, the curve has gone like this. It's done a complete dog leg.

The consumption of GPUs is just on a such a different scale that the tricks to keep it under control before don't work anymore. So we are really in a sort of, we've reached a tipping point. And it is because, partly people are like generating like millions of Ghibli images, starter packs or, I'm simplifying a lot, but my, I'm, what I'm questioning is, how, when you look at that graph, how much of this activity is really useful? How much of it is curing cancer or, or the greatest joke of all, fixing climate change? When it's, happening is it's making it worse. And that this, again, this dog leg is so sharp that we can't build nuclear power quickly enough to fill up this demand. So what's happening is that, coal burning energy generators, or gas, are being kept open so that we can keep, making those images and doing our homework and all that sort of thing. So that is in a nutshell is, is why we need frugal AI. And we need it also because the, it has been built in a way.

If you, if you haven't read, book Empire of AI yet, by Karen Hao, it's very strongly recommended, because of the things it explains is that the genesis of OpenAI, at some point they decided, that bigger your model is, the more, basically the more compute power it uses, the better it will be. And they've just been built building on that premise ever since the launch of ChatGPT. Whereas the fact is, the most recent versions of ChatGPT or GPT, actually hallucinate more than the less powerful version. So why do we need to throw all that power at it? When, as we see from talking to people like the amazing Sasha Luccioni, with LLMs for example, you have models that are 30 to 60 times smaller, which can do just a, just as well a job, just as good a job. So these are the sort of conversations that you can have a lot, in France, which is really sort of standing out today as a frugal AI pioneer. The fact that the, over 90% of French electricity is carbon free is, that helps a lot. That's something that Mistral in particular, on a lot, say, we've got clean energy, therefore we are green. Watch out for the AWS effect. But it is a very important point, because all the ChatGPT and other impact that's happening in America. And so I was very happy to see because of, big tech's opacity, Ecologits, which as you mentioned is a Python library, it very quickly became a global reference because that's all we had.

Chris Adams: Okay, so when the bar's on the floor, it doesn't need to be very high, right? 

James Martin: Yeah exactly. It's like, my favorite tweet, I think my favorite tweet of the year so far is Sam Altman. I can even share the link to the tweet because I love slash love it so much. It basically said when all these hundreds of thousands of millions of Ghibli images happened, and he joked that GpUs were melting. He said, he shared this completely ridiculous graph, which said, this is the water impact of one ChatGPT query, is the water impact of one Burger.

Chris Adams: Yeah.

James Martin: Sam Altman's comment in the tweet was. Anti AI people making up shit about the impact of ChatGPT whilst eating burgers. And I just found it so cynical because A, I'm not anti AI, I'm just, I'm anti waste. And the, so that's the third point. the reason that people have to make shit up is because they don't declare access to any of the numbers. Yeah. if they did, we wouldn't even be having this conversation. We would be able to say, ChatGPT is this, is this, Llama is this. And we'd be able to compare everyone on a, on the same playing field. But

Chris Adams: On their merits. Yeah.

James Martin: Yeah. So coming back to France. Because I'm wary of going off on a rant. The French government is really, sort of, has been incredible on this topic. So they, around the time of that AI Action Summit, they supported A frugal AI challenge whereby people were encouraged to complete AI tasks across audio, text, and image. And they, you would win the challenge by doing, completing the tasks, whilst using x times less energy than the big LLMs. And so the projects that won, they used 60 times, one of them used 60 times less energy big LLM. Proving that these big LLMs are not necessary.

Chris Adams: And it was solving the same task. 'cause I think from memory, there was like, there was a few challenges which were like, you know, combat disinformation online, discover something useful there. The things like, which were, they weren't, they weren't something which was like, you know, these were considered socially useful problems, but people were free to use any kind of approach they were gonna, they were to take. And what, so what you're saying is that okay, you could use an LLM to solve one of them, but what, solve it one way, but there's other ways that they solved it. And some of the winners were quite, you know, 60 times more efficient, essentially 60 time less consumptive.

Right.

James Martin: Exactly. So, yeah, it's great to have projects like that. The French Government is also has obtained funding for around a dozen frugal AI projects, which are being run by municipalities all over France. So they're using it to optimize energy usage or to detect garbage in the street or that sort of thing. So that's great. The French government also supports the frugal AI guidelines of AFNOR. AFNOR is France's International, sorry, is France's Official Standards Organization, and what they've done is like basically to say for your AI to be frugal, it needs to correspond with these criteria. The first criteria, which I love is, can you prove that this solution cannot be solved by anything else than AI? And it's pretty strict. There are three first steps, but then it goes into a lot of detail about what is or is not frugal AI, and that's such pioneering work it's on track to become EU standard. That's some really some great work there. But I think, for me, one of the best arguments that I use about why should you bother with frugal AI is, very simply, the French Ministry for the Environment has said to startups, if you want to work with us, you have to prove that your AI is frugal first. So,

Chris Adams: Oh, okay. So it's like they're creating demand pool then essentially to like, so like, you know, this is how this is your carrot. Your carrot is a fat government contract, but you need to demonstrate that you're actually following these principles in what you do.

James Martin: I love that because it shows that doing things frugally can actually be good for your business.

Chris Adams: Okay. Alright. So, wow. I think we should definitely make sure we've got some links for a bunch of that stuff. 'Cause I wasn't aware that there were, I know that France in the kind of world of W3C, they have, I can never put, I never, it's the RGESN and I forget I'm not gonna, yeah. I'm not gonna butcher the pronunciation, but it broadly translates to like a general policy for EcoDesign, and I know that's like a standards track for Europe. 

James Martin: Yes. 

Chris Adams: If I can find the actual French words, I might try to share it, but, or maybe you might be to help me with that one because my French is not as, is, nowhere good enough to spell it properly. But I'm also aware that France is actually one of the first countries in the world to actually have like a digital sustainability law. There was one in 2020, the REEN, the Oh yeah.

James Martin: That's it. That's it. Yeah. I was very focused on AI with all those examples. But yeah, France is the only country which has a Digital Responsibility Act, called REEN, basically says, for example, that any municipality with over 50,000 inhabitants has to publish their digital responsibility strategy, even if it's just, we are going to buy older, we are going to keep our PCs going for longer or, sort of simple stuff like that. They, the, this French law demands that localities, municipalities, only make an effort on these things, but they show that they are making an effort. So 

Chris Adams: I see.

James Martin: in a sort of a great incentive.

Chris Adams: Ah, okay. So that I now understand. So the, with the RGESN, as I understand it, that was essentially something like a guide sort of guidelines for France. Ah, so,

James Martin: yeah, it's two different things. RGSN, the guidelines for econ conception. so the how to make your website not only more energy efficient, but also more accessible to people of varying abilities. There's also a law that just came into effect here in France to make websites more accessible. So that, it is great to see those two things going hand in hand. They also announced at the AI Action Summit that they were going to invest hundred billion in new data centers for AI by the end of the decade. You win some, you lose some. But maybe better to do that here with lower carbon than in the states, which is generally speaking, 10 times more carbon in the electricity.

Chris Adams: Okay. It sounds like there's a lot happening in France. So not only that, are they talking, so there is this whole, not only is this, there's an idea of like frugal AI in digital sobriety, which is this other French term, which when translated in English, always sounds really strange to my ears, but there's actually quite a lot of, for want of a better word, like policy support behind this stuff to actually encourage people to work in this way, basically, huh?

James Martin: Absolutely. And again, I would give a, another heads up to Data for Good for that because they were instrumental in that frugal AI challenge along with Sasha Luccioni.

Chris Adams: Okay.

James Martin: By the way, we'll be, we'll be speaking at Viva Tech. So, Viva Tech is France's biggest tech event. It's actually one of the biggest
tech events in Europe. Unfortunately, they had Elon Musk as their keynote last year and the year before. Fortunately they won't this year.

Chris Adams: Yeah.

James Martin: Sasha is going to be one of their keynotes this year, which is also great, I think it's a good sign.
And she will also be speaking on a panel as part of a sustainability summit with Kate Kallot, which is of Amini AI. And I'll be that conversation. So I'm happy these sort of conversations are happening. Not

Chris Adams: But more mainstream by the sounds of things.

James Martin: Not only between, people like you and me who care, and are, who understand all the tech. But it's super important, as I was saying at the beginning, to be having these conversations with as broad an audience as possible, because otherwise nothing's gonna change.

Chris Adams: Okay, so we've spoke about, we've gone quite deeply into talking about AI and hardware and water and stuff like that. If we pull back out. So you are, we talk about how people might engage with this topic in the first place.

If there's one thing you could change about how people talk about sustainability, particularly in technology, what would you change, James?

James Martin: I suppose I'd presume it as, don't believe the hype. And the hype tech is usually, bigger is better. What I would like people to try and really integrate is that bigger isn't always better. As we said before, it is very important to look at the holistic picture of impacts rather than just the individual ones. It's more important to pressure companies to change as you see with that French government example, rather than making users feel guilty because again, it's not their fault. And I just think people, what I try, what I'm trying to do as often as I can, Chris, is just bring people back to that sort of gold standard of green IT, which is only use the right tools for the right needs.

This is why this sort of bigger is better thing is just so irritating to me. The way AI is being done right now, it's a classic in tech. It's using a bazooka to swat a fly. It's not necessary. And it's actually, not only is it ridiculous, but it's also very bad the planet. So, if you only need to do this much, you only need a tool that does this much, not this much. And that's one of the reasons that why,when I hear the term AI for Good, which we hear a lot of at the moment, I would say that I would challenge that and I would encourage people to challenge that too by saying, "are sure this AI is for good? Are you sure this tech is for good? Are you sure? That the good, that it does, far outweighs the potential harm that it has?"

Because it's not always the case. A lot of the AI for good examples see at the moment, are just. they can't be backed with scientific data at all.And that comes back to another of my points. If you can't prove that it's for good, then it's not, and it's probably greenwashing.

Chris Adams: Okay. So show us your receipts then. Basically, yeah.

James Martin: Yeah. 

Chris Adams: Okay. Well thanks for that, James. James. we're just coming up to time now. So if people have found this interesting and they wanted to learn more about either your writing or where you'll be next, where should people be looking? Is there like, maybe, I mean, you mentioned the website for example, is there anywhere else people should be looking to kind of keep up with, like updates from you or anything like that?

The website is BetterTech.blog. So yeah, that's the main, that's where you can find a lot more resources about my work on the impact AI and on other things. I also post frequently on LinkedIn about, about this sort of thing, like things like the last one was about frugal prompting.

James Martin: That's, my latest discovery. and, yeah, those are the two, main sources. And, I'll work together to make sure that the.

Chris Adams: We have all the links for the show notes and everything like that.

James Martin: of this, of this episode.

Chris Adams: Brilliant. Well, James, thank you so much for giving me the time, and to everyone's listening, for all of this. And I hope you enjoy the rest of the day in what look appears to be sunny Paris behind you.

James Martin: It is been, it's been sunnier, but it's fine.

Chris Adams: Okay.

James Martin: It's still Paris, so grumble. Thanks very much.

Chris Adams: Indeed.

James Martin: Thanks very much, Chris. It's like I said, it's been a real honor to be on this podcast and I hope we've been able that's useful for people.

Chris Adams: Merci beaucoup, James.

James Martin: Merci as well, Chris. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Host Chris Adams speaks with James Martin about how to communicate the environmental impact of software to a general audience. Drawing on his background in journalism and sustainability communications, James shares strategies for translating complex digital sustainability issues into accessible narratives, explains why AI's growing resource demands require scrutiny, and highlights France’s leadership in frugal AI policy and standards. From impact calculators to debunking greenwashing, this episode unpacks how informed storytelling can drive responsible tech choices.

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TRANSCRIPT BELOW:

James Martin: When I hear the term AI for Good, which we hear a lot of at the moment, I would say that I would challenge that and I would encourage people to challenge that too by saying "are sure this AI is for good? Are you sure this tech is for good? Are you sure that the good that it does, far outweighs the potential harm that it has?"

Because it's not always the case. A lot of the AI for good examples see at the moment are just, they can't be backed with scientific data at all.

And that comes back to another of my points. If you can't prove that it's for good, then it's not, and it's probably greenwashing.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Welcome to Environment Variables, where we bring you the latest news and updates from the Board of Sustainable Software Development. I'm your host, Chris Adams. Our guest today is James Martin, a content and communications expert who has spent years translating complex text strategies into compelling narratives that drive change.

From leading communications with a special focus on sustainability at Scaleway, to founding BetterTech.blog, James has been at the forefront of making green tech more actionable and accessible. He's spoken at major climate and tech events, most recently ChangeNOW. He's written a comprehensive white paper on green IT, and played a key role in Gen AI Impact a French NGO working to measure the impact of AI. And also he's a Green Software Foundation champion.

So, James, thank you so much for joining the podcast. Really lovely to see you again after we last crossed paths in, I guess Paris, I think. Maybe I've tried to introduce you a little bit, but I figure there's maybe some things you might wanna talk about as well. So, can I give you the floor to just like introduce yourself and, talk a little bit about yourself?

James Martin: Yeah, thanks very much, Chris. First and foremost, I just wanted to say I'm really happy to, be on this podcast with you because, this podcast is one of the things that really got me excited, and it started me off on my green IT adventure. So, thanks to you and Anne for putting all, putting out all these amazing episodes.

Basically what I'm speaking today in the name of BetterTech, which is my blog, which I founded 2018. So I've been a, I've been a journalist for most of my career. And, so for about 15 years I was writing for a French cultural magazine. I had a page in that two weeks. And I started off writing out, "here's a new iPhone, here's a new console." And after that I got a bit bored of just saying the same thing every time. So I was drawn towards more responsible topics, like how do you reduce your screen time, how do you protect your data?

And also, of course, what is the impact of technology on the planet? So that started in that, in that magazine, and then I got so into it, i founded my own blog on the topic.

And then that was pretty much when an opportunity came up, in 2020, 2021 to work at Scaleway. I thought that sounds really interesting because, that is a European cloud provider, so not American. And also they were already very, communicating a lot about the sustainable aspect of what they do. So, yeah, I was very happy to join them and lead their communications from 2021 with this huge focus sustainability.

Chris Adams: Ah.

James Martin: Yeah, that's how, that's basically where it started. At that time, Scaleway had its centers and one of them called DC five, which is one of the most sustainable Europe because it doesn't have air conditioning, so it uses a lot less energy. That's it. It has adiabatic cooling. So we focused a lot of communication efforts on that. But then after year or two, Scaleway decided to sell its data centers. I had to look at are the other ways I could talk about sustainability in the cloud? So from digging around into green IT, especially into some green Software Foundation resources,

I basically understood that not just data centers, it's hardware and software. So I also, with a bit of help from one of a pivotal meeting, was meeting Neil Fryer from who the Green Software Foundation at a conference. I got him to come and speak at Scaleway to people like me were sort of concerned about the impact of tech. And then that led to the white paper that you mentioned that I erase in 2023, which is basic. It's basically how engineers can reduce the impact of of technology. So, and then that led to speaking opportunities and then to realize that, yeah, I'm not a, I'm not a developer. I'm not an engineer. I may be the first non-developer on this podcast. So I can't build Green Tech, but I can explain how it works and I think that's an important thing to be able to do, if we want to convince as many people as possible of how important this is, then it needs to be communicated properly.

And, yeah, so that's what I've been doing ever since.

Chris Adams: Okay, thanks. Okay, so I'm, I appreciate that you're coming here as not as a non, as someone who's not like a full-time techie who's like using GitHub on the daily and everything like that, because I think that means you, you get a bit of a chance to like see how normal people see this who aren't conversant in like object storage or block storage or stuff like that.

So maybe we can talk a little bit about that then, because when people start to think about, say, the environmental footprint of digital services, right? It's often coming from a very low base. And it's like people might start thinking about like the carbon footprint of their emails, and that's like the thing they should be focusing on first.

And like if you do have a bit of domain knowledge, you'll often realize that actually that's probably not where you'd start if you have a kind of more, more developed understanding of the problem. Now you've spent some of your time being this translator between techies and like people who are not full, you know, who, who aren't writing code and building applications all day long, for example.

So maybe we could talk a little bit about like the misunderstandings people have when they come to this in the first place and how you might address some of this because this seems to be your day job and this might be something that could, that might help who are other techies realize how they might change the way they talk about this for other people to make a bit more accessible and intelligible.

James Martin: Yes. So, thank you for mentioning day job first and foremost, because, so Scaleway was my former day job and I have another day job working for another french scale app. But here I'm very much speaking in the name of my blog. It's because I care so much about topics that I continue to talk about them, to write about them on the side because it's just, I just think something that needs to be done. So this is why today with my BetterTech hat

Chris Adams: Hat on. Yeah.

James Martin: so yeah, just wanted to make that clear. The first thing that people do when people misunderstand stuff, the first thing I want to say is it's not their fault. Sometimes they are led down the wrong path. Like,  a few years ago, French environment Minister said people should stop trying to send so many funny email attachments.

Chris Adams: Oh, really? 

James Martin: Like when you send a joking video to all your colleagues, you should stop doing that because it's not good for the planet. It honestly, the minister could say something that misguided because that's not where we, you and I know, not where the impact is. The impact is in the cloud.
The impact is in hardware. So it is sort of, about the communication is repetition and I always start with, digital is 4% of global emissions, and 1% of that is data centers, 3% of that is hardware, and software is sort of all over the place. That's the thing I, the figure I use the most to get things started. I think the, there's number one misconception that people need to get their heads around is the people tend to think that tech is, immaterial. It's because of expressions like the cloud. It just sounds,

Chris Adams: Like this floaty thing rather than massive industrial concrete things. Yeah.

James Martin: Need to make it more physical. If, I can't remember who said that if data centers could fly, then it would make our job easier. But no, that's where you need to always come back to the figures. 4% is double the emissions of planes. And yet, the AIrline industry gets tens of hundreds times more hassle than the tech industry in terms of trying to keep control of their emissions. So what you need is a lot more examples, and you need people to explain this impact over time, so you need to move away from bad examples, like funny email attachments or the thing about, we keep hearing in AI is, one ChatGPT prompt is 10 times more energy than Google. That may or may not be true, but it's a bit, again, it's a bit of the, it's the wrong example, because it doesn't. It doesn't focus on the bigger picture. 

Chris Adams: Yeah. That kind of implies that if I just like reduce my usage of this, then I'm gonna have like 10 times the impact. I'm gonna, you know, that's all I need to, that feels a bit kind of individual, a bit like individualizing the problem, surely. Right?

James Martin: And it's putting it on people's, it's putting the onus on the users, whereas it's not their fault. You need to see the bigger picture. And this is what I've been repeating since I wrote that white paper actually, you can't say you have a green IT approach if you're only focusing on data centers, hardware or software. You've got to focus on, yeah, exactly. Holistically.

That said, you should also encourage people to have greener habits because that's, me stopping using ChatGPT just on my own won't have much impact, but it will if I can convince, if I can tell my family, if I can tell my friends, if I can talk about it in podcasts and conferences, then maybe the more people question their usage, then maybe the providers of that tech start providing more frugal examples. But

Chris Adams: Ah, I see. So that's like maybe almost like choice architecture, giving people like, you know, foregrounding some of the options. So, you know, making it easier to do, possibly the more sustainable thing, rather than making people at the right, at the end of the process do all the hard work. You, it sounds like you're suggesting that okay, as a professional, part of my role is to kind of put different choices in front of someone who's maybe using my service to make it easier for them to do more sustainable things, rather than like things which are much more environmentally destructive, for example.

James Martin: Yes. And I would add a final thing, which is sort of super important because people, there are a topics like electric cars for example, which people get really emotional and angry about, 'cause people are very attached to their cars and yet cars are the number one source of emissions in most Western countries. The way around the emotion is to use, I really focus on only using science-based facts. If it's from the IPCC, if it's from the IEA, if it's like really serious scientific studies then you can use it. If it's just someone speculating on LinkedIn, no. So I always make sure that data I use as fully backed by science, by a sort of by all the GHG protocols, looking at all three scopes all that sort of thing. Because otherwise you just can't, it could be greenwashing.

Chris Adams: Okay. Alright, so maybe this is actually a nice segue for the next question because when people talk about, say, well basically in footprint from here, one of the challenges people have is like, like having some numbers, having tr, having some figures for any of this stuff. For example, if I'm using maybe a chat bot, i-, it's very hard for me to understand what the footprint might be. So in the absence of that, you can kind of see how people end up with an idea saying, oh yeah, every query is the same as a, you know, bottle of water, for example. Simply because there is a kind of dearth of information. And this is something that I think that I remember when you presented at Green IO, a conference around kind of green IT, you were talking about how this is actually something that you've had quite a lot of firsthand experience with now, particularly when you're working at Scaleway because there's like new calculators published and stuff like that. I mean, we can talk about the AI thing in a bit more detail later and, but I wanted to ask you a little bit about the impact calculators that I saw you present before.

So are there any principles or any kind of approaches that you think are really helpful when you're helping people engage with a topic like this when they're trying to use a calculator to kind of modify or like improve the footprint as like a professional.

James Martin: Yes. Well, one of the things that sort of peaked my curiosity when we were looking into the topic at Scaleway is, what percentage of servers or instances are really used?

And I was inspired by that, by the work of Holly Cummins from, from Red Hat, who famously said that instances possibly represent around 25% of cloud activity. When I asked around, do cloud providers in general try and identify that, that zombie activity and to just to shut it down, the, from asking around various cloud providers, the consensus I seem to get was, well, no, because people are paying for those instances. So we are just gonna, we are just going to, why would we flag that sort of thing?

So that also shows this sort of, the sort of pushback that a, that an environmental calculator might get. Even though, I mean, you could argue that, the fact that there are zombie instances is potentially more the client's fault than the cloud provider's fault. But yeah, the, building a project like that is just to say that you're going up against of habits where people haven't really, if you want more resources, you can have them, even if you've got too many. It's a

Chris Adams: Yeah, I guess the incentives. 

James Martin: Yeah, the cloud has been a, pretty much a all you can eat service in general for since it was invented. So going sort of try and get to get people to use it more responsibly can be seen a bit as going against the grain, but the good news is, it was, got lots of really positive feedback from clients about it and, I don't know how it's doing now, but I'm sure it's doing some really useful work.

Chris Adams: So you said, so I just wanna check one thing, 'cause we, you, we said, this idea of zombie instances. My, my guess when you say that is, that's basically a running virtual machine or something like that, that's consuming resources, but it doesn't appear to be doing any obviously useful work. Is that what a zombie is in this context?

Right. Okay, cool. And, I can kind of see like why you might not want to kind of turn people's stuff off, along that, because if you are, I mean, if you are running a data center, you're kind of incentivized to keep things up and if you're selling stuff, you're kind of incentivized to kind of make sure there's always stuff available.

Right. But I do, I, kind of see your point, like if you, if you're not at least making this visible to people, then yeah, how are people able to make kind of maybe any responsible choices about, okay, is this really the right size, for example? And if like a chunk of your revenue is reliant on that, that's probably another reason that you might not wanna do some of that stuff, so. Oh, okay. Alright. So there's like a change of incentives that we may need to think about, but I know that one thing that I have seen people talking about in France a lot is actually not just looking at energy and, yeah, okay, France has quite a clean grid because there's lots of things like low carbon energy, like nuclear and stuff like that, but is there something else to that? Like why, is it just because the energy's clean, there's nothing else to do? Or is there a bigger thing that you need to be aware of if you are building a calculator or making some of these, figures available to people?

Is energy the full picture or is there more to it that we should be thinking about?

James Martin: No. Exactly. That was the, that was really the real unique point about Scaleway's calculator, is it wasn't just the carbon calculators and so not just energy and emissions, but also the impact of hardware and also the impact of water, how much water is your data center using? And was a really important part of the project. And I remember my colleagues telling me the most challenging part of the project was actually getting the hardware data off the manufacturers. 'Cause they don't necessarily declare it. 

Nvidia, for example, still gives no lifecycle analysis data on their GPUs. So, it's incredible. But, there it is. So basically, what Scaleway set out to do is the opposite of what AWS does, which is, AWS says, we've bought all this green energy, renewable energy, we've bought enough carbon credits to cover us for the next seven years. Therefore, your cloud is green.

Chris Adams: Nothing to do. No changes. Yeah.

James Martin: Yeah. Which is completely false because it's ignoring the scope three, which is the biggest share of emissions, the emissions. So all of that is ignored. I worked out from a report a while ago that nearly 65% of the tech sector's emissions are unaccounted for. It's a complete, in the dark. Then if you consider that only 11% of tech impacts our emissions, the rest is hardware,

then we're really, what the information that we've got so far is like, it's portion of the real impact. So that was why, it was such a big deal that Scaleway was setting out to, to cover much of the real impact as possible. Because

once you have as broad a picture of as possible of that impact, then you can make the right decisions. As you were saying, Chris, the, then you can choose, I'm going to go for data centers in France because as they say, as you, they, because they have this lower carbon intensity, I might try and use this type of product because it uses less energy. I'd say that is a, that is an added value provider can bring that should attract more clients, I'd have thought, with what with, you've got things like CSRD and all sorts of other 

Chris Adams: Yeah, it's literally written into the standards that you need to declare scope three for cloud and services and data centers now. So if getting that number is easier, then yeah, I can see why that would be helpful actually.

James Martin: Absolutely.

Chris Adams: All right. We'll share a link to that specific part of the European Sustainability reporting standards. 'Cause it kind of blew my mind when I saw it actually. Like I didn't realize it was really that explicit. And that's something that we have.  So you mentioned Nvidia and you mentioned there's a kind of like somewhat known environmental footprint associated with the actual hardware itself. And as I understand it, you mentioned GenAI Impact, which is an organization that's been doing some work to make. Some of these numbers a bit more visible to people when they're using some of that. Maybe, I could just ask you a little bit, and I know as I understand it, is GenAI impact, is it based primarily in France? Is that

James Martin: Yeah. So the sort of my origin story for that was, it was again, Green IO more hats off to Gael. So that was at Green IO Paris 2023. It ended with a, from, Théo Alves Da Costa, who is the co-president of Data for Good, is ONG, which has this like 6,000 data scientists, engineers who are all putting their skills to for good, basically as volunteers. And so he did it this presentation, which, notably drew on a white paper from Data for Good, which said that we didn't really know that much at the time, but that the impact of inference could be anything from 20 to 200 times more than the impact of training.

And he showed it with these bubbles, and you just, and I just looked at it and went, oh my God, this is beyond the, this goes way beyond any level of cloud impact that we've been used to before. So, yeah, that drew me to get interested in, I went to Data for Good's next meeting launched, GenAI Impact, which is the, project which ended up producing Ecologits.ai, which is a super handy calculator for.

Chris Adams: this is a tool to give you to like plugs into like if you're using any kind generative AI tools it as I understand it, like, 'cause we looked through it ourselves. Like if you're using maybe some Python code to call ChatGPT or Mistral or something, it will give you some of the numbers as you do it and it'll give you like the hardware, the water usage and stuff like that.

It gives you some figures, right?

James Martin: Exactly. And the way it does it is, pretty clever so it will mostly measure open source models, easy because you know what their parameters are all the data is open. And it will compare that with closed models. So it will be able to give you an estimation of the impact closed models like ChatGPT so you can use it to say, what is the impact of writing a tweet with, chat g PT versus what is the impact of doing it with llama or whatever? And, because big tech is so opaque, and this is one of my big, bug bears, it means that it gives us a sort of

Chris Adams: That's the best you've got to go on for like me. Yeah.

James Martin: very educated guess, and which is something that should, people to use frugal, AI. That's the idea.

Chris Adams: Okay. So I, this is one thing that I'm always amazed by when I go to France because there seems to be the, field seems to be further along quite a, definitely in Ger than Germany, for example. And like for example, France had the AI Action Summit this year. It's the only country in the world where the kind of government supported frugal AI channel.

You've mentioned this a few times and I'm, might give you a bit of space to actually tell people what frugal AI actually is. I mean, maybe we could talk, how does a conversation About AI spec, for example, how does it differ in France compared to maybe somewhere else in the world, like, that you've experienced because I, it does feel different to me, but I'm not quite sure why.

And I figure as someone who's in France, you've probably got a better idea about what's different and what's driving that.

James Martin: Yeah, it's, it really is a, it is the place to be. So let's say. If you've seen that the Paris just moved ahead of London as the sort of one of the best places for startups to be at the moment. And one of the reason for that is that very strong AI ecosystem. Everyone thinks of Mistral first and foremost, but are lots of others. But yeah, I just wanted to talk first, before I get into that, I wanted why do we need frugal AI? Because, it's not something that people think about on a daily basis, like I was saying before. you can, My wife the other day was, she's a teacher and she was preparing her, she was using ChatGPT to prepare help prepare her lesson. And I was like, no, don't use that. There are lots of, there are lots of other alternative, but to her it's just of course, there and to 800 million people who use every week. They do it because it's free and they do it because works really well. But, what they don't know is that because of tools like, like ChatGPT and we know that ChatGPT is amongst the highest impact of, model. Data center energy consumption is going to triple or maybe even quadruple by the end of the decade. And data center water consumption is going to quadruple by the end of the decade. And there are lots of very serious studies which all, they all came out at the end of last year. Most of them, they all concur that this is, or, all of these, if you put all of their graphs together, they are very, they're very similar and the scariest thing about them, in fact, is that they show that data center energy consumption has been pretty much flat for the past years because whilst cloud usage has been surging something like 500%, the data center operators like Scaleway and lots of other companies have been able to optimize that energy usage and keep it flat. The problem is that AI is, because this has all been based on CPUs, because AI uses GPUs, which use four times more energy and heat up 2.5 times more than CPUs, the curve has gone like this. It's done a complete dog leg.

The consumption of GPUs is just on a such a different scale that the tricks to keep it under control before don't work anymore. So we are really in a sort of, we've reached a tipping point. And it is because, partly people are like generating like millions of Ghibli images, starter packs or, I'm simplifying a lot, but my, I'm, what I'm questioning is, how, when you look at that graph, how much of this activity is really useful? How much of it is curing cancer or, or the greatest joke of all, fixing climate change? When it's, happening is it's making it worse. And that this, again, this dog leg is so sharp that we can't build nuclear power quickly enough to fill up this demand. So what's happening is that, coal burning energy generators, or gas, are being kept open so that we can keep, making those images and doing our homework and all that sort of thing. So that is in a nutshell is, is why we need frugal AI. And we need it also because the, it has been built in a way.

If you, if you haven't read, book Empire of AI yet, by Karen Hao, it's very strongly recommended, because of the things it explains is that the genesis of OpenAI, at some point they decided, that bigger your model is, the more, basically the more compute power it uses, the better it will be. And they've just been built building on that premise ever since the launch of ChatGPT. Whereas the fact is, the most recent versions of ChatGPT or GPT, actually hallucinate more than the less powerful version. So why do we need to throw all that power at it? When, as we see from talking to people like the amazing Sasha Luccioni, with LLMs for example, you have models that are 30 to 60 times smaller, which can do just a, just as well a job, just as good a job. So these are the sort of conversations that you can have a lot, in France, which is really sort of standing out today as a frugal AI pioneer. The fact that the, over 90% of French electricity is carbon free is, that helps a lot. That's something that Mistral in particular, on a lot, say, we've got clean energy, therefore we are green. Watch out for the AWS effect. But it is a very important point, because all the ChatGPT and other impact that's happening in America. And so I was very happy to see because of, big tech's opacity, Ecologits, which as you mentioned is a Python library, it very quickly became a global reference because that's all we had.

Chris Adams: Okay, so when the bar's on the floor, it doesn't need to be very high, right? 

James Martin: Yeah exactly. It's like, my favorite tweet, I think my favorite tweet of the year so far is Sam Altman. I can even share the link to the tweet because I love slash love it so much. It basically said when all these hundreds of thousands of millions of Ghibli images happened, and he joked that GpUs were melting. He said, he shared this completely ridiculous graph, which said, this is the water impact of one ChatGPT query, is the water impact of one Burger.

Chris Adams: Yeah.

James Martin: Sam Altman's comment in the tweet was. Anti AI people making up shit about the impact of ChatGPT whilst eating burgers. And I just found it so cynical because A, I'm not anti AI, I'm just, I'm anti waste. And the, so that's the third point. the reason that people have to make shit up is because they don't declare access to any of the numbers. Yeah. if they did, we wouldn't even be having this conversation. We would be able to say, ChatGPT is this, is this, Llama is this. And we'd be able to compare everyone on a, on the same playing field. But

Chris Adams: On their merits. Yeah.

James Martin: Yeah. So coming back to France. Because I'm wary of going off on a rant. The French government is really, sort of, has been incredible on this topic. So they, around the time of that AI Action Summit, they supported A frugal AI challenge whereby people were encouraged to complete AI tasks across audio, text, and image. And they, you would win the challenge by doing, completing the tasks, whilst using x times less energy than the big LLMs. And so the projects that won, they used 60 times, one of them used 60 times less energy big LLM. Proving that these big LLMs are not necessary.

Chris Adams: And it was solving the same task. 'cause I think from memory, there was like, there was a few challenges which were like, you know, combat disinformation online, discover something useful there. The things like, which were, they weren't, they weren't something which was like, you know, these were considered socially useful problems, but people were free to use any kind of approach they were gonna, they were to take. And what, so what you're saying is that okay, you could use an LLM to solve one of them, but what, solve it one way, but there's other ways that they solved it. And some of the winners were quite, you know, 60 times more efficient, essentially 60 time less consumptive.

Right.

James Martin: Exactly. So, yeah, it's great to have projects like that. The French Government is also has obtained funding for around a dozen frugal AI projects, which are being run by municipalities all over France. So they're using it to optimize energy usage or to detect garbage in the street or that sort of thing. So that's great. The French government also supports the frugal AI guidelines of AFNOR. AFNOR is France's International, sorry, is France's Official Standards Organization, and what they've done is like basically to say for your AI to be frugal, it needs to correspond with these criteria. The first criteria, which I love is, can you prove that this solution cannot be solved by anything else than AI? And it's pretty strict. There are three first steps, but then it goes into a lot of detail about what is or is not frugal AI, and that's such pioneering work it's on track to become EU standard. That's some really some great work there. But I think, for me, one of the best arguments that I use about why should you bother with frugal AI is, very simply, the French Ministry for the Environment has said to startups, if you want to work with us, you have to prove that your AI is frugal first. So,

Chris Adams: Oh, okay. So it's like they're creating demand pool then essentially to like, so like, you know, this is how this is your carrot. Your carrot is a fat government contract, but you need to demonstrate that you're actually following these principles in what you do.

James Martin: I love that because it shows that doing things frugally can actually be good for your business.

Chris Adams: Okay. Alright. So, wow. I think we should definitely make sure we've got some links for a bunch of that stuff. 'Cause I wasn't aware that there were, I know that France in the kind of world of W3C, they have, I can never put, I never, it's the RGESN and I forget I'm not gonna, yeah. I'm not gonna butcher the pronunciation, but it broadly translates to like a general policy for EcoDesign, and I know that's like a standards track for Europe. 

James Martin: Yes. 

Chris Adams: If I can find the actual French words, I might try to share it, but, or maybe you might be to help me with that one because my French is not as, is, nowhere good enough to spell it properly. But I'm also aware that France is actually one of the first countries in the world to actually have like a digital sustainability law. There was one in 2020, the REEN, the Oh yeah.

James Martin: That's it. That's it. Yeah. I was very focused on AI with all those examples. But yeah, France is the only country which has a Digital Responsibility Act, called REEN, basically says, for example, that any municipality with over 50,000 inhabitants has to publish their digital responsibility strategy, even if it's just, we are going to buy older, we are going to keep our PCs going for longer or, sort of simple stuff like that. They, the, this French law demands that localities, municipalities, only make an effort on these things, but they show that they are making an effort. So 

Chris Adams: I see.

James Martin: in a sort of a great incentive.

Chris Adams: Ah, okay. So that I now understand. So the, with the RGESN, as I understand it, that was essentially something like a guide sort of guidelines for France. Ah, so,

James Martin: yeah, it's two different things. RGSN, the guidelines for econ conception. so the how to make your website not only more energy efficient, but also more accessible to people of varying abilities. There's also a law that just came into effect here in France to make websites more accessible. So that, it is great to see those two things going hand in hand. They also announced at the AI Action Summit that they were going to invest hundred billion in new data centers for AI by the end of the decade. You win some, you lose some. But maybe better to do that here with lower carbon than in the states, which is generally speaking, 10 times more carbon in the electricity.

Chris Adams: Okay. It sounds like there's a lot happening in France. So not only that, are they talking, so there is this whole, not only is this, there's an idea of like frugal AI in digital sobriety, which is this other French term, which when translated in English, always sounds really strange to my ears, but there's actually quite a lot of, for want of a better word, like policy support behind this stuff to actually encourage people to work in this way, basically, huh?

James Martin: Absolutely. And again, I would give a, another heads up to Data for Good for that because they were instrumental in that frugal AI challenge along with Sasha Luccioni.

Chris Adams: Okay.

James Martin: By the way, we'll be, we'll be speaking at Viva Tech. So, Viva Tech is France's biggest tech event. It's actually one of the biggest
tech events in Europe. Unfortunately, they had Elon Musk as their keynote last year and the year before. Fortunately they won't this year.

Chris Adams: Yeah.

James Martin: Sasha is going to be one of their keynotes this year, which is also great, I think it's a good sign.
And she will also be speaking on a panel as part of a sustainability summit with Kate Kallot, which is of Amini AI. And I'll be that conversation. So I'm happy these sort of conversations are happening. Not

Chris Adams: But more mainstream by the sounds of things.

James Martin: Not only between, people like you and me who care, and are, who understand all the tech. But it's super important, as I was saying at the beginning, to be having these conversations with as broad an audience as possible, because otherwise nothing's gonna change.

Chris Adams: Okay, so we've spoke about, we've gone quite deeply into talking about AI and hardware and water and stuff like that. If we pull back out. So you are, we talk about how people might engage with this topic in the first place.

If there's one thing you could change about how people talk about sustainability, particularly in technology, what would you change, James?

James Martin: I suppose I'd presume it as, don't believe the hype. And the hype tech is usually, bigger is better. What I would like people to try and really integrate is that bigger isn't always better. As we said before, it is very important to look at the holistic picture of impacts rather than just the individual ones. It's more important to pressure companies to change as you see with that French government example, rather than making users feel guilty because again, it's not their fault. And I just think people, what I try, what I'm trying to do as often as I can, Chris, is just bring people back to that sort of gold standard of green IT, which is only use the right tools for the right needs.

This is why this sort of bigger is better thing is just so irritating to me. The way AI is being done right now, it's a classic in tech. It's using a bazooka to swat a fly. It's not necessary. And it's actually, not only is it ridiculous, but it's also very bad the planet. So, if you only need to do this much, you only need a tool that does this much, not this much. And that's one of the reasons that why,when I hear the term AI for Good, which we hear a lot of at the moment, I would say that I would challenge that and I would encourage people to challenge that too by saying, "are sure this AI is for good? Are you sure this tech is for good? Are you sure? That the good, that it does, far outweighs the potential harm that it has?"

Because it's not always the case. A lot of the AI for good examples see at the moment, are just. they can't be backed with scientific data at all.And that comes back to another of my points. If you can't prove that it's for good, then it's not, and it's probably greenwashing.

Chris Adams: Okay. So show us your receipts then. Basically, yeah.

James Martin: Yeah. 

Chris Adams: Okay. Well thanks for that, James. James. we're just coming up to time now. So if people have found this interesting and they wanted to learn more about either your writing or where you'll be next, where should people be looking? Is there like, maybe, I mean, you mentioned the website for example, is there anywhere else people should be looking to kind of keep up with, like updates from you or anything like that?

The website is BetterTech.blog. So yeah, that's the main, that's where you can find a lot more resources about my work on the impact AI and on other things. I also post frequently on LinkedIn about, about this sort of thing, like things like the last one was about frugal prompting.

James Martin: That's, my latest discovery. and, yeah, those are the two, main sources. And, I'll work together to make sure that the.

Chris Adams: We have all the links for the show notes and everything like that.

James Martin: of this, of this episode.

Chris Adams: Brilliant. Well, James, thank you so much for giving me the time, and to everyone's listening, for all of this. And I hope you enjoy the rest of the day in what look appears to be sunny Paris behind you.

James Martin: It is been, it's been sunnier, but it's fine.

Chris Adams: Okay.

James Martin: It's still Paris, so grumble. Thanks very much.

Chris Adams: Indeed.

James Martin: Thanks very much, Chris. It's like I said, it's been a real honor to be on this podcast and I hope we've been able that's useful for people.

Chris Adams: Merci beaucoup, James.

James Martin: Merci as well, Chris. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Why You Need Hardware Standards for Green Software Why You Need Hardware Standards for Green Software Thu, 15 May 2025 07:00:00 GMT 50:34 80qqzl20 false https://podcasts.castplus.fm/e/vn55m92n-hardware-standards-working-group 68dc7d077be17a7f01356615 68dc7cef46a2532cdd8d1c23 Chris Adams is joined by Zachary Smith and My Truong both members of the Hardware Standards Working Group at the GSF. They dive into the challenges of improving hardware efficiency in data centers, the importance of standardization, and how emerging technologies like two-phase liquid cooling systems can reduce emissions, improve energy reuse, and even support power grid stability. They also discuss grid operation and the potential of software-hardware coordination to drastically cut infrastructure impact. 108 Chris Adams is joined by Zachary Smith and My Truong both members of the Hardware Standards Working Group at the GSF. They dive into the challenges of improving hardware efficiency in data centers, the importance of standardization, and how emerging technologies like two-phase liquid cooling systems can reduce emissions, improve energy reuse, and even support power grid stability. They also discuss grid operation and the potential of software-hardware coordination to drastically cut infrastructure impact. 

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TRANSCRIPT BELOW:

Zachary Smith: We've successfully made data centers into cloud computing over the past 20 or 25 years, where most people who use and consume data centers never actually see them or touch them. And so it's out of sight, out of mind in terms of the impacts of the latest and greatest hardware or refresh. What happens to a 2-year-old Nvidia server when it goes to die? Does anybody really know Hello, and welcome to Environment Variables,

Chris Adams: brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to Environment Variables, the podcast where we explore the latest in sustainable software development. I'm your host, Chris Adams. Since this podcast started in 2022, we've spoken a lot about green software, how to make code more efficient so it consumes fewer resources or runs on a wider range of hardware to avoid needless hardware upgrades, and so on.

We've also covered how to deploy services into data centers where energy is the cleanest, or even when energy is the cleanest, by timing compute jobs to coincide with an abundance of clean energy on the grid. However, for many of these interventions to work, they rely on the next layer down from software,

the hardware layer, to play along. And for that to work at scale, you really need standards. Earlier this year, the SSIA, the Sustainable and Scalable Infrastructure Alliance, joined the Green Software Foundation. So now there's a hardware standards working group for HSWG within the Green Software Foundation too.

Today we're joined by two leaders in the field who are shaping the future of sustainable software. So, oops, sustainable hardware. We've got Zachary Smith formerly of Packet and Equinix, and My Truong from ZutaCore. We'll be discussing hardware efficiency, how it fits into the bigger sustainability picture, the role of the Open19 standard, and the challenges and opportunities of making data centers greener.

So let's get started. So, Zachary Smith, you are alphabetically ahead of My Truong, Mr. Truong. So can I give you the floor first to introduce yourself and tell a little bit about yourself for the listeners?

Zachary Smith: Sure. Thanks so much, Chris. It's a pleasure being here and getting to work with My on this podcast. As you mentioned, my name's Zachary Smith. I've been an entrepreneur, primarily in cloud computing for, I guess it's about 25 years now. I went to Juilliard. I studied music and ended up figuring that wasn't gonna pay my rent here in New York City and in the early two thousands joined a Linux-based hosting company. That really gave me just this full stack view on having to put together hardware. We had to build our own computers, ran data center space, oftentimes helped build some of the data centers, connect them all with networks, travel all around the world, setting that up for our customers. And so I feel really fortunate because I got to touch kind of all layers of the stack. My career evolved touch further into hardware. It just became a true passion about where we could connect software and hardware together through automation, through accessible interfaces, and other kinds of standardized protocols, and led me to start a company called Packet, where we did that across different architectures, X86 and ARM, which was really coming to the data center in the 2014/15 timeframe. That business Equinix, one of the world's largest data center operators. And at that point we really had a different viewpoint on how we could impact scale, with the sustainability groups within Equinix as one of the largest green power purchasers in the world, and start thinking more fundamentally about how we use hardware within data centers, how data centers could speak more or be accessible to software users which as we'll, unpack in this conversation, are pretty disparate types of users and don't often get to communicate in good ways. So, I've had the pleasure of being at operating companies. I now invest primarily businesses around the use of data centers and technology as well as circular models to improve efficiency and the sustainability of products.

Chris Adams: Cool. Thank you Zachary. And, My, can I give you the floor as well to introduce yourself from what looks like your spaceship in California?

My Truong: Thanks. Thanks, Chris. Yes. So pleasure being here as well. Yeah, My Truong, I'm the CTO at ZutaCore, a small two-phase liquid cooling organization, very focused on bringing sustainable liquid cooling to the marketplace. Was very fortunate to cross over with Zach at Packet and Equinix and have since taken my journey in a slightly different direction to liquid cooling. Super excited to join here. Come from, unfortunately I'm not a musician by a classical training. I am a double E by training. I'm joining here from California on the west coast of the Bay Area.

Chris Adams: Cool. Thank you for that, My. Alright then. So, my name is Chris. If you're new to this podcast, I work in the Green Web Foundation, which is a small Dutch nonprofit focused on an entirely fossil free internet by 2030. And I'm also the co-chair of the policy working group within the Green Software Foundation.

Everything that we talk about, we'll do our best to share links to in the show notes. And if there's any particular thing you heard us talking about that you're really interested that isn't in the show notes, please do get in touch with us because we want to help you in your quest to learn more about green software and now green hardware.

Alright then looks like you folks are sitting comfortably. Shall we start?

Zachary Smith: Let's do it.

Chris Adams: All right then. Cool. Okay. To start things off, Zachary, I'll put this one to you first. Can you just give our listeners an overview of what a hardware standards working group actually does and why having standards with like data centers actually helps?

I mean, you can assume that our listeners might know that there are web standards that make websites more accessible and easier to run on different devices, so there's a sustainability angle there, but a lot of our listeners might not know that much about data centers and might not know where standards would be helpful.

So maybe you can start with maybe a concrete case of where this is actually useful in helping make any kind of change to the sustainability properties of maybe a data center or a facility.

Zachary Smith: Yeah. That's great. Well, let me give my viewpoint on hardware standards and why they're so critical. We're really fortunate actually to enjoy a significant amount of standardization in consumer products, I would say. there's working groups, things like the USB Alliance, that have Really provided, just in recent times, for example, standardization, whether that's through market forces or regulation around something like USB C, right, which allowed manufacturers and accessories and cables and consumers to not have extra or throw away good devices because they didn't have the right cable to match the port.

Right? And so beyond this interoperability aspect to make these products work better across an intricate supply chain and ecosystem, they also could provide real sustainability benefits in terms of just reuse. Okay. In data centers, amazing thing, being that we can unpack some of the complexities related to the supply chain. These are incredibly complex buildings full of very highly engineered systems that are changing at a relatively rapid pace. But the real issue from my standpoint is, we've successfully made data centers into cloud computing over the past 20 or 25 years, where most people who use and consume data centers never actually see them or touch them. And so it's out of sight, out of mind in terms of the impacts of the latest and greatest hardware or refresh. What happens to a 2-year-old, Nvidia server when it goes to die? Does anybody really know? You kind of know in your home or with your consumer electronics, and you have this real waste problem, so then you have to deal with it.

You know not to put lithium ion batteries in the trash, so,

you find the place to put them. But you know, when it's the internet and it's so far away, it's a little bit hazy for, I think most people to understand the kind of impact of hardware and the related technology as well as what happens to it. And so that's, I'm gonna say, one of the challenges

in the broader sustainability space for data center and cloud computing. One of the opportunities is that maybe different from consumer, we know actually almost exactly where most of this physical infrastructure shows up. Data centers don't move around usually. Um, And so they're usually pretty big. They're usually attached to long-term physical plants, and there's not millions of them. There's thousands of them, but not millions. And so that represents a really interesting opportunity for implementing really interesting, which would seem complex, models. For example, upgrade cycles or parts replacement or upskilling, of hardware. Those things are actually almost more doable logistically in data centers than they are in the broader consumer world because of where they end up. The challenge is that we have this really disparate group of manufacturers that frankly don't always have all the, or aligned incentives, for making things work together. Some of them actually define their value by, "did I put my, logo on the left or did I put my cable on the right?" You have, a business model, which would be the infamous Intel TikTok model, which is now maybe Nvidia. My, what's NVIDIA's version of this?

IDK. But its 18 month refresh cycles are really like put out as a pace of innovation, which are, I would say in many ways quite good, but in another way, it requires this giant purchasing cycle to happen and people build highly engineered products around one particular set of technology and then expect the world to upgrade everything around it when you have data centers and the and related physical plant, maybe 90 or 95% of this infrastructure Can, be very consistent. Things like sheet metal and power supplies and cables and so like, I think that's where we started focusing a couple years ago was "how could we create a standard that would allow different parts of the value chain throughout data center hardware, data centers, and related to, benefit from an industry-wide interoperability. And that came to like really fundamental things that take years to go through supply chain, and that's things like power systems, now what My is working on related cooling systems, as well as operating models for that hardware in terms of upgrade or life cycling and recycling. I'm not sure if that helps but, this is why its such a hard problem, but also so important to make a reality.

Chris Adams: So if I'm understanding, one of the advantages having the standards here is that you get to decide where you compete and where you cooperate here with the idea being that, okay, we all have a shared goal of reducing the embodied carbon in maybe some of the materials you might use, but people might have their own specialized chips.

And by providing some agreed standards for how they work with each other, you're able to use say maybe different kinds of cooling, or different kinds of chips without, okay. I think I know, I think I know more or less where you're going with that then.

Zachary Smith: I mean, I would give a couple of very practical examples. Can we make computers that you can pop out the motherboard and have an upgraded CPU, but still use

Like the rest of the band. Yeah.

the power supplies, et cetera. Is that a possibility? Only with standardization could that work. Some sort of open standard. And standards are a little bit different in hardware.

I'm sure My can give you some color, having recently built Open19 V2 standard. It's different than the software, right? Which is relatively, I'm gonna say, quick to create,

quick to change.

And also different licensing models, but hardware specifications are their own beast and come with some unique challenges.

Chris Adams: Cool. Thank you for that, Zach. My, I'm gonna come bring to the next question to you because we did speak a little bit about Open19 and that was one thing that was a big thing with the SSIA. So as I understand it, the Open19 spec, which we referenced, that was one of the big things that the SSIA was a kind of steward of. And as I understand it, there's already an existing different standard that def, that defines like the dimensions of like say a 19 inch rack in a data center.

So, need to be the same size and everything like that. But that has nothing to say about the power that goes in and how you cool it or things like that. I assume this is what some of the Open19 spec was concerning itself with. I mean, maybe you could talk a little bit about why you even needed that or if that's what it really looks into and why that's actually relevant now, or why that's more important in, say, halfway through the 2020s, for example.

My Truong: Yeah, so Open19, the spec itself originated from a group of folks starting with the LinkedIn or organization at the time. Yuval Bachar put it together along with a few others.

As that organization grew, it was inherited by SsIA, which was, became a Linx Foundation project. What we did when we became a Linux Foundation project is rev the spec. the original spec was built around 12 volt power. It had a power envelope that was maybe a little bit lower than what we knew we needed to go to in the industry. And so what we did when we revised the spec was to bring, both 48 volt power, a much higher TDP to it, and brought some consistency the design itself.

So, as you were saying earlier, EIATIA has a 19 inch spec that defines like a rail to rail, but no additional dimensions beyond just a rail to rail dimension. And so what we did was we built a full, I'm gonna air quote, a "mechanical API" for software folk. So like, do we consistently deliver something you can create variation inside of that API, but the API itself is very consistent on how you go both mechanically, bring hardware into a location, how you power it up, how do you cool it? For variations of cooling, but have a consistent API for bringing cooling into that IT asset. What it doesn't do is really dive into the rest of the physical infrastructure delivery. And that was very important in building a hardware spec, was that we didn't go over and above what we needed to consistently deliver hardware into a location. And when you do that, what you do is you allow for a tremendous amount of freedom on how you go and bring the rest of the infrastructure to the IT asset.

So, in the same way when you build a software spec, you don't really concern yourself about what language you put in behind it, how the rest of that infrastructure, if you have like, a communication bus or is it like a semi API driven with a callback mechanism? You don't really try to think too heavily around that.

You build the API and you expect the API to behave correctly. And so what that gave us the freedom to do is when we started bringing 48 volt power, we could then start thinking about the rest of the infrastructure a little bit differently when you bring consistent sets of APIs to cooling and to power. And so when we started thinking about it, we saw this trend line here about like. We knew that we needed to go think about 400 volt power. We saw the EV industry coming. There was a tread line towards 400 volt power delivery. What we did inside of that hardware spec was we left some optionality inside of the spec to go and change the way that we would go do work, right?

So we gave some optional parameters the, infrastructure teams to go and change up what they needed to go do so that they could deliver that hardware, that infrastructure a little bit more carefully or correctly for their needs. So, we didn't over specify, in particular areas where, I'll give you a counter example and in other specifications out there you'll see like a very consistent busbar in the back of the infrastructure that delivers power. It's great when you're at a

Chris Adams: So if I can just stop for you for a second there, My. The busbar, that's the thing you plug a power thing instead of a socket. Is that what you're referring to there?

My Truong: Oh, so, good question Chris. So in what you see in some of the Hyperscale rack at a time designs, you'll

see two copper bars sitting in the middle of the rack in the back delivering power. And that looks great for an at scale design pattern, but may not fit the needs of smaller or more nuanced design patterns that are out there. Does that make sense?

Chris Adams: Yeah. Yeah. So instead of having a typical, kinda like three-way kind of kettle style plug, the servers just connect directly to this bar to provide the power. That's that's what one of those bars is. Yeah. Gotcha.

My Truong: Yep. And so we went a slightly different way on that, where we had a dedicated power connection per device that went into the Open19 spec. And the spec is up, I think it's up still up on our ssia.org, website. And so anybody can go take a look at it and see the, mechanical spec there.

It's a little bit different.

Chris Adams: Okay. All right. So basically previously there was just a spec said "computers need to be this shape if they're gonna be server computers in rack." And then Open19 was a little bit more about saying, "okay, if you're gonna run all these at scale, then you should probably have some standards about how power goes in and how power goes out."

Because if nothing else that allows 'em to be maybe some somewhat more efficient. And there's things like, and there's various considerations like that, that you can take into account. And you spoke about shifting from maybe 48 volts to like 400 volts and that there is efficiency gained by, which we probably don't need to go into too much detail about, when you do things like that because it allows you to use, maybe it allows you to move along more power without so much being wasted, for example.

These are some of the things that the standards are looking into and well, in the last 10 years, we've seen a massive, we've seen a shift from data center racks, which use quite a lot of power to some things which use significantly more. So maybe 10 years ago you might had a cloud rack would be between five and 15 kilowatts of power.

That's like, tens of homes. And now you we're looking at racks, which might be say, half a megawatt or a megawatt power, which is maybe hundreds if not thousands of homes worth of power. And therefore you need say, refreshed and updated standards. And that's where the V2 thing is moving towards.

Right.

My Truong: Okay.

Chris Adams: Okay, cool. So yeah.

Zachary Smith: Just, the hard thing about hardware standards, where the manufacturing supply chain moves slow

unless you are end-to-end verticalizer, like some of the hyperscale customers can really verticalize. They build the data center, they build the hardware, lots of the same thing.

They can force that. But a broader industry has to rely upon a supply chain. Maybe OEMs, third party data center operators, 'cause they don't build their own data center,

they use somebody else's. And so what we accomplish with V2 was allow for this kind of innovation within the envelope and do the, one of our guiding principles was how could we provide the minimal amount of standardization that we would allow for more adoption to occur while still gaining the benefits?

Chris Adams: Ah.

Zachary Smith: And so that it's a really difficult friction point because your natural reaction is to like, solve the problem. Let's solve the problem as best we can.

The that injects so much opinion that it's very hard to get adopted throughout the broader industry. And so even things like cooling,

single phase or two phase, full immersion or not, this kind of liquid or this way, different types of pressure, whatever. There's all kinds of implications, whether those are technology use, those are regulatory situations across different environments, so I think like that's the challenge that we've had with hardware standards, is how to make it meaningful while still allowing it to evolve for specific use cases. 

Chris Adams: Alright. Okay. So, I think I am, I'm understanding a bit now. And like I'll try and put it in context to some of the other podcast episodes we've done. So we've had people come into this podcast from like Google for example, or Microsoft, and they talk about all these cool things that they're entirely vertically designed data centers where they're in the entire supply chain. They do all these cool things with the grid, right? But all those designs, a lot of the time they, there's maybe these might be custom designs in the case of Google when no one gets to see them. Or in some cases, like say Meta or some other ones, it may be open compute, which is a, it's a different size to most people's data centers, for example. So you can't just like drop that stuff in, like there's a few of them arouned, but it's still 19 inches that's the default standard in lots of places. And if I understand it, one of the things that, one of the goals of Open19 is to essentially bring everyone else along who already have standardized on these kind of sides so they can start doing some of the cool grid aware, carbon aware stuff that you see people talking about that you probably don't have that much access to if you're not already meta Google or Facebook with literally R&D budgets in the hundreds of millions.

Zachary Smith: Yeah, maybe add some zeros there.

Yeah, I think absolutely, right, which is democratizing access to some of this innovation, right? while still relying upon and helping within the broader supply chain. For example, if EVs are moving into 400 volt, like we can slipstream and bring that capability to data centre hardware supply chains.

'Cause the people making power supplies or components or cabling are moving in those directions, right? But then it's also just allowing for the innovation, right? Like, I think, we firmly seen this in software. I think this is a great part of Linux Foundation, which is, 

no one company owns the, you know, monopoly on innovation. And what we really wanna see was not to like, can we make a better piece of hardware, but can we provide, some more foundational capabilities so that hundreds of startups or different types of organizations that might have different ideas or different needs or different goals could innovate around the sustainability aspect of data center hardware and, I think what we're focused on now within GSF is really taking that to a more foundational level. There's just a huge opportunity right now with the data center construction industry happening to really find a even more interesting place where we can take some of those learnings from hardware specifications and apply it to an even broader impact base 

Chris Adams: Ah, okay. Alright. I'll come back to some of this because there's, I know there's a project called Project Mycelium that Asim Hussain, the executive Director of the Green Software Foundation is continually talking about. But like we've spoken a little about, you mentioned, if I understand it, like, this allows you to maybe have more freedom to talk about maybe, instead of having like tiny fans, which scream at massive, thousands and thousands of RPM, there's other ways that you could maybe call down chips for example. And like, this is one thing that I know that the hardware standards working group is looking at, is finding ways to keep the servers cool, for example. Like as I understand it,

using liquid is, can be more efficient, quite a bit more efficient than having tiny fans to cool at massive RPM to cool things down. But also, I guess there's a whole discussion about, well there's different kinds of, there's different ways of cooling things which might reduce the kind of local power draw, local water usage in a data center, for example.

And like, maybe this is one thing we could talk a little bit more about then, 'cause I dunno that, we've had people talk about, say, liquid calling and things like that before, as like, these are some alternative ways to more sustainably cool down data centers in terms of how much power they need, but also what their local footprint could actually be.

But we've never had people who actually have that much deep expertise in this. So maybe I could put the questions to one of you. Like, let's say you're gonna switch to liquid calling, for example, Instead of using itty bitty fans or even just bigger, slightly bigger fans, running a little bit slower. Like, how does that actually improve it? Maybe you could, maybe I could put this to you, My. 'Cause I think this is one thing that you've spent quite a lot of time looking into, like, yeah, where are the benefits? Like what, how does, how did the benefits materialize if you switch from, say, air to a liquid calling approach like this?

My Truong: Yeah, so on the liquid cooling front, there's a number of pieces here. The fans that you were describing earlier, they're moving air, which is effectively a liquid when you're using it in a cooling mode at 25,000 RPM, you're trying to more air across the surface and it doesn't have a great amount of, 

Zachary Smith: Heat transfer capability. 

My Truong: removal and rejection. Yeah. heat transfer capabilities. Right. So in this world where we're not moving heat with air, we're moving it with some sort of liquid, either a single phase liquid, like water or a two-phase liquid taking advantage of two phase heat transfer properties.

There's a lot of significant gains and those gains really start magnifying here in this AI space that we're in today. And I think this is where Project Mycelium started to come into fruition was to really think about that infrastructure end to end. When you're looking at some of these AI workloads, especially AI training workloads, their ability to go and move hundreds of megawatts of power simultaneously and instantaneously becomes a tricky cooling challenge and infrastructure challenge. And so really what we wanted to be able to think through is how do we go and allow software to signal all the way through into hardware and get hardware to help go and deal with this problem in a way that makes sense.

So I'll give you a concrete example. 

If you're in the single phase space and you are in the 100 megawatt or 200 megawatt data center site, which is, this is what xAI built out Memphis, Tennessee. When you're going and swinging that workload, you are swinging a workload from zero to a hundred percent back to zero quite quickly. In the timescale of around 40 milliseconds or so, you can move a workload from zero to 200 megawatts back down to zero. When you're connected to a grid, when you're connected to a grid,

Chris Adams: right.

My Truong: that's called a grid distorting power event, right?

You can go swing an entire grid 200 megawatt, which is, probably like, maybe like a quarter of the LA area of like the ability to go and distort a grid pretty quickly. When you're an isolated grid like Ercot, this becomes like a very, tricky proposition for the grid to go and manage correctly. On the flip side of that, like once you took the power you, created about 200 megawatt of heat as well. And when you start doing that, you have to really think about what are you doing on your cooling infrastructure. If you're a pump based system, like single phase, that means that you're probably having to spool up and spool down your pump system quite rapidly to go respond to that swing in power demand. But how do you know? How do you prep the system? How do you tell that this is going to happen? And this is where we really need to start thinking about, these software hardware interfaces. Wouldn't it be great if your software workload could start signaling to your software or your hardware infrastructure? "Hey I'm about, to go and start up this workload, and I'm about to go and swing this workload quite quickly." You would really want to go signal to your infrastructure and say, "yes, I'm about to go do this to you," and maybe you want to even signal to your grid, "I'm about to go do this for you" as well. You can start thinking about other, options for managing your power systems correctly, maybe using like a battery system to go and shave off that peak inside of the grid and manage that appropriately. So we can start thinking about this. Once we have this ability to go signal from software to hardware to infrastructure and building that communication path, it becomes an interesting thought exercise that we can realize that this is just a software problem.

have been in this hardware, software space, we've seen this before. And is it worth synchronizing this data up? Is it worth signaling this correctly through the infrastructure? This is like the big question that we have with Project Mycelium. Like, it would be amazing for us to be able to do this.

Chris Adams: Ah, I see.

My Truong: The secondary effects of this is to really go think through, now, if you're in Dublin where you have offshore power and you now have one hour resolution on data that's coming through about the amount of green power that's about to come through, it would be amazing for you to signal up and signal down your infrastructure to say, you should really spool up your workload and maybe run it at 150% for a while, right?

This would be a great time to go really take green power off grid and drive your workload on green power for this duration. And then as that power spools off, you can go roll that power need off for a time window. So being able to think about these things that we can create between the software hardware interface is really where I think that we have this opportunity to really make game changing and really economy changing outcomes. 

Chris Adams: Okay. 

Zachary Smith: I have a viewpoint on that, Chris, 

Chris Adams: too.

Yeah, please do.

Zachary Smith: My TLDR summary is like, infrastructure has gotten much more complicated and the interplay between workload and that physical infrastructure is no longer, "set it in there and just forget it and the fans will blow and the servers will work and nobody will notice the difference in the IT room."

These are incredibly complex workloads. Significant amount of our world is interacting with this type of infrastructure through software services. It's just got more complicated, right? And what we haven't really done is provide more efficient and advanced ways to collaborate between that infrastructure and the kind of workload. It's, still working under some paradigms that like, data centers, you put everything in there and the computers just run. And that's just not the case anymore. Right. I think that's what My was illustrating so nicely, is that workload is so different and so dynamic and so complex that we need to step up with some ways to, for the infrastructure and that software workload to communicate.

Chris Adams: Ah, I see. Okay. So I'll try and translate some of that for some of the listeners that we've had here. So you said something about, okay. A 200 megawatt like power swing, that's like, that's not that far away from a half a million people appearing on the grid, then disappearing on the grid every 14 milliseconds.

And like obviously that's gonna piss off people who have to operate the grid. But that by itself is one thing, and that's also a change from what we had before because typically cloud data centers were known for being good customers because they're really like flat, predictable power draw.

And now rather than having like a flat kind of line, you have something more like a kind of seesaw, a saw tooth, like up, down, up, down, up, down, up, down. And like if you just pass that straight through to the grid, that's a really good way to just like totally mess with the grid and do all kinds of damage to the rest of the grid.

But what it sounds like you're saying is actually, if you have some degree of control within the data center, you might say, "well, all this crazy spikiness, rather than pulling it from the grid, can I pull it from batteries, for example?" And then I might expose, or that I might expose that familiar flat pattern to the rest of the grid, for example.

And that might be a way to make you more popular with grid operators, but also that might be a way to actually make the system more efficient. So that's one of the things you said there. So that's one kind of helpful thing there. But also you said that there is a chance to like dynamically scale up how, when there is loads and loads of green energy, so you end up turning into a bit more of a kind of like better neighbor on the grid essentially.

And that can have implications for the fact that because we are moving to a, like you said before, there's complexity at the power level and it allows the data centers to rather than make that worse, that gonna address some of those things. So it's complimentary to the grid, is that what you're saying?

My Truong: Yeah. I think you got it, Chris. You got it, Chris. Yeah.

Exactly. So that's on the power side. I think that we have this other opportunity now that as we're starting to introduce liquid cooling to the space as well, we're effectively, efficeintly removing heat from the silicon. Especially in Europe

this is becoming like a very front and center, conversation of data centers operating in Europe is that this energy doesn't need to go to waste and be evacuated into the atmosphere. We have this tremendous opportunity to go and bring that heat into local municipal heat loops

and really think about that much more, in a much more cohesive way. And so this is again, like where we really, like, as Zach was saying, we really need to think about this a bit comprehensively and really rethink our architectures to some degree with these types of workloads coming through. And so bringing standards around the hardware, the software interface, and then as we start thinking through the rest of the ecosystem, how do we think through bringing consistency to some of this interface so that we can communicate "workload is going up, workload is going down. The city needs x amount of gigawatt of power into a municipal heat loop," like help the entire ecosystem out a little bit better. In the winter, probably Berlin or Frankfurt would be excited to have gigawatts of power in a heat loop to go and drive a carbon free heating footprint inside of the area. But then on the flip side of that, going and building a site that expects that in the winter, but in the summer where you're not able to take that heat off, how do we think about more innovative ways of driving cooling then as well? How do we go and use that heat in a more effective way to drive a cooling infrastructure?

Chris Adams: So, okay, so this is that. I'm glad you mentioned the example, 'cause I live in Germany and our biggest driver of fossil fuel use is heating things up when it gets cold. So that's one of the good, good ways to, like, if, there's a way to actually use heat, which doesn't involve burning more fossil fuels, totally. Or I'm all for that. There is actually, one question I might ask actually is like, what are the coolants that people use for this kind of stuff? Because the, when you, I mean, when we move away from air, you're not norm, you're not typically just using water in some, all of these cases, there may be different kinds of chemicals or different kinds of coolants in use, right?

I mean, maybe you could talk a little bit about that, because I know that we had switches from when we've looked at how we use coolant elsewhere, there's been different generations of coolants for our, and in Europe, I know one thing we, there's a whole ongoing discussion about saying, "okay, if we're gonna have liquid cooling, can we at least make sure that the liquid, the coolants we're using are actually not the things which end up being massively emitting in their own right," because one of the big drivers of emissions is like end of life refrigerants and things like that. Maybe you could talk a little bit about like what your options are if you're gonna do liquid cooling and like, what's on the table right now?

To actually do something which is more efficient, but is also a bit more kind of non-toxic and safe if you're gonna have this inside a, in inside a given space.

My Truong: Yeah. So in liquid cooling there's a number of fluids that we can use. the most well understood of the fluids, as used both in the facility and the technical loop side is standard de-ionized water. Just water across the cold plate. There's variations that are used out there with a propylene glycol mix to manage microbial growth. The organization that I'm part of, we use a two-phase approach where we're taking a two-phase fluid, and taking advantage of phase change to remove that heat from the silicon. And in this space, this is where we have a lot of conversations around fluids and fluid safety and how we're thinking about that fluid and end of life usage of that fluid. Once you're removing heat with that fluid and putting it into a network, most of these heat networks are water-based heat networks where you're using some sort of water with a microbial treatment and going through treatment regimes to manage that water quality through the system.

So this is a very conventional approach. Overall, there's good and bads to every system. Water is very good at removing heat from systems. But as you start getting towards megawatt scale, the size of plumbing that you're requiring to go remove that heat and bring that fluid through, becomes a real technical challenge.

And also

at megawatts. Yeah. Yeah.

Zachary Smith: If I'm not mistaken.

Also, there's challenges if you're not doing a two-phase, approach to actually removing heat at a hot enough temperature that you can use it for something else, right?

My Truong: Correct. Correct, Zach. It's, so there's, like, a number of like very like technical angles to this. So as you're, going down that path, Zach, so in single phase what we do is we have to move fluid across that surface a good enough clip to make sure that we're removing heat keeping that silicon from overheating. Downside of this is like, as silicon requires colder and colder temperatures to keep them operating well, their opportunity to drive that heat source up high enough to be able to use in a municipal heat loop becomes lower and lower. So let's say, for example, your best in class silicon today asking for what's known as a 65 degree TJ. That's a number that we see in the silicon side. So you're basically saying, "I need my silicon to be 65 degrees Celsius or lower to be able to operate properly." flip side of that is you're gonna ask your infrastructure go deliver water between 12 to 17 degrees Celsius to make sure that, that cooling is supplied. But the flip side of that is that if you allow for, let's say, a 20 degree Celsius rise, your exit temperature on that water is only gonna 20 degrees higher than the 70 degrees inlet, so that water temperature is so low

And that's not a very nice shower, basically. Yeah.

You're in a lukewarm shower at best.

So, we have to do, then we have to tr spend a tremendous amount of energy then bring that heat quality up so that we can use it in a heat network. And two phase approaches, what we're taking advantage of is the physics of two-phase heat transfer, where, during phase change, you have exactly one temperature, which that fluid will phase change.

To a gas. Yeah. Yeah.

To a gas. Exactly.

Yeah.

And so the easiest way, like, we'll use the water example, but this is not typically what's used in two phase tech technologies, is that water at a atmospheric pressure will always phase change about a hundred degrees Celsius. It's not 101, it's not 99. It's always a hundred degrees Celsius at hemispheric pressure. So your silicon underneath that will always be at a, around a hundred degree Celsius or maybe a little bit higher depending on what your heat transfer, characteristics look like. And this is the physics that we take advantage of. So when you're doing that, the vapor side this becomes like a very valuable energy source and you can actually do some very creative things with it on two phase.

So that's, there's some, every technology has a, is a double-edged sword and we're taking advantage of the physics of heat transfer to effectively and efficiently remove heat in two-phase solutions.

Chris Adams: Ah, so I have one kind of question about the actual, how that changes what data center feels like to be inside, because I've been inside data centers and they are not quiet places to be. Like, I couldn't believe just how uncomfortably loud they are. And like, if you're moving away from fans, does that change how they sound, for example?

Because if, even if you're outside some buildings, people talk about some of the noise pollution aspects. Does a move to something like this mean that it changes some of it at all?

Zachary Smith: Oh yeah.

My Truong: In inside of the white space. Absolutely. Like one of the things that we fear the most inside of a data center is dead silence.

You might actually be able to end up in a data center where there's dead silence, soon.

And that being a good thing. Yeah.

With no fans. Yeah. We'd love to remove the parasitic draw of power from fans moving air across data centers, just to allow that power to go back into the workload itself.

Chris Adams: So for context, maybe someone, if you haven't been in a data center... I mean, it was around, I think it felt like 80 to 90 decibels for me, which felt like a, I mean, defects have a

Yeah, plus could have been more actually. Yeah. So I mean, it was a, I mean, if you have like an, if you have a something on a wearable, on a phone, as soon as it's above 90 degrees, 90 decibels, that's like

louder than lots of nightclubs, basically. Like maybe there's a comp. So this is one thing that I fell and this sounds like it does, like it can introduce some changes there as well rather than actually just, we're just talking about energy and water usage. Right.

Zachary Smith: Yeah, most data center technicians wear ear protectors all the time, can't talk on the phone, have to scream at each other, because it's so loud. Certainly there's, some really nice quality of life improvements that can happen when you're not blowing that much air around and spinning up multiple thousand

My Truong: 25,000 to 30,000 RPM fans will, require you double hearing protection to be able to even function as out of the space.

Yeah, that's the thing.

A lot of energy there.

Chris Adams: Oh, okay. Cool. So, so this is the, these are some of the, this is some of the shifts that make possible. So the idea, you can have, you might have data centers of what you're able to be more active in terms of actually working with the grid because for all the kind of things we might do as software engineers, there's actually a standard which makes sure that the things that we see Google doing or Meta talking about in academic papers could be more accessible to more people.

That's one of the things that having standards and for like Open19 things might be because there's just so many more people using 19 inch racks and things like that. That seems to be one thing. So maybe I could actually ask you folks like. This is one thing that you've been working on and My, you obviously running an organization, Zuta Core here, and Zach, it sounds like you're working on a number of these projects.

Are there any particular like open source projects or papers or things with really some of these. Some of the more wacky ideas or more interesting projects that you would point people to? Because when I talk about data centers and things like this, there's a paper from, that's called the Ecoviser paper, which is all about virtualizing power so that you could have power from batteries going to certain workloads and power from the grid going to other workloads.

And we've always thought about it as going one way, but it sounds like with things like Project Mycelium, you can go have things going the other way. Like for people who are really into this stuff, are there any, are there any good repos that you would point people to? Or is there a particular paper that you found exciting that you would direct people to who are still with us and still being able to keep up with the kind of, honestly, quite technical discussion we've had here.?

Zachary Smith: Well, I would, not to tout My's horn, but, reading the Open19 V2 specification, I think is worthwhile. Some of the challenges we dealt with at a kind of server and rack level, I think are indicative of where the market is and where it's going. There's also great stuff within the OCP Advanced Cooling working group. And I found it very interesting, especially to see some of what's coming from Hyperscale where they are able to move faster through a verticalized integration approach. And then I've just been really interested in following along the power systems, and related from the EV industry, I think there's, that's an exciting area where we can start to see data centers not as buildings for IT, but data centers as energy components.

So when you're looking at, whether it's EV or grid scale kind of renewable management, I think there's some really interesting tie-ins that our industry, frankly is not very good at yet.

Ah.

Most people who are working in data centers are not actually power experts from a generation or storage perspective.

And so there's some just educational opportunities there. I've found, just as one resource, My, I don't know if they have it, at the, the seven by 24 conference group, which is the critical infrastructure conference, which everything from like water systems, power systems to data centers, has been really a great learning place for me.

But I'm not sure if they have a publication that is useful. We, have some work to do in moving our industry into transparent Git repos.

My Truong: Chris, my favorite is actually the open BMC codebase. It provides a tremendous gateway where this used to be a very closed ecosystem, and very hard for us to think about being able to look through a code repo of a redfish API, and able to rev that spec in a way that could be useful and, implementable into an ecosystem has been like my favorite place outside of hardware specifications like

Chris Adams: Ah, okay. So I might try and translate that 'cause I, the BMC thing, this is basically the bit of computing, which essentially tells software what's going on inside of data, how much power it's using and stuff like that. Is that what you're referring to? And is Open BMC, like something used to be proprietary, there is now a more open standard so that there's a visibility that wasn't there before.

Is that what it is? 

My Truong: Right. that's exactly right. So there you have to, in years past, had a closed ecosystem on the service controller or the BMC, the baseboard controller dule inside of a server and being able to look into that code base was always very difficult at best and traumatic at worst. But having open BMC reference code out there,

being look and see an implementation and port that code base into running systems has been very useful, I think, for the ecosystem to go and get more transparency, as Zach was saying, into API driven interfaces.

oh.

What I'm seeing is that prevalence of that code base now showing up in a, number of different places and the patterns are being designated into, as Zach was saying, power systems. We're seeing this, become more and more prevalent in power shelves, power control, 

places where we used to not have access or we used to use programmable logic controllers to drive this. They're now becoming much more software ecosystem driven and opening up a lot form possibilities for us.

Chris Adams: Okay. I'm now understanding the whole idea behind Mycelium, like roots reaching down further down into the actual hardware to do things that couldn't be done before that. Okay. This now makes a lot more sense. Yeah.

Peel it back. One more layer.

Okay. Stacks within Stacks. Brilliant. Okay. This makes sense. Okay folks, well, thank you very much for actually sharing that and diving into those other projects.

We'll add some, if we can, we'll add some links to some of those things. 'Cause I think the open BMC, that's one thing that is actually in production in a few places. I know that Oxide Computer use some of this, but there's other providers who also have that as part of their stack now that you can see.

Right.

My Truong: We also put into production when we were part of the Packet Equinix team. So we have a little bit of experience in running this tech base in, real production workloads.

Chris Adams: Oh wow. I might ask you some questions outside this podcast 'cause this is one thing that we always struggle with is finding who's actually exposing any of these numbers for people who are actually further up the stack because it's a real challenge. Alright. Okay, we're coming up to time, so, I just wanna leave one question with you folks, if I may.

If people have found this interesting and they want to like, follow what's going on with Zach Smith and My Truong, where do they look? Where do they go? Like, can you just give us some pointers about where we should be following and what we should be linking to in the show notes? 'Cause I think there's quite a lot of stuff we've covered here and I think there's space for a lot more learning actually.

Zachary Smith: Well, I can't say I'm using X or related on a constant basis, but I'm on LinkedIn @zsmith, connect with me there. Follow. I post occasionally on working groups and other parts that I'm part of. And I'd encourage, if folks are interested, like we're very early in this hardware working group within the GSF.

There's so much opportunity. We need more help. We need more ideas. We need more places to try. And so if you're interested, I'd suggest joining or coming to some of our working group sessions. It's very early and we're open to all kinds of ideas as long as you're willing to, copy a core value from Equinix,

as long as you can speak up and then step up, we'd love the help. there's a lot to do.

Brilliant, Zach. And My, over to you.

My Truong: LinkedIn as well. Love to see people here as part of our working groups, and see what we can move forward here in the industry.

Chris Adams: Brilliant. Okay. Well, gentlemen, thank you so much for taking me through this tour all the way down the stack into the depths that we as software developers don't really have that much visibility into. And I hope you have a lovely morning slash day slash afternoon depending on where you are in the world.

Alright, cheers fellas.

Thanks Chris.

Thanks so much. 

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]]> Chris Adams is joined by Zachary Smith and My Truong both members of the Hardware Standards Working Group at the GSF. They dive into the challenges of improving hardware efficiency in data centers, the importance of standardization, and how emerging technologies like two-phase liquid cooling systems can reduce emissions, improve energy reuse, and even support power grid stability. They also discuss grid operation and the potential of software-hardware coordination to drastically cut infrastructure impact. 

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TRANSCRIPT BELOW:

Zachary Smith: We've successfully made data centers into cloud computing over the past 20 or 25 years, where most people who use and consume data centers never actually see them or touch them. And so it's out of sight, out of mind in terms of the impacts of the latest and greatest hardware or refresh. What happens to a 2-year-old Nvidia server when it goes to die? Does anybody really know Hello, and welcome to Environment Variables,

Chris Adams: brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to Environment Variables, the podcast where we explore the latest in sustainable software development. I'm your host, Chris Adams. Since this podcast started in 2022, we've spoken a lot about green software, how to make code more efficient so it consumes fewer resources or runs on a wider range of hardware to avoid needless hardware upgrades, and so on.

We've also covered how to deploy services into data centers where energy is the cleanest, or even when energy is the cleanest, by timing compute jobs to coincide with an abundance of clean energy on the grid. However, for many of these interventions to work, they rely on the next layer down from software,

the hardware layer, to play along. And for that to work at scale, you really need standards. Earlier this year, the SSIA, the Sustainable and Scalable Infrastructure Alliance, joined the Green Software Foundation. So now there's a hardware standards working group for HSWG within the Green Software Foundation too.

Today we're joined by two leaders in the field who are shaping the future of sustainable software. So, oops, sustainable hardware. We've got Zachary Smith formerly of Packet and Equinix, and My Truong from ZutaCore. We'll be discussing hardware efficiency, how it fits into the bigger sustainability picture, the role of the Open19 standard, and the challenges and opportunities of making data centers greener.

So let's get started. So, Zachary Smith, you are alphabetically ahead of My Truong, Mr. Truong. So can I give you the floor first to introduce yourself and tell a little bit about yourself for the listeners?

Zachary Smith: Sure. Thanks so much, Chris. It's a pleasure being here and getting to work with My on this podcast. As you mentioned, my name's Zachary Smith. I've been an entrepreneur, primarily in cloud computing for, I guess it's about 25 years now. I went to Juilliard. I studied music and ended up figuring that wasn't gonna pay my rent here in New York City and in the early two thousands joined a Linux-based hosting company. That really gave me just this full stack view on having to put together hardware. We had to build our own computers, ran data center space, oftentimes helped build some of the data centers, connect them all with networks, travel all around the world, setting that up for our customers. And so I feel really fortunate because I got to touch kind of all layers of the stack. My career evolved touch further into hardware. It just became a true passion about where we could connect software and hardware together through automation, through accessible interfaces, and other kinds of standardized protocols, and led me to start a company called Packet, where we did that across different architectures, X86 and ARM, which was really coming to the data center in the 2014/15 timeframe. That business Equinix, one of the world's largest data center operators. And at that point we really had a different viewpoint on how we could impact scale, with the sustainability groups within Equinix as one of the largest green power purchasers in the world, and start thinking more fundamentally about how we use hardware within data centers, how data centers could speak more or be accessible to software users which as we'll, unpack in this conversation, are pretty disparate types of users and don't often get to communicate in good ways. So, I've had the pleasure of being at operating companies. I now invest primarily businesses around the use of data centers and technology as well as circular models to improve efficiency and the sustainability of products.

Chris Adams: Cool. Thank you Zachary. And, My, can I give you the floor as well to introduce yourself from what looks like your spaceship in California?

My Truong: Thanks. Thanks, Chris. Yes. So pleasure being here as well. Yeah, My Truong, I'm the CTO at ZutaCore, a small two-phase liquid cooling organization, very focused on bringing sustainable liquid cooling to the marketplace. Was very fortunate to cross over with Zach at Packet and Equinix and have since taken my journey in a slightly different direction to liquid cooling. Super excited to join here. Come from, unfortunately I'm not a musician by a classical training. I am a double E by training. I'm joining here from California on the west coast of the Bay Area.

Chris Adams: Cool. Thank you for that, My. Alright then. So, my name is Chris. If you're new to this podcast, I work in the Green Web Foundation, which is a small Dutch nonprofit focused on an entirely fossil free internet by 2030. And I'm also the co-chair of the policy working group within the Green Software Foundation.

Everything that we talk about, we'll do our best to share links to in the show notes. And if there's any particular thing you heard us talking about that you're really interested that isn't in the show notes, please do get in touch with us because we want to help you in your quest to learn more about green software and now green hardware.

Alright then looks like you folks are sitting comfortably. Shall we start?

Zachary Smith: Let's do it.

Chris Adams: All right then. Cool. Okay. To start things off, Zachary, I'll put this one to you first. Can you just give our listeners an overview of what a hardware standards working group actually does and why having standards with like data centers actually helps?

I mean, you can assume that our listeners might know that there are web standards that make websites more accessible and easier to run on different devices, so there's a sustainability angle there, but a lot of our listeners might not know that much about data centers and might not know where standards would be helpful.

So maybe you can start with maybe a concrete case of where this is actually useful in helping make any kind of change to the sustainability properties of maybe a data center or a facility.

Zachary Smith: Yeah. That's great. Well, let me give my viewpoint on hardware standards and why they're so critical. We're really fortunate actually to enjoy a significant amount of standardization in consumer products, I would say. there's working groups, things like the USB Alliance, that have Really provided, just in recent times, for example, standardization, whether that's through market forces or regulation around something like USB C, right, which allowed manufacturers and accessories and cables and consumers to not have extra or throw away good devices because they didn't have the right cable to match the port.

Right? And so beyond this interoperability aspect to make these products work better across an intricate supply chain and ecosystem, they also could provide real sustainability benefits in terms of just reuse. Okay. In data centers, amazing thing, being that we can unpack some of the complexities related to the supply chain. These are incredibly complex buildings full of very highly engineered systems that are changing at a relatively rapid pace. But the real issue from my standpoint is, we've successfully made data centers into cloud computing over the past 20 or 25 years, where most people who use and consume data centers never actually see them or touch them. And so it's out of sight, out of mind in terms of the impacts of the latest and greatest hardware or refresh. What happens to a 2-year-old, Nvidia server when it goes to die? Does anybody really know? You kind of know in your home or with your consumer electronics, and you have this real waste problem, so then you have to deal with it.

You know not to put lithium ion batteries in the trash, so,

you find the place to put them. But you know, when it's the internet and it's so far away, it's a little bit hazy for, I think most people to understand the kind of impact of hardware and the related technology as well as what happens to it. And so that's, I'm gonna say, one of the challenges

in the broader sustainability space for data center and cloud computing. One of the opportunities is that maybe different from consumer, we know actually almost exactly where most of this physical infrastructure shows up. Data centers don't move around usually. Um, And so they're usually pretty big. They're usually attached to long-term physical plants, and there's not millions of them. There's thousands of them, but not millions. And so that represents a really interesting opportunity for implementing really interesting, which would seem complex, models. For example, upgrade cycles or parts replacement or upskilling, of hardware. Those things are actually almost more doable logistically in data centers than they are in the broader consumer world because of where they end up. The challenge is that we have this really disparate group of manufacturers that frankly don't always have all the, or aligned incentives, for making things work together. Some of them actually define their value by, "did I put my, logo on the left or did I put my cable on the right?" You have, a business model, which would be the infamous Intel TikTok model, which is now maybe Nvidia. My, what's NVIDIA's version of this?

IDK. But its 18 month refresh cycles are really like put out as a pace of innovation, which are, I would say in many ways quite good, but in another way, it requires this giant purchasing cycle to happen and people build highly engineered products around one particular set of technology and then expect the world to upgrade everything around it when you have data centers and the and related physical plant, maybe 90 or 95% of this infrastructure Can, be very consistent. Things like sheet metal and power supplies and cables and so like, I think that's where we started focusing a couple years ago was "how could we create a standard that would allow different parts of the value chain throughout data center hardware, data centers, and related to, benefit from an industry-wide interoperability. And that came to like really fundamental things that take years to go through supply chain, and that's things like power systems, now what My is working on related cooling systems, as well as operating models for that hardware in terms of upgrade or life cycling and recycling. I'm not sure if that helps but, this is why its such a hard problem, but also so important to make a reality.

Chris Adams: So if I'm understanding, one of the advantages having the standards here is that you get to decide where you compete and where you cooperate here with the idea being that, okay, we all have a shared goal of reducing the embodied carbon in maybe some of the materials you might use, but people might have their own specialized chips.

And by providing some agreed standards for how they work with each other, you're able to use say maybe different kinds of cooling, or different kinds of chips without, okay. I think I know, I think I know more or less where you're going with that then.

Zachary Smith: I mean, I would give a couple of very practical examples. Can we make computers that you can pop out the motherboard and have an upgraded CPU, but still use

Like the rest of the band. Yeah.

the power supplies, et cetera. Is that a possibility? Only with standardization could that work. Some sort of open standard. And standards are a little bit different in hardware.

I'm sure My can give you some color, having recently built Open19 V2 standard. It's different than the software, right? Which is relatively, I'm gonna say, quick to create,

quick to change.

And also different licensing models, but hardware specifications are their own beast and come with some unique challenges.

Chris Adams: Cool. Thank you for that, Zach. My, I'm gonna come bring to the next question to you because we did speak a little bit about Open19 and that was one thing that was a big thing with the SSIA. So as I understand it, the Open19 spec, which we referenced, that was one of the big things that the SSIA was a kind of steward of. And as I understand it, there's already an existing different standard that def, that defines like the dimensions of like say a 19 inch rack in a data center.

So, need to be the same size and everything like that. But that has nothing to say about the power that goes in and how you cool it or things like that. I assume this is what some of the Open19 spec was concerning itself with. I mean, maybe you could talk a little bit about why you even needed that or if that's what it really looks into and why that's actually relevant now, or why that's more important in, say, halfway through the 2020s, for example.

My Truong: Yeah, so Open19, the spec itself originated from a group of folks starting with the LinkedIn or organization at the time. Yuval Bachar put it together along with a few others.

As that organization grew, it was inherited by SsIA, which was, became a Linx Foundation project. What we did when we became a Linux Foundation project is rev the spec. the original spec was built around 12 volt power. It had a power envelope that was maybe a little bit lower than what we knew we needed to go to in the industry. And so what we did when we revised the spec was to bring, both 48 volt power, a much higher TDP to it, and brought some consistency the design itself.

So, as you were saying earlier, EIATIA has a 19 inch spec that defines like a rail to rail, but no additional dimensions beyond just a rail to rail dimension. And so what we did was we built a full, I'm gonna air quote, a "mechanical API" for software folk. So like, do we consistently deliver something you can create variation inside of that API, but the API itself is very consistent on how you go both mechanically, bring hardware into a location, how you power it up, how do you cool it? For variations of cooling, but have a consistent API for bringing cooling into that IT asset. What it doesn't do is really dive into the rest of the physical infrastructure delivery. And that was very important in building a hardware spec, was that we didn't go over and above what we needed to consistently deliver hardware into a location. And when you do that, what you do is you allow for a tremendous amount of freedom on how you go and bring the rest of the infrastructure to the IT asset.

So, in the same way when you build a software spec, you don't really concern yourself about what language you put in behind it, how the rest of that infrastructure, if you have like, a communication bus or is it like a semi API driven with a callback mechanism? You don't really try to think too heavily around that.

You build the API and you expect the API to behave correctly. And so what that gave us the freedom to do is when we started bringing 48 volt power, we could then start thinking about the rest of the infrastructure a little bit differently when you bring consistent sets of APIs to cooling and to power. And so when we started thinking about it, we saw this trend line here about like. We knew that we needed to go think about 400 volt power. We saw the EV industry coming. There was a tread line towards 400 volt power delivery. What we did inside of that hardware spec was we left some optionality inside of the spec to go and change the way that we would go do work, right?

So we gave some optional parameters the, infrastructure teams to go and change up what they needed to go do so that they could deliver that hardware, that infrastructure a little bit more carefully or correctly for their needs. So, we didn't over specify, in particular areas where, I'll give you a counter example and in other specifications out there you'll see like a very consistent busbar in the back of the infrastructure that delivers power. It's great when you're at a

Chris Adams: So if I can just stop for you for a second there, My. The busbar, that's the thing you plug a power thing instead of a socket. Is that what you're referring to there?

My Truong: Oh, so, good question Chris. So in what you see in some of the Hyperscale rack at a time designs, you'll

see two copper bars sitting in the middle of the rack in the back delivering power. And that looks great for an at scale design pattern, but may not fit the needs of smaller or more nuanced design patterns that are out there. Does that make sense?

Chris Adams: Yeah. Yeah. So instead of having a typical, kinda like three-way kind of kettle style plug, the servers just connect directly to this bar to provide the power. That's that's what one of those bars is. Yeah. Gotcha.

My Truong: Yep. And so we went a slightly different way on that, where we had a dedicated power connection per device that went into the Open19 spec. And the spec is up, I think it's up still up on our ssia.org, website. And so anybody can go take a look at it and see the, mechanical spec there.

It's a little bit different.

Chris Adams: Okay. All right. So basically previously there was just a spec said "computers need to be this shape if they're gonna be server computers in rack." And then Open19 was a little bit more about saying, "okay, if you're gonna run all these at scale, then you should probably have some standards about how power goes in and how power goes out."

Because if nothing else that allows 'em to be maybe some somewhat more efficient. And there's things like, and there's various considerations like that, that you can take into account. And you spoke about shifting from maybe 48 volts to like 400 volts and that there is efficiency gained by, which we probably don't need to go into too much detail about, when you do things like that because it allows you to use, maybe it allows you to move along more power without so much being wasted, for example.

These are some of the things that the standards are looking into and well, in the last 10 years, we've seen a massive, we've seen a shift from data center racks, which use quite a lot of power to some things which use significantly more. So maybe 10 years ago you might had a cloud rack would be between five and 15 kilowatts of power.

That's like, tens of homes. And now you we're looking at racks, which might be say, half a megawatt or a megawatt power, which is maybe hundreds if not thousands of homes worth of power. And therefore you need say, refreshed and updated standards. And that's where the V2 thing is moving towards.

Right.

My Truong: Okay.

Chris Adams: Okay, cool. So yeah.

Zachary Smith: Just, the hard thing about hardware standards, where the manufacturing supply chain moves slow

unless you are end-to-end verticalizer, like some of the hyperscale customers can really verticalize. They build the data center, they build the hardware, lots of the same thing.

They can force that. But a broader industry has to rely upon a supply chain. Maybe OEMs, third party data center operators, 'cause they don't build their own data center,

they use somebody else's. And so what we accomplish with V2 was allow for this kind of innovation within the envelope and do the, one of our guiding principles was how could we provide the minimal amount of standardization that we would allow for more adoption to occur while still gaining the benefits?

Chris Adams: Ah.

Zachary Smith: And so that it's a really difficult friction point because your natural reaction is to like, solve the problem. Let's solve the problem as best we can.

The that injects so much opinion that it's very hard to get adopted throughout the broader industry. And so even things like cooling,

single phase or two phase, full immersion or not, this kind of liquid or this way, different types of pressure, whatever. There's all kinds of implications, whether those are technology use, those are regulatory situations across different environments, so I think like that's the challenge that we've had with hardware standards, is how to make it meaningful while still allowing it to evolve for specific use cases. 

Chris Adams: Alright. Okay. So, I think I am, I'm understanding a bit now. And like I'll try and put it in context to some of the other podcast episodes we've done. So we've had people come into this podcast from like Google for example, or Microsoft, and they talk about all these cool things that they're entirely vertically designed data centers where they're in the entire supply chain. They do all these cool things with the grid, right? But all those designs, a lot of the time they, there's maybe these might be custom designs in the case of Google when no one gets to see them. Or in some cases, like say Meta or some other ones, it may be open compute, which is a, it's a different size to most people's data centers, for example. So you can't just like drop that stuff in, like there's a few of them arouned, but it's still 19 inches that's the default standard in lots of places. And if I understand it, one of the things that, one of the goals of Open19 is to essentially bring everyone else along who already have standardized on these kind of sides so they can start doing some of the cool grid aware, carbon aware stuff that you see people talking about that you probably don't have that much access to if you're not already meta Google or Facebook with literally R&D budgets in the hundreds of millions.

Zachary Smith: Yeah, maybe add some zeros there.

Yeah, I think absolutely, right, which is democratizing access to some of this innovation, right? while still relying upon and helping within the broader supply chain. For example, if EVs are moving into 400 volt, like we can slipstream and bring that capability to data centre hardware supply chains.

'Cause the people making power supplies or components or cabling are moving in those directions, right? But then it's also just allowing for the innovation, right? Like, I think, we firmly seen this in software. I think this is a great part of Linux Foundation, which is, 

no one company owns the, you know, monopoly on innovation. And what we really wanna see was not to like, can we make a better piece of hardware, but can we provide, some more foundational capabilities so that hundreds of startups or different types of organizations that might have different ideas or different needs or different goals could innovate around the sustainability aspect of data center hardware and, I think what we're focused on now within GSF is really taking that to a more foundational level. There's just a huge opportunity right now with the data center construction industry happening to really find a even more interesting place where we can take some of those learnings from hardware specifications and apply it to an even broader impact base 

Chris Adams: Ah, okay. Alright. I'll come back to some of this because there's, I know there's a project called Project Mycelium that Asim Hussain, the executive Director of the Green Software Foundation is continually talking about. But like we've spoken a little about, you mentioned, if I understand it, like, this allows you to maybe have more freedom to talk about maybe, instead of having like tiny fans, which scream at massive, thousands and thousands of RPM, there's other ways that you could maybe call down chips for example. And like, this is one thing that I know that the hardware standards working group is looking at, is finding ways to keep the servers cool, for example. Like as I understand it,

using liquid is, can be more efficient, quite a bit more efficient than having tiny fans to cool at massive RPM to cool things down. But also, I guess there's a whole discussion about, well there's different kinds of, there's different ways of cooling things which might reduce the kind of local power draw, local water usage in a data center, for example.

And like, maybe this is one thing we could talk a little bit more about then, 'cause I dunno that, we've had people talk about, say, liquid calling and things like that before, as like, these are some alternative ways to more sustainably cool down data centers in terms of how much power they need, but also what their local footprint could actually be.

But we've never had people who actually have that much deep expertise in this. So maybe I could put the questions to one of you. Like, let's say you're gonna switch to liquid calling, for example, Instead of using itty bitty fans or even just bigger, slightly bigger fans, running a little bit slower. Like, how does that actually improve it? Maybe you could, maybe I could put this to you, My. 'Cause I think this is one thing that you've spent quite a lot of time looking into, like, yeah, where are the benefits? Like what, how does, how did the benefits materialize if you switch from, say, air to a liquid calling approach like this?

My Truong: Yeah, so on the liquid cooling front, there's a number of pieces here. The fans that you were describing earlier, they're moving air, which is effectively a liquid when you're using it in a cooling mode at 25,000 RPM, you're trying to more air across the surface and it doesn't have a great amount of, 

Zachary Smith: Heat transfer capability. 

My Truong: removal and rejection. Yeah. heat transfer capabilities. Right. So in this world where we're not moving heat with air, we're moving it with some sort of liquid, either a single phase liquid, like water or a two-phase liquid taking advantage of two phase heat transfer properties.

There's a lot of significant gains and those gains really start magnifying here in this AI space that we're in today. And I think this is where Project Mycelium started to come into fruition was to really think about that infrastructure end to end. When you're looking at some of these AI workloads, especially AI training workloads, their ability to go and move hundreds of megawatts of power simultaneously and instantaneously becomes a tricky cooling challenge and infrastructure challenge. And so really what we wanted to be able to think through is how do we go and allow software to signal all the way through into hardware and get hardware to help go and deal with this problem in a way that makes sense.

So I'll give you a concrete example. 

If you're in the single phase space and you are in the 100 megawatt or 200 megawatt data center site, which is, this is what xAI built out Memphis, Tennessee. When you're going and swinging that workload, you are swinging a workload from zero to a hundred percent back to zero quite quickly. In the timescale of around 40 milliseconds or so, you can move a workload from zero to 200 megawatts back down to zero. When you're connected to a grid, when you're connected to a grid,

Chris Adams: right.

My Truong: that's called a grid distorting power event, right?

You can go swing an entire grid 200 megawatt, which is, probably like, maybe like a quarter of the LA area of like the ability to go and distort a grid pretty quickly. When you're an isolated grid like Ercot, this becomes like a very, tricky proposition for the grid to go and manage correctly. On the flip side of that, like once you took the power you, created about 200 megawatt of heat as well. And when you start doing that, you have to really think about what are you doing on your cooling infrastructure. If you're a pump based system, like single phase, that means that you're probably having to spool up and spool down your pump system quite rapidly to go respond to that swing in power demand. But how do you know? How do you prep the system? How do you tell that this is going to happen? And this is where we really need to start thinking about, these software hardware interfaces. Wouldn't it be great if your software workload could start signaling to your software or your hardware infrastructure? "Hey I'm about, to go and start up this workload, and I'm about to go and swing this workload quite quickly." You would really want to go signal to your infrastructure and say, "yes, I'm about to go do this to you," and maybe you want to even signal to your grid, "I'm about to go do this for you" as well. You can start thinking about other, options for managing your power systems correctly, maybe using like a battery system to go and shave off that peak inside of the grid and manage that appropriately. So we can start thinking about this. Once we have this ability to go signal from software to hardware to infrastructure and building that communication path, it becomes an interesting thought exercise that we can realize that this is just a software problem.

have been in this hardware, software space, we've seen this before. And is it worth synchronizing this data up? Is it worth signaling this correctly through the infrastructure? This is like the big question that we have with Project Mycelium. Like, it would be amazing for us to be able to do this.

Chris Adams: Ah, I see.

My Truong: The secondary effects of this is to really go think through, now, if you're in Dublin where you have offshore power and you now have one hour resolution on data that's coming through about the amount of green power that's about to come through, it would be amazing for you to signal up and signal down your infrastructure to say, you should really spool up your workload and maybe run it at 150% for a while, right?

This would be a great time to go really take green power off grid and drive your workload on green power for this duration. And then as that power spools off, you can go roll that power need off for a time window. So being able to think about these things that we can create between the software hardware interface is really where I think that we have this opportunity to really make game changing and really economy changing outcomes. 

Chris Adams: Okay. 

Zachary Smith: I have a viewpoint on that, Chris, 

Chris Adams: too.

Yeah, please do.

Zachary Smith: My TLDR summary is like, infrastructure has gotten much more complicated and the interplay between workload and that physical infrastructure is no longer, "set it in there and just forget it and the fans will blow and the servers will work and nobody will notice the difference in the IT room."

These are incredibly complex workloads. Significant amount of our world is interacting with this type of infrastructure through software services. It's just got more complicated, right? And what we haven't really done is provide more efficient and advanced ways to collaborate between that infrastructure and the kind of workload. It's, still working under some paradigms that like, data centers, you put everything in there and the computers just run. And that's just not the case anymore. Right. I think that's what My was illustrating so nicely, is that workload is so different and so dynamic and so complex that we need to step up with some ways to, for the infrastructure and that software workload to communicate.

Chris Adams: Ah, I see. Okay. So I'll try and translate some of that for some of the listeners that we've had here. So you said something about, okay. A 200 megawatt like power swing, that's like, that's not that far away from a half a million people appearing on the grid, then disappearing on the grid every 14 milliseconds.

And like obviously that's gonna piss off people who have to operate the grid. But that by itself is one thing, and that's also a change from what we had before because typically cloud data centers were known for being good customers because they're really like flat, predictable power draw.

And now rather than having like a flat kind of line, you have something more like a kind of seesaw, a saw tooth, like up, down, up, down, up, down, up, down. And like if you just pass that straight through to the grid, that's a really good way to just like totally mess with the grid and do all kinds of damage to the rest of the grid.

But what it sounds like you're saying is actually, if you have some degree of control within the data center, you might say, "well, all this crazy spikiness, rather than pulling it from the grid, can I pull it from batteries, for example?" And then I might expose, or that I might expose that familiar flat pattern to the rest of the grid, for example.

And that might be a way to make you more popular with grid operators, but also that might be a way to actually make the system more efficient. So that's one of the things you said there. So that's one kind of helpful thing there. But also you said that there is a chance to like dynamically scale up how, when there is loads and loads of green energy, so you end up turning into a bit more of a kind of like better neighbor on the grid essentially.

And that can have implications for the fact that because we are moving to a, like you said before, there's complexity at the power level and it allows the data centers to rather than make that worse, that gonna address some of those things. So it's complimentary to the grid, is that what you're saying?

My Truong: Yeah. I think you got it, Chris. You got it, Chris. Yeah.

Exactly. So that's on the power side. I think that we have this other opportunity now that as we're starting to introduce liquid cooling to the space as well, we're effectively, efficeintly removing heat from the silicon. Especially in Europe

this is becoming like a very front and center, conversation of data centers operating in Europe is that this energy doesn't need to go to waste and be evacuated into the atmosphere. We have this tremendous opportunity to go and bring that heat into local municipal heat loops

and really think about that much more, in a much more cohesive way. And so this is again, like where we really, like, as Zach was saying, we really need to think about this a bit comprehensively and really rethink our architectures to some degree with these types of workloads coming through. And so bringing standards around the hardware, the software interface, and then as we start thinking through the rest of the ecosystem, how do we think through bringing consistency to some of this interface so that we can communicate "workload is going up, workload is going down. The city needs x amount of gigawatt of power into a municipal heat loop," like help the entire ecosystem out a little bit better. In the winter, probably Berlin or Frankfurt would be excited to have gigawatts of power in a heat loop to go and drive a carbon free heating footprint inside of the area. But then on the flip side of that, going and building a site that expects that in the winter, but in the summer where you're not able to take that heat off, how do we think about more innovative ways of driving cooling then as well? How do we go and use that heat in a more effective way to drive a cooling infrastructure?

Chris Adams: So, okay, so this is that. I'm glad you mentioned the example, 'cause I live in Germany and our biggest driver of fossil fuel use is heating things up when it gets cold. So that's one of the good, good ways to, like, if, there's a way to actually use heat, which doesn't involve burning more fossil fuels, totally. Or I'm all for that. There is actually, one question I might ask actually is like, what are the coolants that people use for this kind of stuff? Because the, when you, I mean, when we move away from air, you're not norm, you're not typically just using water in some, all of these cases, there may be different kinds of chemicals or different kinds of coolants in use, right?

I mean, maybe you could talk a little bit about that, because I know that we had switches from when we've looked at how we use coolant elsewhere, there's been different generations of coolants for our, and in Europe, I know one thing we, there's a whole ongoing discussion about saying, "okay, if we're gonna have liquid cooling, can we at least make sure that the liquid, the coolants we're using are actually not the things which end up being massively emitting in their own right," because one of the big drivers of emissions is like end of life refrigerants and things like that. Maybe you could talk a little bit about like what your options are if you're gonna do liquid cooling and like, what's on the table right now?

To actually do something which is more efficient, but is also a bit more kind of non-toxic and safe if you're gonna have this inside a, in inside a given space.

My Truong: Yeah. So in liquid cooling there's a number of fluids that we can use. the most well understood of the fluids, as used both in the facility and the technical loop side is standard de-ionized water. Just water across the cold plate. There's variations that are used out there with a propylene glycol mix to manage microbial growth. The organization that I'm part of, we use a two-phase approach where we're taking a two-phase fluid, and taking advantage of phase change to remove that heat from the silicon. And in this space, this is where we have a lot of conversations around fluids and fluid safety and how we're thinking about that fluid and end of life usage of that fluid. Once you're removing heat with that fluid and putting it into a network, most of these heat networks are water-based heat networks where you're using some sort of water with a microbial treatment and going through treatment regimes to manage that water quality through the system.

So this is a very conventional approach. Overall, there's good and bads to every system. Water is very good at removing heat from systems. But as you start getting towards megawatt scale, the size of plumbing that you're requiring to go remove that heat and bring that fluid through, becomes a real technical challenge.

And also

at megawatts. Yeah. Yeah.

Zachary Smith: If I'm not mistaken.

Also, there's challenges if you're not doing a two-phase, approach to actually removing heat at a hot enough temperature that you can use it for something else, right?

My Truong: Correct. Correct, Zach. It's, so there's, like, a number of like very like technical angles to this. So as you're, going down that path, Zach, so in single phase what we do is we have to move fluid across that surface a good enough clip to make sure that we're removing heat keeping that silicon from overheating. Downside of this is like, as silicon requires colder and colder temperatures to keep them operating well, their opportunity to drive that heat source up high enough to be able to use in a municipal heat loop becomes lower and lower. So let's say, for example, your best in class silicon today asking for what's known as a 65 degree TJ. That's a number that we see in the silicon side. So you're basically saying, "I need my silicon to be 65 degrees Celsius or lower to be able to operate properly." flip side of that is you're gonna ask your infrastructure go deliver water between 12 to 17 degrees Celsius to make sure that, that cooling is supplied. But the flip side of that is that if you allow for, let's say, a 20 degree Celsius rise, your exit temperature on that water is only gonna 20 degrees higher than the 70 degrees inlet, so that water temperature is so low

And that's not a very nice shower, basically. Yeah.

You're in a lukewarm shower at best.

So, we have to do, then we have to tr spend a tremendous amount of energy then bring that heat quality up so that we can use it in a heat network. And two phase approaches, what we're taking advantage of is the physics of two-phase heat transfer, where, during phase change, you have exactly one temperature, which that fluid will phase change.

To a gas. Yeah. Yeah.

To a gas. Exactly.

Yeah.

And so the easiest way, like, we'll use the water example, but this is not typically what's used in two phase tech technologies, is that water at a atmospheric pressure will always phase change about a hundred degrees Celsius. It's not 101, it's not 99. It's always a hundred degrees Celsius at hemispheric pressure. So your silicon underneath that will always be at a, around a hundred degree Celsius or maybe a little bit higher depending on what your heat transfer, characteristics look like. And this is the physics that we take advantage of. So when you're doing that, the vapor side this becomes like a very valuable energy source and you can actually do some very creative things with it on two phase.

So that's, there's some, every technology has a, is a double-edged sword and we're taking advantage of the physics of heat transfer to effectively and efficiently remove heat in two-phase solutions.

Chris Adams: Ah, so I have one kind of question about the actual, how that changes what data center feels like to be inside, because I've been inside data centers and they are not quiet places to be. Like, I couldn't believe just how uncomfortably loud they are. And like, if you're moving away from fans, does that change how they sound, for example?

Because if, even if you're outside some buildings, people talk about some of the noise pollution aspects. Does a move to something like this mean that it changes some of it at all?

Zachary Smith: Oh yeah.

My Truong: In inside of the white space. Absolutely. Like one of the things that we fear the most inside of a data center is dead silence.

You might actually be able to end up in a data center where there's dead silence, soon.

And that being a good thing. Yeah.

With no fans. Yeah. We'd love to remove the parasitic draw of power from fans moving air across data centers, just to allow that power to go back into the workload itself.

Chris Adams: So for context, maybe someone, if you haven't been in a data center... I mean, it was around, I think it felt like 80 to 90 decibels for me, which felt like a, I mean, defects have a

Yeah, plus could have been more actually. Yeah. So I mean, it was a, I mean, if you have like an, if you have a something on a wearable, on a phone, as soon as it's above 90 degrees, 90 decibels, that's like

louder than lots of nightclubs, basically. Like maybe there's a comp. So this is one thing that I fell and this sounds like it does, like it can introduce some changes there as well rather than actually just, we're just talking about energy and water usage. Right.

Zachary Smith: Yeah, most data center technicians wear ear protectors all the time, can't talk on the phone, have to scream at each other, because it's so loud. Certainly there's, some really nice quality of life improvements that can happen when you're not blowing that much air around and spinning up multiple thousand

My Truong: 25,000 to 30,000 RPM fans will, require you double hearing protection to be able to even function as out of the space.

Yeah, that's the thing.

A lot of energy there.

Chris Adams: Oh, okay. Cool. So, so this is the, these are some of the, this is some of the shifts that make possible. So the idea, you can have, you might have data centers of what you're able to be more active in terms of actually working with the grid because for all the kind of things we might do as software engineers, there's actually a standard which makes sure that the things that we see Google doing or Meta talking about in academic papers could be more accessible to more people.

That's one of the things that having standards and for like Open19 things might be because there's just so many more people using 19 inch racks and things like that. That seems to be one thing. So maybe I could actually ask you folks like. This is one thing that you've been working on and My, you obviously running an organization, Zuta Core here, and Zach, it sounds like you're working on a number of these projects.

Are there any particular like open source projects or papers or things with really some of these. Some of the more wacky ideas or more interesting projects that you would point people to? Because when I talk about data centers and things like this, there's a paper from, that's called the Ecoviser paper, which is all about virtualizing power so that you could have power from batteries going to certain workloads and power from the grid going to other workloads.

And we've always thought about it as going one way, but it sounds like with things like Project Mycelium, you can go have things going the other way. Like for people who are really into this stuff, are there any, are there any good repos that you would point people to? Or is there a particular paper that you found exciting that you would direct people to who are still with us and still being able to keep up with the kind of, honestly, quite technical discussion we've had here.?

Zachary Smith: Well, I would, not to tout My's horn, but, reading the Open19 V2 specification, I think is worthwhile. Some of the challenges we dealt with at a kind of server and rack level, I think are indicative of where the market is and where it's going. There's also great stuff within the OCP Advanced Cooling working group. And I found it very interesting, especially to see some of what's coming from Hyperscale where they are able to move faster through a verticalized integration approach. And then I've just been really interested in following along the power systems, and related from the EV industry, I think there's, that's an exciting area where we can start to see data centers not as buildings for IT, but data centers as energy components.

So when you're looking at, whether it's EV or grid scale kind of renewable management, I think there's some really interesting tie-ins that our industry, frankly is not very good at yet.

Ah.

Most people who are working in data centers are not actually power experts from a generation or storage perspective.

And so there's some just educational opportunities there. I've found, just as one resource, My, I don't know if they have it, at the, the seven by 24 conference group, which is the critical infrastructure conference, which everything from like water systems, power systems to data centers, has been really a great learning place for me.

But I'm not sure if they have a publication that is useful. We, have some work to do in moving our industry into transparent Git repos.

My Truong: Chris, my favorite is actually the open BMC codebase. It provides a tremendous gateway where this used to be a very closed ecosystem, and very hard for us to think about being able to look through a code repo of a redfish API, and able to rev that spec in a way that could be useful and, implementable into an ecosystem has been like my favorite place outside of hardware specifications like

Chris Adams: Ah, okay. So I might try and translate that 'cause I, the BMC thing, this is basically the bit of computing, which essentially tells software what's going on inside of data, how much power it's using and stuff like that. Is that what you're referring to? And is Open BMC, like something used to be proprietary, there is now a more open standard so that there's a visibility that wasn't there before.

Is that what it is? 

My Truong: Right. that's exactly right. So there you have to, in years past, had a closed ecosystem on the service controller or the BMC, the baseboard controller dule inside of a server and being able to look into that code base was always very difficult at best and traumatic at worst. But having open BMC reference code out there,

being look and see an implementation and port that code base into running systems has been very useful, I think, for the ecosystem to go and get more transparency, as Zach was saying, into API driven interfaces.

oh.

What I'm seeing is that prevalence of that code base now showing up in a, number of different places and the patterns are being designated into, as Zach was saying, power systems. We're seeing this, become more and more prevalent in power shelves, power control, 

places where we used to not have access or we used to use programmable logic controllers to drive this. They're now becoming much more software ecosystem driven and opening up a lot form possibilities for us.

Chris Adams: Okay. I'm now understanding the whole idea behind Mycelium, like roots reaching down further down into the actual hardware to do things that couldn't be done before that. Okay. This now makes a lot more sense. Yeah.

Peel it back. One more layer.

Okay. Stacks within Stacks. Brilliant. Okay. This makes sense. Okay folks, well, thank you very much for actually sharing that and diving into those other projects.

We'll add some, if we can, we'll add some links to some of those things. 'Cause I think the open BMC, that's one thing that is actually in production in a few places. I know that Oxide Computer use some of this, but there's other providers who also have that as part of their stack now that you can see.

Right.

My Truong: We also put into production when we were part of the Packet Equinix team. So we have a little bit of experience in running this tech base in, real production workloads.

Chris Adams: Oh wow. I might ask you some questions outside this podcast 'cause this is one thing that we always struggle with is finding who's actually exposing any of these numbers for people who are actually further up the stack because it's a real challenge. Alright. Okay, we're coming up to time, so, I just wanna leave one question with you folks, if I may.

If people have found this interesting and they want to like, follow what's going on with Zach Smith and My Truong, where do they look? Where do they go? Like, can you just give us some pointers about where we should be following and what we should be linking to in the show notes? 'Cause I think there's quite a lot of stuff we've covered here and I think there's space for a lot more learning actually.

Zachary Smith: Well, I can't say I'm using X or related on a constant basis, but I'm on LinkedIn @zsmith, connect with me there. Follow. I post occasionally on working groups and other parts that I'm part of. And I'd encourage, if folks are interested, like we're very early in this hardware working group within the GSF.

There's so much opportunity. We need more help. We need more ideas. We need more places to try. And so if you're interested, I'd suggest joining or coming to some of our working group sessions. It's very early and we're open to all kinds of ideas as long as you're willing to, copy a core value from Equinix,

as long as you can speak up and then step up, we'd love the help. there's a lot to do.

Brilliant, Zach. And My, over to you.

My Truong: LinkedIn as well. Love to see people here as part of our working groups, and see what we can move forward here in the industry.

Chris Adams: Brilliant. Okay. Well, gentlemen, thank you so much for taking me through this tour all the way down the stack into the depths that we as software developers don't really have that much visibility into. And I hope you have a lovely morning slash day slash afternoon depending on where you are in the world.

Alright, cheers fellas.

Thanks Chris.

Thanks so much. 

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]]> Cloud Infrastructure, Efficiency and Sustainability Cloud Infrastructure, Efficiency and Sustainability Thu, 08 May 2025 07:00:00 GMT 55:13 v17vyl90 false https://podcasts.castplus.fm/e/4892y9wn-cloud-infrastructure-efficiency-and-sustainability 68dc7d076d92c33f9c88f2d8 68dc7cef46a2532cdd8d1c23 Host Anne Currie is joined by Charles Humble; a writer, podcaster, and former CTO with a decade’s experience helping technologists build better systems—both technically and ethically. Together, they discuss how developers and companies can make smarter, greener choices in the cloud, as well as the trade-offs that should be considered. They discuss the road that led to the present state of generative AI, the effect it has had on the planet, as well as their hopes for a more sustainable future. 107 Host Anne Currie is Joined by the esteemed Charles Humble, a figure in the world of sustainable technology. Charles Humble is a writer, podcaster, and former CTO with a decade’s experience helping technologists build better systems—both technically and ethically. Together, they discuss how developers and companies can make smarter, greener choices in the cloud, as well as the trade-offs that should be considered. They discuss the road that led to the present state of generative AI, the effect it has had on the planet, as well as their hopes for a more sustainable future.

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TRANSCRIPT BELOW:

Charles Humble: In general, if you are working with vendors, whether they're AI vendors or whatever, it is entirely reasonable to go and say, "well, I want to know what your carbon story looks like." And if they won't tell you, go somewhere else. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. Today I'm your guest host Anne Currie, and we'll be zooming in on an increasingly important topic, cloud infrastructure, efficiency and sustainability.

Using the cloud well is about making some really clever choices, really difficult choices upfront. And they have an enormous, those choices an enormous impact on our carbon footprint, but we often just don't make them. So our guest today is someone who's thought very deeply about this.

So Charles Humble is a writer, podcaster, and former CTO who has spent the past decade helping technologists build better systems, both technically and ethically. He's the author of The Developer's Guide to Cloud Infrastructure, Efficiency and Sustainability, a book that breaks down how cloud choices intersects with environmental impacts and performance.

So before we go on, Charles, please introduce yourself.

Charles Humble: Thank you. Yes, so as you said, I'm Charles Humble. I work mainly as a consultant and also an author and a technologist. I have a, my own business is a company called Conissaunce, which I run. And I'm very excited to be here. I speak a lot at conferences, most recently, mainly about sustainability. I've written a bunch of stuff with O'Reilly, including a series of shortcut articles called Professional Skills for Software Engineers, and as you mentioned most recently, this ebook, which I think is why you've invited me on.

Anne Currie: It is indeed. Yes. So, to introduce myself, my name is Anne Currie. I've been in the tech industry for pretty a long time. Pretty much the same as Charles, about 30 years. And I am one of the authors of O'Reilly's new book, building Green Software, which is entirely and completely aimed at the folks who will be listening to this podcast today.

So if you haven't listened to it, if you haven't read it or listened to it because it is available in an audio version as well, then please do so, you'd enjoy it. So, let's get on with the questions that we want to ask about today. So, Charles, you've written this great ebook, which is also something everybody who's listening to the podcast should be reading.

And we'll link to it in the show notes below. In fact, everything we'll be talking about today will be linked to in the show notes below. But let's start with one of the key insights from your book, which is that choices matter. Things like VM choices matter, but they're often overlooked when it comes to planning your cloud infrastructure.

What did you learn about that? What do you feel about that, Charles? 

Charles Humble: it's such an interesting place to start. So I think, when I was thinking about this book and how I was putting it together, my kind of starting point was, I wanted like a really easy on-ramp for people. And that came from, you know, speaking a lot at conferences and through some of the consulting work I've done and having people come up to me and say, "well, I kind of want to do the right thing, but I'm not very clear what the right thing is." 

And I think one of the things that's happened, we've been very good about talking about some of the carbon aware competing stuff, you know, demand shifting and shaping and those sorts of things. But that's quite a, quite an ambitious place to start. And oftentimes there are so many kind of easier wins, I think. And I kind of feel like I want to get us talking a little bit more about some of the easy stuff. 'Cause it's stuff that we can just do. The other thing is, you know, human beings, we make assumptions and we learn things and then we don't go back and reexamine those things later on. So I've occasionally thought to myself, I ought to write a work called something like Things That Were True But Aren't Anymore or something like that 

because we all have these things. Like my mental model of how a CPU works until probably about two years ago is basically a Pentium two .And CPUs haven't looked like a Pentium two for a very long time, and I have a feeling I'm not the only one. So, you were specifically asking about like CPUs and VM choices, and I think a lot of the time, those of us, certainly those of us of a certain age, but I don't think it's just us, came through this era where Windows and Intel were totally dominant. And so we naturally default to well, "Intel will be fine"

because it was right for a long time.

Anne Currie: Yeah.

Intel 

Charles Humble: was the right 

Anne Currie: Who could ever have imagined that Intel would lose the data center? It's 

Charles Humble: Absolutely it is extraordinary. I mean obviously they lost mobile mainly to ARM and that was very much a sort of power efficiency thing. Fair enough. But yes, the idea that they might be losing the data center or might have lost the data center is extraordinary. But you know, the reality is first of all, if you are thinking about running your workloads. So, AMD processors, more or less how a cross compatible of Intel wants. It's not totally true, but it kind of is. So they have an X86 compatible instruction set. So for the most part, your workloads that will run on Intel will run on AMD.

But not only will they run on AMD, they will probably run on AMD better.

Again, for the most part, there are places where Intel probably has an edge, I would think. If you're doing a lot of floating point maths, then, maybe they still have an edge. I'm not a hundred percent sure, but as a rule of thumb, AMD is going to be, you know, faster and cheaper. And the reason for that has a great deal to do with core density. So AMD has more cores per chip than Intel does, and what that means is you end up with more processing per server, which means you need fewer servers to run the same workload. I ran some tests for the ebook and that came out,

so I had a 2000 VM instance and we had 11 AMD powered servers. So running, epic, the AMD Epic chips and we needed 17 Intel powered servers to do the same job. Right? So that's roughly 35% fewer servers. It's not, by the way, 35% less power use. It's actually about 29%, something like that, less power use 'cause the chips are quite power hungry, but still that's a big saving, right? And it's also, by the way, a cost saving as well. So the other part of this is, you know, it is probably about 13% cheaper to be running your workload on AMD than Intel. Now obviously your mileage may vary and you need to verify everything I'm saying.

Don't just assume, "well, Charles Humble said it's true, so it must be." 

It'll be a foolish thing to do, but as a rule of fault, the chances are in most cases you're better off and I'll wager that you are a lot of the time when you are setting up your VMs on your cloud provider, your cloud providers probably default to Intel and you probably just think, "well, that'll be fine."

Right?

So kind of a case of trying to flip that script. So maybe you default to AMD, maybe you evaluate whether ARM processors will work. We are seeing another surge of ARM in datacenters. Though, as I said, that comes with some it. In mobile, the trade offs are pretty straightforward with ARM to anything else. In data centers it is a little bit more nuanced. But basically it's that, and I think it's, I think it's this thing of, as I say, of these assumptions that we've just built up over time that we don't, we're not very good at going back and reexamining our opinions or our assumptions. And then the other thing that I think feeds into this is we build layers of abstractions, right? That's what computer science does, and we get more and more abstracted away from what the actual hardware is doing. I found myself this morning when I was thinking about coming on the show, thinking a bit about some of the stuff Martin Thompson's been talking about for years, about mechanical sympathy.

I'm sure you have experiences of this, and I know I have,

where, you know, I've been brought into a company that's having performance problems. And you look at, there's one that I actually remember vividly from decades ago, but it was, an internet banking app. So it was a new internet bank that was written in visual basic, weird choice, but anyway, go with me here. And they were reading. It was all MQ series, so IBM MQ series under the hood, right? So basically you've got messages that were written in XML being passed around between little programs. It looks a bit like microservices, but 20 years ago before we had the term roughly. And what they were doing, so when you read a message off an MQQ, you read it off essentially one byte at a time.

And what they were doing in a loop in Visual Basic was they were basically saying string equals string plus next byte. Does that make sense? So, string equals string plus new string. That kind of idea. Now under the cover, they're doing a deep string copy every single time they do that. But they had no idea 'cause they were visual basic programmers and didn't know what a deep string copy even was.

Fair enough. And then they were going, "why is our audit process grinding to a halt?"

And the reason is, well, 'cause you'll, we just need like an API. But what I'm getting at is we have these, we get very abstracted away from what the hardware is doing

because most of the time that's fine, right?

That's what we want, except that our abstractions leak in weird ways. And so sometimes you kind of need to be able to draw on this is what's actually happening to understand. So as I say, in the case of,

in the case of CPUs, if you haven't been paying attention to CPUs for a while, you probably think Intel still has the edge, but right now, sorry, Intel, they don't.

Hope that changes. Competition is always good. But you know, it's just a great example of, you probably don't even think about it. You probably haven't thought about it for years. I know, honestly I hadn't.

Anne Currie: Yeah.

Charles Humble: But then you start running these numbers and go, "gosh, that's, you know, like a 30% power saving."

That's, at any sort of scale, that's quite a big deal. And so a lot of the things that I was trying to do in the book was really that. It was just saying, well, what are some of the things that we can do that are easy things,

that make a massive difference?

Anne Currie: It's interesting. What you're saying there reminds me a little bit of somebody who was a big name in tech back in our early, you'll remember it very well. Joel Spolsky 

used to write a thing about, you know, what would Joel do? He used to work on, do a lot of work on usability, studying usability.

And he'd say, well, you're not looking for, to change the world and rewrite all these systems. You are often just looking for the truffles, the small changes that will have an outsize effect. And what you're saying is that, for example, moving from Intel to AMD is a small truffle that will have an outsized effect. If you do it at the right time,

it's, actually you could probably, it's not so much, as you say, the trouble with go with going to an ARM ship or, you know, Graviton servers that's been pushed very heavily by AWS at the moment. Big improvement in energy use and reductions in cost. But that is not a lift, that's not an instant

oh, flick of switch and you go over. They, you know, there are services that are no longer available. There are, you know, you're gonna have to retest and recompile and do all the things, but it's not such an obvious truffle. But you are saying that really that the intel AMD might be a really easy win for you.

Charles Humble: Yeah, absolutely. Absolutely. It's funny you mentioned Joel Spolsky there. 'Cause actually his, so I read his User Interface Design for Programmers, I think the book is called, about 30 years ago probably. It's just, I still, like everything I know about user interface, I swear it comes from that like book.

It was such a brilliant, it's also hysterically funny. It has all sorts of examples of just, it's very wittily written and has some wonderful examples of, you know, just terrible bits of user interface. Like the Windows 95 start button, which is in the bottom left hand corner. Except that if you drag to the bottom left hand corner of the screen, which is one of the easy places on a screen to hit, you miss the start button because aesthetically it looked wrong without a border around it.

But then no one thought, well, maybe we should just make it so if you miss, but you are there, you know, like it's full of just examples like that. It's very funny. And yeah, absolutely. This, business of, as I say, so much of, we have as an industry, been very profligate, right? 

We've been quite casual about our energy use and our

hardware use. So there's another example, which is to do with infrastructure and right sizing.

Again, this is just one of those things, it's such an easy, quick win for people

and it's another thing that connects to this business of our old assumptions. So when I started in the industry, and probably when you started in the industry and we ran everything in our own data centers, procurement was very slow, right?

If I needed a new server, I probably had to fill in a form and 10 people had to sign it, and then it would go off to procurement and it would sit doing, heaven knows what for a couple of months, and then eventually someone might get around to buying a server and then they'd install the software on it and then it would get racked.

And you know, like six months of my life could have gone by, right.

And so what that meant was if I was putting a new app in, and at some point someone would come along to you and go, "we're putting this new app in. How many servers do you need?" And what you do is you'd run a bunch of load tests on, I dunno, load runner or something like that.

You'd work out what the maximum possible concurrent, like, oh, sorry, concurrent was a poor choice of word there.

Simultaneous number of users on your system, rather.

Yeah.

Right. You simulate that loads, that would tell you how many boxes you needed. So suppose that said four servers, you go to procurement and you go "eight, please."

Anne Currie: Indeed. 

Charles Humble: Right. And no one would ever say "why do you need eight?" Because, right. And that's just. That's just what we do. And what's weird is we still do it, right. Even though elastic compute on the cloud means surely we don't need to. We kind of have this mindset of, "well, I'll just, I'll add a bit more just to be on the safe side 'cause I'm not

too confident about my numbers.

Anne Currie: There is a logic to it if it's easy because it, the thing that you fear is that you'll under provisioning and it'll fall over. So there's a big risk to that. Over provisioning, yes, it cost you more, but it's hard. It's really hard to get the provisioning perfect.

So we over provision and then you always intend to come back later and right size. And of course you never do because you never get a chance to come back and do things later. 

Charles Humble: Something I say a lot to the companies that I consult to is "well just run an audit."

Anne Currie: Yes, indeed. Yeah.

Have 

Charles Humble: a three month process or a, you know, like a three month or a six month mission where we are gonna do a right sizing exercise. We're gonna look for zombie machines. So those are machines that were, you know, once doing something useful but are doing nothing useful anymore. And also look for machines that are just sitting idle and get rid of them. You actually have an amazing story in the, in your O'Reilly book, the Building Green Software book from Martin Lippert. So he was tools and lead sustainability for VMware, Broadcom, part of the old Spring team.

He talks about, so in 2019, I think it was in VMware, they consolidated a datacenter in Singapore. They were moving the data center and basically they found that something like 66% of all the host machines were zombies. 66%.

Yeah. And that's untypical. 

Anne Currie: No, it's not.

Charles Humble: I've gone and done audits. 

50% plus is quite normal.

 So I have this like thing that I quite often say to people, I reckon you can halve your carbon emissions

in your IT practice just by running an audit and getting rid of things you don't need. 

And it may even be more than that. 

Anne Currie: Yeah, indeed. As VMware discovered, and people do it at a time when they move data centers. I often think this is probably a major reason why when people go, "oh, you know, I repatriated, I moved away from the cloud back in and I saved a whole load of money."

Yeah, you would've made, saved that money doing that kind of exercise in the cloud as well. Probably more because the cloud, the trouble with the cloud is both amazing, it has amazing potential for efficiency because it has great servers that are written to be very efficient and you wouldn't be able to write them that efficiently yourselves.

So there's amazing potential. Spot instances, burstable instance types, serverless, you know, there's loads of services that can really help you be efficient. But it's so easy to overprovision that inevitably everybody over provisions massively. And especially if you lift and shift into the cloud, you massively over provision.

Charles Humble: There's a related thing there as well because it's so easy to

and then you just forget about it. Evevn on my own, like sort of, you know, personal projects, I've suddenly got a bill from Google or something and I've been like, "oh hello, that then?"

And you know, it's something that I spun up three months ago for an article I was writing or something and I'd just totally forgotten about. And it's been sitting there running ever since, you know, like, and you could imagine how much worse that is as an enterprise, this is just like me on my own doing it.

And it's that kind of thing. I think. So thinking about things like auto sizing, you know,

scaling up remembering, to scale back down again. People often scale up and don't scale down again. There's some of the Holly Cummings stuff around Lightswith Ops. This idea of, you know, basically you want to be able to spin your systems back up again really easily.

That sort of stuff. Again, this is all stuff that's quite easy to do, relatively speaking.

Anne Currie: Relatively. So much easier than rewriting your systems in Rust or C, I can assure you of that.

Charles Humble: Well, a hundred percent, right? And, again, you know, I've made this, 

I've made this joke a few times on stage and it's absolutely true. We kind of, because we're programmers, we automatically think, "oh, I'll go and look at a benchmark that tells me what the most efficient language is," and it will be C or C++ or something.

And like "we will rewrite everything in C or C++ or Rust." Well that would be insane. And your company would go bust and nobody is gonna sponsor you to do that for very good reason. And

what you want to be doing is you want to be saying, "well, you know, what are the pragmatic things we can do that will make a huge difference?"

And a lot of those things are. You know, rightsizing. It's a really good example. 

Anne Currie: Yeah, I mean, I clearly we're, this is something that you and I have discussed many times and it was one of the reasons why at the end of Building Green Software, we devised the Green Software Maturity Matrix that we donated to the Green Software Foundation, 

Charles Humble: Yes. 

Anne Currie: because the, what we found over and over again when we talked to conferences, went out and spoke to people is that they had a tendency to leap right to the end, rewrite things in.

You know, they say, "well, we couldn't rewrite everything in C or Rust or we'd go outta business, so we won't do anything at all." And they step over all the most important, they step over all the truffles, which are switching your CPU choice, switching your VM choice, doing a right sizing, audits, doing a basic audit of your systems and turning off stuff, doing a security audit because a lot of the, these zombie systems actually should be turned off in a security audit because if they're there and they're running and they're not being patched and nobody owns them anymore, nobody knows what they're doing anymore, they will get hacked.

They are the ways into your system. So sometimes the way to pitch this is a security audit.

Charles Humble: Absolutely. Yes, and I do, I use the Maturity Matrix quite a lot in this ebook. Actually, it's one of the things that I reference all the way through it for exactly this reason, because it's, as I said, I think we tend to go to the end a lot. And actually a lot of the stuff is so much earlier on than that.

And I think it's just a, yeah, 

I think it's a really important thing to realize that there's a huge amount you can do. And actually as well, it's gonna save you an awful lot of money. And given the kind of very uncertain business environment that we're in, and people are very kind of worried about investing at the moment for all sorts of quite sensible reasons, this is one of those moments where actually if you're thinking about "I want to get my business onto a more, or my IT within my company onto a more sustainable footing," this is absolutely the right time to be having those conversations with your CFO, with your execs because, you know, this is the time where businesses need to be thinking, "well, how do I cut cost?" And there's a huge amount of waste. I guarantee you if you've not looked at this, there will be a huge amount of waste in your IT you can just get of

and be a bit of a hero and, you know, do good by the planet at the same time.

It's like, what's not to like?

Anne Currie: Yeah, because I mean, different companies, different enterprises, different entities have different roles in the energy transition. For most enterprises, your role is to adopt modern DevOps practices really, it's a new start. You don't mean you don't have to start there. You can start with the, as you say, manual audit.

Sometimes I've heard it called the thriftathon, where you just go through and you go, "do you know that machine? Turn it off." You know, you can use that kind of, they use the screen test method of "you don't think anyone's using it, turn it off. Find out if anybody was using it." And then you can use that to kind of step yourself up to the next level.

You and I both know holly Cummins, who was a guest, cut two back, one back, on this podcast. And she introduced the idea of, Lightswitch Ops, which is the, first kind of automation. If you haven't done any automation up till now and you want to learn how to do automation, a really good bit of automation is the ability to turn machines off automatically, maybe for a period overnight or, and you try that out on machines like your test suites, to just get yourself into the, to the simplest form of automation. It can also, if you are on the right, it depends if you're on the right models and you're in the cloud potentially, or you have the right

infrastructure, then that can save you money. It might not always save you money because you have to have made the right infrastructure choices. It might just that be that the machine sits on and doesn't really do anything. You've just turned off your application. But you really want to be turning things off to save power.

You know, and it's a really good way of getting you into the DevOps mindset, which is where everybody needs to be with so many payoffs.

Charles Humble: Yes.

Anne Currie: But yes. So, we'll go back to, do ask the questions. So, in part of, in, well, one of your talks is writing greener software, even when you are stuck on prem, and you talk about the fact that not everybody has the option to move into the cloud.

So what, then? What do you do if you can't move into the cloud?

Charles Humble: Yeah, that's, it is such an interesting question, that. So obviously there are things you can't do or can't do very easily, and one of the most obvious of those is you can't choose green locations on the whole if you're running stuff in your own data centers. So again, going back to these easy wins, an easy win is to use something like Electricity Maps, which is a tool which basically tells you what the energy mix is in a given region.

Oh.

And then you say, "I shall run my workloads there 'cause that looks good." There's a little bit more to it than that. You kind of want a location that not only has the greenest energy mix at the moment, but also has like credible plans for that to keep improving.

Anne Currie: Yeah.

Charles Humble: Obviously that's really hard to do with your own data centers.

Anne Currie: Yeah.

Charles Humble: As a rule of thumb, you probably don't want to be building new data centers if you can help it because, pouring concrete is not great. There's a lot of costs associated. That said, you do have some advantages in your own data centers 'cause you have some things that you can control that people on cloud can't. I would say, I mean, you know, like being honest about it, if you can move things to public cloud, that's probably going to be better. But if you can't, there are still things you can do. So one of those things is you have control over the lifetime of your hardware. This gets a little bit complex, but it's basically down to, so hardware has an embodied carbon cost.

That's the cost that it takes to construct it, transport it, dispose it at the end of its use, like useful lifetime. I mean, it also has the cost it takes to charge it. Now for your laptops, your mobile phones, your end user devices, the embodied carbon absolutely dwarfs the carbon cost used to charge it in its lifetime.

Anne Currie: Yeah.

Charles Humble: What we talk about with end user devices is like basically extend the life. Say, you know, 10 years or something like that, keep it. We want to make less of them, is really the point. Servers and TPUs and GPUs and those sorts of things, it's a bit more complicated. The reason it's a bit more complicated is because we are getting an awful lot better at making more efficient servers for all sorts of reasons. so what that means is the trade-offs with each new generation is more complicated. As an example, a lot of your energy use in your data center is actually gonna be cooling. So a CPU or a TPU that's running less hot requires less cooling. That's a big win. These sorts of things are sufficiently important that actually, until gen AI came along, so really three or four years ago, though we were adding massive amounts of compute, the emissions from our data centers was pretty flat. I mean, it was climbing, but not much. So the point here with your own data centers is you have control over that lifetime. So what you can do is you can do the calculations, assuming you can get the embodied carbon costs from your suppliers, you can do the calculations and think about, "well, how long do I keep this piece of hardware going before I turn it over?" Now, I don't want to give you a heuristic on that because it's kind of dangerous, but it's probably not 10 years, right?

It's probably five years-ish. Maybe something like that, but run the maths. But it's absolutely something you can do. You can also take advantage of things like your servers will have power saving modes that you probably don't turn on because we used to worry about that kind of thing.

'Cause we have this like, again, one of our old assumptions. We used to imagine that if you power a server down, it might not come back quite the same. Actually that's kind of still true, but, you know, it's fixable, right? So enable power saving across your entire fleet, that will make a huge difference, particularly if you've over provisioned, like we were saying earlier, right? 50% of your servers are idle. Well, they can be asleep all the time, and that helps. It's not the same as turning 'em off, but helpful. You can also look at voltage ranges. So your hardware will have a supported voltage range, and you've probably never thought about it, and I'll admit I hadn't until quite recently.

But actually again, if you're running at scale, if you send the lowest voltage that your servers will support, at a big scale that will a considerable difference. And then again, some of the other things we talked about, your CPU choice again, will make a difference. So think about, you know, "do I need to be buying Intel servers all the time, or could I be buying AMD ones or ARM ones?"

And also look at your cooling. But that's a whole, that's a whole nother complicated topic for all sorts of reasons. Often, well, in brief, some of the most energy efficient methods of cooling have their own set of problems, which make the trade offs really hard. So, like water-based cooling tends to be very efficient,

tends not to be great for local water tables.

Anne Currie: Yeah.

Charles Humble: It's, complicated. But, yeah, as I say, there are, so, there are a lot of things are that are definitely harder. And if you have a choice, if you're running in like a hybrid environment, chances are if you have a choice of going public cloud or own data center, public cloud is probably better. It's absolutely in Google and AWS and Microsoft's interests to run their data centers as efficiently as possible. 'Cause that's where their cloud profit margin is, right?

Anne Currie: Absolutely.

Less 

Charles Humble: it's costing them to run the, you are still paying the same amount, the more money they make. 

Anne Currie: Well, I, and I think I always laugh when I see the numbers on Graviton. So when AWS attempt to, persuade you quite correctly to, if you can move from Intel chips to run on, to run your applications onto ARM chips. They say, "oh, this will save, 40% on your hosting bill and 60% on your carbon emissions."

And you think, I think you've just pocketed quite a lot, a big. That suggest to me you've just pocketed quite a nice upgrade in your, in your, profitability. And I have no problem with that whatsoever, as things get better, I have no problem with making profits out of it. So I'm gonna pick you up on something that, I think everything you've said there is very true.

And I'm gonna take a slightly different take on it, which is that remember what that, what Charles is saying, there is quite detailed stuff about not everybody here will be a hardware person and that you will have specialists within your organization who can do all these hardware judgements.

The interesting thing is that they can. And it is always the case that if you can, if you have specialists in your organization, the best way to do better is to persuade them that they want to do better. So, if, you could persuade your specialists that actually to actually take an interest in this and to find ways of improving the efficiency of your systems, cutting the carbon emissions, they will do better at it than you will.

Charles Humble: 100%.

Anne Currie: Best thing you could do is persuade them to focus their in giant specialist brains on the subject because the likelihood is that the real issue is they probably aren't thinking about it, or they probably don't, you know, they, it is not top of their mind. They maybe think they're not even allowed to start thinking about it.

If it at a high level, you can actually get your specialists to turn their attention to these. efficiency issues to these carbon reduction issues, that's so much more effective than you going and reading up on it yourself. Get them involved. Go out and talk to people. Persuade, use your powers of persuasion, because, what you should take away from Charles, what's lots of people listening should take away from what Charles

just said then is that there is a lot of stuff that can be done by your specialist teams that they might not be thinking about doing, or they might not be, they might feel they don't have the time or focus to do. You can potentially help them by focusing them or giving them some budgets or some time to work on it.

Charles Humble: Definitely. Absolutely. Yeah. Yeah. No, I'm a big believer in specialization in our industry, and I think actually this idea that we are almost know everything isn't, is not helpful. Like absolutely, if you've got hardware people, go and tell the hardware people, and it's a thing of incentivizing.

It's like, you know, "we can save money by doing some of these things, or we can reduce our carbon by doing some of these things, and those are good things to do." Yeah, a hundred percent agree with all of that. No disagreements at all.

Anne Currie: Yeah, no, it's interesting isn't it, that most of human progress has come from the realization that specialists kick the butt of generalists. And I'm a generalist, so you know, I wish it wasn't true. My job is to kind of encourage specialists to be specialists and, you know, this is not new news.

It was the, it's the theme of Adam Smith's the Wealth of Nations that he wrote in the 1770s about why the industrial revolution was happening. It wasn't to do with any kind of technology or anything else. It was the discovery that specialists kick the butt of generalists.

Charles Humble: Hundred percent, yes.

Anne Currie: But now we're gonna get to the final tricky question that we have for you, Charles, that you'll be thinking about. You've been thinking about, so I'm, your work often emphasizes the importance of transparency, knowing the carbon footprint of what we build. What tools and practices do you recommend for people to do that? 

Charles Humble: Oh, that is a hard question. Yes. Frustratingly hard actually, we, so the first thing is we often end up using proxies

and the reason we end up using proxies is 'cause measurement is genuinely quite difficult. So cost is a quite a good proxy. In Bill Gates' book, blanking on the name of the book, oh, How to Avoid a Climate Disaster, 

Anne Currie: Oh yeah. Which is excellent. And again, everybody listening to this should be reading it. Yeah.

Charles Humble: Absolutely. So he, in that book, he does a bunch of calculations, which he calls green premiums and they're

basically the cost of going green.

Now, He doesn't do one for our industry, but I would wager, because we are also profligate, I would wager that our green premium, and I haven't worked this out, I will admit it, but I would think our green premium is probably a negative number.

So, that's to say,

going green is probably cheaper for us. Right.

Anne Currie: I agree.

Charles Humble: So cost is a very good proxy. It is an imperfect proxy. One of the reasons it's an imperfect proxy is because, for example, if you're running a green energy mix, that's not going to be reflected in your electricity bill at the moment. That may change, but at the

moment it doesn't happen.

Right. So it is imperfect, but

Anne Currie: Well, it doesn't happen in some places and in other places it does. So if you are on prem and you're in a country with dynamic pricing like Spain or zonal pricing, like talking about the UK having in future, that's still very up in the air, then it does. But if you're in the cloud, even in those areas, it doesn't at the moment.

Charles Humble: Absolutely. But nevertheless, 'cause as I was saying, you know, like probably half of your servers are doing nothing useful. So cost is a pretty good starting point. Another thing is CPU utilization. So there's something we haven't really talked about, which is this idea, Google calls it energy proportionality,

Anne Currie: Yeah.

Charles Humble: the observation that when you turn a machine on, you turn a server on, it has a static power draw, and that static power draw is quite a lot. How much depends on how efficient the server is, but it might be 50% or something like that. So when it's sitting idle, it's actually drawing a lot of power. The upshot of this is you'd usually have like an optimum envelope for a given server, and that might be somewhere between 50 and about 80%.

It may be a bit lower than that depending on how good the chips are. Above about 80% you tend to get key contention and those sorts of things going on. Not great. But around and about that operating window. So it's again, keeping your CPU utilization hard but not, high rather, but not maxed out is another good one.

Hardware utilization is another good one. Beyond that, so all of the cloud providers have tools of varying usefulness. Google's carbon footprint tool is probably best in class, at least in my experience. I think they take this stuff very seriously and they've done a lot of very good work.

Microsoft Azure tools are also pretty good. AWS's ones, so they have just released an update literally as we're recording this, and I hadn't had a chance to go and look at what's in the updated version. I'm going to say I think AWS is still a long way behind their competitors in terms of reporting.

Anne Currie: Yeah.

With 

Charles Humble: a slight proviso that I hadn't looked at what's in the new tool properly. But again, there, there are all things there that you can use. There's a tool called Cloud Carbon Footprint, which is an open source thing, by ThoughtWorks and that's quite good. It will work across different cloud providers, so that's kind of nice. You could probably adapt it for your own data centers, I would imagine. Of course the GSF has a formula for calculated carbon intensity as well. So that's more of a sort of product carbon footprint or lifecycle assessment type approach. It's not really suitable for corporate level accounting or reporting or that sort of thing, but that's quite a good tool as well. And there are a variety of other things you can use, but as I say, if we're talking the very beginnings, you probably start with the proxies. If you've got a choice of cloud provider, think about the cloud provider that gives you the tooling you need.

And you know, that might, again, going back to our assumptions, time was you would choose AWS. Maybe you shouldn't be choosing AWS now, or at least maybe you should be thinking about is AWS the right choice.

At least until they, you know, until they sort put their house in order a bit more. These are things, questions that we can reasonably ask. And in general, if you are working with vendors, whether they're AI vendors or whatever, it is entirely reasonable to go and say, "well, I want to know what your carbon story looks like." And if they won't tell you, go somewhere else. In the case of AI, none of the AI companies will tell you. They absolutely won't. And so my advice, if you're looking at running generative AI, other than. Everything we just said applies to AI, like it applies to everything else. There are a bunch of very specific AI related techniques, distillation, quantization, pruning, those sorts of things. Fine. But really my advice is well, using an open source model, and look at something like the ML leaderboard from ml.energy leaderboard, which will give you an idea of, what the carbon cost looks like. And don't use AI from a company that won't tell you, would be my advice. You know, and maybe we can embarrass some of these companies into doing the right things. You never know.

Anne Currie: Be nice, wouldn't it? It's so, it's interesting, the, this, so in April, Eric Schmidt got up in front of the US government in one of their, in one of their, committees and said, well, you know, if we, at the current rates, AI is going to take up 99% of the grid electricity in the US.

And you think "it's interesting, isn't it," because that's not a law of nature. There are plenty of countries that are looking at more efficient AI, so China, are certainly looking at more efficient AI. They don't want, they want to compete. They wanna be able to run AI because in the end, the business that's going to collapse if AI requires 99% of the US grid is AI because it cannot, you know, it's kind of, if something cannot go on, it will stop. 

Charles Humble: It's a desperate source of frustration for me because it is completely unnecessary.

Anne Currie: Well, it's, you just have to be a bit efficient.

Charles Humble: Just in brief, 'cause again, this is like a whole separate podcast probably,

but just in brief, there are a bunch of things that you can do

Anne Currie: Absolutely.

Charles Humble: that make a huge difference, both when you are collecting your data, when you are training your models, when you're running them in production afterwards. I have just done a piece of work for the News Stack on federated learning, and in the process of doing that, I talked to somebody called Professor Nick Lane, who is at Cambridge University, and he talked about, so one of the solutions to the data center cooling problem, which we touched on earlier, is basically what you do with the waste heat. And there are lots of companies in Europe that are looking at using it for things like heating homes or using, you know, heating municipal swimming pools, that sort of thing, right? You can't do that with an Amazon or a Google or a Microsoft facility, because you have to construct the data center close to where the waste is gonna be used.

But there are lots of these small data centers, particularly in Europe. There are companies like T Loop that are doing a lot of this work. And he made the point that with federated learning, you can actually combine these smaller facilities together and then, you know, be training potentially very large models on much, much smaller data centers, which I thought was fascinating. There's a guy called, Chung is his surname, and apologies to him, i'm blanking on Jae-Won Chung. He's done some extraordinary work looking at, so when we split stuff across GPUs,

that has to be synchronized, right? So we divide the workload up because it's too big to fit in a GPU and we split it across a bunch of different GPUs and we run all of those GPUs at full tilt, but we don't have to. Because we can't divide the workloads up evenly.

So you have some workloads that are tiny but this GPU is still running at full power, and what he worked out was, well, if we slow those GPUs down, the job will still end at the same point, but it'll use a lot less energy. So he's built something called Perseus, on his tasks with things like Bloom and GPT-3, they're about, it's about 30% less energy use just from using that

for exactly the same throughput. So there's no throughput loss, there's no hardware modification. The end results are exactly the same, and you just save 30% of your energy bill, which is a big deal.

Then you go, as I say, things like distillation and quantizing and pruning and shrinking your model size, all of that stuff.

So it frustrates me because it's so unnecessary. I think we need a carbon tax and I think the carbon tax needs to be prohibitive. And I think, you know, bluntly, I think companies like OpenAI should be pushed outta business if they don't get their house in it's time. I thrilled.

Hannah Richie's book, not The End of the World, which is my, possibly my favorite book on climate. And again, it's a book, everyone haven't read it, go and read it. She has a wonderful quote in there where she says, "I've talked to lots of economists and all of the economists I've spoken to agree that we need some sort of carbon tax."

And then she goes on to say, "it's maybe the only thing that economists agree on," which I thought was a fine and excellent line.

Anne Currie: It is really interesting 'cause I, we disagree slightly on, you're not a huge AI fan. I'm a massive AI fan. I want AI and I also want a livable climate. And they are not mutually exclusive. They can be done. I mean, you have, you don't love AI, you don't love AI as much as I love AI, but we are both in agreement that it is not physically impossible to have AI and effective control of climate change because as you were saying about the federated learning and, you know, optimizing your GPU towards the bottleneck tasks and then things like that, as long as you, workloads that are time insensitive that can be shifted in time and maybe delayed and maybe separated and then glob together again,

they're very good workloads to run on renewable power, which is variably available. So in fact, AI is potentially incredibly alignable with the energy transition. The fact that we don't always do it is a travesty and it's so bad for AI as well as being bad for the planet.

Charles Humble: I want to push back slightly on you saying I'm not a fan of AI. So I have. Quite strong concerns specifically about generative AI that are ethical and moral as well as environmental.

Anne Currie: Which I can see.

Charles Humble: And in essence it comes down to the fact you are taking a bunch of other people's work and you are building a machine that plagiarizes that work and you are not compensating those people for it. And you are also, basically you have to do tuning of the model. So reinforcement learning with human feedback and the way that, that's done is pretty horrifying when you dig into it. It usually involves, you know, people in places like Kenya being paid $3 an hour to look at the worst contents of the internet for day after day.

I mean, one can imagine what that does to you. So I have quite specific reservations with generative AI, the way that we are doing it. As it goes, I think there are ways that we could build generative AI that wouldn't, I wouldn't have these ethical problems with, that we're not doing. More generally, think generative AI is interesting. I don't know that it's useful, but I do think it's interesting. And more broadly, I'm not against AI at all. I'm like, you know, 

I've done work with a company that, for example, is using AI to look at, , increase the window that you can treat stroke patients with, by like hours.

And it's amazing. Amazing work. So they're basically doing image processing to identify different types of stroke. And some stroke patients, the window is much wider. So, you know, we

think of it as being 4.5 hours but it's much bigger. Stuff like

that. There's, and, as you say, like grid balancing is gonna get more complicated with renewables, and AI probably has a role to play there.

And I'm not anti. I'm not anti, I just think that there are things that we are doing as an industry which are reckless and ill-judged and you know, in my tiny little way I want. I mean, I'm aware that it's like, you know, blowing a kazoo in a thunderstorm, it's quite amusing, but it doesn't actually do much for anybody. But I, in my own little way, I want to be sort of beating the drum. As an industry, I think we need to get better. Right. And part of the reason I think we need to get better is because the work that we do has a huge impact on the whole planet now and on society and all sorts of things. And we are still like acting like we're a little cottage industry and what we do is inconsequential but it's not true.

 So my reservations with gen AI is, I think it's being done in a desperately irresponsible way, but that doesn't mean it has to be. It just means that's what we're doing. And hey, I might be wrong. You know, I'm not an ethicist. I just like, I just have reservations. Also, I am a writer. And a musician, right?

So, you know, like I do have skin in the game. I kind of want generative AI not to work. 'Cause otherwise I don't really have a living anymore, which is a bit of a worry. So, you know, I'm not a neutral observer on this at all, but I just think the way we're doing this is morally, ethically dubious, as well as being very bad for the climate. And I don't think it has to be any of those things.

Anne Currie: Yeah, I, so it's an interesting, we have a slightly different, 'cause I'm also a writer and a painter. but I've always been so rubbish at making money out of writing and painting that I don't really, don't have anything to say. So we have, that's, but that is my own fault.

A little bit. 

Charles Humble: The last question, I'm looking at your script now. Sorry. 'cause it's a shared Gigle doc, and your last question is about, so I write in my free time in a band called Twofish. And the question is, if you could score the soundtrack for a more sustainable future, what would it sound like? 

Anne Currie: I forgot about the question. Yeah.

Charles Humble: Interesting have get it in. So we did the opposite thing actually. We did, so there's a piece on the last two Fish album, called Floe, and that was my kind of, I started, everything is written as, by two of us. But I started that one and when I started it, what I was trying to do is describe what climate breakdown might sound like in music.

That was kind of my starting point. Not sure anyone hearing it would get that, but what I did was I went and recorded a bunch of like, field recordings. So, you know, California wild fires and that sort of thing. Tune them all to A flat minor as you do, and then wrote this very dark, scary,

that gets a bit drum and bassy as it goes on. It's very black and industrial and dark and quite grim and I rather like it. So I think we just have to go the opposite, right? We'd have to go the other end of this. 

Anne Currie: So Twofish, what's the name of your last album? In fact, which album would you recommend? 

Charles Humble: It's called At Least a Hundred Fingers. That's the last album. And, yeah, Twofish is the band, TWA as in the encryption algorithm, fellow nerds. So yeah, so with this one, the climate break, with the sustainable future one, I think some of my favorite composers, classical composers, would be like early, late 19th, early 20th century.

People like that. They were very inspired by the natural world, and they tended also to draw a lot on their, the folk tunes of the countries where worked. So I think melodically your, my starting point might be to go to a folk tune, and then use very traditional instruments. So have like a, maybe a string section, you know, sort of violins, violas, cello. So try and get some of that lift and air and that sort of thing into it. And then have the, more electronic stuff for stuff that I typically do, be very kind of intricate, interconnected, kind of supporting lines so that you have something melodic that is folk, quite traditional instruments, and then this kind of sense of interconnectedness and sort of mechanisms working, something like that. I might have a go at that actually. Perhaps there'll be a third Twofish album that has that on it. You never know. Yeah, that. If you want to look my stuff up, so my website, my company is Conissaunce com, www.conissaunce.com. I'm Charles Humble on LinkedIn. I'm also

Anne Currie: There will be, we'll have links below in the show notes.

Charles Humble: So yeah, you can find me on all of those. And you can find the music there as well.

Anne Currie: Excellent. And I really recommend the albums. I like them a lot. They're great. 

Charles Humble: Thank you.

Anne Currie: So thank you very much, and thank you to all the listeners today. As reminder again that all the links that we've talked about today, we have slightly overrun, will be in the show notes below. So, until the next time, thank you very much for listening and happy building Green Software.

Charles Humble: Thank you very much indeed for having me. It's been a pleasure. Thanks for listening and goodbye.

Anne Currie: Goodbye. 

Chris Skipper: Hey everyone, thanks for listening. As a special treat, we're going to play you out with the piece that Charles was talking about, Floe by Twofish. If you want to listen to more podcasts by the Green Software Foundation, head to podcast.greensoftware.foundation to listen to more.

Bye for now. 



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]]> Host Anne Currie is Joined by the esteemed Charles Humble, a figure in the world of sustainable technology. Charles Humble is a writer, podcaster, and former CTO with a decade’s experience helping technologists build better systems—both technically and ethically. Together, they discuss how developers and companies can make smarter, greener choices in the cloud, as well as the trade-offs that should be considered. They discuss the road that led to the present state of generative AI, the effect it has had on the planet, as well as their hopes for a more sustainable future.

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TRANSCRIPT BELOW:

Charles Humble: In general, if you are working with vendors, whether they're AI vendors or whatever, it is entirely reasonable to go and say, "well, I want to know what your carbon story looks like." And if they won't tell you, go somewhere else. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. Today I'm your guest host Anne Currie, and we'll be zooming in on an increasingly important topic, cloud infrastructure, efficiency and sustainability.

Using the cloud well is about making some really clever choices, really difficult choices upfront. And they have an enormous, those choices an enormous impact on our carbon footprint, but we often just don't make them. So our guest today is someone who's thought very deeply about this.

So Charles Humble is a writer, podcaster, and former CTO who has spent the past decade helping technologists build better systems, both technically and ethically. He's the author of The Developer's Guide to Cloud Infrastructure, Efficiency and Sustainability, a book that breaks down how cloud choices intersects with environmental impacts and performance.

So before we go on, Charles, please introduce yourself.

Charles Humble: Thank you. Yes, so as you said, I'm Charles Humble. I work mainly as a consultant and also an author and a technologist. I have a, my own business is a company called Conissaunce, which I run. And I'm very excited to be here. I speak a lot at conferences, most recently, mainly about sustainability. I've written a bunch of stuff with O'Reilly, including a series of shortcut articles called Professional Skills for Software Engineers, and as you mentioned most recently, this ebook, which I think is why you've invited me on.

Anne Currie: It is indeed. Yes. So, to introduce myself, my name is Anne Currie. I've been in the tech industry for pretty a long time. Pretty much the same as Charles, about 30 years. And I am one of the authors of O'Reilly's new book, building Green Software, which is entirely and completely aimed at the folks who will be listening to this podcast today.

So if you haven't listened to it, if you haven't read it or listened to it because it is available in an audio version as well, then please do so, you'd enjoy it. So, let's get on with the questions that we want to ask about today. So, Charles, you've written this great ebook, which is also something everybody who's listening to the podcast should be reading.

And we'll link to it in the show notes below. In fact, everything we'll be talking about today will be linked to in the show notes below. But let's start with one of the key insights from your book, which is that choices matter. Things like VM choices matter, but they're often overlooked when it comes to planning your cloud infrastructure.

What did you learn about that? What do you feel about that, Charles? 

Charles Humble: it's such an interesting place to start. So I think, when I was thinking about this book and how I was putting it together, my kind of starting point was, I wanted like a really easy on-ramp for people. And that came from, you know, speaking a lot at conferences and through some of the consulting work I've done and having people come up to me and say, "well, I kind of want to do the right thing, but I'm not very clear what the right thing is." 

And I think one of the things that's happened, we've been very good about talking about some of the carbon aware competing stuff, you know, demand shifting and shaping and those sorts of things. But that's quite a, quite an ambitious place to start. And oftentimes there are so many kind of easier wins, I think. And I kind of feel like I want to get us talking a little bit more about some of the easy stuff. 'Cause it's stuff that we can just do. The other thing is, you know, human beings, we make assumptions and we learn things and then we don't go back and reexamine those things later on. So I've occasionally thought to myself, I ought to write a work called something like Things That Were True But Aren't Anymore or something like that 

because we all have these things. Like my mental model of how a CPU works until probably about two years ago is basically a Pentium two .And CPUs haven't looked like a Pentium two for a very long time, and I have a feeling I'm not the only one. So, you were specifically asking about like CPUs and VM choices, and I think a lot of the time, those of us, certainly those of us of a certain age, but I don't think it's just us, came through this era where Windows and Intel were totally dominant. And so we naturally default to well, "Intel will be fine"

because it was right for a long time.

Anne Currie: Yeah.

Intel 

Charles Humble: was the right 

Anne Currie: Who could ever have imagined that Intel would lose the data center? It's 

Charles Humble: Absolutely it is extraordinary. I mean obviously they lost mobile mainly to ARM and that was very much a sort of power efficiency thing. Fair enough. But yes, the idea that they might be losing the data center or might have lost the data center is extraordinary. But you know, the reality is first of all, if you are thinking about running your workloads. So, AMD processors, more or less how a cross compatible of Intel wants. It's not totally true, but it kind of is. So they have an X86 compatible instruction set. So for the most part, your workloads that will run on Intel will run on AMD.

But not only will they run on AMD, they will probably run on AMD better.

Again, for the most part, there are places where Intel probably has an edge, I would think. If you're doing a lot of floating point maths, then, maybe they still have an edge. I'm not a hundred percent sure, but as a rule of thumb, AMD is going to be, you know, faster and cheaper. And the reason for that has a great deal to do with core density. So AMD has more cores per chip than Intel does, and what that means is you end up with more processing per server, which means you need fewer servers to run the same workload. I ran some tests for the ebook and that came out,

so I had a 2000 VM instance and we had 11 AMD powered servers. So running, epic, the AMD Epic chips and we needed 17 Intel powered servers to do the same job. Right? So that's roughly 35% fewer servers. It's not, by the way, 35% less power use. It's actually about 29%, something like that, less power use 'cause the chips are quite power hungry, but still that's a big saving, right? And it's also, by the way, a cost saving as well. So the other part of this is, you know, it is probably about 13% cheaper to be running your workload on AMD than Intel. Now obviously your mileage may vary and you need to verify everything I'm saying.

Don't just assume, "well, Charles Humble said it's true, so it must be." 

It'll be a foolish thing to do, but as a rule of fault, the chances are in most cases you're better off and I'll wager that you are a lot of the time when you are setting up your VMs on your cloud provider, your cloud providers probably default to Intel and you probably just think, "well, that'll be fine."

Right?

So kind of a case of trying to flip that script. So maybe you default to AMD, maybe you evaluate whether ARM processors will work. We are seeing another surge of ARM in datacenters. Though, as I said, that comes with some it. In mobile, the trade offs are pretty straightforward with ARM to anything else. In data centers it is a little bit more nuanced. But basically it's that, and I think it's, I think it's this thing of, as I say, of these assumptions that we've just built up over time that we don't, we're not very good at going back and reexamining our opinions or our assumptions. And then the other thing that I think feeds into this is we build layers of abstractions, right? That's what computer science does, and we get more and more abstracted away from what the actual hardware is doing. I found myself this morning when I was thinking about coming on the show, thinking a bit about some of the stuff Martin Thompson's been talking about for years, about mechanical sympathy.

I'm sure you have experiences of this, and I know I have,

where, you know, I've been brought into a company that's having performance problems. And you look at, there's one that I actually remember vividly from decades ago, but it was, an internet banking app. So it was a new internet bank that was written in visual basic, weird choice, but anyway, go with me here. And they were reading. It was all MQ series, so IBM MQ series under the hood, right? So basically you've got messages that were written in XML being passed around between little programs. It looks a bit like microservices, but 20 years ago before we had the term roughly. And what they were doing, so when you read a message off an MQQ, you read it off essentially one byte at a time.

And what they were doing in a loop in Visual Basic was they were basically saying string equals string plus next byte. Does that make sense? So, string equals string plus new string. That kind of idea. Now under the cover, they're doing a deep string copy every single time they do that. But they had no idea 'cause they were visual basic programmers and didn't know what a deep string copy even was.

Fair enough. And then they were going, "why is our audit process grinding to a halt?"

And the reason is, well, 'cause you'll, we just need like an API. But what I'm getting at is we have these, we get very abstracted away from what the hardware is doing

because most of the time that's fine, right?

That's what we want, except that our abstractions leak in weird ways. And so sometimes you kind of need to be able to draw on this is what's actually happening to understand. So as I say, in the case of,

in the case of CPUs, if you haven't been paying attention to CPUs for a while, you probably think Intel still has the edge, but right now, sorry, Intel, they don't.

Hope that changes. Competition is always good. But you know, it's just a great example of, you probably don't even think about it. You probably haven't thought about it for years. I know, honestly I hadn't.

Anne Currie: Yeah.

Charles Humble: But then you start running these numbers and go, "gosh, that's, you know, like a 30% power saving."

That's, at any sort of scale, that's quite a big deal. And so a lot of the things that I was trying to do in the book was really that. It was just saying, well, what are some of the things that we can do that are easy things,

that make a massive difference?

Anne Currie: It's interesting. What you're saying there reminds me a little bit of somebody who was a big name in tech back in our early, you'll remember it very well. Joel Spolsky 

used to write a thing about, you know, what would Joel do? He used to work on, do a lot of work on usability, studying usability.

And he'd say, well, you're not looking for, to change the world and rewrite all these systems. You are often just looking for the truffles, the small changes that will have an outsize effect. And what you're saying is that, for example, moving from Intel to AMD is a small truffle that will have an outsized effect. If you do it at the right time,

it's, actually you could probably, it's not so much, as you say, the trouble with go with going to an ARM ship or, you know, Graviton servers that's been pushed very heavily by AWS at the moment. Big improvement in energy use and reductions in cost. But that is not a lift, that's not an instant

oh, flick of switch and you go over. They, you know, there are services that are no longer available. There are, you know, you're gonna have to retest and recompile and do all the things, but it's not such an obvious truffle. But you are saying that really that the intel AMD might be a really easy win for you.

Charles Humble: Yeah, absolutely. Absolutely. It's funny you mentioned Joel Spolsky there. 'Cause actually his, so I read his User Interface Design for Programmers, I think the book is called, about 30 years ago probably. It's just, I still, like everything I know about user interface, I swear it comes from that like book.

It was such a brilliant, it's also hysterically funny. It has all sorts of examples of just, it's very wittily written and has some wonderful examples of, you know, just terrible bits of user interface. Like the Windows 95 start button, which is in the bottom left hand corner. Except that if you drag to the bottom left hand corner of the screen, which is one of the easy places on a screen to hit, you miss the start button because aesthetically it looked wrong without a border around it.

But then no one thought, well, maybe we should just make it so if you miss, but you are there, you know, like it's full of just examples like that. It's very funny. And yeah, absolutely. This, business of, as I say, so much of, we have as an industry, been very profligate, right? 

We've been quite casual about our energy use and our

hardware use. So there's another example, which is to do with infrastructure and right sizing.

Again, this is just one of those things, it's such an easy, quick win for people

and it's another thing that connects to this business of our old assumptions. So when I started in the industry, and probably when you started in the industry and we ran everything in our own data centers, procurement was very slow, right?

If I needed a new server, I probably had to fill in a form and 10 people had to sign it, and then it would go off to procurement and it would sit doing, heaven knows what for a couple of months, and then eventually someone might get around to buying a server and then they'd install the software on it and then it would get racked.

And you know, like six months of my life could have gone by, right.

And so what that meant was if I was putting a new app in, and at some point someone would come along to you and go, "we're putting this new app in. How many servers do you need?" And what you do is you'd run a bunch of load tests on, I dunno, load runner or something like that.

You'd work out what the maximum possible concurrent, like, oh, sorry, concurrent was a poor choice of word there.

Simultaneous number of users on your system, rather.

Yeah.

Right. You simulate that loads, that would tell you how many boxes you needed. So suppose that said four servers, you go to procurement and you go "eight, please."

Anne Currie: Indeed. 

Charles Humble: Right. And no one would ever say "why do you need eight?" Because, right. And that's just. That's just what we do. And what's weird is we still do it, right. Even though elastic compute on the cloud means surely we don't need to. We kind of have this mindset of, "well, I'll just, I'll add a bit more just to be on the safe side 'cause I'm not

too confident about my numbers.

Anne Currie: There is a logic to it if it's easy because it, the thing that you fear is that you'll under provisioning and it'll fall over. So there's a big risk to that. Over provisioning, yes, it cost you more, but it's hard. It's really hard to get the provisioning perfect.

So we over provision and then you always intend to come back later and right size. And of course you never do because you never get a chance to come back and do things later. 

Charles Humble: Something I say a lot to the companies that I consult to is "well just run an audit."

Anne Currie: Yes, indeed. Yeah.

Have 

Charles Humble: a three month process or a, you know, like a three month or a six month mission where we are gonna do a right sizing exercise. We're gonna look for zombie machines. So those are machines that were, you know, once doing something useful but are doing nothing useful anymore. And also look for machines that are just sitting idle and get rid of them. You actually have an amazing story in the, in your O'Reilly book, the Building Green Software book from Martin Lippert. So he was tools and lead sustainability for VMware, Broadcom, part of the old Spring team.

He talks about, so in 2019, I think it was in VMware, they consolidated a datacenter in Singapore. They were moving the data center and basically they found that something like 66% of all the host machines were zombies. 66%.

Yeah. And that's untypical. 

Anne Currie: No, it's not.

Charles Humble: I've gone and done audits. 

50% plus is quite normal.

 So I have this like thing that I quite often say to people, I reckon you can halve your carbon emissions

in your IT practice just by running an audit and getting rid of things you don't need. 

And it may even be more than that. 

Anne Currie: Yeah, indeed. As VMware discovered, and people do it at a time when they move data centers. I often think this is probably a major reason why when people go, "oh, you know, I repatriated, I moved away from the cloud back in and I saved a whole load of money."

Yeah, you would've made, saved that money doing that kind of exercise in the cloud as well. Probably more because the cloud, the trouble with the cloud is both amazing, it has amazing potential for efficiency because it has great servers that are written to be very efficient and you wouldn't be able to write them that efficiently yourselves.

So there's amazing potential. Spot instances, burstable instance types, serverless, you know, there's loads of services that can really help you be efficient. But it's so easy to overprovision that inevitably everybody over provisions massively. And especially if you lift and shift into the cloud, you massively over provision.

Charles Humble: There's a related thing there as well because it's so easy to

and then you just forget about it. Evevn on my own, like sort of, you know, personal projects, I've suddenly got a bill from Google or something and I've been like, "oh hello, that then?"

And you know, it's something that I spun up three months ago for an article I was writing or something and I'd just totally forgotten about. And it's been sitting there running ever since, you know, like, and you could imagine how much worse that is as an enterprise, this is just like me on my own doing it.

And it's that kind of thing. I think. So thinking about things like auto sizing, you know,

scaling up remembering, to scale back down again. People often scale up and don't scale down again. There's some of the Holly Cummings stuff around Lightswith Ops. This idea of, you know, basically you want to be able to spin your systems back up again really easily.

That sort of stuff. Again, this is all stuff that's quite easy to do, relatively speaking.

Anne Currie: Relatively. So much easier than rewriting your systems in Rust or C, I can assure you of that.

Charles Humble: Well, a hundred percent, right? And, again, you know, I've made this, 

I've made this joke a few times on stage and it's absolutely true. We kind of, because we're programmers, we automatically think, "oh, I'll go and look at a benchmark that tells me what the most efficient language is," and it will be C or C++ or something.

And like "we will rewrite everything in C or C++ or Rust." Well that would be insane. And your company would go bust and nobody is gonna sponsor you to do that for very good reason. And

what you want to be doing is you want to be saying, "well, you know, what are the pragmatic things we can do that will make a huge difference?"

And a lot of those things are. You know, rightsizing. It's a really good example. 

Anne Currie: Yeah, I mean, I clearly we're, this is something that you and I have discussed many times and it was one of the reasons why at the end of Building Green Software, we devised the Green Software Maturity Matrix that we donated to the Green Software Foundation, 

Charles Humble: Yes. 

Anne Currie: because the, what we found over and over again when we talked to conferences, went out and spoke to people is that they had a tendency to leap right to the end, rewrite things in.

You know, they say, "well, we couldn't rewrite everything in C or Rust or we'd go outta business, so we won't do anything at all." And they step over all the most important, they step over all the truffles, which are switching your CPU choice, switching your VM choice, doing a right sizing, audits, doing a basic audit of your systems and turning off stuff, doing a security audit because a lot of the, these zombie systems actually should be turned off in a security audit because if they're there and they're running and they're not being patched and nobody owns them anymore, nobody knows what they're doing anymore, they will get hacked.

They are the ways into your system. So sometimes the way to pitch this is a security audit.

Charles Humble: Absolutely. Yes, and I do, I use the Maturity Matrix quite a lot in this ebook. Actually, it's one of the things that I reference all the way through it for exactly this reason, because it's, as I said, I think we tend to go to the end a lot. And actually a lot of the stuff is so much earlier on than that.

And I think it's just a, yeah, 

I think it's a really important thing to realize that there's a huge amount you can do. And actually as well, it's gonna save you an awful lot of money. And given the kind of very uncertain business environment that we're in, and people are very kind of worried about investing at the moment for all sorts of quite sensible reasons, this is one of those moments where actually if you're thinking about "I want to get my business onto a more, or my IT within my company onto a more sustainable footing," this is absolutely the right time to be having those conversations with your CFO, with your execs because, you know, this is the time where businesses need to be thinking, "well, how do I cut cost?" And there's a huge amount of waste. I guarantee you if you've not looked at this, there will be a huge amount of waste in your IT you can just get of

and be a bit of a hero and, you know, do good by the planet at the same time.

It's like, what's not to like?

Anne Currie: Yeah, because I mean, different companies, different enterprises, different entities have different roles in the energy transition. For most enterprises, your role is to adopt modern DevOps practices really, it's a new start. You don't mean you don't have to start there. You can start with the, as you say, manual audit.

Sometimes I've heard it called the thriftathon, where you just go through and you go, "do you know that machine? Turn it off." You know, you can use that kind of, they use the screen test method of "you don't think anyone's using it, turn it off. Find out if anybody was using it." And then you can use that to kind of step yourself up to the next level.

You and I both know holly Cummins, who was a guest, cut two back, one back, on this podcast. And she introduced the idea of, Lightswitch Ops, which is the, first kind of automation. If you haven't done any automation up till now and you want to learn how to do automation, a really good bit of automation is the ability to turn machines off automatically, maybe for a period overnight or, and you try that out on machines like your test suites, to just get yourself into the, to the simplest form of automation. It can also, if you are on the right, it depends if you're on the right models and you're in the cloud potentially, or you have the right

infrastructure, then that can save you money. It might not always save you money because you have to have made the right infrastructure choices. It might just that be that the machine sits on and doesn't really do anything. You've just turned off your application. But you really want to be turning things off to save power.

You know, and it's a really good way of getting you into the DevOps mindset, which is where everybody needs to be with so many payoffs.

Charles Humble: Yes.

Anne Currie: But yes. So, we'll go back to, do ask the questions. So, in part of, in, well, one of your talks is writing greener software, even when you are stuck on prem, and you talk about the fact that not everybody has the option to move into the cloud.

So what, then? What do you do if you can't move into the cloud?

Charles Humble: Yeah, that's, it is such an interesting question, that. So obviously there are things you can't do or can't do very easily, and one of the most obvious of those is you can't choose green locations on the whole if you're running stuff in your own data centers. So again, going back to these easy wins, an easy win is to use something like Electricity Maps, which is a tool which basically tells you what the energy mix is in a given region.

Oh.

And then you say, "I shall run my workloads there 'cause that looks good." There's a little bit more to it than that. You kind of want a location that not only has the greenest energy mix at the moment, but also has like credible plans for that to keep improving.

Anne Currie: Yeah.

Charles Humble: Obviously that's really hard to do with your own data centers.

Anne Currie: Yeah.

Charles Humble: As a rule of thumb, you probably don't want to be building new data centers if you can help it because, pouring concrete is not great. There's a lot of costs associated. That said, you do have some advantages in your own data centers 'cause you have some things that you can control that people on cloud can't. I would say, I mean, you know, like being honest about it, if you can move things to public cloud, that's probably going to be better. But if you can't, there are still things you can do. So one of those things is you have control over the lifetime of your hardware. This gets a little bit complex, but it's basically down to, so hardware has an embodied carbon cost.

That's the cost that it takes to construct it, transport it, dispose it at the end of its use, like useful lifetime. I mean, it also has the cost it takes to charge it. Now for your laptops, your mobile phones, your end user devices, the embodied carbon absolutely dwarfs the carbon cost used to charge it in its lifetime.

Anne Currie: Yeah.

Charles Humble: What we talk about with end user devices is like basically extend the life. Say, you know, 10 years or something like that, keep it. We want to make less of them, is really the point. Servers and TPUs and GPUs and those sorts of things, it's a bit more complicated. The reason it's a bit more complicated is because we are getting an awful lot better at making more efficient servers for all sorts of reasons. so what that means is the trade-offs with each new generation is more complicated. As an example, a lot of your energy use in your data center is actually gonna be cooling. So a CPU or a TPU that's running less hot requires less cooling. That's a big win. These sorts of things are sufficiently important that actually, until gen AI came along, so really three or four years ago, though we were adding massive amounts of compute, the emissions from our data centers was pretty flat. I mean, it was climbing, but not much. So the point here with your own data centers is you have control over that lifetime. So what you can do is you can do the calculations, assuming you can get the embodied carbon costs from your suppliers, you can do the calculations and think about, "well, how long do I keep this piece of hardware going before I turn it over?" Now, I don't want to give you a heuristic on that because it's kind of dangerous, but it's probably not 10 years, right?

It's probably five years-ish. Maybe something like that, but run the maths. But it's absolutely something you can do. You can also take advantage of things like your servers will have power saving modes that you probably don't turn on because we used to worry about that kind of thing.

'Cause we have this like, again, one of our old assumptions. We used to imagine that if you power a server down, it might not come back quite the same. Actually that's kind of still true, but, you know, it's fixable, right? So enable power saving across your entire fleet, that will make a huge difference, particularly if you've over provisioned, like we were saying earlier, right? 50% of your servers are idle. Well, they can be asleep all the time, and that helps. It's not the same as turning 'em off, but helpful. You can also look at voltage ranges. So your hardware will have a supported voltage range, and you've probably never thought about it, and I'll admit I hadn't until quite recently.

But actually again, if you're running at scale, if you send the lowest voltage that your servers will support, at a big scale that will a considerable difference. And then again, some of the other things we talked about, your CPU choice again, will make a difference. So think about, you know, "do I need to be buying Intel servers all the time, or could I be buying AMD ones or ARM ones?"

And also look at your cooling. But that's a whole, that's a whole nother complicated topic for all sorts of reasons. Often, well, in brief, some of the most energy efficient methods of cooling have their own set of problems, which make the trade offs really hard. So, like water-based cooling tends to be very efficient,

tends not to be great for local water tables.

Anne Currie: Yeah.

Charles Humble: It's, complicated. But, yeah, as I say, there are, so, there are a lot of things are that are definitely harder. And if you have a choice, if you're running in like a hybrid environment, chances are if you have a choice of going public cloud or own data center, public cloud is probably better. It's absolutely in Google and AWS and Microsoft's interests to run their data centers as efficiently as possible. 'Cause that's where their cloud profit margin is, right?

Anne Currie: Absolutely.

Less 

Charles Humble: it's costing them to run the, you are still paying the same amount, the more money they make. 

Anne Currie: Well, I, and I think I always laugh when I see the numbers on Graviton. So when AWS attempt to, persuade you quite correctly to, if you can move from Intel chips to run on, to run your applications onto ARM chips. They say, "oh, this will save, 40% on your hosting bill and 60% on your carbon emissions."

And you think, I think you've just pocketed quite a lot, a big. That suggest to me you've just pocketed quite a nice upgrade in your, in your, profitability. And I have no problem with that whatsoever, as things get better, I have no problem with making profits out of it. So I'm gonna pick you up on something that, I think everything you've said there is very true.

And I'm gonna take a slightly different take on it, which is that remember what that, what Charles is saying, there is quite detailed stuff about not everybody here will be a hardware person and that you will have specialists within your organization who can do all these hardware judgements.

The interesting thing is that they can. And it is always the case that if you can, if you have specialists in your organization, the best way to do better is to persuade them that they want to do better. So, if, you could persuade your specialists that actually to actually take an interest in this and to find ways of improving the efficiency of your systems, cutting the carbon emissions, they will do better at it than you will.

Charles Humble: 100%.

Anne Currie: Best thing you could do is persuade them to focus their in giant specialist brains on the subject because the likelihood is that the real issue is they probably aren't thinking about it, or they probably don't, you know, they, it is not top of their mind. They maybe think they're not even allowed to start thinking about it.

If it at a high level, you can actually get your specialists to turn their attention to these. efficiency issues to these carbon reduction issues, that's so much more effective than you going and reading up on it yourself. Get them involved. Go out and talk to people. Persuade, use your powers of persuasion, because, what you should take away from Charles, what's lots of people listening should take away from what Charles

just said then is that there is a lot of stuff that can be done by your specialist teams that they might not be thinking about doing, or they might not be, they might feel they don't have the time or focus to do. You can potentially help them by focusing them or giving them some budgets or some time to work on it.

Charles Humble: Definitely. Absolutely. Yeah. Yeah. No, I'm a big believer in specialization in our industry, and I think actually this idea that we are almost know everything isn't, is not helpful. Like absolutely, if you've got hardware people, go and tell the hardware people, and it's a thing of incentivizing.

It's like, you know, "we can save money by doing some of these things, or we can reduce our carbon by doing some of these things, and those are good things to do." Yeah, a hundred percent agree with all of that. No disagreements at all.

Anne Currie: Yeah, no, it's interesting isn't it, that most of human progress has come from the realization that specialists kick the butt of generalists. And I'm a generalist, so you know, I wish it wasn't true. My job is to kind of encourage specialists to be specialists and, you know, this is not new news.

It was the, it's the theme of Adam Smith's the Wealth of Nations that he wrote in the 1770s about why the industrial revolution was happening. It wasn't to do with any kind of technology or anything else. It was the discovery that specialists kick the butt of generalists.

Charles Humble: Hundred percent, yes.

Anne Currie: But now we're gonna get to the final tricky question that we have for you, Charles, that you'll be thinking about. You've been thinking about, so I'm, your work often emphasizes the importance of transparency, knowing the carbon footprint of what we build. What tools and practices do you recommend for people to do that? 

Charles Humble: Oh, that is a hard question. Yes. Frustratingly hard actually, we, so the first thing is we often end up using proxies

and the reason we end up using proxies is 'cause measurement is genuinely quite difficult. So cost is a quite a good proxy. In Bill Gates' book, blanking on the name of the book, oh, How to Avoid a Climate Disaster, 

Anne Currie: Oh yeah. Which is excellent. And again, everybody listening to this should be reading it. Yeah.

Charles Humble: Absolutely. So he, in that book, he does a bunch of calculations, which he calls green premiums and they're

basically the cost of going green.

Now, He doesn't do one for our industry, but I would wager, because we are also profligate, I would wager that our green premium, and I haven't worked this out, I will admit it, but I would think our green premium is probably a negative number.

So, that's to say,

going green is probably cheaper for us. Right.

Anne Currie: I agree.

Charles Humble: So cost is a very good proxy. It is an imperfect proxy. One of the reasons it's an imperfect proxy is because, for example, if you're running a green energy mix, that's not going to be reflected in your electricity bill at the moment. That may change, but at the

moment it doesn't happen.

Right. So it is imperfect, but

Anne Currie: Well, it doesn't happen in some places and in other places it does. So if you are on prem and you're in a country with dynamic pricing like Spain or zonal pricing, like talking about the UK having in future, that's still very up in the air, then it does. But if you're in the cloud, even in those areas, it doesn't at the moment.

Charles Humble: Absolutely. But nevertheless, 'cause as I was saying, you know, like probably half of your servers are doing nothing useful. So cost is a pretty good starting point. Another thing is CPU utilization. So there's something we haven't really talked about, which is this idea, Google calls it energy proportionality,

Anne Currie: Yeah.

Charles Humble: the observation that when you turn a machine on, you turn a server on, it has a static power draw, and that static power draw is quite a lot. How much depends on how efficient the server is, but it might be 50% or something like that. So when it's sitting idle, it's actually drawing a lot of power. The upshot of this is you'd usually have like an optimum envelope for a given server, and that might be somewhere between 50 and about 80%.

It may be a bit lower than that depending on how good the chips are. Above about 80% you tend to get key contention and those sorts of things going on. Not great. But around and about that operating window. So it's again, keeping your CPU utilization hard but not, high rather, but not maxed out is another good one.

Hardware utilization is another good one. Beyond that, so all of the cloud providers have tools of varying usefulness. Google's carbon footprint tool is probably best in class, at least in my experience. I think they take this stuff very seriously and they've done a lot of very good work.

Microsoft Azure tools are also pretty good. AWS's ones, so they have just released an update literally as we're recording this, and I hadn't had a chance to go and look at what's in the updated version. I'm going to say I think AWS is still a long way behind their competitors in terms of reporting.

Anne Currie: Yeah.

With 

Charles Humble: a slight proviso that I hadn't looked at what's in the new tool properly. But again, there, there are all things there that you can use. There's a tool called Cloud Carbon Footprint, which is an open source thing, by ThoughtWorks and that's quite good. It will work across different cloud providers, so that's kind of nice. You could probably adapt it for your own data centers, I would imagine. Of course the GSF has a formula for calculated carbon intensity as well. So that's more of a sort of product carbon footprint or lifecycle assessment type approach. It's not really suitable for corporate level accounting or reporting or that sort of thing, but that's quite a good tool as well. And there are a variety of other things you can use, but as I say, if we're talking the very beginnings, you probably start with the proxies. If you've got a choice of cloud provider, think about the cloud provider that gives you the tooling you need.

And you know, that might, again, going back to our assumptions, time was you would choose AWS. Maybe you shouldn't be choosing AWS now, or at least maybe you should be thinking about is AWS the right choice.

At least until they, you know, until they sort put their house in order a bit more. These are things, questions that we can reasonably ask. And in general, if you are working with vendors, whether they're AI vendors or whatever, it is entirely reasonable to go and say, "well, I want to know what your carbon story looks like." And if they won't tell you, go somewhere else. In the case of AI, none of the AI companies will tell you. They absolutely won't. And so my advice, if you're looking at running generative AI, other than. Everything we just said applies to AI, like it applies to everything else. There are a bunch of very specific AI related techniques, distillation, quantization, pruning, those sorts of things. Fine. But really my advice is well, using an open source model, and look at something like the ML leaderboard from ml.energy leaderboard, which will give you an idea of, what the carbon cost looks like. And don't use AI from a company that won't tell you, would be my advice. You know, and maybe we can embarrass some of these companies into doing the right things. You never know.

Anne Currie: Be nice, wouldn't it? It's so, it's interesting, the, this, so in April, Eric Schmidt got up in front of the US government in one of their, in one of their, committees and said, well, you know, if we, at the current rates, AI is going to take up 99% of the grid electricity in the US.

And you think "it's interesting, isn't it," because that's not a law of nature. There are plenty of countries that are looking at more efficient AI, so China, are certainly looking at more efficient AI. They don't want, they want to compete. They wanna be able to run AI because in the end, the business that's going to collapse if AI requires 99% of the US grid is AI because it cannot, you know, it's kind of, if something cannot go on, it will stop. 

Charles Humble: It's a desperate source of frustration for me because it is completely unnecessary.

Anne Currie: Well, it's, you just have to be a bit efficient.

Charles Humble: Just in brief, 'cause again, this is like a whole separate podcast probably,

but just in brief, there are a bunch of things that you can do

Anne Currie: Absolutely.

Charles Humble: that make a huge difference, both when you are collecting your data, when you are training your models, when you're running them in production afterwards. I have just done a piece of work for the News Stack on federated learning, and in the process of doing that, I talked to somebody called Professor Nick Lane, who is at Cambridge University, and he talked about, so one of the solutions to the data center cooling problem, which we touched on earlier, is basically what you do with the waste heat. And there are lots of companies in Europe that are looking at using it for things like heating homes or using, you know, heating municipal swimming pools, that sort of thing, right? You can't do that with an Amazon or a Google or a Microsoft facility, because you have to construct the data center close to where the waste is gonna be used.

But there are lots of these small data centers, particularly in Europe. There are companies like T Loop that are doing a lot of this work. And he made the point that with federated learning, you can actually combine these smaller facilities together and then, you know, be training potentially very large models on much, much smaller data centers, which I thought was fascinating. There's a guy called, Chung is his surname, and apologies to him, i'm blanking on Jae-Won Chung. He's done some extraordinary work looking at, so when we split stuff across GPUs,

that has to be synchronized, right? So we divide the workload up because it's too big to fit in a GPU and we split it across a bunch of different GPUs and we run all of those GPUs at full tilt, but we don't have to. Because we can't divide the workloads up evenly.

So you have some workloads that are tiny but this GPU is still running at full power, and what he worked out was, well, if we slow those GPUs down, the job will still end at the same point, but it'll use a lot less energy. So he's built something called Perseus, on his tasks with things like Bloom and GPT-3, they're about, it's about 30% less energy use just from using that

for exactly the same throughput. So there's no throughput loss, there's no hardware modification. The end results are exactly the same, and you just save 30% of your energy bill, which is a big deal.

Then you go, as I say, things like distillation and quantizing and pruning and shrinking your model size, all of that stuff.

So it frustrates me because it's so unnecessary. I think we need a carbon tax and I think the carbon tax needs to be prohibitive. And I think, you know, bluntly, I think companies like OpenAI should be pushed outta business if they don't get their house in it's time. I thrilled.

Hannah Richie's book, not The End of the World, which is my, possibly my favorite book on climate. And again, it's a book, everyone haven't read it, go and read it. She has a wonderful quote in there where she says, "I've talked to lots of economists and all of the economists I've spoken to agree that we need some sort of carbon tax."

And then she goes on to say, "it's maybe the only thing that economists agree on," which I thought was a fine and excellent line.

Anne Currie: It is really interesting 'cause I, we disagree slightly on, you're not a huge AI fan. I'm a massive AI fan. I want AI and I also want a livable climate. And they are not mutually exclusive. They can be done. I mean, you have, you don't love AI, you don't love AI as much as I love AI, but we are both in agreement that it is not physically impossible to have AI and effective control of climate change because as you were saying about the federated learning and, you know, optimizing your GPU towards the bottleneck tasks and then things like that, as long as you, workloads that are time insensitive that can be shifted in time and maybe delayed and maybe separated and then glob together again,

they're very good workloads to run on renewable power, which is variably available. So in fact, AI is potentially incredibly alignable with the energy transition. The fact that we don't always do it is a travesty and it's so bad for AI as well as being bad for the planet.

Charles Humble: I want to push back slightly on you saying I'm not a fan of AI. So I have. Quite strong concerns specifically about generative AI that are ethical and moral as well as environmental.

Anne Currie: Which I can see.

Charles Humble: And in essence it comes down to the fact you are taking a bunch of other people's work and you are building a machine that plagiarizes that work and you are not compensating those people for it. And you are also, basically you have to do tuning of the model. So reinforcement learning with human feedback and the way that, that's done is pretty horrifying when you dig into it. It usually involves, you know, people in places like Kenya being paid $3 an hour to look at the worst contents of the internet for day after day.

I mean, one can imagine what that does to you. So I have quite specific reservations with generative AI, the way that we are doing it. As it goes, I think there are ways that we could build generative AI that wouldn't, I wouldn't have these ethical problems with, that we're not doing. More generally, think generative AI is interesting. I don't know that it's useful, but I do think it's interesting. And more broadly, I'm not against AI at all. I'm like, you know, 

I've done work with a company that, for example, is using AI to look at, , increase the window that you can treat stroke patients with, by like hours.

And it's amazing. Amazing work. So they're basically doing image processing to identify different types of stroke. And some stroke patients, the window is much wider. So, you know, we

think of it as being 4.5 hours but it's much bigger. Stuff like

that. There's, and, as you say, like grid balancing is gonna get more complicated with renewables, and AI probably has a role to play there.

And I'm not anti. I'm not anti, I just think that there are things that we are doing as an industry which are reckless and ill-judged and you know, in my tiny little way I want. I mean, I'm aware that it's like, you know, blowing a kazoo in a thunderstorm, it's quite amusing, but it doesn't actually do much for anybody. But I, in my own little way, I want to be sort of beating the drum. As an industry, I think we need to get better. Right. And part of the reason I think we need to get better is because the work that we do has a huge impact on the whole planet now and on society and all sorts of things. And we are still like acting like we're a little cottage industry and what we do is inconsequential but it's not true.

 So my reservations with gen AI is, I think it's being done in a desperately irresponsible way, but that doesn't mean it has to be. It just means that's what we're doing. And hey, I might be wrong. You know, I'm not an ethicist. I just like, I just have reservations. Also, I am a writer. And a musician, right?

So, you know, like I do have skin in the game. I kind of want generative AI not to work. 'Cause otherwise I don't really have a living anymore, which is a bit of a worry. So, you know, I'm not a neutral observer on this at all, but I just think the way we're doing this is morally, ethically dubious, as well as being very bad for the climate. And I don't think it has to be any of those things.

Anne Currie: Yeah, I, so it's an interesting, we have a slightly different, 'cause I'm also a writer and a painter. but I've always been so rubbish at making money out of writing and painting that I don't really, don't have anything to say. So we have, that's, but that is my own fault.

A little bit. 

Charles Humble: The last question, I'm looking at your script now. Sorry. 'cause it's a shared Gigle doc, and your last question is about, so I write in my free time in a band called Twofish. And the question is, if you could score the soundtrack for a more sustainable future, what would it sound like? 

Anne Currie: I forgot about the question. Yeah.

Charles Humble: Interesting have get it in. So we did the opposite thing actually. We did, so there's a piece on the last two Fish album, called Floe, and that was my kind of, I started, everything is written as, by two of us. But I started that one and when I started it, what I was trying to do is describe what climate breakdown might sound like in music.

That was kind of my starting point. Not sure anyone hearing it would get that, but what I did was I went and recorded a bunch of like, field recordings. So, you know, California wild fires and that sort of thing. Tune them all to A flat minor as you do, and then wrote this very dark, scary,

that gets a bit drum and bassy as it goes on. It's very black and industrial and dark and quite grim and I rather like it. So I think we just have to go the opposite, right? We'd have to go the other end of this. 

Anne Currie: So Twofish, what's the name of your last album? In fact, which album would you recommend? 

Charles Humble: It's called At Least a Hundred Fingers. That's the last album. And, yeah, Twofish is the band, TWA as in the encryption algorithm, fellow nerds. So yeah, so with this one, the climate break, with the sustainable future one, I think some of my favorite composers, classical composers, would be like early, late 19th, early 20th century.

People like that. They were very inspired by the natural world, and they tended also to draw a lot on their, the folk tunes of the countries where worked. So I think melodically your, my starting point might be to go to a folk tune, and then use very traditional instruments. So have like a, maybe a string section, you know, sort of violins, violas, cello. So try and get some of that lift and air and that sort of thing into it. And then have the, more electronic stuff for stuff that I typically do, be very kind of intricate, interconnected, kind of supporting lines so that you have something melodic that is folk, quite traditional instruments, and then this kind of sense of interconnectedness and sort of mechanisms working, something like that. I might have a go at that actually. Perhaps there'll be a third Twofish album that has that on it. You never know. Yeah, that. If you want to look my stuff up, so my website, my company is Conissaunce com, www.conissaunce.com. I'm Charles Humble on LinkedIn. I'm also

Anne Currie: There will be, we'll have links below in the show notes.

Charles Humble: So yeah, you can find me on all of those. And you can find the music there as well.

Anne Currie: Excellent. And I really recommend the albums. I like them a lot. They're great. 

Charles Humble: Thank you.

Anne Currie: So thank you very much, and thank you to all the listeners today. As reminder again that all the links that we've talked about today, we have slightly overrun, will be in the show notes below. So, until the next time, thank you very much for listening and happy building Green Software.

Charles Humble: Thank you very much indeed for having me. It's been a pleasure. Thanks for listening and goodbye.

Anne Currie: Goodbye. 

Chris Skipper: Hey everyone, thanks for listening. As a special treat, we're going to play you out with the piece that Charles was talking about, Floe by Twofish. If you want to listen to more podcasts by the Green Software Foundation, head to podcast.greensoftware.foundation to listen to more.

Bye for now. 



Hosted on Acast. See acast.com/privacy for more information.

]]> Backstage: Green AI Committee Backstage: Green AI Committee Thu, 01 May 2025 07:00:00 GMT 18:01 71vlxk51 false https://podcasts.castplus.fm/e/pnm5237n-backstage-green-ai-committee 68dc7d0ad5c8a1f9b326f293 68dc7cef46a2532cdd8d1c23 106
In this special backstage episode of Environment Variables, producer Chris Skipper spotlights the Green AI Committee, an initiative of the Green Software Foundation launched in 2024. Guests Thomas Lewis and Sanjay Podder share the committee’s mission to reduce AI's environmental impact through strategic focus on measurement, policy influence, and lifecycle optimization. The episode explores the committee’s approach to defining and implementing “green AI,” its contributions to public policy and ISO standards, and collaborative efforts to build tools, best practices, and educational resources that promote sustainable AI development.

Learn more about our people:

Find out more about the GSF:

Resources:

Events:

If you enjoyed this episode then please either:

TRANSCRIPT BELOW:

​Chris Skipper: Welcome to Environment Variables, where we bring you the latest news from the world of sustainable software development. I'm the producer of this podcast, Chris Skipper, and today we are thrilled to bring you another episode of Backstage, where we dive into the stories, challenges, and triumphs of the people shaping the future of green software.

 In this episode, we're turning the spotlight on the Green AI Committee, a pivotal initiative approved by the Green Software Foundation in March, 2024. With the rapid rise of AI, this committee has been at the forefront of shaping how companies innovate sustainably while reducing AI's environmental impact . From driving policies and standards, to fostering collaborations and crafting new tools, the Green AI Committee is charting a path toward a more sustainable AI future. Joining us today are Thomas Lewis, the founder of the committee, along with co-chair Sanjay Podder.

Together, they'll share insights on the committee's goals, their strategies for tackling AI's carbon footprint, and the critical role this initiative plays in ensuring AI development supports global net zero ambitions. And as always, everything we discuss today will be linked in the show notes below. So without further ado, let's dive into our conversation about the Green AI Committee.

First, I'll let Thomas Lewis introduce himself.

Thomas Lewis: Hi, I'm Thomas Lewis. I'm a green software developer advocate at Microsoft, and excited to be here. I also work in artificial intelligence, spatial computing, and I've recently been involved in becoming a book nerd again.

Chris Skipper: My first question to Thomas was, what inspired the creation of the Green AI Committee and how does it aim to shape the GFS approach to ensuring AI innovation aligns with sustainability goals? 

Thomas Lewis: Yeah, so we noticed that we were getting a lot of inquiries. We were getting them from legislators and a lot of technologists. Everybody from, you know, people working at your, you know, typical enterprise to folks who were doing research at universities and learning institutions.

And they were reaching out to try to get a better understanding of how the green software principles that we talk about and those practices applied to this growing impact of AI. It was not unusual to see on social media a lot of interest in this kind of intersection of green software or sustainability with artificial intelligence.

And, you know, this kind of shaped the GSF's approach because in a way we take a slow, methodical approach to thinking about the challenges of green AI and we tend to bring in a lot of experts who have thought about this space from quite a few different viewpoints. And we don't just look at it in a binary way of good or bad.

And I think a lot of times, especially online, it can be like, well, you know, AI is, you know, burning the planet down. And you know, and that the resources needed to run these AIs are significant, which is not untrue. And that's the thing I appreciate with the GSF is that you know, we look at those elephants in the room.

But with acknowledging those challenges, we also look at AI to help support sustainability efforts by, again, looking at it from those different vectors and then thinking of a viewpoint and also backing it up with the appropriate tools, technologies, and education that may be needed.

Chris Skipper: The committee's manifesto emphasizes focusing on reducing the environmental impact of AI. Could you elaborate on why this focus was chosen rather than areas like AI for sustainability or responsible AI?

Thomas Lewis: That's a good question. We tend to look at things from a variety of vectors and don't necessarily limit ourselves if we think it is important to dig into these other areas. But one of the things I do like, about the GSF is that typically when we start a committee or start a project, we always start with a workshop.

And what we do is we ask for a lot of experts to come to the, you know, virtual table, so to speak, and walk actually through it. So, everyone gets a voice and gets to put out an opinion and to brainstorm and think about these things. And these workshops are over multiple days. And so, typically the first day is kind of like just getting everything on the board.

And then the, you know, second time that we get together is really about how to kind of say, "okay, how do we prioritize these? What do we think are the most important? What should we start on first? And then what are the things that, you know, we put on the backlog?" 

And then the third, you know, one is typically where we're really getting sort of precise about "here's where our focus is going to be." 

So the conversation is always very broad in the beginning, right? Because you have all of these people coming to the table to say what's important. But as we kind of go through that, so, after a lot of that discussion, we decide on a prioritized focus. But of course we'll come back to others as we iterate because there are gonna be opportunities where, hey, maybe it is more important that we focus on a certain thing.

So, like, for example for the GSF, it is about building out the SCI for AI. So, if you're familiar with our Software Carbon Intensity spec, that now is a standard, that is one of, kind of the projects that came out of that workshop and that thinking, because, you know, first thing you kind of have to do if you wanna make a change in what you do is you have to measure it, right?

You have to measure what your carbon intensity is, whether it's AI or gaming or blockchain or what have you. And so I think by having this process of doing these workshops that's really what gets us to our priority. So I don't think that there's always sort of a kind of a crisp thing of like, why we did this or not do this, or why we prioritize it a way.

It's really that kind of collective coming together, which I think is what really makes the foundation very powerful because everyone has a voice in it.

Chris Skipper: The committee recently responded to a bill drafted by US Senators to investigate AI's environmental impact. How do you see the role of the Green AI Committee in shaping public policy and regulations?

Thomas Lewis: I've always seen the Green AI Committee's role in this as a trusted advisor, backed up with technical credibility and intellectual honesty. Our intent is not to rubber stamp legislation or just be another endorsement on a bill, but to review bills and papers that come to us with experts in this field and to call out things that we think are important to sustainability or also question things. What I really have appreciated is what comes to us is there has never been an intention for us just to say, "this is good" and give the check mark. But it really is, has been like, "hey, we want your feedback. We wanna understand how we can make these things better for our constituents."

And the other thing is that the committee also works very closely with our own policy group within the GSF because many of the members, including myself, don't work with legislators and politicians normally. And so there's a vernacular to the things that they talk about and how they approach things.

And so our policy group is also very helpful in this. So, you know, our committees aren't based on, "hey, everything related to AI will come through this committee." We have a lot of different groups, and those groups may be like the policy group, it may be the open source projects that are within the GSF and some of our education opportunities that are there.

But yeah, I would say from my perspective the role is mostly as a trusted advisor. And I think that if that is how people reflected the relationship regarding policy and advocacy, I would think that we are doing a good thing.

Chris Skipper: From the initial stages of founding the Green AI Committee to where it stands now, what have been the most valuable lessons learned that could guide other organizations aiming to promote sustainability in AI?

Thomas Lewis: I would say, first take a thoughtful approach in how you wanna approach things. Not only is green software a significant amount of tech, people and communities, but AI builds on top of that and has its own things, and the innovation is happening way faster than most people can keep up.

And so you've gotta take the time to figure out what you wanna focus on first. You can't say you're just gonna try to cover every angle and every thing. Second, I would say take a less dogmatic approach to your efforts. It's easy to say "things should be this way," right? Or, "hey, we're gonna do something 100%, or it's considered a failure."

This space is rapidly changing. This environment especially. So what you have to do is kind of take the time to get a wide variety of insights and motivations, and then methodically figure out what a hopefully optimal approach is going to look like. And then the third which, you know, may not be just related to, you know, green software and AI, but surround yourself with people who are smarter and more knowledgeable than yourself.

One of the things that I absolutely love being on this committee is there are just super smart people that I get to work with, like the people that are on this podcast. And I learned so much because we all have different contexts, we have different viewpoints and we have various experiences, right?

So we've got you know, folks who are in big companies and people who are in small companies and people who are just starting their sustainability journey. There's people who have been doing this for a long time. We have students, we have researchers. There's all kinds of people. So the more that you can kind of understand where a lot of people are coming from,

and again, what their context is, you're gonna find that you're gonna really be able to do a whole lot more than you have been able to before. And you may get ideas from places that you think you didn't before. And again, this isn't just with the Green AI Committee, I think this is in life, you know, and again, if you surround yourself with people who are smarter and more knowledgeable than yourself I always think that you're going to be in a better place and you'll end up being a better person for it.

Chris Skipper: Thanks to Thomas for sharing those insights with us. Next up we have Sanjay Podder. Sanjay is not only co-chair of the Green AI Committee, but also host of our other podcast here at the Green Software Foundation, CXO Bytes. My first question to Sanjay was how does the Green AI Committee contribute to reducing AI's carbon footprint?

And can you share specific strategies or tools the committee is exploring to achieve these goals?

Sanjay Podder: The Green AI Committee brings together experts from across the industry to shape what it truly means to build AI sustainably. Our goal is to not only define green AI, but to make it practical and actionable for developers, data scientists, and technology leaders alike. We started by creating a simple developer-friendly definition of green AI.

One that anyone in the ecosystem can understand and apply. But we did not stop there. We have taken a lifecycle approach breaking down the environmental impact of AI at every stage from data processing and model training to deployment and inference. This helps pinpoint where emissions are highest and where optimization efforts can have the biggest impact.

We are also actively working on strategies and tools to support these goals. By embedding best practices across the AI lifecycle, we are driving a shift towards AI systems that are not just powerful, but also responsible and sustainable.

Chris Skipper: The manifesto highlights the importance of partnerships with nonprofits, governments, and regulators.

Could you share some examples of how collaborations have advanced the Green AI committee's mission?

Sanjay Podder: The committee understands that tackling AI's environmental impact demands broad collaboration with various stakeholders to create comprehensive standards. These standards will focus on transparency software and hardware efficiency and environmental accountability. Engaging a wide range of AI and ICT organizations will help build consensus and ensure that sustainability is a core design principle from the start.

Chris Skipper: The committee is tasked with supporting projects like the development of an ISO standard for measuring AI's environmental impact. What milestones have been achieved in this area so far, and what are the next steps?

Sanjay Podder: Despite rapid advancement in AI, practitioners and users currently lack clear guidance and knowledge on how to measure, reduce, and report, AI impacts. This absence limits public awareness and hinders efforts to address AI's environmental footprint, making it more challenging to develop AI sustainably.

To address these challenges, the committee is actively pursuing initiatives to provide practitioners and users with the necessary knowledge and tools to minimize AI's environmental footprint. The goal is to increase awareness of green AI principles and promote sustainable AI development practices. For example, Green AI Practitioners course to increase the awareness of green AI and understanding of the implications of AI development on the environment.

It'll explain the fundamental principles of green AI developments and solutions and, provide practical, actionable recommendations for practitioners, including guidelines for measurement. Software Carbon Intensity for AI to address the challenges of measuring AI carbon emission to the AI lifecycle, and support more informed decision making and promote accountability in AI development.

Chris Skipper: And finally, what are some of the long-term goals for the Green AI Committee, and how do you see these objectives evolving with advancements in AI technology? 

Sanjay Podder: Our goals are evolving to reduce the ecological footprint of AI systems. Green AI isn't just a standalone solution. It's a core component of a broader sustainability ecosystem. As we advance in this mission, we urge more organizations to join the conversation and help build a more sustainable future for AI, developing and regularly updating standardized methodologies to measure AI's environmental impact will be essential for driving sustainable and scalable AI development.

Chris Skipper: Thanks to Sanjay for those insights. Next up, we have some events coming up in the next few weeks that we'd like to announce. First up, a virtual event from our friends at Electricity Maps, Carbon-aware IT: The new standard for sustainable tech infrastructure, on May the fifth at 6:00 PM CEST.

Explore how organizations optimize IT infrastructure to meet their net zero goals. Then for those of you in Germany, there is a hybrid event in Karlsruhe run by Green Software Development Karlsruhe, called Inside CO2.js - Measuring the Emissions of the Web, happening on May the sixth at 6:30 PM CEST.

This is also a hybrid event, so there will be an online element. Learn how to make emissions estimates and use CO2.js, a JavaScript library from regular environment variables host, Chris Adams and the Green Web Foundation. Then we have another event that is purely virtual happening on May 6th at 6:30 PM CEST, called Monitoring for Software Environmental Sustainability.

Learn how to incorporate software sustainability metrics into your monitoring system. And finally in New York, the Green IO and Apidays conference, green io, New York, happening from May the 14th until May the 15th. Get the latest insights from thought leaders in tech sustainability and actionable hands-on feedback from practitioners scaling green IT. So we've reached the end of this special backstage episode on the Green AI Committee Project at the GSF. Thanks to both Thomas and Sanjay for their contributions. I hope you enjoyed the podcast. To listen to more podcasts about green software, please visit podcast.greensoftware.foundation, and we'll see you on the next episode.

Bye for now. 

Hosted on Acast. See acast.com/privacy for more information.

]]>
In this special backstage episode of Environment Variables, producer Chris Skipper spotlights the Green AI Committee, an initiative of the Green Software Foundation launched in 2024. Guests Thomas Lewis and Sanjay Podder share the committee’s mission to reduce AI's environmental impact through strategic focus on measurement, policy influence, and lifecycle optimization. The episode explores the committee’s approach to defining and implementing “green AI,” its contributions to public policy and ISO standards, and collaborative efforts to build tools, best practices, and educational resources that promote sustainable AI development.

Learn more about our people:

Find out more about the GSF:

Resources:

Events:

If you enjoyed this episode then please either:

TRANSCRIPT BELOW:

​Chris Skipper: Welcome to Environment Variables, where we bring you the latest news from the world of sustainable software development. I'm the producer of this podcast, Chris Skipper, and today we are thrilled to bring you another episode of Backstage, where we dive into the stories, challenges, and triumphs of the people shaping the future of green software.

 In this episode, we're turning the spotlight on the Green AI Committee, a pivotal initiative approved by the Green Software Foundation in March, 2024. With the rapid rise of AI, this committee has been at the forefront of shaping how companies innovate sustainably while reducing AI's environmental impact . From driving policies and standards, to fostering collaborations and crafting new tools, the Green AI Committee is charting a path toward a more sustainable AI future. Joining us today are Thomas Lewis, the founder of the committee, along with co-chair Sanjay Podder.

Together, they'll share insights on the committee's goals, their strategies for tackling AI's carbon footprint, and the critical role this initiative plays in ensuring AI development supports global net zero ambitions. And as always, everything we discuss today will be linked in the show notes below. So without further ado, let's dive into our conversation about the Green AI Committee.

First, I'll let Thomas Lewis introduce himself.

Thomas Lewis: Hi, I'm Thomas Lewis. I'm a green software developer advocate at Microsoft, and excited to be here. I also work in artificial intelligence, spatial computing, and I've recently been involved in becoming a book nerd again.

Chris Skipper: My first question to Thomas was, what inspired the creation of the Green AI Committee and how does it aim to shape the GFS approach to ensuring AI innovation aligns with sustainability goals? 

Thomas Lewis: Yeah, so we noticed that we were getting a lot of inquiries. We were getting them from legislators and a lot of technologists. Everybody from, you know, people working at your, you know, typical enterprise to folks who were doing research at universities and learning institutions.

And they were reaching out to try to get a better understanding of how the green software principles that we talk about and those practices applied to this growing impact of AI. It was not unusual to see on social media a lot of interest in this kind of intersection of green software or sustainability with artificial intelligence.

And, you know, this kind of shaped the GSF's approach because in a way we take a slow, methodical approach to thinking about the challenges of green AI and we tend to bring in a lot of experts who have thought about this space from quite a few different viewpoints. And we don't just look at it in a binary way of good or bad.

And I think a lot of times, especially online, it can be like, well, you know, AI is, you know, burning the planet down. And you know, and that the resources needed to run these AIs are significant, which is not untrue. And that's the thing I appreciate with the GSF is that you know, we look at those elephants in the room.

But with acknowledging those challenges, we also look at AI to help support sustainability efforts by, again, looking at it from those different vectors and then thinking of a viewpoint and also backing it up with the appropriate tools, technologies, and education that may be needed.

Chris Skipper: The committee's manifesto emphasizes focusing on reducing the environmental impact of AI. Could you elaborate on why this focus was chosen rather than areas like AI for sustainability or responsible AI?

Thomas Lewis: That's a good question. We tend to look at things from a variety of vectors and don't necessarily limit ourselves if we think it is important to dig into these other areas. But one of the things I do like, about the GSF is that typically when we start a committee or start a project, we always start with a workshop.

And what we do is we ask for a lot of experts to come to the, you know, virtual table, so to speak, and walk actually through it. So, everyone gets a voice and gets to put out an opinion and to brainstorm and think about these things. And these workshops are over multiple days. And so, typically the first day is kind of like just getting everything on the board.

And then the, you know, second time that we get together is really about how to kind of say, "okay, how do we prioritize these? What do we think are the most important? What should we start on first? And then what are the things that, you know, we put on the backlog?" 

And then the third, you know, one is typically where we're really getting sort of precise about "here's where our focus is going to be." 

So the conversation is always very broad in the beginning, right? Because you have all of these people coming to the table to say what's important. But as we kind of go through that, so, after a lot of that discussion, we decide on a prioritized focus. But of course we'll come back to others as we iterate because there are gonna be opportunities where, hey, maybe it is more important that we focus on a certain thing.

So, like, for example for the GSF, it is about building out the SCI for AI. So, if you're familiar with our Software Carbon Intensity spec, that now is a standard, that is one of, kind of the projects that came out of that workshop and that thinking, because, you know, first thing you kind of have to do if you wanna make a change in what you do is you have to measure it, right?

You have to measure what your carbon intensity is, whether it's AI or gaming or blockchain or what have you. And so I think by having this process of doing these workshops that's really what gets us to our priority. So I don't think that there's always sort of a kind of a crisp thing of like, why we did this or not do this, or why we prioritize it a way.

It's really that kind of collective coming together, which I think is what really makes the foundation very powerful because everyone has a voice in it.

Chris Skipper: The committee recently responded to a bill drafted by US Senators to investigate AI's environmental impact. How do you see the role of the Green AI Committee in shaping public policy and regulations?

Thomas Lewis: I've always seen the Green AI Committee's role in this as a trusted advisor, backed up with technical credibility and intellectual honesty. Our intent is not to rubber stamp legislation or just be another endorsement on a bill, but to review bills and papers that come to us with experts in this field and to call out things that we think are important to sustainability or also question things. What I really have appreciated is what comes to us is there has never been an intention for us just to say, "this is good" and give the check mark. But it really is, has been like, "hey, we want your feedback. We wanna understand how we can make these things better for our constituents."

And the other thing is that the committee also works very closely with our own policy group within the GSF because many of the members, including myself, don't work with legislators and politicians normally. And so there's a vernacular to the things that they talk about and how they approach things.

And so our policy group is also very helpful in this. So, you know, our committees aren't based on, "hey, everything related to AI will come through this committee." We have a lot of different groups, and those groups may be like the policy group, it may be the open source projects that are within the GSF and some of our education opportunities that are there.

But yeah, I would say from my perspective the role is mostly as a trusted advisor. And I think that if that is how people reflected the relationship regarding policy and advocacy, I would think that we are doing a good thing.

Chris Skipper: From the initial stages of founding the Green AI Committee to where it stands now, what have been the most valuable lessons learned that could guide other organizations aiming to promote sustainability in AI?

Thomas Lewis: I would say, first take a thoughtful approach in how you wanna approach things. Not only is green software a significant amount of tech, people and communities, but AI builds on top of that and has its own things, and the innovation is happening way faster than most people can keep up.

And so you've gotta take the time to figure out what you wanna focus on first. You can't say you're just gonna try to cover every angle and every thing. Second, I would say take a less dogmatic approach to your efforts. It's easy to say "things should be this way," right? Or, "hey, we're gonna do something 100%, or it's considered a failure."

This space is rapidly changing. This environment especially. So what you have to do is kind of take the time to get a wide variety of insights and motivations, and then methodically figure out what a hopefully optimal approach is going to look like. And then the third which, you know, may not be just related to, you know, green software and AI, but surround yourself with people who are smarter and more knowledgeable than yourself.

One of the things that I absolutely love being on this committee is there are just super smart people that I get to work with, like the people that are on this podcast. And I learned so much because we all have different contexts, we have different viewpoints and we have various experiences, right?

So we've got you know, folks who are in big companies and people who are in small companies and people who are just starting their sustainability journey. There's people who have been doing this for a long time. We have students, we have researchers. There's all kinds of people. So the more that you can kind of understand where a lot of people are coming from,

and again, what their context is, you're gonna find that you're gonna really be able to do a whole lot more than you have been able to before. And you may get ideas from places that you think you didn't before. And again, this isn't just with the Green AI Committee, I think this is in life, you know, and again, if you surround yourself with people who are smarter and more knowledgeable than yourself I always think that you're going to be in a better place and you'll end up being a better person for it.

Chris Skipper: Thanks to Thomas for sharing those insights with us. Next up we have Sanjay Podder. Sanjay is not only co-chair of the Green AI Committee, but also host of our other podcast here at the Green Software Foundation, CXO Bytes. My first question to Sanjay was how does the Green AI Committee contribute to reducing AI's carbon footprint?

And can you share specific strategies or tools the committee is exploring to achieve these goals?

Sanjay Podder: The Green AI Committee brings together experts from across the industry to shape what it truly means to build AI sustainably. Our goal is to not only define green AI, but to make it practical and actionable for developers, data scientists, and technology leaders alike. We started by creating a simple developer-friendly definition of green AI.

One that anyone in the ecosystem can understand and apply. But we did not stop there. We have taken a lifecycle approach breaking down the environmental impact of AI at every stage from data processing and model training to deployment and inference. This helps pinpoint where emissions are highest and where optimization efforts can have the biggest impact.

We are also actively working on strategies and tools to support these goals. By embedding best practices across the AI lifecycle, we are driving a shift towards AI systems that are not just powerful, but also responsible and sustainable.

Chris Skipper: The manifesto highlights the importance of partnerships with nonprofits, governments, and regulators.

Could you share some examples of how collaborations have advanced the Green AI committee's mission?

Sanjay Podder: The committee understands that tackling AI's environmental impact demands broad collaboration with various stakeholders to create comprehensive standards. These standards will focus on transparency software and hardware efficiency and environmental accountability. Engaging a wide range of AI and ICT organizations will help build consensus and ensure that sustainability is a core design principle from the start.

Chris Skipper: The committee is tasked with supporting projects like the development of an ISO standard for measuring AI's environmental impact. What milestones have been achieved in this area so far, and what are the next steps?

Sanjay Podder: Despite rapid advancement in AI, practitioners and users currently lack clear guidance and knowledge on how to measure, reduce, and report, AI impacts. This absence limits public awareness and hinders efforts to address AI's environmental footprint, making it more challenging to develop AI sustainably.

To address these challenges, the committee is actively pursuing initiatives to provide practitioners and users with the necessary knowledge and tools to minimize AI's environmental footprint. The goal is to increase awareness of green AI principles and promote sustainable AI development practices. For example, Green AI Practitioners course to increase the awareness of green AI and understanding of the implications of AI development on the environment.

It'll explain the fundamental principles of green AI developments and solutions and, provide practical, actionable recommendations for practitioners, including guidelines for measurement. Software Carbon Intensity for AI to address the challenges of measuring AI carbon emission to the AI lifecycle, and support more informed decision making and promote accountability in AI development.

Chris Skipper: And finally, what are some of the long-term goals for the Green AI Committee, and how do you see these objectives evolving with advancements in AI technology? 

Sanjay Podder: Our goals are evolving to reduce the ecological footprint of AI systems. Green AI isn't just a standalone solution. It's a core component of a broader sustainability ecosystem. As we advance in this mission, we urge more organizations to join the conversation and help build a more sustainable future for AI, developing and regularly updating standardized methodologies to measure AI's environmental impact will be essential for driving sustainable and scalable AI development.

Chris Skipper: Thanks to Sanjay for those insights. Next up, we have some events coming up in the next few weeks that we'd like to announce. First up, a virtual event from our friends at Electricity Maps, Carbon-aware IT: The new standard for sustainable tech infrastructure, on May the fifth at 6:00 PM CEST.

Explore how organizations optimize IT infrastructure to meet their net zero goals. Then for those of you in Germany, there is a hybrid event in Karlsruhe run by Green Software Development Karlsruhe, called Inside CO2.js - Measuring the Emissions of the Web, happening on May the sixth at 6:30 PM CEST.

This is also a hybrid event, so there will be an online element. Learn how to make emissions estimates and use CO2.js, a JavaScript library from regular environment variables host, Chris Adams and the Green Web Foundation. Then we have another event that is purely virtual happening on May 6th at 6:30 PM CEST, called Monitoring for Software Environmental Sustainability.

Learn how to incorporate software sustainability metrics into your monitoring system. And finally in New York, the Green IO and Apidays conference, green io, New York, happening from May the 14th until May the 15th. Get the latest insights from thought leaders in tech sustainability and actionable hands-on feedback from practitioners scaling green IT. So we've reached the end of this special backstage episode on the Green AI Committee Project at the GSF. Thanks to both Thomas and Sanjay for their contributions. I hope you enjoyed the podcast. To listen to more podcasts about green software, please visit podcast.greensoftware.foundation, and we'll see you on the next episode.

Bye for now. 

Hosted on Acast. See acast.com/privacy for more information.

]]> The Economics of AI The Economics of AI Thu, 24 Apr 2025 07:00:00 GMT 34:35 k18m92v0 false https://podcasts.castplus.fm/e/rn7y56zn-the-economics-of-ai 68dc7cf97be17a7f01356113 68dc7cef46a2532cdd8d1c23 105 Chris Adams sits down in-person with Max Schulze, founder of the Sustainable Digital Infrastructure Alliance (SDIA), to explore the economics of AI, digital infrastructure, and green software. They unpack the EU's Energy Efficiency Directive and its implications for data centers, the importance of measuring and reporting digital resource use, and why current conversations around AI and cloud infrastructure often miss the mark without reliable data. Max also introduces the concept of "digital resources" as a clearer way to understand and allocate environmental impact in cloud computing. The conversation highlights the need for public, transparent reporting to drive better policy and purchasing decisions in digital sustainability. 

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TRANSCRIPT BELOW:

Max Schulze: The measurement piece is key. Having transparency and understanding always helps. What gets measured gets fixed. It's very simple, but the step that comes after that, I think we're currently jumping the gun on that because we haven't measured a lot of stuff. 

Chris Adams: Hello and welcome to Environment Variables, brought to you by the Green Software Foundation.

In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect. Candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development.

I'm your host, Chris Adams. We're doing something a bit different today. Because a friend and frequent guest of the pod, Max Schulzer is actually turning up to Berlin in person where I'm recording today. So I figured it'd be nice to catch up with Max, see what he's up to, and yeah, just like catch up really.

So Max, we've been on this podcast a few times together, 

but not everyone has listened to every single word we've ever shared. So maybe if I give you some space to introduce yourself, 

I'll do it myself and then we'll move from there. Okay. Sounds good. All right then Max, so what brings you to this here?

Can you introduce yourself today? Yeah. 

Max Schulze: Yeah. I think the first question, why am I in Berlin? I think there's a lot of going on in Europe in terms of policies around tech. In the EU, there's the Cloud and AI Development Act. There's a lot of questions now about datacenters, and I think you and I can both be very grateful for the invention of AI because everything we ever talked about, now everybody's talking about 10x, which is quite nice.

Like everybody's thinking about it now. Yep. My general introduction, my name is Max. For everybody who doesn't know me, I'm the founder of the SDIA, the Sustainable Digital Infrastructure Alliance. And in the past we've done a lot of research on software, on datacenters, on energy use, on efficiency, on philosophical questions around sustainability.

I think the outcome that we generated that was probably the most well known is the Energy Efficiency Directive, which is forcing datacenters in Europe to be more transparent now. Unfortunately, the data will not be public, which is a loss. But at least a lot of digital infrastructure now needs to, Yeah,

be more transparent on their resource use. And the other thing that I think we got quite well known for is our explanation model. The way we think about the connection between infrastructure, digital resources, which is a term that we came up with and how that all interrelates to software. Because there's this conception too that we are building datacenters for the sake of datacenters.

But we are, of course, building them in response to software and software needs resources. And these resources need to be made somewhere. 

Chris Adams: Ah, I see. 

Max Schulze: And that's, I think what we were well known for. 

Chris Adams: Okay. Those two things I might jump into a little bit later on in a bit more detail.

So, if you're new to this podcast, my name is Chris Adams. I am the policy chair in the Green Software Foundation's Policy Working Group, and I'm also the director of technology and policy in the confusingly, but similarly named Green Web Foundation. Alright. Max, you spoke about two things that, if I can, I'd like to go dive into in a little bit more detail.

So, first of all, you spoke about this law called the Energy Efficiency Directive, which, as I understand it, essentially is intended to compel every datacenter above a certain size to start recording information, and in many ways it's like sustainability-adjacent information with the idea being that it should be published eventually.

Could we just talk a little bit about that first and maybe some of your role there, and then we'll talk a little bit about the digital resource thing that you mentioned. 

Max Schulze: Yeah. I think on the Energy Efficiency Directive, even one step up, europe has this ambition to conserve resources at any time and point.

Now, critical raw materials are also in that energy efficiency. Normally, actually, this law sets thresholds. Like it is supposed to say, "a building shall not consume more power than X." 

And with datacenters, what they realized, like, actually we can't set those thresholds because we don't know, like reliably how many resources have you consumed?

So we can't say "this should be the limit." Therefore, the first step was to say, well, first of all, everybody needs to report into a register. And what's interesting about that, it's not just the number that in datacenter land everybody likes to talk about, which is PUE, power usage effectiveness. And so how much overhead do I generate with cooling and other things on top of the IT, but also that it for the first time has water in there.

It has IT utilization ranges in there. It even has, which I think is very funny., The amount of traffic that goes in and out of a datacenter, which is a bit like, I don't know what we're trying to measure with this, but you know, sometimes you gotta leave the funny things in there to humor everybody. And it goes really far in terms of metrics on like really trying to see what resources go in a datacenter, how efficiently are there being used, and to a certain degree also what comes out of it. Maybe traffic. Yeah. 

Chris Adams: Ah, I see. Okay. Alright, so it's basically, essentially trying to bring the datacenter industry in line with some of other sectors where they already have this notion of, okay, we know they should be this efficient, and like we've had a lack of information in the datacenter industry, which made it difficult to do that.

Now I'm speaking to you in Berlin, and I don't normally sound like I'm in Berlin, but I am in Berlin, and you definitely sound like you are from Germany, even though you're not necessarily living in Germany. 

Max Schulze: I'm German. 

Chris Adams: Oh yeah. Maybe it might be worth just briefly touching on how this law kind of manifests in various countries, because I know that like this might be a bit inside baseball, but I've learned from you that Germany was one of the countries that was really pushing quite hard for this energy efficiency law in the first place, and they were one of the first countries who actually kinda write into their own national law.

Maybe we could touch a little bit on that before we start talking about world of digital resources and things like that. 

Max Schulze: Yeah, I think even funnier, and then you always know in the Europe that a certain country's really interested in something, they actually implemented it before the directive even was finalized.

So for everybody who doesn't know European policies, so the EU makes directives and then every country actually has to, it's called transpose it, into national law. So just because the EU, it's a very confusing thing, makes something, doesn't mean it's law. It just means that the countries should now implement it, but they don't have to and they can still change it.

So what Germany, for example, did, in the directive it's not mandatory to have heat recovery. So we're using the waste heat that comes out of the datacenter. But also the EU did not set release thresholds. But of course Germany was like, "no, we have to be harsher than this." So they actually said, for datacenters above a certain size, that needs to be powered by renewable energy, you need to have heat recovery,

it's mandatory for a certain size. And of course the industry is not pleased. So I think we will see a re revision of this, but it was a very ambitious, very strong, "let's manage how they build these things."

Chris Adams: I see. Okay. There is a, I think, is there a German phrase? Trust is nice, control is better.

Yes. Well, something like that. Yeah. Yeah. Okay. All right. So if I'm just gonna put my program ahead on, so when I think of a directive, it's a little bit like maybe an abstract class, right? Yes. And then if I'm Germany, I'm making a kind of concrete, I've implemented that class in my German law basically.

Yes. 

Max Schulze: Interfaces and implementations. Okay. 

Chris Adams: Alright. You've explained it into nerd for me. That makes a bit more sense. Thank you for that. Alright, so that's the ED, you kind of, you essentially were there to, to use another German phrase, watch the sausage get made. Yeah. So you've seen how that's turned up and now we have a law in Germany where essentially you've got datacenters regulated in a meaningful way for the first time, for example. Yeah. And we're dealing with all the kind of fallout from all that, for example. And we also spoke a little bit about this idea of digital resources. This is one other thing that you spend quite a lot of intellectual effort and time on helping people develop some of this language themselves and we've used ourselves in some of our own reports when we talk to policy makers or people who don't build datacenters themselves. 'Cause a lot of the time people don't necessarily know what, how a datacenter relates to software and how that relates to maybe them using a smartphone. Maybe you could talk a little about what a digital resource is in this context and why it's even useful to have this language.

Max Schulze: Yeah, and let me try to also connect it to the conversation about the ED. I think when, as a developer, you hear transparency and okay, they have to report data. What you're thinking is, "oh, they're gonna have an API where I can pull this information, also, let's say from the inside of the datacenter." Now in Germany, it is also funny for everybody listening, one way to fulfill that because the law was not specific,

datacenters now are hanging a piece of paper, I'm not kidding, on their fence with this information, right? So this is like them reporting this. And of course we as, I'm also a software engineer, so we as technical people, what we need is the datacenter to have an API that basically assigns the environmental impact of the entire datacenter to something.

And that something has always bothered me that we say, oh, it's the server. Or it's the, I don't know, the rack or the cluster, but ultimately, what does software consume? Software consumes basically three things. We call it compute, network, and storage, but in more philosophical terms, it's the ability to store, process and transfer data.

And that is the resource that software consumes. A software does not consume a datacenter or a server. It consumes these three things. And a server makes those things, turns actually energy and a lot of raw materials into digital resources. Then the datacenter in turn provides the shell in which the server can do that function.

Right? It's, the factory building is the datacenter. The machine that makes the t-shirts is the server. And the t-shirt is what people wear. Right?

Chris Adams: Ah, I see. Okay. So that actually helps when I think about, say, cloud computing. Like when I'm purchasing cloud computing, right, I'm paying for compute. I'm not really that bothered about whether it's an Intel server or something like that.

And to a degree, a lot of that is abstracted away from me anyway, so, and there's good sides to that and downsides to that. But essentially that seems to be that idea of kind of like cloud you compute and there being maybe for want of a better term, primitives you build services with, that's essentially some of the language that you are, you've been repurposing for people who aren't cloud engineers, essentially, to understand how modern software gets built these days.

Right. 

Max Schulze: And I think. That's also the real innovation of cloud, right? They gotta give them credit for that. They disaggregated these things. So on. When AWS was first launched, it was S3 for storage, EC2 for compute, and VPC for networks, right? So they basically said like, whatever you need, we will give it to you at scale in infinite pools of however much you need and want, and you pay only for it by the hour.

Which before you had to rent a server, the server always came with everything. It came with network, it came with storage, and you had to build the disaggregation yourself. But as a developer, fundamentally all you want, sometimes you just want compute. Now we have LLMs. I definitely just want compute. Then you realize, oh, I also need a lot storage to train an LLM.

Then you want some more storage. And then you're like, okay, well I need a massive network inside that, and you can buy each of these pieces by themselves because of cloud. That is really what it is about. 

Chris Adams: Oh, I see. Okay. And this is why it's little bit can be a bit difficult when you're trying to work out the environmental footprint of something because if we are trying to measure, say a server, but the resources are actually cloud and there's all these different ways you can provide that cloud,

then obviously it's gonna be complicated when you try to measure this stuff. 

Max Schulze: Yeah. Think about a gigabyte of storage on S3. There may be hundreds of servers behind it providing redundancy, providing the control layer, doing monitoring, right? Like in a way that gigabyte of storage is not like a disc inside a server somewhere.

It is a system that enables that gigabyte. And on thinking on that, like trying to say the gigabyte needs to come from somewhere is the much more interesting conversation than to go from the server up. Ah. It's misleading otherwise. 

Chris Adams: Alright. Okay. So. I'm gonna try and use a analogy from say, the energy sector, just to kinda help me understand this because I think there's quite a few key ideas inside this. So in the same way that I am buying maybe units of electricity, like kilowatt hours I'm buying that, I'm not really buying like an entire power station or even a small generator when I'm paying for something. There's all these different ways I can provide it, but really I care about is the resources. And this is the kind of key thing that you've been speaking to policy makers or people who are trying to understand how they should be thinking about datacenters and what they're good for and what they're bound for, right? Yes. Okay. Alright, cool. So you are in Berlin and it's surprisingly sunny today, which is really nice. We've made it through the kind of depressing German winter and I've actually like, you know, you, we've crossed parts quite a few times in the last few weeks because you've been bouncing between where you live in Harlem, Netherlands, and Brussels and Berlin quite a lot.

And I like trains and I imagine you like trains, but that's not the only reason you are zipping around here. Are there any projects related to digital sustainability that you could talk about that have been taking up your time, like that you're allowed to talk about these days?

Max Schulze: Yeah, I there's a lot.

There's too many actually, which is a bit overwhelming. We are doing a lot of work still on software also related to AI and I don't think it's so interesting to go into that. I think everybody from this podcast knows that there's an environmental impact. We now have a lot of tools to measure it, so my work is really focused on how do I get policy makers to act. And one project that I just recently came out and now that the elections are over in Germany, we can also talk about it, is we basically wrote a 200 page monster, call it the German Datacenter, not a strategy yet, it's an assessment and there's a lot of like, how much power are they gonna use?

That's not from us. But what we, for the first time we're able to do is to really explain the layers. So there's a lot of misconception that say building a datacenter creates jobs. But I think everybody in software knows that, and I think actually all of you should be more offended when datacenters claim that they are creating jobs because it is always software that runs there that is actually creating the benefit, right?

A datacenter building is just an empty building, and what we've been able to explain is to really say, okay, I build a datacenter, then there is somebody bringing servers, running IT infrastructure, maybe a hoster. That hoster in turn provides services to, let's say an agency. That agency creates a website. And that's a really complex system of actors that each add value,

and what we've shown is that a datacenter, per megawatt, depending on who's building it, can be three to six jobs. And a megawatt is already a very large datacenter, just can be 10,000 servers. If you compare that to the people on top, like if you go to that agency that can go to up to 300 to 600 jobs per megawatt.

And the value creation is really in the software and not anywhere else. And we believe that the German government and all sort of regions, and this applies to any region around the world, should really think like, "okay, if I did, I will build this datacenter, but how do I create that ecosystem around it? You know, in Amsterdam is always a good example.

You have Adyen, you have booking.com, you have really big tech companies, and you're like, "I'm sure they're using a Dutch datacenter." Of course not. They're running on AWS in Ireland. So you don't get the ecosystem benefit. But your policy makers think they do, but you don't connect the dots, so to say. 

Chris Adams: Ah, okay.

So if I understand this, so essentially the federal German government, third largest economy, I think it's third or fourth largest economy in the world. Yes. They need to figure out what to do with the fact there's lots and lots of demand for digital infrastructure. They're not quite sure what to do with it, and they also know they have like binding climate goals. So they're trying to work out how to square their circle. And there is also, I mean, most countries right now do wanna have some notion of like being able to kind of economically grow. So they're trying to understand, okay, what role do these play? And a lot of the time there has been a bit of a misunderstanding between what the datacenter provides and where the jobs actually come from.

And so you've essentially done for the first time some of this real, actually quite rigorous and open research into, "okay, how do jobs and how is economic opportunity created when you do this? And what happens if you have the datacenter in one place, but the job where the agencies or the startups in another place?" 

For example, because there seems to be this idea that if you just have a datacenter, you automatically get all the startups and all the jobs and everything in the same place.

And that sounds like that might not always be the case without deliberate decisions, right? 

Max Schulze: Yes. Without like really like designing it that way. And it becomes even more obvious when you look at Hyperscale and cloud providers, where you see these massive companies with massive profits and let's say they go to a region, they come to Berlin,

and they tell Berlin, you know, having actually Amazon and Spain also sent a really big press release, like, "we're gonna add 3% to your GDP. We're going to create millions of jobs." 

And of course every software engineer know is like just building a datacenter for a cloud provider does not do that.

And what they're also trying to distract, which we've shown in the report by going through their financial records, is that they don't, they pay property tax, so they pay local tax, in Germany is very low. But they of course, don't pay any corporate income tax in these regions. So the region thinks, "oh, I'm gonna get 10% of the revenue that a company like Microsoft makes."

That's not true. And in return, the company ask for energy infrastructure, which is socialized cost, meaning taxpayers pay for this. They ask for land, not always available, or scars. And then they don't really give much back. And that's really, I'm not saying we shouldn't build datacenters or you know, but you have to be really mindful that you need the job creation.

The tax creation is something that comes from above this, like on top of a datacenter stack. Yeah. And you need to be deliberate in bringing that all together, like everything else is just an illusion in that sense. 

Chris Adams: Oh, I see. Okay. So this helps me understand why you place so much emphasis on help helping people understand this whole stack of resources being created and where some of the value might actually be.

'Cause it's a little bit like if you are, let's imagine like say you're looking at, say, generating power for example, and you're like, you're opening a power station. Creating a power station by itself isn't necessarily the thing that generates the wealth or it's maybe people being able to use it in some of the higher services, further up the stack as it were.

Correct. And that's the kind of framing that you helping people understand so they can have a more sophisticated way of thinking about the role that datacenters play when they advance their economies, for example. 

Max Schulze: I love that you're using the energy analogy because everybody will hear that, or who's hearing this on the podcast will probably be like, "oh yeah, that's obvious, right?"

But for digital it, to a lot of people, it's not so obvious. They think that the power station is the thing, but actually it's the chemical industry next to it that should actually create, that's where the value is created. 

Chris Adams: I see. Okay. Alright. That's actually quite helpful. So one of the pieces of work you did was actually.

Providing new ways to think about how digital infrastructure ends up being, like how it's useful for maybe a country, for example. But one thing that I think you spoke about for some of this report was actually the role that software can actually play in like blunting some of the kind of expected growth in demand for electricity and things like that.

And obviously that has gonna have climate implications for example. Can we talk a little bit about the role that designing software in a more thoughtful way actually can blunt some of this expected growth so we can actually hit some of the goals that we had. 'Cause this is something that I know that you spend about fair amount of time thinking about and writing about as well.

Max Schulze: Yeah, 

I think 

it's really difficult. The measurement piece is key, but having transparency and understanding always helps. What gets measured gets fixed. It's very simple. But the step that comes after that, I think we're currently jumping the gun on that because we haven't measured a lot of stuff. We don't have a public database of say, this SAP system, this Zoom call is using this much.

We have very little data to work with and we're immediately jumping through solutions that like, oh, but we, if we shift the workloads, but if we're, for example, workload shifting on cloud, it's, unless the server has turned off, the impact is zero. Or that zero is extreme, but it's very limited because the cloud provider then has an incentive to, to fill it with some other workload.

You, it's, we've talked about this before. If everybody sells oil stocks because they're protesting against oil companies, it just means somebody else gonna buy the oil stock. You know? And it ultimately brings them spot prices down. But that's a different conversation. So I think, let's not jump to that.

Let's first get measurement really, right? And then it raises to me the question, what's the incentive for big software vendors or companies using software to actually measure and then also publish the results? Because, let's be honest, without public data, we can't do scientific research and even communities like the Green Software Foundation will have a hard time, you know, making report or giving good, making good analysis if we don't have publicly available data on certain software applications.

Chris Adams: I see. Okay. This does actually ring some bells 'cause I remember when I was involved in some of the early things related to working out, say software carbon intensity scores. We found that it's actually very, difficult to just get the energy numbers from a lot of services simply because that's not the thing that, 'cause a lot of the time,

if you're a company, you might not want to share this 'cause you might consider that as commercially sensitive information. There's a whole separate project called the Real Time Cloud project within the Green Software Foundation where the idea is to, and there's been some progress putting out, say, region by region figures for the carbon intensity of different places you might run cloud in, for example, and this is actually like a step forward, but at best we're finding that we could get maybe the figures for the carbon intensity of the energy that's there, but we don't actually have access to how much power is being used by a particular instance, for example. We're still struggling with this stuff and this is one thing that we keep bumping up against. So I can see where you're coming from there. So, alright, so this is one thing that you've been spending a bit of time thinking through, like where do we go from here then?

Max Schulze: Yeah, I think first we need to give ourselves a clap on the back because if you look at the amount of tools that can now do measurement like commercial tools, open source tools, I think it's amazing, right? We have, it's all there. Dashboards, promoters things, report interfaces, you know, it's all there. Now, the next step, and I think that's, as software people, we like to skip that step because we think, well, everybody's now gonna do it.

Well, it's not the reality. Now it's about incentives. And I think, for example, one organization we work with is called Seafit and it's a conglomerate of government purchasers, iT purchasers, who say, "okay, we want to purchase sustainable software." And to me it's very difficult to say, and I think you have the same experience, here are the 400 things you should put in your contracts to make the software more sustainable.

Instead, what we recommend is to simply say, well, please send me an annual report of all the environmental impacts created from my usage of your software, and very important phrase we always put in this end, please also publish it. Yeah. Again, and I think, right now, that's what we need to focus on. We need to focus on creating that incentive for somebody who's buying, even like Google Workplace, more like notion to really say, "Hey, by the way, before I buy this, I want to see the report," right?

I want to see the report from my workplace, and even for all the people listening to this, any service you use, like any API you use commercially, send them just an email and say, "Hey, I'm buying your product. I'm paying 50 euro a month, or 500 or 5,000 euros a month. Can I please get that report? Would you mind?"

Yeah. And that creates a whole chain reaction of everybody in the company thinking, "oh my God, all our customers are asking for this." Yeah, we need this. One of our largest accounts wants this figured out. And then they go to the Green Software Foundation or go to all the open source tools.

They learn about it, they implement a measurement. Then they realize, "oh, our cloud providers are not giving us data." So then they're sending a letter to all the cloud providers saying like, "guys, can you please provide us those numbers?" 

Chris Adams: Yeah. Yes. 

Max Schulze: And this is the chain reaction that requires all of us to focus and act now to trigger.

Chris Adams: Okay. So that sounds like, okay. When you, when I first met you, you were looking at, say, how do you quantify this and how do you build some of these measurement tools? And I know that some, there was a German project called, is It SoftAware, which was very, you know, the German take on SoftAware that does try to figure these out to like come up with some meaningful numbers. And now the thing it looks like you're spending some time thinking about is, okay, how do you get organizations with enough clout to essentially write in the level of disclosure that's needed for us to actually know if we're making progress or not?

Right? Yeah. 

Max Schulze: Correct. Little side anecdote on SoftAware. The report is also a 200 page piece. It's been finished for a year and it's not published yet because it's still in review in the, so it's a bit, it's a bit to pain. But fundamentally what we concluded is that, and I, there's other people that have already, while we are writing it, built better tools than we have.

And again, research-wise, this topic is, I don't wanna say solved. All the knowledge is out there and it's totally possible. And that's also what we basically set the report. Like if you can attach to the digital resource, if I can attach to the gigabyte of S3 storage, that is highly redundant or less redundant, an environmental product declaration.

So how much, physical resources went in it, how much energy went into it, how much water? Then any developer building a software application can basically then do that calculation themselves. If I use 400 gigabytes of search, it's just 400 x what I got environment important for, and that information is still not there.

But it's not there because we can't measure it. It's there because people don't want to, like you said, they don't want to have that in public. 

Chris Adams: Okay. So that's quite an interesting insight that you shared there, is that, 'cause when we first started looking at, I don't know, building digital services,

there was a whole thing about saying, well, if my webpage is twice the size, it must have twice the carbon footprint. And there's been a whole debate saying, well actually no, we shouldn't think about that. It doesn't scale that way. And it sounds like you're suggesting yes, you can go down that route where you directly measure every single thing, but in aggregate, if you wanna take a zoom out, if you wanna zoom out to actually achieve some systemic level of change, the thing you might actually need is kind of lower level per primitive kind of allocation of environmental footprint and just say, well, if I know the thing I'm purchasing and building with is say, gigabytes of storage, maybe I should just be thinking about in terms of each gigabyte of storage has this much, so therefore I should just reduce that number rather than worrying too much about if I halve my half, halve the numbers, it's not gonna be precisely a halving in emissions because you're looking at a kind of wider systemic level.

Max Schulze: First of all, I never talk about emissions because that's already like a proxy. Again, I think if you take the example of the browser, what you just said, I think there it becomes very obvious, what you really want is HP, Apple, Dell, any laptop they sell, they say, you know, there's 32 gigs of memory per gigabyte of memory.

This is the environmental impact per CPU cycle. This is the environmental impact. How easy would it be then to say, well, this browser is using 30% CPU, half of the memory, and then again, assigning it to each tab. It becomes literally just a division and forwarding game mathematically. But the scarcity, that the vendors don't ultimately release it on that level makes it incredibly painful for anyone to 

kinda reverse 

engineer and work backwards. Exactly. You get it for the server for the whole thing. Yeah. But that server also, of which configuration was it? Which, how much memory did it have? And this subdivision, that needs to happen.

But again, that's a feature that I think we need to see in the measurement game. But I would say, again, slap on the back for all of us and everybody listening, the measurement is good enough. For AI we really see it like, I think for the first time, it is at a scale that everybody's like, it doesn't really matter if we get it 40 or 60% right. It's pretty bad. Yeah. Right. And instead of now saying like, oh, let's immediately move to optimizing the models. Let's first create an incentive that we get all the model makers and then especially those service providers and the APIs, to just give everybody these reports so that we have facts.

That's really important to make policy, but also then to have an incentive to get better. 

Chris Adams: Okay. So look, have a data informed discussion essentially. Alright, so you need data for a data informed discussion basically. 

Max Schulze: Yes. 

Chris Adams: Alright. 

Max Schulze: To add to that, it's really because you like analogies and I like analogies

it's a market that is liquid with information. What I mean by that, if I want to buy a stock of a company, I download their 400 page financial report and it gives me a lot of information about how good that company's doing. Now for software, what are we, what is the liquidity of information in the market?

It's, for environmental impact, it's zero. The only liquidity we have is features. There are so many videos for every product on how many features and how to use them. So we have even the financial records of most software companies you can't actually get, 'cause they're private. So we have very scarcity of information and therefore competition in software is all about features.

Not about environmental impact. And I'm trying to create information liquidity in the market so that you and I and anybody buying software can make better choices. 

Chris Adams: Ah, okay. And this helps me understand why, I guess you pointed to there was less that French open example of something equivalent to like word processing.

I think we, it should be this French equivalent to like Google Docs. Yeah. Or which is literally called Docs. Yeah. And their entire thing was it's, it looks very much, very similar to some, to the kind of tool you might use for like note taking and everything like that. But because it's on an entirely open stack, it is possible to like see what's happening inside it and understand that, okay, well this is how the impacts scale based on my usage here, for example.

Max Schulze: But now. Now one of our friends, Anna, from Green Coding, would say, yeah, you can just run it through my tool and then you see it, but it's still just research information. We need liquidity on the information of, okay, the Ministry of Foreign Affairs in France is using docs. It has 4,000 documents and 3000 active data users.

Now that's the where I want the environmental impact data, right? I don't want a lab report. I don't wanna scale it in the lab. I want the real usage data. 

Chris Adams: Okay. So that feels like some of the next direction we might be moving to is almost looking at some of these things, seeing, like sacrificing some of the precision for maybe higher frequency information at like of things in production essentially.

So you can start getting a better idea about, okay, when this is in production or deployed for an entire department, for example, what, how will the changes I make there scale across rather than just making an assumption based on a single system that might not be quite as accurate as the changes I'm seeing in the real world?

Max Schulze: And you and I have two different bets on this that go in a different direction. Your bet was very much on sustainability reporting requirements, both CSRD or even financial disclosures. And my bet is if purchasers ask for it, then it will become public. And those are complimentary, but they're bets on the same exact thing. Information liquidity on environmental impact information. 

Chris Adams: Okay. All right. Well, Max, that sounds, this has been quite fun actually. I've gotta ask just before we wrap up now, if people are curious, and I've found some of the stuff you're talking about, interesting. Where should people be looking if they'd like to learn more?

Like is there a website you'd point people to or should they just look up Max Schulze on LinkedIn, for example? 

Max Schulze: That's always a good idea. If you want angry white men raging about stuff, that's LinkedIn, so you can follow me there. We, the SDIA is now focused on really helping regional governments developing digital ecosystems.

So if you're interested in that, go there. If you're interested more in the macro policy work, especially around software, we have launched a new brand that's our think tank now, which is called Leitmotiv. And I'm sure we're gonna include the note, the link somewhere in the notes. Of natürlich. Yeah. Yeah. Very nice.

And yeah, I urge you to check that out. We are completely independently funded now. No companies behind us. So a lot of what you read is like the brutal truth and not some kind of washed lobbying positions. So maybe you enjoy reading it. 

Chris Adams: Okay then. All right, so we've got Leitmotiv, and we've got the SDIA and then just Max Shulzer on LinkedIn.

These are the three places to be looking for this sort. Yeah. Alright, Max, it's lovely chatting to you in person and I hope you have a lovely weekend and enjoy some of this sunshine now that we've made it through the Berlin winter. Thanks, Max. Thanks Chris. 

Hey everyone. Thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts.

And please do leave a rating and review if you like what we're doing. It helps other people discover the show. And of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again and see you in the next episode.



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]]> Chris Adams sits down in-person with Max Schulze, founder of the Sustainable Digital Infrastructure Alliance (SDIA), to explore the economics of AI, digital infrastructure, and green software. They unpack the EU's Energy Efficiency Directive and its implications for data centers, the importance of measuring and reporting digital resource use, and why current conversations around AI and cloud infrastructure often miss the mark without reliable data. Max also introduces the concept of "digital resources" as a clearer way to understand and allocate environmental impact in cloud computing. The conversation highlights the need for public, transparent reporting to drive better policy and purchasing decisions in digital sustainability. 

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TRANSCRIPT BELOW:

Max Schulze: The measurement piece is key. Having transparency and understanding always helps. What gets measured gets fixed. It's very simple, but the step that comes after that, I think we're currently jumping the gun on that because we haven't measured a lot of stuff. 

Chris Adams: Hello and welcome to Environment Variables, brought to you by the Green Software Foundation.

In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect. Candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development.

I'm your host, Chris Adams. We're doing something a bit different today. Because a friend and frequent guest of the pod, Max Schulzer is actually turning up to Berlin in person where I'm recording today. So I figured it'd be nice to catch up with Max, see what he's up to, and yeah, just like catch up really.

So Max, we've been on this podcast a few times together, 

but not everyone has listened to every single word we've ever shared. So maybe if I give you some space to introduce yourself, 

I'll do it myself and then we'll move from there. Okay. Sounds good. All right then Max, so what brings you to this here?

Can you introduce yourself today? Yeah. 

Max Schulze: Yeah. I think the first question, why am I in Berlin? I think there's a lot of going on in Europe in terms of policies around tech. In the EU, there's the Cloud and AI Development Act. There's a lot of questions now about datacenters, and I think you and I can both be very grateful for the invention of AI because everything we ever talked about, now everybody's talking about 10x, which is quite nice.

Like everybody's thinking about it now. Yep. My general introduction, my name is Max. For everybody who doesn't know me, I'm the founder of the SDIA, the Sustainable Digital Infrastructure Alliance. And in the past we've done a lot of research on software, on datacenters, on energy use, on efficiency, on philosophical questions around sustainability.

I think the outcome that we generated that was probably the most well known is the Energy Efficiency Directive, which is forcing datacenters in Europe to be more transparent now. Unfortunately, the data will not be public, which is a loss. But at least a lot of digital infrastructure now needs to, Yeah,

be more transparent on their resource use. And the other thing that I think we got quite well known for is our explanation model. The way we think about the connection between infrastructure, digital resources, which is a term that we came up with and how that all interrelates to software. Because there's this conception too that we are building datacenters for the sake of datacenters.

But we are, of course, building them in response to software and software needs resources. And these resources need to be made somewhere. 

Chris Adams: Ah, I see. 

Max Schulze: And that's, I think what we were well known for. 

Chris Adams: Okay. Those two things I might jump into a little bit later on in a bit more detail.

So, if you're new to this podcast, my name is Chris Adams. I am the policy chair in the Green Software Foundation's Policy Working Group, and I'm also the director of technology and policy in the confusingly, but similarly named Green Web Foundation. Alright. Max, you spoke about two things that, if I can, I'd like to go dive into in a little bit more detail.

So, first of all, you spoke about this law called the Energy Efficiency Directive, which, as I understand it, essentially is intended to compel every datacenter above a certain size to start recording information, and in many ways it's like sustainability-adjacent information with the idea being that it should be published eventually.

Could we just talk a little bit about that first and maybe some of your role there, and then we'll talk a little bit about the digital resource thing that you mentioned. 

Max Schulze: Yeah. I think on the Energy Efficiency Directive, even one step up, europe has this ambition to conserve resources at any time and point.

Now, critical raw materials are also in that energy efficiency. Normally, actually, this law sets thresholds. Like it is supposed to say, "a building shall not consume more power than X." 

And with datacenters, what they realized, like, actually we can't set those thresholds because we don't know, like reliably how many resources have you consumed?

So we can't say "this should be the limit." Therefore, the first step was to say, well, first of all, everybody needs to report into a register. And what's interesting about that, it's not just the number that in datacenter land everybody likes to talk about, which is PUE, power usage effectiveness. And so how much overhead do I generate with cooling and other things on top of the IT, but also that it for the first time has water in there.

It has IT utilization ranges in there. It even has, which I think is very funny., The amount of traffic that goes in and out of a datacenter, which is a bit like, I don't know what we're trying to measure with this, but you know, sometimes you gotta leave the funny things in there to humor everybody. And it goes really far in terms of metrics on like really trying to see what resources go in a datacenter, how efficiently are there being used, and to a certain degree also what comes out of it. Maybe traffic. Yeah. 

Chris Adams: Ah, I see. Okay. Alright, so it's basically, essentially trying to bring the datacenter industry in line with some of other sectors where they already have this notion of, okay, we know they should be this efficient, and like we've had a lack of information in the datacenter industry, which made it difficult to do that.

Now I'm speaking to you in Berlin, and I don't normally sound like I'm in Berlin, but I am in Berlin, and you definitely sound like you are from Germany, even though you're not necessarily living in Germany. 

Max Schulze: I'm German. 

Chris Adams: Oh yeah. Maybe it might be worth just briefly touching on how this law kind of manifests in various countries, because I know that like this might be a bit inside baseball, but I've learned from you that Germany was one of the countries that was really pushing quite hard for this energy efficiency law in the first place, and they were one of the first countries who actually kinda write into their own national law.

Maybe we could touch a little bit on that before we start talking about world of digital resources and things like that. 

Max Schulze: Yeah, I think even funnier, and then you always know in the Europe that a certain country's really interested in something, they actually implemented it before the directive even was finalized.

So for everybody who doesn't know European policies, so the EU makes directives and then every country actually has to, it's called transpose it, into national law. So just because the EU, it's a very confusing thing, makes something, doesn't mean it's law. It just means that the countries should now implement it, but they don't have to and they can still change it.

So what Germany, for example, did, in the directive it's not mandatory to have heat recovery. So we're using the waste heat that comes out of the datacenter. But also the EU did not set release thresholds. But of course Germany was like, "no, we have to be harsher than this." So they actually said, for datacenters above a certain size, that needs to be powered by renewable energy, you need to have heat recovery,

it's mandatory for a certain size. And of course the industry is not pleased. So I think we will see a re revision of this, but it was a very ambitious, very strong, "let's manage how they build these things."

Chris Adams: I see. Okay. There is a, I think, is there a German phrase? Trust is nice, control is better.

Yes. Well, something like that. Yeah. Yeah. Okay. All right. So if I'm just gonna put my program ahead on, so when I think of a directive, it's a little bit like maybe an abstract class, right? Yes. And then if I'm Germany, I'm making a kind of concrete, I've implemented that class in my German law basically.

Yes. 

Max Schulze: Interfaces and implementations. Okay. 

Chris Adams: Alright. You've explained it into nerd for me. That makes a bit more sense. Thank you for that. Alright, so that's the ED, you kind of, you essentially were there to, to use another German phrase, watch the sausage get made. Yeah. So you've seen how that's turned up and now we have a law in Germany where essentially you've got datacenters regulated in a meaningful way for the first time, for example. Yeah. And we're dealing with all the kind of fallout from all that, for example. And we also spoke a little bit about this idea of digital resources. This is one other thing that you spend quite a lot of intellectual effort and time on helping people develop some of this language themselves and we've used ourselves in some of our own reports when we talk to policy makers or people who don't build datacenters themselves. 'Cause a lot of the time people don't necessarily know what, how a datacenter relates to software and how that relates to maybe them using a smartphone. Maybe you could talk a little about what a digital resource is in this context and why it's even useful to have this language.

Max Schulze: Yeah, and let me try to also connect it to the conversation about the ED. I think when, as a developer, you hear transparency and okay, they have to report data. What you're thinking is, "oh, they're gonna have an API where I can pull this information, also, let's say from the inside of the datacenter." Now in Germany, it is also funny for everybody listening, one way to fulfill that because the law was not specific,

datacenters now are hanging a piece of paper, I'm not kidding, on their fence with this information, right? So this is like them reporting this. And of course we as, I'm also a software engineer, so we as technical people, what we need is the datacenter to have an API that basically assigns the environmental impact of the entire datacenter to something.

And that something has always bothered me that we say, oh, it's the server. Or it's the, I don't know, the rack or the cluster, but ultimately, what does software consume? Software consumes basically three things. We call it compute, network, and storage, but in more philosophical terms, it's the ability to store, process and transfer data.

And that is the resource that software consumes. A software does not consume a datacenter or a server. It consumes these three things. And a server makes those things, turns actually energy and a lot of raw materials into digital resources. Then the datacenter in turn provides the shell in which the server can do that function.

Right? It's, the factory building is the datacenter. The machine that makes the t-shirts is the server. And the t-shirt is what people wear. Right?

Chris Adams: Ah, I see. Okay. So that actually helps when I think about, say, cloud computing. Like when I'm purchasing cloud computing, right, I'm paying for compute. I'm not really that bothered about whether it's an Intel server or something like that.

And to a degree, a lot of that is abstracted away from me anyway, so, and there's good sides to that and downsides to that. But essentially that seems to be that idea of kind of like cloud you compute and there being maybe for want of a better term, primitives you build services with, that's essentially some of the language that you are, you've been repurposing for people who aren't cloud engineers, essentially, to understand how modern software gets built these days.

Right. 

Max Schulze: And I think. That's also the real innovation of cloud, right? They gotta give them credit for that. They disaggregated these things. So on. When AWS was first launched, it was S3 for storage, EC2 for compute, and VPC for networks, right? So they basically said like, whatever you need, we will give it to you at scale in infinite pools of however much you need and want, and you pay only for it by the hour.

Which before you had to rent a server, the server always came with everything. It came with network, it came with storage, and you had to build the disaggregation yourself. But as a developer, fundamentally all you want, sometimes you just want compute. Now we have LLMs. I definitely just want compute. Then you realize, oh, I also need a lot storage to train an LLM.

Then you want some more storage. And then you're like, okay, well I need a massive network inside that, and you can buy each of these pieces by themselves because of cloud. That is really what it is about. 

Chris Adams: Oh, I see. Okay. And this is why it's little bit can be a bit difficult when you're trying to work out the environmental footprint of something because if we are trying to measure, say a server, but the resources are actually cloud and there's all these different ways you can provide that cloud,

then obviously it's gonna be complicated when you try to measure this stuff. 

Max Schulze: Yeah. Think about a gigabyte of storage on S3. There may be hundreds of servers behind it providing redundancy, providing the control layer, doing monitoring, right? Like in a way that gigabyte of storage is not like a disc inside a server somewhere.

It is a system that enables that gigabyte. And on thinking on that, like trying to say the gigabyte needs to come from somewhere is the much more interesting conversation than to go from the server up. Ah. It's misleading otherwise. 

Chris Adams: Alright. Okay. So. I'm gonna try and use a analogy from say, the energy sector, just to kinda help me understand this because I think there's quite a few key ideas inside this. So in the same way that I am buying maybe units of electricity, like kilowatt hours I'm buying that, I'm not really buying like an entire power station or even a small generator when I'm paying for something. There's all these different ways I can provide it, but really I care about is the resources. And this is the kind of key thing that you've been speaking to policy makers or people who are trying to understand how they should be thinking about datacenters and what they're good for and what they're bound for, right? Yes. Okay. Alright, cool. So you are in Berlin and it's surprisingly sunny today, which is really nice. We've made it through the kind of depressing German winter and I've actually like, you know, you, we've crossed parts quite a few times in the last few weeks because you've been bouncing between where you live in Harlem, Netherlands, and Brussels and Berlin quite a lot.

And I like trains and I imagine you like trains, but that's not the only reason you are zipping around here. Are there any projects related to digital sustainability that you could talk about that have been taking up your time, like that you're allowed to talk about these days?

Max Schulze: Yeah, I there's a lot.

There's too many actually, which is a bit overwhelming. We are doing a lot of work still on software also related to AI and I don't think it's so interesting to go into that. I think everybody from this podcast knows that there's an environmental impact. We now have a lot of tools to measure it, so my work is really focused on how do I get policy makers to act. And one project that I just recently came out and now that the elections are over in Germany, we can also talk about it, is we basically wrote a 200 page monster, call it the German Datacenter, not a strategy yet, it's an assessment and there's a lot of like, how much power are they gonna use?

That's not from us. But what we, for the first time we're able to do is to really explain the layers. So there's a lot of misconception that say building a datacenter creates jobs. But I think everybody in software knows that, and I think actually all of you should be more offended when datacenters claim that they are creating jobs because it is always software that runs there that is actually creating the benefit, right?

A datacenter building is just an empty building, and what we've been able to explain is to really say, okay, I build a datacenter, then there is somebody bringing servers, running IT infrastructure, maybe a hoster. That hoster in turn provides services to, let's say an agency. That agency creates a website. And that's a really complex system of actors that each add value,

and what we've shown is that a datacenter, per megawatt, depending on who's building it, can be three to six jobs. And a megawatt is already a very large datacenter, just can be 10,000 servers. If you compare that to the people on top, like if you go to that agency that can go to up to 300 to 600 jobs per megawatt.

And the value creation is really in the software and not anywhere else. And we believe that the German government and all sort of regions, and this applies to any region around the world, should really think like, "okay, if I did, I will build this datacenter, but how do I create that ecosystem around it? You know, in Amsterdam is always a good example.

You have Adyen, you have booking.com, you have really big tech companies, and you're like, "I'm sure they're using a Dutch datacenter." Of course not. They're running on AWS in Ireland. So you don't get the ecosystem benefit. But your policy makers think they do, but you don't connect the dots, so to say. 

Chris Adams: Ah, okay.

So if I understand this, so essentially the federal German government, third largest economy, I think it's third or fourth largest economy in the world. Yes. They need to figure out what to do with the fact there's lots and lots of demand for digital infrastructure. They're not quite sure what to do with it, and they also know they have like binding climate goals. So they're trying to work out how to square their circle. And there is also, I mean, most countries right now do wanna have some notion of like being able to kind of economically grow. So they're trying to understand, okay, what role do these play? And a lot of the time there has been a bit of a misunderstanding between what the datacenter provides and where the jobs actually come from.

And so you've essentially done for the first time some of this real, actually quite rigorous and open research into, "okay, how do jobs and how is economic opportunity created when you do this? And what happens if you have the datacenter in one place, but the job where the agencies or the startups in another place?" 

For example, because there seems to be this idea that if you just have a datacenter, you automatically get all the startups and all the jobs and everything in the same place.

And that sounds like that might not always be the case without deliberate decisions, right? 

Max Schulze: Yes. Without like really like designing it that way. And it becomes even more obvious when you look at Hyperscale and cloud providers, where you see these massive companies with massive profits and let's say they go to a region, they come to Berlin,

and they tell Berlin, you know, having actually Amazon and Spain also sent a really big press release, like, "we're gonna add 3% to your GDP. We're going to create millions of jobs." 

And of course every software engineer know is like just building a datacenter for a cloud provider does not do that.

And what they're also trying to distract, which we've shown in the report by going through their financial records, is that they don't, they pay property tax, so they pay local tax, in Germany is very low. But they of course, don't pay any corporate income tax in these regions. So the region thinks, "oh, I'm gonna get 10% of the revenue that a company like Microsoft makes."

That's not true. And in return, the company ask for energy infrastructure, which is socialized cost, meaning taxpayers pay for this. They ask for land, not always available, or scars. And then they don't really give much back. And that's really, I'm not saying we shouldn't build datacenters or you know, but you have to be really mindful that you need the job creation.

The tax creation is something that comes from above this, like on top of a datacenter stack. Yeah. And you need to be deliberate in bringing that all together, like everything else is just an illusion in that sense. 

Chris Adams: Oh, I see. Okay. So this helps me understand why you place so much emphasis on help helping people understand this whole stack of resources being created and where some of the value might actually be.

'Cause it's a little bit like if you are, let's imagine like say you're looking at, say, generating power for example, and you're like, you're opening a power station. Creating a power station by itself isn't necessarily the thing that generates the wealth or it's maybe people being able to use it in some of the higher services, further up the stack as it were.

Correct. And that's the kind of framing that you helping people understand so they can have a more sophisticated way of thinking about the role that datacenters play when they advance their economies, for example. 

Max Schulze: I love that you're using the energy analogy because everybody will hear that, or who's hearing this on the podcast will probably be like, "oh yeah, that's obvious, right?"

But for digital it, to a lot of people, it's not so obvious. They think that the power station is the thing, but actually it's the chemical industry next to it that should actually create, that's where the value is created. 

Chris Adams: I see. Okay. Alright. That's actually quite helpful. So one of the pieces of work you did was actually.

Providing new ways to think about how digital infrastructure ends up being, like how it's useful for maybe a country, for example. But one thing that I think you spoke about for some of this report was actually the role that software can actually play in like blunting some of the kind of expected growth in demand for electricity and things like that.

And obviously that has gonna have climate implications for example. Can we talk a little bit about the role that designing software in a more thoughtful way actually can blunt some of this expected growth so we can actually hit some of the goals that we had. 'Cause this is something that I know that you spend about fair amount of time thinking about and writing about as well.

Max Schulze: Yeah, 

I think 

it's really difficult. The measurement piece is key, but having transparency and understanding always helps. What gets measured gets fixed. It's very simple. But the step that comes after that, I think we're currently jumping the gun on that because we haven't measured a lot of stuff. We don't have a public database of say, this SAP system, this Zoom call is using this much.

We have very little data to work with and we're immediately jumping through solutions that like, oh, but we, if we shift the workloads, but if we're, for example, workload shifting on cloud, it's, unless the server has turned off, the impact is zero. Or that zero is extreme, but it's very limited because the cloud provider then has an incentive to, to fill it with some other workload.

You, it's, we've talked about this before. If everybody sells oil stocks because they're protesting against oil companies, it just means somebody else gonna buy the oil stock. You know? And it ultimately brings them spot prices down. But that's a different conversation. So I think, let's not jump to that.

Let's first get measurement really, right? And then it raises to me the question, what's the incentive for big software vendors or companies using software to actually measure and then also publish the results? Because, let's be honest, without public data, we can't do scientific research and even communities like the Green Software Foundation will have a hard time, you know, making report or giving good, making good analysis if we don't have publicly available data on certain software applications.

Chris Adams: I see. Okay. This does actually ring some bells 'cause I remember when I was involved in some of the early things related to working out, say software carbon intensity scores. We found that it's actually very, difficult to just get the energy numbers from a lot of services simply because that's not the thing that, 'cause a lot of the time,

if you're a company, you might not want to share this 'cause you might consider that as commercially sensitive information. There's a whole separate project called the Real Time Cloud project within the Green Software Foundation where the idea is to, and there's been some progress putting out, say, region by region figures for the carbon intensity of different places you might run cloud in, for example, and this is actually like a step forward, but at best we're finding that we could get maybe the figures for the carbon intensity of the energy that's there, but we don't actually have access to how much power is being used by a particular instance, for example. We're still struggling with this stuff and this is one thing that we keep bumping up against. So I can see where you're coming from there. So, alright, so this is one thing that you've been spending a bit of time thinking through, like where do we go from here then?

Max Schulze: Yeah, I think first we need to give ourselves a clap on the back because if you look at the amount of tools that can now do measurement like commercial tools, open source tools, I think it's amazing, right? We have, it's all there. Dashboards, promoters things, report interfaces, you know, it's all there. Now, the next step, and I think that's, as software people, we like to skip that step because we think, well, everybody's now gonna do it.

Well, it's not the reality. Now it's about incentives. And I think, for example, one organization we work with is called Seafit and it's a conglomerate of government purchasers, iT purchasers, who say, "okay, we want to purchase sustainable software." And to me it's very difficult to say, and I think you have the same experience, here are the 400 things you should put in your contracts to make the software more sustainable.

Instead, what we recommend is to simply say, well, please send me an annual report of all the environmental impacts created from my usage of your software, and very important phrase we always put in this end, please also publish it. Yeah. Again, and I think, right now, that's what we need to focus on. We need to focus on creating that incentive for somebody who's buying, even like Google Workplace, more like notion to really say, "Hey, by the way, before I buy this, I want to see the report," right?

I want to see the report from my workplace, and even for all the people listening to this, any service you use, like any API you use commercially, send them just an email and say, "Hey, I'm buying your product. I'm paying 50 euro a month, or 500 or 5,000 euros a month. Can I please get that report? Would you mind?"

Yeah. And that creates a whole chain reaction of everybody in the company thinking, "oh my God, all our customers are asking for this." Yeah, we need this. One of our largest accounts wants this figured out. And then they go to the Green Software Foundation or go to all the open source tools.

They learn about it, they implement a measurement. Then they realize, "oh, our cloud providers are not giving us data." So then they're sending a letter to all the cloud providers saying like, "guys, can you please provide us those numbers?" 

Chris Adams: Yeah. Yes. 

Max Schulze: And this is the chain reaction that requires all of us to focus and act now to trigger.

Chris Adams: Okay. So that sounds like, okay. When you, when I first met you, you were looking at, say, how do you quantify this and how do you build some of these measurement tools? And I know that some, there was a German project called, is It SoftAware, which was very, you know, the German take on SoftAware that does try to figure these out to like come up with some meaningful numbers. And now the thing it looks like you're spending some time thinking about is, okay, how do you get organizations with enough clout to essentially write in the level of disclosure that's needed for us to actually know if we're making progress or not?

Right? Yeah. 

Max Schulze: Correct. Little side anecdote on SoftAware. The report is also a 200 page piece. It's been finished for a year and it's not published yet because it's still in review in the, so it's a bit, it's a bit to pain. But fundamentally what we concluded is that, and I, there's other people that have already, while we are writing it, built better tools than we have.

And again, research-wise, this topic is, I don't wanna say solved. All the knowledge is out there and it's totally possible. And that's also what we basically set the report. Like if you can attach to the digital resource, if I can attach to the gigabyte of S3 storage, that is highly redundant or less redundant, an environmental product declaration.

So how much, physical resources went in it, how much energy went into it, how much water? Then any developer building a software application can basically then do that calculation themselves. If I use 400 gigabytes of search, it's just 400 x what I got environment important for, and that information is still not there.

But it's not there because we can't measure it. It's there because people don't want to, like you said, they don't want to have that in public. 

Chris Adams: Okay. So that's quite an interesting insight that you shared there, is that, 'cause when we first started looking at, I don't know, building digital services,

there was a whole thing about saying, well, if my webpage is twice the size, it must have twice the carbon footprint. And there's been a whole debate saying, well actually no, we shouldn't think about that. It doesn't scale that way. And it sounds like you're suggesting yes, you can go down that route where you directly measure every single thing, but in aggregate, if you wanna take a zoom out, if you wanna zoom out to actually achieve some systemic level of change, the thing you might actually need is kind of lower level per primitive kind of allocation of environmental footprint and just say, well, if I know the thing I'm purchasing and building with is say, gigabytes of storage, maybe I should just be thinking about in terms of each gigabyte of storage has this much, so therefore I should just reduce that number rather than worrying too much about if I halve my half, halve the numbers, it's not gonna be precisely a halving in emissions because you're looking at a kind of wider systemic level.

Max Schulze: First of all, I never talk about emissions because that's already like a proxy. Again, I think if you take the example of the browser, what you just said, I think there it becomes very obvious, what you really want is HP, Apple, Dell, any laptop they sell, they say, you know, there's 32 gigs of memory per gigabyte of memory.

This is the environmental impact per CPU cycle. This is the environmental impact. How easy would it be then to say, well, this browser is using 30% CPU, half of the memory, and then again, assigning it to each tab. It becomes literally just a division and forwarding game mathematically. But the scarcity, that the vendors don't ultimately release it on that level makes it incredibly painful for anyone to 

kinda reverse 

engineer and work backwards. Exactly. You get it for the server for the whole thing. Yeah. But that server also, of which configuration was it? Which, how much memory did it have? And this subdivision, that needs to happen.

But again, that's a feature that I think we need to see in the measurement game. But I would say, again, slap on the back for all of us and everybody listening, the measurement is good enough. For AI we really see it like, I think for the first time, it is at a scale that everybody's like, it doesn't really matter if we get it 40 or 60% right. It's pretty bad. Yeah. Right. And instead of now saying like, oh, let's immediately move to optimizing the models. Let's first create an incentive that we get all the model makers and then especially those service providers and the APIs, to just give everybody these reports so that we have facts.

That's really important to make policy, but also then to have an incentive to get better. 

Chris Adams: Okay. So look, have a data informed discussion essentially. Alright, so you need data for a data informed discussion basically. 

Max Schulze: Yes. 

Chris Adams: Alright. 

Max Schulze: To add to that, it's really because you like analogies and I like analogies

it's a market that is liquid with information. What I mean by that, if I want to buy a stock of a company, I download their 400 page financial report and it gives me a lot of information about how good that company's doing. Now for software, what are we, what is the liquidity of information in the market?

It's, for environmental impact, it's zero. The only liquidity we have is features. There are so many videos for every product on how many features and how to use them. So we have even the financial records of most software companies you can't actually get, 'cause they're private. So we have very scarcity of information and therefore competition in software is all about features.

Not about environmental impact. And I'm trying to create information liquidity in the market so that you and I and anybody buying software can make better choices. 

Chris Adams: Ah, okay. And this helps me understand why, I guess you pointed to there was less that French open example of something equivalent to like word processing.

I think we, it should be this French equivalent to like Google Docs. Yeah. Or which is literally called Docs. Yeah. And their entire thing was it's, it looks very much, very similar to some, to the kind of tool you might use for like note taking and everything like that. But because it's on an entirely open stack, it is possible to like see what's happening inside it and understand that, okay, well this is how the impacts scale based on my usage here, for example.

Max Schulze: But now. Now one of our friends, Anna, from Green Coding, would say, yeah, you can just run it through my tool and then you see it, but it's still just research information. We need liquidity on the information of, okay, the Ministry of Foreign Affairs in France is using docs. It has 4,000 documents and 3000 active data users.

Now that's the where I want the environmental impact data, right? I don't want a lab report. I don't wanna scale it in the lab. I want the real usage data. 

Chris Adams: Okay. So that feels like some of the next direction we might be moving to is almost looking at some of these things, seeing, like sacrificing some of the precision for maybe higher frequency information at like of things in production essentially.

So you can start getting a better idea about, okay, when this is in production or deployed for an entire department, for example, what, how will the changes I make there scale across rather than just making an assumption based on a single system that might not be quite as accurate as the changes I'm seeing in the real world?

Max Schulze: And you and I have two different bets on this that go in a different direction. Your bet was very much on sustainability reporting requirements, both CSRD or even financial disclosures. And my bet is if purchasers ask for it, then it will become public. And those are complimentary, but they're bets on the same exact thing. Information liquidity on environmental impact information. 

Chris Adams: Okay. All right. Well, Max, that sounds, this has been quite fun actually. I've gotta ask just before we wrap up now, if people are curious, and I've found some of the stuff you're talking about, interesting. Where should people be looking if they'd like to learn more?

Like is there a website you'd point people to or should they just look up Max Schulze on LinkedIn, for example? 

Max Schulze: That's always a good idea. If you want angry white men raging about stuff, that's LinkedIn, so you can follow me there. We, the SDIA is now focused on really helping regional governments developing digital ecosystems.

So if you're interested in that, go there. If you're interested more in the macro policy work, especially around software, we have launched a new brand that's our think tank now, which is called Leitmotiv. And I'm sure we're gonna include the note, the link somewhere in the notes. Of natürlich. Yeah. Yeah. Very nice.

And yeah, I urge you to check that out. We are completely independently funded now. No companies behind us. So a lot of what you read is like the brutal truth and not some kind of washed lobbying positions. So maybe you enjoy reading it. 

Chris Adams: Okay then. All right, so we've got Leitmotiv, and we've got the SDIA and then just Max Shulzer on LinkedIn.

These are the three places to be looking for this sort. Yeah. Alright, Max, it's lovely chatting to you in person and I hope you have a lovely weekend and enjoy some of this sunshine now that we've made it through the Berlin winter. Thanks, Max. Thanks Chris. 

Hey everyone. Thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts.

And please do leave a rating and review if you like what we're doing. It helps other people discover the show. And of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

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]]> OCP, Wooden Datacentres and Cleaning up Datacentre Diesel OCP, Wooden Datacentres and Cleaning up Datacentre Diesel Thu, 17 Apr 2025 07:00:00 GMT 1:01:26 p0kpz861 false https://podcasts.castplus.fm/e/q80q6w0n-ocp-wooden-datacentres-and-cleaning-up-datacentre-diesel 68dc7d107be17a7f0135694b 68dc7cef46a2532cdd8d1c23 Host Chris Adams is joined by special guest Karl Rabe, founder of WoodenDataCenter and co-lead of the Open Compute Project’s Data Center Facilities group, to discuss sustainable data center design and operation. They explore how colocating data centers with renewable energy sources like wind farms can reduce carbon emissions, and how using novel materials like cross-laminated timber can significantly cut the embodied carbon of data center infrastructure. Karl discusses replacing traditional diesel backup generators with cleaner alternatives like HVO, as well as designing modular, open-source hardware for increased sustainability and transparency. The conversation also covers the growing need for energy-integrated, community-friendly data centers to support the evolving demands of AI and the energy transition in a sustainable fashion. 104 Host Chris Adams is joined by special guest Karl Rabe, founder of WoodenDataCenter and co-lead of the Open Compute Project’s Data Center Facilities group, to discuss sustainable data center design and operation. They explore how colocating data centers with renewable energy sources like wind farms can reduce carbon emissions, and how using novel materials like cross-laminated timber can significantly cut the embodied carbon of data center infrastructure. Karl discusses replacing traditional diesel backup generators with cleaner alternatives like HVO, as well as designing modular, open-source hardware for increased sustainability and transparency. The conversation also covers the growing need for energy-integrated, community-friendly data centers to support the evolving demands of AI and the energy transition in a sustainable fashion.

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TRANSCRIPT BELOW:

Karl Rabe: That's a perfect analogy, having like a good neighbor approach saying, "look, we are here now, we look ugly, we always box, you know, but we help, you know, powering your homes, we reduce the cost of the energy transition, and we also heat your homes." 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello, and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. How do you green the bits of a computing system that you can't normally control with software? We've discussed before that one option that you can do might be to shift where you run computing jobs from one part of the world to another part of the world where the energy is greener.

And we've spoken about how this is essentially a way to run the same code, doing the same thing, but with a lower carbon footprint. But even if you have two data centers with the same efficiency on the same grid, one can still be greener than the other simply because of the energy gone into making the data center in the first place and the materials used. So does this make a meaningful difference though, and can it make a meaningful difference? I didn't know this. 

So I asked Karl Rabe the founder of Wooden Data Center and Windcloud, and now increasingly involved in the Open Compute Project, to come on and help me navigate these questions as he is the first person who turned me onto the idea that there are all these options available to green the shell, the stuff around the servers that we have that also has an impact on the software we run.

Karl, thank you so much for joining me. Can I just give you the floor to introduce yourself before we start?

Karl Rabe: Thanks, Chris. This is an absolute honor and I'll have to admit, you know, you're a big part on my carbon aware journey, and so I'm very glad that we finally get to speak. I'm Karl, based out of North Germany. We initially, I always say I had a one proper job. I'm a technical engineer by training,

and then I moved into the data. Then I fell into the data center business, we can touch on it a little later, which was Windcloud, which remains, which was data center thought from the energy perspective, which is a very important idea in 2025. But we pivoted about four years ago to Wooden Data Center, probably can touch upon those a little later, in also realizing there is this supply chain component to the data center.

And there are also tools to action against those. And I'm learning and supporting and providing, you know, as a co-lead in the data center facilities group of the OCP where we work, you know, with the biggest organizations directly in order to shape and define the latest trends in the data center

and especially navigating the AI buildout in somewhat of a, yeah, sustainable way.

Chris Adams: Okay, cool. And when you say OCP, you're referring to the Open Compute Project, the kind of project with Microsoft, Meta, various other companies, designing essentially open source server designs, right?

Karl Rabe: Correct. That is the, initially started by then Facebook now Meta, in order yeah, to create or to cut out waste on the server design. It meanwhile involves and grew into cooling environments, data center design, chiplet design. It's a whole range of initiatives.

Very interesting to look into. And, happy to talk about some of those projects. Yeah.

Chris Adams: All right, thanks Karl. So if you are new to this podcast, my name is Chris Adams. I am the director of technology and policy at the Green Web Foundation, a small Dutch non-profit focused on a fossil free internet by 2030. And I also work with the Green Software Foundation, the larger industry body, in their policy working group.

And we are gonna talk about various projects and we'll add as many all the show notes to all the links we can think of as we discuss. So if there's any particular things that caught your eye, like the OCP or Wooden Data Centers, if you follow the link to this website, to this podcast's website, you'll see all the links there.

Alright then Karl, are you sitting comfortably?

Karl Rabe: I am sitting very well. Yeah.

Chris Adams: Good stuff. Alright, then I guess we can start. So maybe I should ask you, where are you calling me from today, actually?

Karl Rabe: I'm calling you today from the west coast of the North Sea Shore in northern Germany. We are not a typical data center region for Germany, per se. We, which is Frankfurt, you know, 'cause of the big internet hub there. But we are actually located right within a wind farm.

You know, in my home, which is, initially was, you know, home growing up and turned to my home office and eventually to what was somewhat considered the international headquarter of Wooden Data Center. Yeah, and we're very close to the North Sea and we have a lot of renewable power around.

Chris Adams: Oh, I see. Okay. So near the north of Germany, near Denmark, where Denmark has loads of wind, you've got the similar thing where, okay. So

Karl Rabe: Yeah, absolutely. Yeah.

Chris Adams: Oh, I see. I get you. So, ah, alright. For people who are not familiar with the geography of like Europe, or Northern Europe in particular, the north part of Germany has loads of wind turbines and loads of wind energy, but lots of the power gets used in other parts of it.

So, Karl is in the windiest part of Germany, basically.

Karl Rabe: That's correct, yeah. We basically have offshore conditions on shore. And it's a community owned wind farm, which is also a special setup, which is very easy to get, you know, the people's acceptance. We have about a megawatt per inhabitant of this small community.

And so this is becoming, you know, the biggest, yeah, economic factor of the small community.

Chris Adams: Wow. A megawatt per, okay, so just for context, for people who are not familiar with megawatts and kilowatts, the typical house might use what may be about half a kilowatt of constant draw on average over the year. So that's a lot of power per person for that space. So that's a, you're in a place of power abundance compared to the scenario people are wondering where's the power gonna be coming from? Wow, I did not know that.

Karl Rabe: No, that, is, yeah, that is the, so it's a bit of that background, so to speak. We are now trying to go from 300 megawatts to 400 megawatts. There has been, you know, Germany's pushing for more renewable energy, and we have still some spots that we can, under new regulations now, build out.

And the goal or the big dream of our organization, the company running this wind farm for us is trying to produce a billion kilowatt hours per year. And so we're now slightly below that and we're trying to, Yeah, add another, yeah. For, we need to reach probably another 25 percent more production. And, it is, so to speak, you are absolutely right, we are in an energy abundance and that was one of the prerequisites for Windcloud. 'Cause you know, the easiest innovations, is one and one is two. And so we have in, we had energy, I was aware that we also had fiber infrastructure in the north to run those set wind, parts.

So we said, why don't we bring a load to those? That was the initial start of Windcloud.

Chris Adams: Okay, so maybe we should talk a little bit about that. I hadn't realized the connection between the geography and the fact that you're literally in the middle of a wind farm, which is why this came together. Okay. So, the, so as I understand it, and now this makes sense why you are so involved in Windcloud.

So for context, my understanding of Windcloud is it's essentially a company where rather than like connecting data centers via big power lines to like somewhere else where the actual generation is miles away from where the data centers are, the ideals instead was to actually put the data centers literally inside the towers of the wind turbines themselves.

So you don't need to have any cables and, well you've obviously got green energy because it's right there, you're literally using the wind turbine. So, apart from this sounding kind of cool, can you tell me like why you do this from a sustainability perspective in the first place?

Karl Rabe: Yeah, so the way we discovered that I wanted to, and this is the, probably the biggest reference that I can give on the software developer front, and I came out of a study in the UK. We had a really nice cohort.

We were constantly bouncing ideas off of each other. I wanted to actually build small aircraft, because we have a wind farm and we have wealth with that. We actually have people building small planes in our location. They told me I needed about 5 million euros to do it, which I didn't have.

So I started pivoting to a software idea. And why the software did to host that, I just quickly discovered, you know, the amount of energy going into data centers, the amount of, you know, associated issues, and back then, 2015, 16, we were literally just discovering the energy aspect of it. We need didn't discuss, you know, water and land use and all of that.

We really focused on the energy and then we say, "look, well wait a second. You know, we have all this excess of energy. We literally cannot deliver that at this point. So we have a very high share of shutting down our wind turbines when there's just too much energy to move it around. Why not bring the data center as a flexible load close to the production, and enable, you know, sustainable compute

to then send package rather than energy, which is way easier, you know, over the global fiber grids." And that's how I got started and fell into the data industry. Big benefit and big learnings from that stand that I didn't know nothing about data centers. And as an engineer, a lot of things were not adding up. 

We looked at the servers back then, and even then it said, okay, this is good, you know, to run from 15 to 32 degrees. I said "32 degrees? Why? What is data center cooling and why is data center cooling? We don't have 32 degrees in the north." Most likely now, we probably ought to do within eight years.

Karl Rabe: But the important thing was really challenging this, and we started with very little money and we couldn't afford like the proper fancy stuff that all of this data center make, you know, like a chillers, you know, spending electric energy to cool something which really does not need cooling, in my opinion, up to now.

That was the start of this, you know, and so this is, the company of Windcloud is still ongoing. We had what we were, what we had as a huge problem. And I was always, my gut feeling for this was always we need to find a way to be able to compete with the Nordics.

So we have renewable energy, but we need to have it cost effective. And that was something that we tried two or two and a half times, I would say with the, with always having a legal way to access the energy in a proper setting. It was always extremely difficult and extremely frustrating also because the German energy system is very complicated.

It is, you know, geared or developed from a centralized view of this, and is benefiting, you know, large scale industry and large scale energy companies, to putting other terms, as you know, in, you're probably familiar with the, Asterix comics. You know, that far off and north in Germany that probably people, you know, there was a bit suspect, you know, what we're doing there or now we start producing energy and now we also want to use the energy so that is not adding up.

It's very hard and close to impossible to access your own produced energy at scale, you know, which is in an abundance. And that was, yeah, that was something what we always faced, which led to other innovations. So we build the first data center or one of the few data centers to reuse the heat in Germany, putting into an algae farm.

And we trying to create really efficient, PUEs already back then, you know, whereas the industry stranded is quite still quite high in ours. Claim I never had enough money to build a data center with a PUE above a 1.2, or even 1.1. The first servers were cooled with a, you know, a temperature regulated fan, you know, out of the, we built with the same guy who built, you know, a pig stale for my father, you know.

that was, you know, we nearly didn't call it Windcloud. We nearly called it Swines and Servers, 

Chris Adams: Okay. Pigcloud.

Yeah.

Karl Rabe: Yeah, Pigcloud, but it could have been, you know, could have been misleading. And the, so the good thing turning out of that, you know, and going back to that, to those struggles in getting started is that we were forced to uncover a lot of the cooling change and the energy distribution change, which are were not, you know, not really adding up for us.

And that is, you know, still one of the biggest support for us to build efficient data centers and to create, you know, sustainable solutions.

Chris Adams: Okay. Cool. Alright then. So. Okay. There's, I didn't realize anything about the Schweins und Servers aspect at all actually. Would you even, I'm not sure what German is for server would actually be in this context. Was it literally gonna be Schweins und Servers, or?

Karl Rabe: Yes. Some. So, yeah, something like that.

Chris Adams: Okay. Wow. That's, I was not expecting that.

I think Windcloud sounds a bit better, to be honest.

Karl Rabe: Yeah, thanks. No, the brand, the name is great. I think it's still, yeah, I'm very simple like that. You know, we had Windcloud, so we take wind, we make cloud. Now we are have, we are Wooden Data Center. We build data centers outta wood. So we, but there's this, but it's, to be fully honest, is right now, is so to speak,

we call Wooden Data Center, but what we do is try to decarbonize the data center. So wood is obviously, is a massive component of that, but we do see real good effort in the supply chains. Happy to go into that a little later, but there are some examples from fluids. We just found, you know, bio-based polycarbonate for hot and cold containments.

So the amount of components throughout the data center that have, that has a bio-based, ergo, a low carbon alternative is ever so increasing. 

Chris Adams: Can I come back to that a little bit later? 'Cause I just wanna, touch on the

Karl Rabe: Yeah. no.

Chris Adams: So the wind thing, so basically Windcloud, the big idea was putting data centers in the actual wind turbines themselves. So that gives you access to green energy straight away, because you're literally using power that otherwise either couldn't be transmitted because there were, because the pipes weren't big enough essentially in some cases.

And, I guess plus point to that in that if you are already using a building that's already there, you don't have to build a whole new building to put the data centers inside. So there's presumably some kind of embodied energy advantage there because there's a load of energy going into, kind of, that goes into making concrete and stuff that you don't have to do because you are already using an existing, like, building, right?

Karl Rabe: Yeah. So to clarify on that, it is good that you touch on that because there is, this is literally is a bit of a crossover because the company you're referring to is Wind Cause, which is a good friend of ours and they are using the turbine towers. 

Chris Adams: Ah, 

Karl Rabe: They can do so because they use a little bit different type of turbine.

And they're also based in the south of Germany, we had the same idea because it's also very difficult to build next to a wind farm. The big difference is that the towers used at Wind Cause, they are concrete and they have quite a lot of space. They're about 27 meters wide. because of the initial, discussion that we have onshore, or offshore conditions onshore, we have steel towers, which are shorter and hence don't have this big diameters.

You know, we build tall. And so we always had the challenge of still needing a data center. And so that's where our learnings inspirations came from For Wooden Data Center. But we still tried to reuse existing infrastructure. So we were at one point within the Windcloud journey,

I was the co-owner of a former military bunker area. And so we wanted to place within those long concrete tubes, we want to place data centers in order to yeah. Have a security aspect and don't need, you know, a lot of additional housing or even bunkering. And there's obviously the dodging bullets where has spent a lot of concrete and steel concrete in order for those facilities.

Chris Adams: I see. So you're reusing some of the existing infrastructure, so rather than building totally new things, you're essentially reusing same, you're reusing stuff that's already had a bunch of like energy and emissions spent to create it in the first place. I see. Okay. All right. So,

Karl Rabe: And, back then, you know, also to, because it's such a short time back then, really need to emphasize that we were, we really, you know, only had a hunch and a feeling, oh yeah, sort of has CO2 associated to it and probably also the building of a data center.

You know, we have, we really, it was so hard to quantify, and I think we still, carbon accounting is still, is somewhat of, not wizardry, but it's really hard to pull the right numbers. You know, only two years ago at the OCP Summit, so in a Google presentation, the range that they mentioned, you know, for steel and concrete carbon was, you know, 7 to 11 for equally both. So the range of the total uncertainty, I feel, is quite high. You know, and this is the biggest, one of the biggest and most funded, best funded organizations in the world. You know, we're still not being able to get it more concrete, you know, and that's something we really need to work with the industry and supply chains in order to be even aware to specify the problem.

Chris Adams: So, can I unpack that for a second before we talk a little bit about this? And so you're basically saying even the largest companies in the world, they don't necessarily have that good access to know how, what the carbon intensity of the concrete they've used in one data center compared to another one,

it can quite, it can vary quite a lot. Is that what you're saying there?

Karl Rabe: So this was basically specifying the global numbers for steel and concrete. So, I do believe that we have now relatively good visibility for our own builds and projects and also what we do now moving forward. But to really try to grasp the global problem of it, that was still, you know, two years ago was still had this high uncertainty, you know, 'cause we were working with numbers,

maybe they're now five years older, we don't know the complete, you know, build out of every city, every building globally. You know, it's just a lot of guesswork in that, globally. And so I especially believe that although we, Wooden Data Center, the amount of innovation that is put into concrete, you know, has the potential to drastically reduce that for buildings.

You know, the, it was a, it's definitely still a huge problem in, for the data quality and the data, yeah, the emissions, you know, guesswork that's in there, you know, and a lot of those things are based on scenarios, you know, and those are getting ever so more real. But the best example for Wooden Data Center is, there's a comparison comparing a steel concrete building to a CLT one,

Chris Adams: Yeah. 

Karl Rabe: and it is assuming that after 20 years, it's only living for 20 years, which, you know, can easily be 200 years, but that afterwards it is being reduced into, you know, building chairs or tools or toys. But if you take then the CLT and burn it, then obviously you have a zero sum gain. Every, all the carbon that was stored.

It's Cross-Laminated Timber, you know.

Chris Adams: Yeah. So this is the kind of like the special, the, this, it's a special kind of, essentially like machined timber that is, that provides some of the kind of strength properties of maybe steel or stuff like that, but is made from wood, basically, right?

Karl Rabe: Correct. So we need to stretch the importance that this is actually a material innovation. It's a relatively young material based on a, I think a thesis, PhD thesis from Austria. And so we only have CLT or cross-laminated timber for about 25 years. 

Chris Adams: Oh, I see.

Karl Rabe: Or maybe now 26, years.

So the, you probably are familiar, or you have seen there are those huge wooden beams in, you know, storage buildings. 

Chris Adams: Yeah. 

Karl Rabe: Those are called GLT, like glue laminated timbers. And the difference is those boards are basically glued in one direction and they're really good for those beams or for posts.

But to have like ceilings, walls, and roofs, those massive panels, you now have the material of cross laminated.

Chris Adams: Oh, okay. In both directions, right? Yeah.

Karl Rabe: Correct. And those now enable like full massive wooden buildouts. And that's something, and so the biggest challenge is that we, if we say wood, then the association we probably will touch on now or later is fire.

Chris Adams: Yeah. 

Karl Rabe: But in reality, in nature, we don't have those massive panels which don't, you know, just flame up. They have, they're fully tested and certified to glim down, which is, you know, they turn black and then they slowly, you know, in a thousand degrees, they slowly, you know,

shrink 

Chris Adams: Like smolder, right? Yeah.

Karl Rabe: Yeah. And so, but, the, how we design data centers is basically factor in this component, and we are able to create really fire secure data centers built out of those new wood materials basically.

Chris Adams: Okay. All right. So a lot of us are typically thinking of data centers as things made entirely with wood and with steel, concrete and plastic all over the place. And essentially you can introduce wood into this place and it's not gonna burn down because you have this material, which is treated in such a way that it is actually very fire resistant.

And that means you could probably replace, I mean, maybe you could talk to him a little bit about which bits you can replace. Like, can you rep, would you replace like a rack or a wall or like a roof? Maybe we can talk about that so we can like, make it a bit easier to kind of picture what this stuff looks like.

Karl Rabe: No, absolutely. I'm afraid I'm still, always very liberal in sending out samples to my clients, you know, but I don't have it here in my hand, but, so that is a very good the question, is basically like, if we would talk like slide deck or something that I'll try to show in terms of scope one, two, and three, what we can do and what we have now, and that it's like the, biggest component is in obviously the housing. You know, what is your building or your room of a data center? When you are touching on existing buildings we CLT is also ideal for building and building concept of existing large storage or logistic buildings to put in data centers.

We can build that up quite quickly out or create rooms very quickly in those, and there is other huge advantage of CLT is that we get those pre-manufactured and they just fit,

Chris Adams: Oh, like stick them together like Lego rather than have to pour 

Karl Rabe: concrete?

Yeah. Little, Yeah, a little bit. You need like, a little bit of leveling foundation.

If you have an existing floor, still, some datas, you know, preferred to in the greenfield also have a new floor. But that is is something that it helps to, with those, we can create the IT room relatively quickly and then have the build out of those averaging up to 40% quicker times than traditional steel sandwich concrete, you know, data center.

So it is enormously easy to work too. It's very precise to pre-design and pre-manufacture and then very easy to work with. If there's something, if there's a problem on site, you know, you just crank out the chainsaw and adapt and adjust.

Chris Adams: Okay. Just to carve it down a bit.

Karl Rabe: And yeah, so to speak. But once you have then those assembled and secured, it has like a lot of mass to it and a lot of volume to that which creates very good fire protective

physical resistance and availability properties. And that is something that we now, it's really being seen as one of the core benefits. You know, the speed what we can build this out.

Chris Adams: Oh, ok.

Karl Rabe: We have introduced wooden racks, and we also see more and more attention for those.

Chris Adams: Wait, sorry. Can I just, you said wooden rack, like as in the big steel rack that holds the servers themselves, you could, that could be made of wood as well now, so you'd have like a rack thing holding a bunch of servers, right?

Karl Rabe: Correct, So we built this also. We have, one of our clients has send us like a server casing and ask to also think about to do the casing, but we probably, we're not a hundred percent there yet. In order to do that, we would have, we would've an idea, in terms of the spirit of OCP, which is, you know, like,

reduce and cut out stuff. You know, one vision of that would be just a wooden, you know, board where you have dedicated spaces. You slide in your main board, connect power, connect liquid cooling, have fans on the back and then cycle through only the boards. Remove, you know, not even fancy, but just base frames of a server.

But right now the, it's a combination, for the 19 inch standard and also the OCP standard, to use, you know, reduce up to 98% of the steel in those constructions and then only have functional parts in order to stick in the servers made from steel railings and have then wooden frames.

And we do that for the OCP format, which is very popular. We get a lot of the special requirements because we are the only ones who producing like a small version of the rack, which, the OCP has a lot of advantage, but the base rack format is a two meter 30 high, which is like a really hyperscale, you know, mass density approach.

Which doesn't fit even through the doors of most data centers I know, you know, they still have relatively, you know, standard two meter high doors or able to fit in like a 42 inch rack. But you need like a very special facilities because those racks come also pre-integrated and then you roll them in place.

So you need a facility that has high doors, ramps with small inclines, you know, or no ramps at all, in order to be able to place a fully integrated rack. We started building OCP racks because back at the time only hyperscalers were really getting those, and we wanted to do more of this open compute format and were able to offer that.

And the version three rack, you know, was a good candidate to convert to a wooden based structure.

Chris Adams: All right, so we'll come to that a little bit later because I actually came across some of your work when you were building, designing some of these on YouTube so people can see what all this stuff looks like. But if I just come back to the original question, essentially, so it sounds like you can replace quite a lot in a data center.

So you can replace the shell of the building, like literally green the shell by replacing the concrete, which is one of the largest sources of, you know, creating concrete and cement is one of the largest sources of emissions globally. So you can switch, you can move from a source of emissions to, is it a sink?

Cause CO2 and carbon gets sucked out of the sky to be turned into trees. So you've gone from something which is a source to a sink and that, and you can replace not just the walls, the outer building, but also quite a lot of the actual structure itself. Just not the servers yet.

So that's probably like a, I mean, maybe I could ask you then like, If I'm switching from maybe regular concrete and regular steel, I mean, is there any, like, do you folks have any idea about like what the kind of changing quantitative terms might actually be if I was to have like an entirely concrete, entirely steel data center and then replaced all of that with, say, wooden alternatives, for example? Like is it like a 5% reduction or is there any, like, what kind of changes are we looking at for the embodied figures, for example? 

Karl Rabe: So the conservative industry figures are somewhat off between minimum 20%, only having the production change up to 40%. So Microsoft, we, the good thing also we have to mention is that we are an industry now. Microsoft announced those productions I know the other hyperscalers are looking at that.

We only, in Germany we had two other companies started getting into construction. That's why it's for us really important to be on the decarbonization path.

Chris Adams: Ah, I 

Karl Rabe: see. 

So we do come with our own data center, even concepts and philosophies, which I can talk about a little later.

But coming back to the point it is still very hard to quantify. And the, but really positive things about carbon accounting or calculations, as I mentioned, we now have as a data center, we have this negative component, which I have to laugh 'cause an engineer immediately and said, can we then just use more wood?

You know, can we make the wall thicker? You know, obviously yes, you could do that, but there's a cost to that and there's also, you know, it be betrays the idea, you know. But, the really exciting thing is that I now go to show, from show to show, and I was two weeks ago in London

and just on the flight somebody showed me, a picture of an air handling unit inside of a wooden enclosure. And I was chasing an hour through the London show, 'cause I assumed it was there, but it was on a, it was on a different show. but that is the kind of things that we can really think about is enclosures.

So also we have started, we have one, well, for the OCP reg or for this AI build out, we have also created a rear door, which is, so to speak, a wooden rear door. So the fans are traditional, the heat exchanges obviously needs to be traditional, but it is also like a micro aluminum micro channel heat exchanger, which is derived from other industries, which is, you know, helping mass production, reducing cost, reducing emissions.

And that is the other thing that is happening in the industry that 

we're trying to find, not data center specific solutions, but rather find mass produced industry solutions and adapt them to the data center in enhanced reducing cost and time.

Chris Adams: Alright. So in the same way that basically cross laminated timber and the use of wood is something that has been in use in not just in the data center industry, like people make, what are they called? Are they called plyscrapers? You know, skyscrapers with wood. Plyscrapers.

It's, so the idea was that, okay, things which are made, being made in volume here can be made more efficiently and like this is one way that you are adapting 'em to a new domain.

And it may be that if people are getting much, much better at making say very efficient heat pumps, 'cause they can cool things down as well as heat them up, that might be another thing you're looking at. Say, "well actually that might be able to be used in this context as well." Okay. Alright. And if I am, so if I go back to the original thing about saying, okay, we're looking at possible savings maybe 20% up to like possibly 40%, like that's the kind of

Karl Rabe: yeah. That's the range that we have, you know, I think, so the problem is do we, if, did Microsoft evaluate with IT or without IT? So for the facility, I think we can potentially come to net zero approach, which we, you know, but by first principle, I think we can at least achieve realistic reductions to let's say 80, 70-80, 85% with those tools that are set, you know, basically the easy steel replacements, the, like, the rack, the enclosures, the housing, fluids is something we have. There's a very interesting, you know, no-brainer replacement for fossil diesel on backend generators.

It's a liquid called HVO

Chris Adams: Yeah. Let's come to that in a second actually. 'Cause I did wanna ask a little bit about some of the things you can do for the fuel here. So maybe if we just, so basically the, so there are some savings available there and these should be something that you could, some, this is something that should show up in some kind of numerical description.

So if you had like, maybe two data centers and one was using wood in strategic places, then the embodied carbon should be lower. So if, I mean, if I was looking for this there like a label to look for or a standard I can look for? Because in the Green Software Foundation we have this idea called Software Carbon Intensity, which includes like the carbon intensity of the energy you use and stuff like that.

But they also look at the building itself. So theoretically, if you had a wooden data center and a bog standard concrete data center, you know, if you run your code in the greener data center, you would probably have a better score if you had access to these, the data or stuff like that. Do you know, like, do, any places share this data or have like a label for anything like this?

Karl Rabe: They definitely share the data. I, for example, so we definitely also need to Eco Data Centers in Sweden's and we, which were, you know, basically we approach to them. Our whole world was shook. You know? It's like, oh, so we come from this energy perspective, but they didn't have idea and they build it, you know, sustainably. They build it sustainably.

So we need to change, you know. That was, you know, it was a huge eyeopener. And they also are the few first ones to, I'm not sure if they used like the LCA method, but they were quantifying the embed carbon and are certifying to you annually too, as a client, which I think is the way to go.

And we need to figure out how to standardize that. I assume there's potentially a standard that we can use. I know that other data center providers are building sustainably and putting this effort forward. But we don't have a unified label yet, I'm afraid.

Chris Adams: Okay. Well this

Karl Rabe: I know that some, also challenge, like, there's like a data center climate neutral act and some of them specifically exclude scope three, which, you know, I know where they're coming from.

Also in Germany and Germans, you know, they're all about energy efficiency. They love to talk about, you know, just the, energy and the scope two, basically. But then, you know,

Chris Adams: Most of the

Karl Rabe: missing out, this dimension, you know,. Missing out the dimension is being faithful to your girlfriend or wife, you know, like three days out of a week.

You know? It is, it's not

Chris Adams: You are not showing the full picture, right?

Karl Rabe: Yeah. You're not, doing it at all basically. Right. I would probably, you know, just need to Google it and there are, you know, building labels that you could be used in construction. Quantifications, I'm sure, but there's not yet like a data center specific label.

There is good work also in OCP to do metrics and key performance indicators, and they're all looking at that and there is, I think they're trying to build towards something like real, like true net zero.

Chris Adams: Oh yeah. Okay.

Karl Rabe: But... 

Chris Adams: So there are some, so there are some initiatives going on to kind of make this something that you could plausibly see, and, but it's quite early at the moment right now. So like, let's say that I, you know, we spoke before about, okay, I can run my computing jobs in one data center or I can choose to run it somewhere else.

These numbers don't show up just yet, but there is work going on. Actually, I've just realized there is actually a embedded carbon working group inside the OCP who have been looking at some of this stuff. So maybe we'll share a link to that, because that's actually one of the logical places you'd look for that.

Okay,

Karl Rabe: And they do real good work. They do a lot of good initiatives, happening there. There's also, it's Swiss from the Swiss Data Center Association. They also have a label, that is looking at some of this, and they want also to include scope three.

So this is coming up, but it's, not as easy as, you know, having an API, you know, pushing it to the software developer and saying, look, we have this offset because this was constructed, you know, with concrete or steel, and this is, you know,

Chris Adams: Okay. So we're not there yet, but that's a, that's the direction we might be heading towards. Okay. Alright. We'll add some links to that. And now I'd like to pick up the other thing you mentioned about HVO and stuff like that because you spoke before about, you know, Windcloud or wind node and like data centers running in,

or like, you know, relying on wind right now, we know it's a really common refrain that the wind doesn't blow all the time. And like it's news to some people that sun, that's, you know, it is not always sunny, for example. So there'll be cases where there'll be times where you need to get the power from somewhere and, you know, in the form of backup power. And like loads of data centers, you said before they rely on like fossil diesel generators, right.

And that can be, it's bad from a climate point of view, it's also quite expensive, but it's also terribly really bad from an air quality point of view as well, because, you know, people are up, you know, you can see elevated cases of like asthma and all kinds of respiratory problems around data centers and things like that.

But you mentioned there's options there to kind of reduce the impact or have like more responsible options there. Maybe we could talk a little bit about like what's available to me there if I wanted to reduce that part, for example.

Karl Rabe: No, happy to go into that. That is something that we are now thinking about quite heavily this year. And we're already presenting on two occasions, a sense. So the easy options in order to reduce your carbon on the scope one part for data center, which is basically, you know, that's just the direct burning of fossil resources and that is the testing of your backup generators. 

The easy option for that is this second gen diesel, HVO 100. And the, when I realized the key feature of this fuel, which about 15% more expensive, is that it doesn't age. Fossil diesel and especially, you know, biodiesel, the first generation and fossil diesel with biogen, there's always, in Europe there you have a certain degree of mixed in of this, it ages through bacteria biologically.

So it's degrading. So, the, which is, you know, really bad because this diesel sitting there in a tank, you run it half an hour every two weeks, and you maybe change the fuel filter twice, once, twice a year. But if you really have an issue, you know, all of a sudden you use this diesel 

for four hours and then your system, your filter clocks, and you still have a problem, right? If

Chris Adams: So your backup isn't a very good backup.

So backup needs to be a good backup. Yeah.

Yeah. 

Karl Rabe: Yeah. so your backup can run

Chris Adams: you had one job, right? Yeah.

Karl Rabe: Yeah. Yeah. And so, how it's mitigated is people try to use 'pure' diesel or, you know, heating oil, you know, which is not so prone to it, but still ages. They are recycling or, you know, really pumping out the fuel and pumping it in again every three years or they continuously filter it.

All of this is either adding energy or cost. And so, the, this new form of biodiesel, which is, you know, your old frying fat, cracked with hydrogen to, is it looks very clear and it's very chemically treated that it's not really aging. People don't know really yet how long it stays.

They certified 10 years, potentially it's stays, good longer and is also burning cleaner. So

Chris Adams: Ah,

so it'sn't going to be bad 

like bad air and stuff as well then?

Karl Rabe: Yeah. So for the majority of your enterprise IT, your standard data center that's around you, you know, cutting out the whole AI discussion, probably that's the easiest way to do something about that.

This is like a drop in replacement. You just, you know, you empty your tank, you put it in, or you burn your old fuel and put a new, that is something that is, you know, easily increasing the availability of your facility and you can change with that. 

Chris Adams: Can I just try to like summarize that? So, because I don't work with data centers on a daily, so there's like basically fossil diesel, the kind of stuff that, you know, you might associate with dieselgate and like all kinds of bad air, air quality issues. And then the, kind of the other option, which is maybe a little bit more expensive, you said around 15%,

there's something called HVO, which is essentially like biodiesel that's been treated in a particular way to get rid of lots of the gunk so it burns more cleanly and works better as a reliable form of backup. So the backup is actually a decent backup rather than a thing which might not be a very good backup.

Oh okay. So that's like one of the things, and that's like the direction that we might be moving towards and that's kind of what we would like to see more of for the case where you need to rely on some kind of liquid fuel power. Right.

Karl Rabe: Yeah.

Chris Adams: Okay.

Karl Rabe: That is, I think is for most people, you know, just a very easy low hang fruit to just replace, you know, it does not, you know, most engines are certified for, nowadays, all engines run on it, you know, it's, it has the same, Yeah, criteria, properties like traditional diesel, the only thing that's different is it's 4% lighter, you know?

Chris Adams: Oh, I see.

Karl Rabe: So that's the only real on the spec sheet 

Chris Adams: Oh, okay. Alright. So if I may, so that's one of the options. These, so you can replace fossil diesel with essentially non-fossil cleaner, slightly less, you know, less toxic diesel. So that's one thing that you might have in for your backup. Now, I've heard various people talking about, say hydrogen, for example.

Now hydrogen can come from fossil sources. So people, most of the time, actually, most hydrogen does come from basically cracking natural gas or methane gas, but it can come from green places. And that's why is, that's another option that you might have to generate power locally.

Karl Rabe: Is that something that people tend to use? 

So I think the best, the best reference for hydrogen is like the champagne of our energy transition. You know, we need, we need to put in a lot of energy to put, to produce it. It's not easy to store, that we need a lot of facilities to actually create green hydrogen.

Karl Rabe: The majority of hydrogen is not green hydrogen, but it's gray or blue, which is basically

Chris Adams: like a carbon capture hydrogen, which is still a bit questionable. Yeah.

Karl Rabe: all based from fossil tracking, you know, so it's, it potentially, you know, you, you also have the same goal. 

everything that we do for our clients is under this extremely short impact of time.

You know, we have solve everything within now, within five years time, not even five years. Right. And so that's also something that I'm always, you know, spark a good discussion. When we talk about SMRs, you know, have the big pushback for nuclear over in the US, and also in Europe we have voices for that.

And the short answer is, the three reasons I don't believe in it. They're not quick, you know, they're not cheap. Two projects were just, a year ago, there were two potential very, you know, hopeful projects for SMRs were canceled in the US, and half a year later it was a big thing.

The big solution. like, what changed, you know? And then the third point is that is the, very German, perspective, you know, all the fears or the, challenges around the fuel, like getting it mostly 70% from Russia or, then the waste, you know, dumping it somewhere is not solved, still.

And so, this is not a 2030 technology basically. 

That's my, the point what we can do and what I'm happy also to link, there's a good article from some of the, hyperscalers looking into solar combined with batteries, combined with gas based backup. The gas based has the one flexibility that it can start fossil, can move to bio, and potentially also can run on hydrogen. So this is, in terms of the speed with which we are now deploying hundreds of megawatts, you know, every data center for AI is now, you know, 100, 200, 300 gigawatts.

You know, things that we did not,

yeah,

yeah. So it's things that we, you know, like yeah, we're discussing, you know, five or one to five gigawatts for the large people. And every other data center is all of a sudden is now a hundred megawatts, which used to be like a mega facility, just two years back.

So 

that build out can only really be achieved not with grids or interconnects, those are too slow. This can only be basically with micro grids.

 

Chris Adams: I see. Okay.

Karl Rabe: Helping, you know, that are battery backed and gas based backed. And the big advantage of this is that if we think about the data center, 

traditionally a data center is a data fortress, right?

You don't get in, data doesn't get out. It is, you know, is like a bank, you know, in terms of the security measures to do that. And also all of the infrastructure was handled that way. But if we are thinking about the UPS, and the genset not being sitting straight at the data center or only sitting straight at the data center but technically belonging to the utility and being able to provide flexible power, you know, because we have this, as mentioned, underlying flexible build out of renewable energy, and we need, you know, reliable switch-on power, which data centers all have. And so if we can put those together, there's a little bit of this working together, finding the right location where it would make most sense, and then allowing for SLAs and with clients to bidirectionally use batteries, gas turbines,

Chris Adams: Oh, I see.

Karl Rabe: engine power.

This would, you know, help our, yeah, help us to transition, especially if we go into, you know, renewable shares, 60% and above and at latest from 80% we need those backup technologies. And then, and that is coming back to the question of hydrogen. Hydrogen is a technology that would, is so expensive that it would need to run all the time, basically.

With renewable energy, we have high loads of

abundance 

of energy and only need short times of flexible energy generation for which gas and batteries is virtually ideal. And so we promote this idea of an energy-integrated data center, which has the electrical part supporting into the grid and is also, you know, taking advantage of the heat reuse, especially for liquid-cooled facilities in order to give heat out.

And the benefit of that is not only from an economical perspective, but also we see more and more discussions about not in my backyard. If a data center is energy integrated, it's not a question, you know, it's a must have. And there's also a reason why it needs to be there, you know, in order to be able to stabilize the, your town grid or your local area.

And so that's what we are trying to promote. We got a lot of good feedback and we see the first, hopefully we'll have the first data center realized with a medium voltage UPS this year, which is like a first step in moving the availability components of a data center, the batteries and the gen sets to a higher area, which, a lot of the cost in a data center is from the low voltage distribution.

The power that you put in the batteries is also first transferred down, and then it's moved, you know, through the data center until it sits in the battery and then needs to go out. And all of those are rectification steps. And all of this makes, yeah, all of

Chris Adams: You lose, so do you lose power every single time you switch between them? Oh, okay. So it sounds like you, there's a shift from, like, data center as a fortress where, you know, you could do that before to like something where you have to be like a bit more symbiotic with your local environment because for a start, if you don't, you're not gonna allow it,

you won't be allowed to build it. But also it's gonna change the economics in your favor if you are prepared to like play nicely and integrate with your, essentially be a good neighbor. All right. That seems to be what you seem to be suggesting. 

Karl Rabe: That's a perfect analogy. Having like a good neighbor approach. Saying, "look, we are here now, we look ugly, we always box, you know, but we help, you know, powering your homes, we reduce the cost of the energy transition, and we also heat your homes."

 You know, and that is then, is then a relatively easy sale.

Chris Adams: Okay. So that points to possibly that, honestly, that points to quite a different strategy required for people who are, whose job it is to get data centers built. They need to figure out how to honestly relate to communities and say, well, which bits are we supposed to be useful? Rather than the approach that you come to sometimes see where people basically say, "well, we're not even gonna tell you who, it is or who the company is, but we're gonna use all your power and use all your water." 

That approach's days are probably numbered, and that's not a very good strategy to use. It does make more sense to actually have a much more like neighborly approach and these are maybe new skills that need to be developed inside the industry then.

Karl Rabe: Absolutely correct. And so, you need the, you need an open collaboration approach to that, and that is, you know, mirrored, so we trying to be a bit of an example there. And if you go, if you talk about, you had a good point in there, which we usually don't have a lot of time to expand on,

but I think podcast a good format for that. You ask like, where do you get the ideas or what's the guiding star on that? And so, 

I was fortunate to be an exchange worker, you know, on a farm in Canada. And they introduced to me the idea of holistic management, which is like a, basically, decision making framework, that is based on financially viable, socially viable, and economically viable. 

And so those three bases are necessary in order to create sustainable decisions or holistic decisions. Those need to be short and long term viable.

And that has been, you know, my guiding star as an entrepreneur and really being able to cut out those things. You know, there's a lot of startups, especially in Germany. We had those Berlin startups who all came from a business school and all of their ideas worked on an Excel sheet,

always cutting out like a social perspective, you know? And so that was, you know, that's the opposite basically of what we are trying to do. And this framework was found by a farmer who first applied it to grass management and cattle farming, technically. But it is, and it is wildly interesting what he's able to do. He's basically retaking, stopping desertification and reversing effects in subtropical, semi arid areas. 

Yeah. So we'll definitely put that in the notes.

It's a tED Talk from Alan Kettle, which I think he's still alive. He's in his, he must be 90 now. And it's fascinating. But that was a guiding star. And in order to promote our ideas, you know, a lot of our designs, you know, we put on YouTube, but we also put the files up.

The, racks, you know, you can download the CD files. There's, we believe they're created with open source tools. So especially in engineering, we only recently really have powerful open source tools for CAD, for single line diagram. So we can give the source files with that.

And that is is something how we believe that open collaboration and openness helps to build, you know, the trust 

Chris Adams: Ah, 

Karl Rabe: to build with speed and to really work together, you know? And that's what we get mirrored in the Open Compute Foundation. Yeah, that is something that we believe is, for challenges that we face as humanity,

you know, I believe that only this open approach, and especially an open source, open hardware, open data, framework can help us.

Chris Adams: All right. Okay, so we're coming up to time and I just wanted, and you did alluded to it a few times and I just wanted to provide a bit of space to let you talk a little bit about that before we kind of finish up. You spoke a few times about the fact that these models, when you work, bunch of designs for the racks and things are like online and available, and did you say that they're on YouTube, like people can see the videos of this or can like download like something in blender to mess around with themselves or work with it? Maybe you could just expand on that a little bit because I haven't come across that before and

Karl Rabe: Okay, sure, sure. So, yeah, we initially, when we started, you know, we designed everything and we put it, we still need to, shamefully, we still need to put, do the push for, to GitLab and GitHub. We use right now, we put those model on a construction setup, of course, called, GrabCAD.

Chris Adams: Mm-hmm. 

Karl Rabe: And for our, it, you know, it's not only our own thoughts to open source this and to build the trust, but it's also our biggest, easiest marketing tool. You know, create a model, publishing it, put a video tape. We are a bit behind. We have a lot of new and great ideas and things to share.

But that's how we approach it, you know, we'll come up with idea, put it out there and, also, you know, make ourselves criticizable, you know, we'll, we are the only ones comfortably saying, look, we have the best data centers in the world, 'cause you can go, you can download, you can fact check our ideas, and if you have something against it, you know, just give us a feedback.

And we are open to change that. And this way forward, you know, helps us also to approach the biggest companies in the world. They really like this open approach, you know, and they're happy to take the files in the models and to work on that.

Chris Adams: So you basically have like models of like, this is a model of

Karl Rabe: Our rack, you know, this is our module data center. These are ideas behind that. And so that's how we are moving this forward. So people can approach this, they can download, they can see if it fits. They can make suggestions. 

Chris Adams: And like see if it's tall enough for the door and all of the basic or the practical things.

Karl Rabe: Yeah. All those things, you know, and see, okay, we have smaller data center, oh, the base design doesn't fit in this setup, or we need to change something where we place, you know, the dry coolers or something like that.

And so that is really, you know, really good feedback and sparks discussions.

Chris Adams: Yeah, I haven't heard about that before. All right. Well, Karl, thank you so. This has been a lot of fun. Now, we've come up to time and I really enjoyed this tour through all the stuff hap that happens below the software stack for engineers like us, for example. If someone does wanna look at this or learn about this or maybe kind of check out any of the models themselves, if they wanted to build any of this stuff themselves, where should they look?

Like, how do we, where do people find you online or any other projects that you're looking at, you're So, working on?

Karl Rabe: So the best thing technically to, is LinkedIn. This is, you know, our strong platform, to be honest, we are very active there. We publish most there. The webpage is still under construction. You know, people already understand what we do from going to that.

LinkedIn is great. Look, go and, you know, trying to reach us and see what we do at the Open Compute Foundations is also often very great. But yeah, just technically why Google is very easy to find us on LinkedIn and to reach 

Chris Adams: So Karl Rabe on LinkedIn, Wooden Data Center, there aren't that many other companies who are called Wooden Data Center. And then for any of the Open Compute Project stuff, that's the other place to look at where you're working. 'Cause you're doing the open compute modular data center stuff.

Those are the ones, yeah?

Karl Rabe: Yeah. Correct.

Chris Adams: Brilliant. Karl, thank you so much for this. This has been loads of fun and I hope that we've had listeners follow us along as well to see all the options and things available to them. Alright, 

Karl Rabe: It was a pleasure. Thanks so much. And, 

Chris Adams: Likewise, Karl. And, hope the wind turbines treat you well

where you're staying. All right, take care mate.

Karl Rabe: Yeah. Thank you. Bye bye. Cheers.  

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Host Chris Adams is joined by special guest Karl Rabe, founder of WoodenDataCenter and co-lead of the Open Compute Project’s Data Center Facilities group, to discuss sustainable data center design and operation. They explore how colocating data centers with renewable energy sources like wind farms can reduce carbon emissions, and how using novel materials like cross-laminated timber can significantly cut the embodied carbon of data center infrastructure. Karl discusses replacing traditional diesel backup generators with cleaner alternatives like HVO, as well as designing modular, open-source hardware for increased sustainability and transparency. The conversation also covers the growing need for energy-integrated, community-friendly data centers to support the evolving demands of AI and the energy transition in a sustainable fashion.

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TRANSCRIPT BELOW:

Karl Rabe: That's a perfect analogy, having like a good neighbor approach saying, "look, we are here now, we look ugly, we always box, you know, but we help, you know, powering your homes, we reduce the cost of the energy transition, and we also heat your homes." 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello, and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. How do you green the bits of a computing system that you can't normally control with software? We've discussed before that one option that you can do might be to shift where you run computing jobs from one part of the world to another part of the world where the energy is greener.

And we've spoken about how this is essentially a way to run the same code, doing the same thing, but with a lower carbon footprint. But even if you have two data centers with the same efficiency on the same grid, one can still be greener than the other simply because of the energy gone into making the data center in the first place and the materials used. So does this make a meaningful difference though, and can it make a meaningful difference? I didn't know this. 

So I asked Karl Rabe the founder of Wooden Data Center and Windcloud, and now increasingly involved in the Open Compute Project, to come on and help me navigate these questions as he is the first person who turned me onto the idea that there are all these options available to green the shell, the stuff around the servers that we have that also has an impact on the software we run.

Karl, thank you so much for joining me. Can I just give you the floor to introduce yourself before we start?

Karl Rabe: Thanks, Chris. This is an absolute honor and I'll have to admit, you know, you're a big part on my carbon aware journey, and so I'm very glad that we finally get to speak. I'm Karl, based out of North Germany. We initially, I always say I had a one proper job. I'm a technical engineer by training,

and then I moved into the data. Then I fell into the data center business, we can touch on it a little later, which was Windcloud, which remains, which was data center thought from the energy perspective, which is a very important idea in 2025. But we pivoted about four years ago to Wooden Data Center, probably can touch upon those a little later, in also realizing there is this supply chain component to the data center.

And there are also tools to action against those. And I'm learning and supporting and providing, you know, as a co-lead in the data center facilities group of the OCP where we work, you know, with the biggest organizations directly in order to shape and define the latest trends in the data center

and especially navigating the AI buildout in somewhat of a, yeah, sustainable way.

Chris Adams: Okay, cool. And when you say OCP, you're referring to the Open Compute Project, the kind of project with Microsoft, Meta, various other companies, designing essentially open source server designs, right?

Karl Rabe: Correct. That is the, initially started by then Facebook now Meta, in order yeah, to create or to cut out waste on the server design. It meanwhile involves and grew into cooling environments, data center design, chiplet design. It's a whole range of initiatives.

Very interesting to look into. And, happy to talk about some of those projects. Yeah.

Chris Adams: All right, thanks Karl. So if you are new to this podcast, my name is Chris Adams. I am the director of technology and policy at the Green Web Foundation, a small Dutch non-profit focused on a fossil free internet by 2030. And I also work with the Green Software Foundation, the larger industry body, in their policy working group.

And we are gonna talk about various projects and we'll add as many all the show notes to all the links we can think of as we discuss. So if there's any particular things that caught your eye, like the OCP or Wooden Data Centers, if you follow the link to this website, to this podcast's website, you'll see all the links there.

Alright then Karl, are you sitting comfortably?

Karl Rabe: I am sitting very well. Yeah.

Chris Adams: Good stuff. Alright, then I guess we can start. So maybe I should ask you, where are you calling me from today, actually?

Karl Rabe: I'm calling you today from the west coast of the North Sea Shore in northern Germany. We are not a typical data center region for Germany, per se. We, which is Frankfurt, you know, 'cause of the big internet hub there. But we are actually located right within a wind farm.

You know, in my home, which is, initially was, you know, home growing up and turned to my home office and eventually to what was somewhat considered the international headquarter of Wooden Data Center. Yeah, and we're very close to the North Sea and we have a lot of renewable power around.

Chris Adams: Oh, I see. Okay. So near the north of Germany, near Denmark, where Denmark has loads of wind, you've got the similar thing where, okay. So

Karl Rabe: Yeah, absolutely. Yeah.

Chris Adams: Oh, I see. I get you. So, ah, alright. For people who are not familiar with the geography of like Europe, or Northern Europe in particular, the north part of Germany has loads of wind turbines and loads of wind energy, but lots of the power gets used in other parts of it.

So, Karl is in the windiest part of Germany, basically.

Karl Rabe: That's correct, yeah. We basically have offshore conditions on shore. And it's a community owned wind farm, which is also a special setup, which is very easy to get, you know, the people's acceptance. We have about a megawatt per inhabitant of this small community.

And so this is becoming, you know, the biggest, yeah, economic factor of the small community.

Chris Adams: Wow. A megawatt per, okay, so just for context, for people who are not familiar with megawatts and kilowatts, the typical house might use what may be about half a kilowatt of constant draw on average over the year. So that's a lot of power per person for that space. So that's a, you're in a place of power abundance compared to the scenario people are wondering where's the power gonna be coming from? Wow, I did not know that.

Karl Rabe: No, that, is, yeah, that is the, so it's a bit of that background, so to speak. We are now trying to go from 300 megawatts to 400 megawatts. There has been, you know, Germany's pushing for more renewable energy, and we have still some spots that we can, under new regulations now, build out.

And the goal or the big dream of our organization, the company running this wind farm for us is trying to produce a billion kilowatt hours per year. And so we're now slightly below that and we're trying to, Yeah, add another, yeah. For, we need to reach probably another 25 percent more production. And, it is, so to speak, you are absolutely right, we are in an energy abundance and that was one of the prerequisites for Windcloud. 'Cause you know, the easiest innovations, is one and one is two. And so we have in, we had energy, I was aware that we also had fiber infrastructure in the north to run those set wind, parts.

So we said, why don't we bring a load to those? That was the initial start of Windcloud.

Chris Adams: Okay, so maybe we should talk a little bit about that. I hadn't realized the connection between the geography and the fact that you're literally in the middle of a wind farm, which is why this came together. Okay. So, the, so as I understand it, and now this makes sense why you are so involved in Windcloud.

So for context, my understanding of Windcloud is it's essentially a company where rather than like connecting data centers via big power lines to like somewhere else where the actual generation is miles away from where the data centers are, the ideals instead was to actually put the data centers literally inside the towers of the wind turbines themselves.

So you don't need to have any cables and, well you've obviously got green energy because it's right there, you're literally using the wind turbine. So, apart from this sounding kind of cool, can you tell me like why you do this from a sustainability perspective in the first place?

Karl Rabe: Yeah, so the way we discovered that I wanted to, and this is the, probably the biggest reference that I can give on the software developer front, and I came out of a study in the UK. We had a really nice cohort.

We were constantly bouncing ideas off of each other. I wanted to actually build small aircraft, because we have a wind farm and we have wealth with that. We actually have people building small planes in our location. They told me I needed about 5 million euros to do it, which I didn't have.

So I started pivoting to a software idea. And why the software did to host that, I just quickly discovered, you know, the amount of energy going into data centers, the amount of, you know, associated issues, and back then, 2015, 16, we were literally just discovering the energy aspect of it. We need didn't discuss, you know, water and land use and all of that.

We really focused on the energy and then we say, "look, well wait a second. You know, we have all this excess of energy. We literally cannot deliver that at this point. So we have a very high share of shutting down our wind turbines when there's just too much energy to move it around. Why not bring the data center as a flexible load close to the production, and enable, you know, sustainable compute

to then send package rather than energy, which is way easier, you know, over the global fiber grids." And that's how I got started and fell into the data industry. Big benefit and big learnings from that stand that I didn't know nothing about data centers. And as an engineer, a lot of things were not adding up. 

We looked at the servers back then, and even then it said, okay, this is good, you know, to run from 15 to 32 degrees. I said "32 degrees? Why? What is data center cooling and why is data center cooling? We don't have 32 degrees in the north." Most likely now, we probably ought to do within eight years.

Karl Rabe: But the important thing was really challenging this, and we started with very little money and we couldn't afford like the proper fancy stuff that all of this data center make, you know, like a chillers, you know, spending electric energy to cool something which really does not need cooling, in my opinion, up to now.

That was the start of this, you know, and so this is, the company of Windcloud is still ongoing. We had what we were, what we had as a huge problem. And I was always, my gut feeling for this was always we need to find a way to be able to compete with the Nordics.

So we have renewable energy, but we need to have it cost effective. And that was something that we tried two or two and a half times, I would say with the, with always having a legal way to access the energy in a proper setting. It was always extremely difficult and extremely frustrating also because the German energy system is very complicated.

It is, you know, geared or developed from a centralized view of this, and is benefiting, you know, large scale industry and large scale energy companies, to putting other terms, as you know, in, you're probably familiar with the, Asterix comics. You know, that far off and north in Germany that probably people, you know, there was a bit suspect, you know, what we're doing there or now we start producing energy and now we also want to use the energy so that is not adding up.

It's very hard and close to impossible to access your own produced energy at scale, you know, which is in an abundance. And that was, yeah, that was something what we always faced, which led to other innovations. So we build the first data center or one of the few data centers to reuse the heat in Germany, putting into an algae farm.

And we trying to create really efficient, PUEs already back then, you know, whereas the industry stranded is quite still quite high in ours. Claim I never had enough money to build a data center with a PUE above a 1.2, or even 1.1. The first servers were cooled with a, you know, a temperature regulated fan, you know, out of the, we built with the same guy who built, you know, a pig stale for my father, you know.

that was, you know, we nearly didn't call it Windcloud. We nearly called it Swines and Servers, 

Chris Adams: Okay. Pigcloud.

Yeah.

Karl Rabe: Yeah, Pigcloud, but it could have been, you know, could have been misleading. And the, so the good thing turning out of that, you know, and going back to that, to those struggles in getting started is that we were forced to uncover a lot of the cooling change and the energy distribution change, which are were not, you know, not really adding up for us.

And that is, you know, still one of the biggest support for us to build efficient data centers and to create, you know, sustainable solutions.

Chris Adams: Okay. Cool. Alright then. So. Okay. There's, I didn't realize anything about the Schweins und Servers aspect at all actually. Would you even, I'm not sure what German is for server would actually be in this context. Was it literally gonna be Schweins und Servers, or?

Karl Rabe: Yes. Some. So, yeah, something like that.

Chris Adams: Okay. Wow. That's, I was not expecting that.

I think Windcloud sounds a bit better, to be honest.

Karl Rabe: Yeah, thanks. No, the brand, the name is great. I think it's still, yeah, I'm very simple like that. You know, we had Windcloud, so we take wind, we make cloud. Now we are have, we are Wooden Data Center. We build data centers outta wood. So we, but there's this, but it's, to be fully honest, is right now, is so to speak,

we call Wooden Data Center, but what we do is try to decarbonize the data center. So wood is obviously, is a massive component of that, but we do see real good effort in the supply chains. Happy to go into that a little later, but there are some examples from fluids. We just found, you know, bio-based polycarbonate for hot and cold containments.

So the amount of components throughout the data center that have, that has a bio-based, ergo, a low carbon alternative is ever so increasing. 

Chris Adams: Can I come back to that a little bit later? 'Cause I just wanna, touch on the

Karl Rabe: Yeah. no.

Chris Adams: So the wind thing, so basically Windcloud, the big idea was putting data centers in the actual wind turbines themselves. So that gives you access to green energy straight away, because you're literally using power that otherwise either couldn't be transmitted because there were, because the pipes weren't big enough essentially in some cases.

And, I guess plus point to that in that if you are already using a building that's already there, you don't have to build a whole new building to put the data centers inside. So there's presumably some kind of embodied energy advantage there because there's a load of energy going into, kind of, that goes into making concrete and stuff that you don't have to do because you are already using an existing, like, building, right?

Karl Rabe: Yeah. So to clarify on that, it is good that you touch on that because there is, this is literally is a bit of a crossover because the company you're referring to is Wind Cause, which is a good friend of ours and they are using the turbine towers. 

Chris Adams: Ah, 

Karl Rabe: They can do so because they use a little bit different type of turbine.

And they're also based in the south of Germany, we had the same idea because it's also very difficult to build next to a wind farm. The big difference is that the towers used at Wind Cause, they are concrete and they have quite a lot of space. They're about 27 meters wide. because of the initial, discussion that we have onshore, or offshore conditions onshore, we have steel towers, which are shorter and hence don't have this big diameters.

You know, we build tall. And so we always had the challenge of still needing a data center. And so that's where our learnings inspirations came from For Wooden Data Center. But we still tried to reuse existing infrastructure. So we were at one point within the Windcloud journey,

I was the co-owner of a former military bunker area. And so we wanted to place within those long concrete tubes, we want to place data centers in order to yeah. Have a security aspect and don't need, you know, a lot of additional housing or even bunkering. And there's obviously the dodging bullets where has spent a lot of concrete and steel concrete in order for those facilities.

Chris Adams: I see. So you're reusing some of the existing infrastructure, so rather than building totally new things, you're essentially reusing same, you're reusing stuff that's already had a bunch of like energy and emissions spent to create it in the first place. I see. Okay. All right. So,

Karl Rabe: And, back then, you know, also to, because it's such a short time back then, really need to emphasize that we were, we really, you know, only had a hunch and a feeling, oh yeah, sort of has CO2 associated to it and probably also the building of a data center.

You know, we have, we really, it was so hard to quantify, and I think we still, carbon accounting is still, is somewhat of, not wizardry, but it's really hard to pull the right numbers. You know, only two years ago at the OCP Summit, so in a Google presentation, the range that they mentioned, you know, for steel and concrete carbon was, you know, 7 to 11 for equally both. So the range of the total uncertainty, I feel, is quite high. You know, and this is the biggest, one of the biggest and most funded, best funded organizations in the world. You know, we're still not being able to get it more concrete, you know, and that's something we really need to work with the industry and supply chains in order to be even aware to specify the problem.

Chris Adams: So, can I unpack that for a second before we talk a little bit about this? And so you're basically saying even the largest companies in the world, they don't necessarily have that good access to know how, what the carbon intensity of the concrete they've used in one data center compared to another one,

it can quite, it can vary quite a lot. Is that what you're saying there?

Karl Rabe: So this was basically specifying the global numbers for steel and concrete. So, I do believe that we have now relatively good visibility for our own builds and projects and also what we do now moving forward. But to really try to grasp the global problem of it, that was still, you know, two years ago was still had this high uncertainty, you know, 'cause we were working with numbers,

maybe they're now five years older, we don't know the complete, you know, build out of every city, every building globally. You know, it's just a lot of guesswork in that, globally. And so I especially believe that although we, Wooden Data Center, the amount of innovation that is put into concrete, you know, has the potential to drastically reduce that for buildings.

You know, the, it was a, it's definitely still a huge problem in, for the data quality and the data, yeah, the emissions, you know, guesswork that's in there, you know, and a lot of those things are based on scenarios, you know, and those are getting ever so more real. But the best example for Wooden Data Center is, there's a comparison comparing a steel concrete building to a CLT one,

Chris Adams: Yeah. 

Karl Rabe: and it is assuming that after 20 years, it's only living for 20 years, which, you know, can easily be 200 years, but that afterwards it is being reduced into, you know, building chairs or tools or toys. But if you take then the CLT and burn it, then obviously you have a zero sum gain. Every, all the carbon that was stored.

It's Cross-Laminated Timber, you know.

Chris Adams: Yeah. So this is the kind of like the special, the, this, it's a special kind of, essentially like machined timber that is, that provides some of the kind of strength properties of maybe steel or stuff like that, but is made from wood, basically, right?

Karl Rabe: Correct. So we need to stretch the importance that this is actually a material innovation. It's a relatively young material based on a, I think a thesis, PhD thesis from Austria. And so we only have CLT or cross-laminated timber for about 25 years. 

Chris Adams: Oh, I see.

Karl Rabe: Or maybe now 26, years.

So the, you probably are familiar, or you have seen there are those huge wooden beams in, you know, storage buildings. 

Chris Adams: Yeah. 

Karl Rabe: Those are called GLT, like glue laminated timbers. And the difference is those boards are basically glued in one direction and they're really good for those beams or for posts.

But to have like ceilings, walls, and roofs, those massive panels, you now have the material of cross laminated.

Chris Adams: Oh, okay. In both directions, right? Yeah.

Karl Rabe: Correct. And those now enable like full massive wooden buildouts. And that's something, and so the biggest challenge is that we, if we say wood, then the association we probably will touch on now or later is fire.

Chris Adams: Yeah. 

Karl Rabe: But in reality, in nature, we don't have those massive panels which don't, you know, just flame up. They have, they're fully tested and certified to glim down, which is, you know, they turn black and then they slowly, you know, in a thousand degrees, they slowly, you know,

shrink 

Chris Adams: Like smolder, right? Yeah.

Karl Rabe: Yeah. And so, but, the, how we design data centers is basically factor in this component, and we are able to create really fire secure data centers built out of those new wood materials basically.

Chris Adams: Okay. All right. So a lot of us are typically thinking of data centers as things made entirely with wood and with steel, concrete and plastic all over the place. And essentially you can introduce wood into this place and it's not gonna burn down because you have this material, which is treated in such a way that it is actually very fire resistant.

And that means you could probably replace, I mean, maybe you could talk to him a little bit about which bits you can replace. Like, can you rep, would you replace like a rack or a wall or like a roof? Maybe we can talk about that so we can like, make it a bit easier to kind of picture what this stuff looks like.

Karl Rabe: No, absolutely. I'm afraid I'm still, always very liberal in sending out samples to my clients, you know, but I don't have it here in my hand, but, so that is a very good the question, is basically like, if we would talk like slide deck or something that I'll try to show in terms of scope one, two, and three, what we can do and what we have now, and that it's like the, biggest component is in obviously the housing. You know, what is your building or your room of a data center? When you are touching on existing buildings we CLT is also ideal for building and building concept of existing large storage or logistic buildings to put in data centers.

We can build that up quite quickly out or create rooms very quickly in those, and there is other huge advantage of CLT is that we get those pre-manufactured and they just fit,

Chris Adams: Oh, like stick them together like Lego rather than have to pour 

Karl Rabe: concrete?

Yeah. Little, Yeah, a little bit. You need like, a little bit of leveling foundation.

If you have an existing floor, still, some datas, you know, preferred to in the greenfield also have a new floor. But that is is something that it helps to, with those, we can create the IT room relatively quickly and then have the build out of those averaging up to 40% quicker times than traditional steel sandwich concrete, you know, data center.

So it is enormously easy to work too. It's very precise to pre-design and pre-manufacture and then very easy to work with. If there's something, if there's a problem on site, you know, you just crank out the chainsaw and adapt and adjust.

Chris Adams: Okay. Just to carve it down a bit.

Karl Rabe: And yeah, so to speak. But once you have then those assembled and secured, it has like a lot of mass to it and a lot of volume to that which creates very good fire protective

physical resistance and availability properties. And that is something that we now, it's really being seen as one of the core benefits. You know, the speed what we can build this out.

Chris Adams: Oh, ok.

Karl Rabe: We have introduced wooden racks, and we also see more and more attention for those.

Chris Adams: Wait, sorry. Can I just, you said wooden rack, like as in the big steel rack that holds the servers themselves, you could, that could be made of wood as well now, so you'd have like a rack thing holding a bunch of servers, right?

Karl Rabe: Correct, So we built this also. We have, one of our clients has send us like a server casing and ask to also think about to do the casing, but we probably, we're not a hundred percent there yet. In order to do that, we would have, we would've an idea, in terms of the spirit of OCP, which is, you know, like,

reduce and cut out stuff. You know, one vision of that would be just a wooden, you know, board where you have dedicated spaces. You slide in your main board, connect power, connect liquid cooling, have fans on the back and then cycle through only the boards. Remove, you know, not even fancy, but just base frames of a server.

But right now the, it's a combination, for the 19 inch standard and also the OCP standard, to use, you know, reduce up to 98% of the steel in those constructions and then only have functional parts in order to stick in the servers made from steel railings and have then wooden frames.

And we do that for the OCP format, which is very popular. We get a lot of the special requirements because we are the only ones who producing like a small version of the rack, which, the OCP has a lot of advantage, but the base rack format is a two meter 30 high, which is like a really hyperscale, you know, mass density approach.

Which doesn't fit even through the doors of most data centers I know, you know, they still have relatively, you know, standard two meter high doors or able to fit in like a 42 inch rack. But you need like a very special facilities because those racks come also pre-integrated and then you roll them in place.

So you need a facility that has high doors, ramps with small inclines, you know, or no ramps at all, in order to be able to place a fully integrated rack. We started building OCP racks because back at the time only hyperscalers were really getting those, and we wanted to do more of this open compute format and were able to offer that.

And the version three rack, you know, was a good candidate to convert to a wooden based structure.

Chris Adams: All right, so we'll come to that a little bit later because I actually came across some of your work when you were building, designing some of these on YouTube so people can see what all this stuff looks like. But if I just come back to the original question, essentially, so it sounds like you can replace quite a lot in a data center.

So you can replace the shell of the building, like literally green the shell by replacing the concrete, which is one of the largest sources of, you know, creating concrete and cement is one of the largest sources of emissions globally. So you can switch, you can move from a source of emissions to, is it a sink?

Cause CO2 and carbon gets sucked out of the sky to be turned into trees. So you've gone from something which is a source to a sink and that, and you can replace not just the walls, the outer building, but also quite a lot of the actual structure itself. Just not the servers yet.

So that's probably like a, I mean, maybe I could ask you then like, If I'm switching from maybe regular concrete and regular steel, I mean, is there any, like, do you folks have any idea about like what the kind of changing quantitative terms might actually be if I was to have like an entirely concrete, entirely steel data center and then replaced all of that with, say, wooden alternatives, for example? Like is it like a 5% reduction or is there any, like, what kind of changes are we looking at for the embodied figures, for example? 

Karl Rabe: So the conservative industry figures are somewhat off between minimum 20%, only having the production change up to 40%. So Microsoft, we, the good thing also we have to mention is that we are an industry now. Microsoft announced those productions I know the other hyperscalers are looking at that.

We only, in Germany we had two other companies started getting into construction. That's why it's for us really important to be on the decarbonization path.

Chris Adams: Ah, I 

Karl Rabe: see. 

So we do come with our own data center, even concepts and philosophies, which I can talk about a little later.

But coming back to the point it is still very hard to quantify. And the, but really positive things about carbon accounting or calculations, as I mentioned, we now have as a data center, we have this negative component, which I have to laugh 'cause an engineer immediately and said, can we then just use more wood?

You know, can we make the wall thicker? You know, obviously yes, you could do that, but there's a cost to that and there's also, you know, it be betrays the idea, you know. But, the really exciting thing is that I now go to show, from show to show, and I was two weeks ago in London

and just on the flight somebody showed me, a picture of an air handling unit inside of a wooden enclosure. And I was chasing an hour through the London show, 'cause I assumed it was there, but it was on a, it was on a different show. but that is the kind of things that we can really think about is enclosures.

So also we have started, we have one, well, for the OCP reg or for this AI build out, we have also created a rear door, which is, so to speak, a wooden rear door. So the fans are traditional, the heat exchanges obviously needs to be traditional, but it is also like a micro aluminum micro channel heat exchanger, which is derived from other industries, which is, you know, helping mass production, reducing cost, reducing emissions.

And that is the other thing that is happening in the industry that 

we're trying to find, not data center specific solutions, but rather find mass produced industry solutions and adapt them to the data center in enhanced reducing cost and time.

Chris Adams: Alright. So in the same way that basically cross laminated timber and the use of wood is something that has been in use in not just in the data center industry, like people make, what are they called? Are they called plyscrapers? You know, skyscrapers with wood. Plyscrapers.

It's, so the idea was that, okay, things which are made, being made in volume here can be made more efficiently and like this is one way that you are adapting 'em to a new domain.

And it may be that if people are getting much, much better at making say very efficient heat pumps, 'cause they can cool things down as well as heat them up, that might be another thing you're looking at. Say, "well actually that might be able to be used in this context as well." Okay. Alright. And if I am, so if I go back to the original thing about saying, okay, we're looking at possible savings maybe 20% up to like possibly 40%, like that's the kind of

Karl Rabe: yeah. That's the range that we have, you know, I think, so the problem is do we, if, did Microsoft evaluate with IT or without IT? So for the facility, I think we can potentially come to net zero approach, which we, you know, but by first principle, I think we can at least achieve realistic reductions to let's say 80, 70-80, 85% with those tools that are set, you know, basically the easy steel replacements, the, like, the rack, the enclosures, the housing, fluids is something we have. There's a very interesting, you know, no-brainer replacement for fossil diesel on backend generators.

It's a liquid called HVO

Chris Adams: Yeah. Let's come to that in a second actually. 'Cause I did wanna ask a little bit about some of the things you can do for the fuel here. So maybe if we just, so basically the, so there are some savings available there and these should be something that you could, some, this is something that should show up in some kind of numerical description.

So if you had like, maybe two data centers and one was using wood in strategic places, then the embodied carbon should be lower. So if, I mean, if I was looking for this there like a label to look for or a standard I can look for? Because in the Green Software Foundation we have this idea called Software Carbon Intensity, which includes like the carbon intensity of the energy you use and stuff like that.

But they also look at the building itself. So theoretically, if you had a wooden data center and a bog standard concrete data center, you know, if you run your code in the greener data center, you would probably have a better score if you had access to these, the data or stuff like that. Do you know, like, do, any places share this data or have like a label for anything like this?

Karl Rabe: They definitely share the data. I, for example, so we definitely also need to Eco Data Centers in Sweden's and we, which were, you know, basically we approach to them. Our whole world was shook. You know? It's like, oh, so we come from this energy perspective, but they didn't have idea and they build it, you know, sustainably. They build it sustainably.

So we need to change, you know. That was, you know, it was a huge eyeopener. And they also are the few first ones to, I'm not sure if they used like the LCA method, but they were quantifying the embed carbon and are certifying to you annually too, as a client, which I think is the way to go.

And we need to figure out how to standardize that. I assume there's potentially a standard that we can use. I know that other data center providers are building sustainably and putting this effort forward. But we don't have a unified label yet, I'm afraid.

Chris Adams: Okay. Well this

Karl Rabe: I know that some, also challenge, like, there's like a data center climate neutral act and some of them specifically exclude scope three, which, you know, I know where they're coming from.

Also in Germany and Germans, you know, they're all about energy efficiency. They love to talk about, you know, just the, energy and the scope two, basically. But then, you know,

Chris Adams: Most of the

Karl Rabe: missing out, this dimension, you know,. Missing out the dimension is being faithful to your girlfriend or wife, you know, like three days out of a week.

You know? It is, it's not

Chris Adams: You are not showing the full picture, right?

Karl Rabe: Yeah. You're not, doing it at all basically. Right. I would probably, you know, just need to Google it and there are, you know, building labels that you could be used in construction. Quantifications, I'm sure, but there's not yet like a data center specific label.

There is good work also in OCP to do metrics and key performance indicators, and they're all looking at that and there is, I think they're trying to build towards something like real, like true net zero.

Chris Adams: Oh yeah. Okay.

Karl Rabe: But... 

Chris Adams: So there are some, so there are some initiatives going on to kind of make this something that you could plausibly see, and, but it's quite early at the moment right now. So like, let's say that I, you know, we spoke before about, okay, I can run my computing jobs in one data center or I can choose to run it somewhere else.

These numbers don't show up just yet, but there is work going on. Actually, I've just realized there is actually a embedded carbon working group inside the OCP who have been looking at some of this stuff. So maybe we'll share a link to that, because that's actually one of the logical places you'd look for that.

Okay,

Karl Rabe: And they do real good work. They do a lot of good initiatives, happening there. There's also, it's Swiss from the Swiss Data Center Association. They also have a label, that is looking at some of this, and they want also to include scope three.

So this is coming up, but it's, not as easy as, you know, having an API, you know, pushing it to the software developer and saying, look, we have this offset because this was constructed, you know, with concrete or steel, and this is, you know,

Chris Adams: Okay. So we're not there yet, but that's a, that's the direction we might be heading towards. Okay. Alright. We'll add some links to that. And now I'd like to pick up the other thing you mentioned about HVO and stuff like that because you spoke before about, you know, Windcloud or wind node and like data centers running in,

or like, you know, relying on wind right now, we know it's a really common refrain that the wind doesn't blow all the time. And like it's news to some people that sun, that's, you know, it is not always sunny, for example. So there'll be cases where there'll be times where you need to get the power from somewhere and, you know, in the form of backup power. And like loads of data centers, you said before they rely on like fossil diesel generators, right.

And that can be, it's bad from a climate point of view, it's also quite expensive, but it's also terribly really bad from an air quality point of view as well, because, you know, people are up, you know, you can see elevated cases of like asthma and all kinds of respiratory problems around data centers and things like that.

But you mentioned there's options there to kind of reduce the impact or have like more responsible options there. Maybe we could talk a little bit about like what's available to me there if I wanted to reduce that part, for example.

Karl Rabe: No, happy to go into that. That is something that we are now thinking about quite heavily this year. And we're already presenting on two occasions, a sense. So the easy options in order to reduce your carbon on the scope one part for data center, which is basically, you know, that's just the direct burning of fossil resources and that is the testing of your backup generators. 

The easy option for that is this second gen diesel, HVO 100. And the, when I realized the key feature of this fuel, which about 15% more expensive, is that it doesn't age. Fossil diesel and especially, you know, biodiesel, the first generation and fossil diesel with biogen, there's always, in Europe there you have a certain degree of mixed in of this, it ages through bacteria biologically.

So it's degrading. So, the, which is, you know, really bad because this diesel sitting there in a tank, you run it half an hour every two weeks, and you maybe change the fuel filter twice, once, twice a year. But if you really have an issue, you know, all of a sudden you use this diesel 

for four hours and then your system, your filter clocks, and you still have a problem, right? If

Chris Adams: So your backup isn't a very good backup.

So backup needs to be a good backup. Yeah.

Yeah. 

Karl Rabe: Yeah. so your backup can run

Chris Adams: you had one job, right? Yeah.

Karl Rabe: Yeah. Yeah. And so, how it's mitigated is people try to use 'pure' diesel or, you know, heating oil, you know, which is not so prone to it, but still ages. They are recycling or, you know, really pumping out the fuel and pumping it in again every three years or they continuously filter it.

All of this is either adding energy or cost. And so, the, this new form of biodiesel, which is, you know, your old frying fat, cracked with hydrogen to, is it looks very clear and it's very chemically treated that it's not really aging. People don't know really yet how long it stays.

They certified 10 years, potentially it's stays, good longer and is also burning cleaner. So

Chris Adams: Ah,

so it'sn't going to be bad 

like bad air and stuff as well then?

Karl Rabe: Yeah. So for the majority of your enterprise IT, your standard data center that's around you, you know, cutting out the whole AI discussion, probably that's the easiest way to do something about that.

This is like a drop in replacement. You just, you know, you empty your tank, you put it in, or you burn your old fuel and put a new, that is something that is, you know, easily increasing the availability of your facility and you can change with that. 

Chris Adams: Can I just try to like summarize that? So, because I don't work with data centers on a daily, so there's like basically fossil diesel, the kind of stuff that, you know, you might associate with dieselgate and like all kinds of bad air, air quality issues. And then the, kind of the other option, which is maybe a little bit more expensive, you said around 15%,

there's something called HVO, which is essentially like biodiesel that's been treated in a particular way to get rid of lots of the gunk so it burns more cleanly and works better as a reliable form of backup. So the backup is actually a decent backup rather than a thing which might not be a very good backup.

Oh okay. So that's like one of the things, and that's like the direction that we might be moving towards and that's kind of what we would like to see more of for the case where you need to rely on some kind of liquid fuel power. Right.

Karl Rabe: Yeah.

Chris Adams: Okay.

Karl Rabe: That is, I think is for most people, you know, just a very easy low hang fruit to just replace, you know, it does not, you know, most engines are certified for, nowadays, all engines run on it, you know, it's, it has the same, Yeah, criteria, properties like traditional diesel, the only thing that's different is it's 4% lighter, you know?

Chris Adams: Oh, I see.

Karl Rabe: So that's the only real on the spec sheet 

Chris Adams: Oh, okay. Alright. So if I may, so that's one of the options. These, so you can replace fossil diesel with essentially non-fossil cleaner, slightly less, you know, less toxic diesel. So that's one thing that you might have in for your backup. Now, I've heard various people talking about, say hydrogen, for example.

Now hydrogen can come from fossil sources. So people, most of the time, actually, most hydrogen does come from basically cracking natural gas or methane gas, but it can come from green places. And that's why is, that's another option that you might have to generate power locally.

Karl Rabe: Is that something that people tend to use? 

So I think the best, the best reference for hydrogen is like the champagne of our energy transition. You know, we need, we need to put in a lot of energy to put, to produce it. It's not easy to store, that we need a lot of facilities to actually create green hydrogen.

Karl Rabe: The majority of hydrogen is not green hydrogen, but it's gray or blue, which is basically

Chris Adams: like a carbon capture hydrogen, which is still a bit questionable. Yeah.

Karl Rabe: all based from fossil tracking, you know, so it's, it potentially, you know, you, you also have the same goal. 

everything that we do for our clients is under this extremely short impact of time.

You know, we have solve everything within now, within five years time, not even five years. Right. And so that's also something that I'm always, you know, spark a good discussion. When we talk about SMRs, you know, have the big pushback for nuclear over in the US, and also in Europe we have voices for that.

And the short answer is, the three reasons I don't believe in it. They're not quick, you know, they're not cheap. Two projects were just, a year ago, there were two potential very, you know, hopeful projects for SMRs were canceled in the US, and half a year later it was a big thing.

The big solution. like, what changed, you know? And then the third point is that is the, very German, perspective, you know, all the fears or the, challenges around the fuel, like getting it mostly 70% from Russia or, then the waste, you know, dumping it somewhere is not solved, still.

And so, this is not a 2030 technology basically. 

That's my, the point what we can do and what I'm happy also to link, there's a good article from some of the, hyperscalers looking into solar combined with batteries, combined with gas based backup. The gas based has the one flexibility that it can start fossil, can move to bio, and potentially also can run on hydrogen. So this is, in terms of the speed with which we are now deploying hundreds of megawatts, you know, every data center for AI is now, you know, 100, 200, 300 gigawatts.

You know, things that we did not,

yeah,

yeah. So it's things that we, you know, like yeah, we're discussing, you know, five or one to five gigawatts for the large people. And every other data center is all of a sudden is now a hundred megawatts, which used to be like a mega facility, just two years back.

So 

that build out can only really be achieved not with grids or interconnects, those are too slow. This can only be basically with micro grids.

 

Chris Adams: I see. Okay.

Karl Rabe: Helping, you know, that are battery backed and gas based backed. And the big advantage of this is that if we think about the data center, 

traditionally a data center is a data fortress, right?

You don't get in, data doesn't get out. It is, you know, is like a bank, you know, in terms of the security measures to do that. And also all of the infrastructure was handled that way. But if we are thinking about the UPS, and the genset not being sitting straight at the data center or only sitting straight at the data center but technically belonging to the utility and being able to provide flexible power, you know, because we have this, as mentioned, underlying flexible build out of renewable energy, and we need, you know, reliable switch-on power, which data centers all have. And so if we can put those together, there's a little bit of this working together, finding the right location where it would make most sense, and then allowing for SLAs and with clients to bidirectionally use batteries, gas turbines,

Chris Adams: Oh, I see.

Karl Rabe: engine power.

This would, you know, help our, yeah, help us to transition, especially if we go into, you know, renewable shares, 60% and above and at latest from 80% we need those backup technologies. And then, and that is coming back to the question of hydrogen. Hydrogen is a technology that would, is so expensive that it would need to run all the time, basically.

With renewable energy, we have high loads of

abundance 

of energy and only need short times of flexible energy generation for which gas and batteries is virtually ideal. And so we promote this idea of an energy-integrated data center, which has the electrical part supporting into the grid and is also, you know, taking advantage of the heat reuse, especially for liquid-cooled facilities in order to give heat out.

And the benefit of that is not only from an economical perspective, but also we see more and more discussions about not in my backyard. If a data center is energy integrated, it's not a question, you know, it's a must have. And there's also a reason why it needs to be there, you know, in order to be able to stabilize the, your town grid or your local area.

And so that's what we are trying to promote. We got a lot of good feedback and we see the first, hopefully we'll have the first data center realized with a medium voltage UPS this year, which is like a first step in moving the availability components of a data center, the batteries and the gen sets to a higher area, which, a lot of the cost in a data center is from the low voltage distribution.

The power that you put in the batteries is also first transferred down, and then it's moved, you know, through the data center until it sits in the battery and then needs to go out. And all of those are rectification steps. And all of this makes, yeah, all of

Chris Adams: You lose, so do you lose power every single time you switch between them? Oh, okay. So it sounds like you, there's a shift from, like, data center as a fortress where, you know, you could do that before to like something where you have to be like a bit more symbiotic with your local environment because for a start, if you don't, you're not gonna allow it,

you won't be allowed to build it. But also it's gonna change the economics in your favor if you are prepared to like play nicely and integrate with your, essentially be a good neighbor. All right. That seems to be what you seem to be suggesting. 

Karl Rabe: That's a perfect analogy. Having like a good neighbor approach. Saying, "look, we are here now, we look ugly, we always box, you know, but we help, you know, powering your homes, we reduce the cost of the energy transition, and we also heat your homes."

 You know, and that is then, is then a relatively easy sale.

Chris Adams: Okay. So that points to possibly that, honestly, that points to quite a different strategy required for people who are, whose job it is to get data centers built. They need to figure out how to honestly relate to communities and say, well, which bits are we supposed to be useful? Rather than the approach that you come to sometimes see where people basically say, "well, we're not even gonna tell you who, it is or who the company is, but we're gonna use all your power and use all your water." 

That approach's days are probably numbered, and that's not a very good strategy to use. It does make more sense to actually have a much more like neighborly approach and these are maybe new skills that need to be developed inside the industry then.

Karl Rabe: Absolutely correct. And so, you need the, you need an open collaboration approach to that, and that is, you know, mirrored, so we trying to be a bit of an example there. And if you go, if you talk about, you had a good point in there, which we usually don't have a lot of time to expand on,

but I think podcast a good format for that. You ask like, where do you get the ideas or what's the guiding star on that? And so, 

I was fortunate to be an exchange worker, you know, on a farm in Canada. And they introduced to me the idea of holistic management, which is like a, basically, decision making framework, that is based on financially viable, socially viable, and economically viable. 

And so those three bases are necessary in order to create sustainable decisions or holistic decisions. Those need to be short and long term viable.

And that has been, you know, my guiding star as an entrepreneur and really being able to cut out those things. You know, there's a lot of startups, especially in Germany. We had those Berlin startups who all came from a business school and all of their ideas worked on an Excel sheet,

always cutting out like a social perspective, you know? And so that was, you know, that's the opposite basically of what we are trying to do. And this framework was found by a farmer who first applied it to grass management and cattle farming, technically. But it is, and it is wildly interesting what he's able to do. He's basically retaking, stopping desertification and reversing effects in subtropical, semi arid areas. 

Yeah. So we'll definitely put that in the notes.

It's a tED Talk from Alan Kettle, which I think he's still alive. He's in his, he must be 90 now. And it's fascinating. But that was a guiding star. And in order to promote our ideas, you know, a lot of our designs, you know, we put on YouTube, but we also put the files up.

The, racks, you know, you can download the CD files. There's, we believe they're created with open source tools. So especially in engineering, we only recently really have powerful open source tools for CAD, for single line diagram. So we can give the source files with that.

And that is is something how we believe that open collaboration and openness helps to build, you know, the trust 

Chris Adams: Ah, 

Karl Rabe: to build with speed and to really work together, you know? And that's what we get mirrored in the Open Compute Foundation. Yeah, that is something that we believe is, for challenges that we face as humanity,

you know, I believe that only this open approach, and especially an open source, open hardware, open data, framework can help us.

Chris Adams: All right. Okay, so we're coming up to time and I just wanted, and you did alluded to it a few times and I just wanted to provide a bit of space to let you talk a little bit about that before we kind of finish up. You spoke a few times about the fact that these models, when you work, bunch of designs for the racks and things are like online and available, and did you say that they're on YouTube, like people can see the videos of this or can like download like something in blender to mess around with themselves or work with it? Maybe you could just expand on that a little bit because I haven't come across that before and

Karl Rabe: Okay, sure, sure. So, yeah, we initially, when we started, you know, we designed everything and we put it, we still need to, shamefully, we still need to put, do the push for, to GitLab and GitHub. We use right now, we put those model on a construction setup, of course, called, GrabCAD.

Chris Adams: Mm-hmm. 

Karl Rabe: And for our, it, you know, it's not only our own thoughts to open source this and to build the trust, but it's also our biggest, easiest marketing tool. You know, create a model, publishing it, put a video tape. We are a bit behind. We have a lot of new and great ideas and things to share.

But that's how we approach it, you know, we'll come up with idea, put it out there and, also, you know, make ourselves criticizable, you know, we'll, we are the only ones comfortably saying, look, we have the best data centers in the world, 'cause you can go, you can download, you can fact check our ideas, and if you have something against it, you know, just give us a feedback.

And we are open to change that. And this way forward, you know, helps us also to approach the biggest companies in the world. They really like this open approach, you know, and they're happy to take the files in the models and to work on that.

Chris Adams: So you basically have like models of like, this is a model of

Karl Rabe: Our rack, you know, this is our module data center. These are ideas behind that. And so that's how we are moving this forward. So people can approach this, they can download, they can see if it fits. They can make suggestions. 

Chris Adams: And like see if it's tall enough for the door and all of the basic or the practical things.

Karl Rabe: Yeah. All those things, you know, and see, okay, we have smaller data center, oh, the base design doesn't fit in this setup, or we need to change something where we place, you know, the dry coolers or something like that.

And so that is really, you know, really good feedback and sparks discussions.

Chris Adams: Yeah, I haven't heard about that before. All right. Well, Karl, thank you so. This has been a lot of fun. Now, we've come up to time and I really enjoyed this tour through all the stuff hap that happens below the software stack for engineers like us, for example. If someone does wanna look at this or learn about this or maybe kind of check out any of the models themselves, if they wanted to build any of this stuff themselves, where should they look?

Like, how do we, where do people find you online or any other projects that you're looking at, you're So, working on?

Karl Rabe: So the best thing technically to, is LinkedIn. This is, you know, our strong platform, to be honest, we are very active there. We publish most there. The webpage is still under construction. You know, people already understand what we do from going to that.

LinkedIn is great. Look, go and, you know, trying to reach us and see what we do at the Open Compute Foundations is also often very great. But yeah, just technically why Google is very easy to find us on LinkedIn and to reach 

Chris Adams: So Karl Rabe on LinkedIn, Wooden Data Center, there aren't that many other companies who are called Wooden Data Center. And then for any of the Open Compute Project stuff, that's the other place to look at where you're working. 'Cause you're doing the open compute modular data center stuff.

Those are the ones, yeah?

Karl Rabe: Yeah. Correct.

Chris Adams: Brilliant. Karl, thank you so much for this. This has been loads of fun and I hope that we've had listeners follow us along as well to see all the options and things available to them. Alright, 

Karl Rabe: It was a pleasure. Thanks so much. And, 

Chris Adams: Likewise, Karl. And, hope the wind turbines treat you well

where you're staying. All right, take care mate.

Karl Rabe: Yeah. Thank you. Bye bye. Cheers.  

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> GreenOps with Greenpixie GreenOps with Greenpixie Thu, 10 Apr 2025 07:00:00 GMT 46:00 j022q8r0 false https://podcasts.castplus.fm/e/2n61xzq8-greenops-with-greenpixie 68dc7d05597bc7d53fb833f8 68dc7cef46a2532cdd8d1c23 Host Chris Adams sits down with James Hall, Head of GreenOps at Greenpixie, to explore the evolving discipline of GreenOps—applying operational practices to reduce the environmental impact of cloud computing. They discuss how Greenpixie helps organizations make informed sustainability decisions using certified carbon data, the challenges of scaling cloud carbon measurement, and why transparency and relevance are just as crucial as accuracy. They also discuss using financial cost as a proxy for carbon, the need for standardization through initiatives like FOCUS, and growing interest in water usage metrics. 103 Host Chris Adams sits down with James Hall, Head of GreenOps at Greenpixie, to explore the evolving discipline of GreenOps—applying operational practices to reduce the environmental impact of cloud computing. They discuss how Greenpixie helps organizations make informed sustainability decisions using certified carbon data, the challenges of scaling cloud carbon measurement, and why transparency and relevance are just as crucial as accuracy. They also discuss using financial cost as a proxy for carbon, the need for standardization through initiatives like FOCUS, and growing interest in water usage metrics.

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TRANSCRIPT BELOW:


James Hall: We want get the carbon data in front of the right people so they can put climate impact as part of the decision making process. Because ultimately, data in and of itself is a catalyst for change. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to Environment Variables where we explore the developing world of sustainable software development. We kicked off this podcast more than two years ago with a discussion about cloud carbon calculators and the open source tool, Cloud Carbon Footprint, and Amazon's cloud carbon calculator.

And since then, the term GreenOps has become a term of art in cloud computing circles when we talk about reducing the environmental impact of cloud computing. But what is GreenOps in the first place? With me today is James Hall, the head of GreenOps at Greenpixie, the cloud computing startup, cloud carbon computing startup,

to help me shed some light on what this term actually means and what it's like to use GreenOps in the trenches. James, we have spoken about this episode as a bit of a intro and I'm wondering if I can ask you a little bit about where this term came from in the first place and how you ended up as the def facto head of GreenOps in your current gig.

Because I've never spoken to a head of GreenOps before, so yeah, maybe I should ask you that.

James Hall: Yeah, well, I've been with Greenpixie right from the start, and we weren't really using the term GreenOps when we originally started. It was cloud sustainability. It was about, you know, changing regions to optimize cloud and right sizing. We didn't know about the FinOps industry either. When we first started, we just knew there was a cloud waste problem and we wanted to do something about it.

You know, luckily when it comes to cloud, there is a big overlap between what saves costs and what saves, what saves carbon. But I think the term GreenOps has existed before we started in the industry. I think it, yeah, actually originally, if you go to Wikipedia, GreenOps, it's actually to do with arthropods and Trilobites from a couple million years ago, funnily enough, I'm not sure when it started becoming, you know, green operations.

But, yeah, it originally had a connotation of like data centers and IT and devices and I think Cloud GreenOps, where Greenpixie specializes, is more of a recent thing because, you know, it used to be about, yeah, well it is about how do you get the right data in front of the right people so they can start making better decisions, ultimately.

And that's kind of what GreenOps means to me. So Greenpixie are a GreenOps data company. We're not here to make decisions for you. We are not a consultancy. 

We want get the carbon data in front of the right people so they can put climate impact as part of the decision making process. Because ultimately, data in and of itself is a catalyst for change.

You know, whether you use this data to reduce carbon or you choose to ignore it, you know, that's up to the organization. But it's all about being more informed, ignoring or, you know, changing your strategy around the carbon data.

Chris Adams: Cool. Thank you for that, James. You mentioning Wikipedia and Greenops being all about Trilobites and Arthropods, it makes me realize we definitely should add that to the show notes and that's the thing I'll quickly just do because I forgot to just do the usual intro folks. Yeah, my name's Chris Adams.

I am one of the policy director, technology and policy director at the Green Web Foundation, and I'm also the chair of the policy working group inside the Green Software Foundation. All the things that James and I'll be talking about, we'll do our best to judiciously add show notes so you can, you too can look up the origins of, well, the etymology of GreenOps and find out all about arthropods and trilobites and other.

And probably a lot more cloud computing as well actually. Okay. Thank you for that James. So you spoke a little and you did a really nice job of actually introducing what Greenpixie does. 'Cause that was something I should have asked you earlier as well. So I have some experience using these tools, like Cloud Carbon Footprint and so on to estimate the environmental impact of digital services. Right. And a lot of the time these things use billing data. So there are tools out there that do already do this stuff. But one thing that I saw that sets Greenpixie apart from some other tools as well, was the actual, the certification process, the fact that you folks have, I think, an ISO 14064 certification.

Now, not all of us read over ISO standards for fun, so can you maybe explain why that matters and what that actually, what that changes at all, or even what that certification means? 'Cause, It sounds kind of impressive and exciting, but I'm not quite sure, and I know there are other standards floating around, like the Software Carbon Intensity standard, for example.

Like yeah, maybe you could just provide an intro, then see how that might be different, for example.

James Hall: Yeah, so ISO 14064 is a kind of set of standards and instructions on how to calculate a carbon number, essentially based on the Greenhouse Gas Protocol. So the process of getting that verification is, you know, you have official auditors who are like certified to give out these certifications, and ultimately they go through all your processes, all your sources, all the inputs of your data, and kind of verify that the outputs and the inputs

make sense. You know, do they align with what the Greenhouse Gas Protocol tells you to do? And, you know, it's quite a, it's a year long process as they get to know absolutely everything about your business and processes, you really gotta show them under the hood. But from a customer perspective, it means you know, that it proves that

the methodology you're using is very rigorous and it gives them confidence that they can use yours. I think if a company that produces carbon data has an ISO badge, then you can probably be sure that when you put this data in your ESG reports or use it to make decisions, the auditors will also agree with it.

'Cause the auditors on the other side, you know, your assurers or from EY and PWC, they'll be using the same set of guidance basically. So it's kind of like getting ahead of the auditing process in the same way, like a security ISO would mean the security that the chief security officer that would need to, you know, check a new vendor that they're about to procure from.

If you've got the ISO already, you know they meet our standards for security, it saves me a job having to go and look through every single data processing agreement that they have.

Chris Adams: Gotcha. Okay. So there's a few different ways that you can kind of establish trust. And so one of the options is have everything entirely open, like say Cloud Carbon Footprint or OpenCost has a bunch of stuff in the open. There's also various other approaches, like we maintain a library called CO2.js, where we try to share our methodologies there and then one of the other options is certification. That's another source of trust. I've gotta ask, is this common? Are there other tools that have this? 'Cause when I think about some of the big cloud calculators, do you know if they have this, let's say I'm using say, a very, one of the big three cloud providers.

Do these have, like today, do you know if they actually have the same certification or is that a thing I should be looking for or I should be asking about if I'm relying on the numbers that I'm seeing from our providers like this.

James Hall: Yeah, they actually don't. Well, technically, Azure. Azure's tool did get one in 2020, but you need to get them renewed and reordered as part of the process. So that one's kind of becoming invalid. And I'm not sure AWS or Google Cloud have actually tried, to be honest, but it's quite a funny thought that, you know, it's arguably because this ISO the, data we give you on GCP and AWS is more accurate than the data, or at least more reliable than the data that comes directly out the cloud providers.

Chris Adams: Okay. Alright. Let's, make sure we don't get sued. So I'm just gonna stop there before we go any further. But that's like one of the things that it provides. Essentially it's an external auditor who's looked through this stuff. So rather than being entirely open, that's one of the other mechanisms that you have.

Okay, cool. So maybe we can talk a little bit more about open source. 'Cause I actually first found out about Greenpixie a few years ago when the Green Software Foundation sent me to Egypt, for COP 27 to try and talk to people about green software. And I won't lie, I mostly got blank looks from most people.

You know, they, the, I, there are, 

people tend to talk about sustainability of tech or sustainability via tech, and people tend not to see them as, most of the time I see people like conflating the two rather than actually realizing no, we're talking about of the technology, not just how it's good for stuff, for example, and he told me, I think one of your colleagues, Rory, was this, yeah.

He was telling me a bit about, that Greenpixie was initially using, when you just first started out, you started looking at some tools like Cloud Carbon Footprint as maybe a starting point, but you've ended up having to make various changes to overcome various technical challenges when you scale the use up to like a large, to well, basically on a larger clients and things like that. Could you maybe talk a little bit about some of the challenges you end up facing when you're trying to implement GreenOps like this? Because it's not something that I have direct experience myself. And it's also a thing that I think a lot of people do reach for some open source tools and they're not quite sure why you might use one over the other or what kind of problems they, that they have to deal with when you start processing that, those levels of like billing and usage data and stuff like that.

James Hall: I think with the, with cloud sustainability methodologies, the two main issues are things like performance and the data volume, and then also the maintenance of it. 'Cause just the very nature of cloud is you know, huge data sets that change rapidly. You know, they get updated on the hour and then you've also got the cloud providers always releasing new services, new instance types, things like that.

So, I mean, like your average enterprises with like a hundred million spend or something? Yeah. Those line items of usage data, if you like, go down to the hour will be billions of rows and terabytes of data. And that is not trivial to process. You know, a lot of the tooling at the moment, including Cloud Carbon Footprint, will try to, you know, use a bunch of SQL queries to truncate it, you know, make it go up to monthly.

So you kind of take out the rows by, you know, a factor of 24 times 30 or whatever that is. It's about 740, I think. Something like that (720). Yeah. Yeah. So, and they'll remove things like, you know, there's certain fields in the usage data that will, that are so unique that when you start removing those and truncating it, you're really reducing the size of the files, but you are really losing a lot of that granularity.

'Cause ultimately this billing data is to be used by engineers and FinOps people. They use all these fields. So when you start removing fields because you can't handle the data, you're losing a lot of the familiarity of the data and a lot of the usability for the people who need to use it to make decisions.

So one of the big challenges is how do you make a processor that can easily handle billions of line items without, you know, falling over. And CCF, one of the issues was the performance really when you start trying to apply it to big data sets. And then on the other side is the maintenance.

You know, arguably it's probably not that difficult to make a methodology of a point in time, but you know, over the six months it takes you to create it, it's way out date. You know, they've released a hundred new instance types across the three providers. There's a new type of storage, there's a brand new services, there's new AI models out there.

And so now, like Greenpixie's main job is how do we make sure the data is more, we have more coverage of all the skews that come out and we can deliver the data faster and customers have more choices of how to ingest it. So if you give customers enough choice and you give it to them quick enough and it's, you know, covering all of their services, then you know, that's what those, lack of those three things is really what's stopping people from doing GreenOps, I think.

Chris Adams: Ah, okay, so one of them was, one of the things you mentioned was just the volume, the fact that you've got, you know, hours multiply the number of different, like a thousand different computers or thousands of computers. That's a lot of data. And then there's a, there's like one of the issues about like the metrics issue, like you, if you wanna provide a simple metric, then you end up losing a lot of data.

So that's one of the things you spoke about. And the other one was just the idea of models themselves not being, there's natural cost associated with having to maintain these models. And as far as I'm aware, there aren't, I mean, are there any kind of open sources of models so that you can say, well this is what the figures probably would be for an Amazon EC, you know, 6XL instance, for example.

That's the stuff you're talking to when you say the models that you, they're hard to actually up to, hard to keep up to date, and you have to do that internally inside the organization. Is that it?

James Hall: Yes, we've got a team dedicated to doing that. But ultimately, like there will always be assumptions in there. 'Cause some of these chip sets you actually can't even get your hands on. So, you know, if Amazon release a new instance type that uses an Intel Xeon 7850C, that is not commercially available.

So how do you get your hands on an Intel Xeon 7850B that is commercially available and you're like, okay, it, these six things are similar in terms of performance in hardware. So we're using this as the proxy for the M5 large or whatever it is. And then once you've got the power consumption of those instance types,

then you can start saying, okay, this is how we, this is how we're mapping instances to real life hardware. And then that's when you've gotta start being really transparent about the assumptions, because ultimately there's no right answer. All you can do is tell people, this is how we do it. Do you like it?

Do you?

And you know, over the four years we've been doing this, you know, there's been a lot of trial and error. Actually, right at the start, one of the questions was, what are my credentials? How did I end up as head of GreenOps? I wouldn't have said four years ago I have any credentials to be, you know, a head of GreenOps.

So it was a while when I was the only head of GreenOps in the world, according to a Sales Navigator. Why me? But I think it's like, you know, they say if you do 10,000 hours of anything, you kind of, you become good at it. And I wouldn't say I'm a master by any means, but I've made more mistakes and probably tried more things than anybody else over the four years.

So, you know, just, from the war stories, I've seen what works. I've seen what doesn't work. And I think that's the kind of, that's the kind of experience people wanna trust. And why Greenpixie made me the head of GreenOps.

Chris Adams: Okay. All right. Thanks for that, James. So maybe this is actually a nice segue to talk about a common starting point that lots of people do actually have. So over the last few years, we've also seen people talk about move from not moved away, not just talking about DevOps, but talking about like FinOps.

This idea that you might apply kind of some financial thinking to how you purchase and consume, say, cloud services for example. And this tends to, as far as I understand, kinda nudge people towards things like serverless or certain kinds of ways of buying it in a way, which is almost is, you know, very much influenced by fi by I guess the financial sector.

And you said before that there's some overlap, but it's not totally over there, it's not, you can't just basically take a bunch of FinOps practices and think it's gonna actually help here. Can we explore that a bit and maybe talk a little bit about what folks get wrong when they try to like map this straight across as if it's the same thing?

Please.

James Hall: Yeah, so one of the big issues is cost proxies, actually. Yeah, a lot of FinOps as well, how do you fix, or how do you optimize from a cost perspective? What already exists? You know, you've already emitted it. How do you now make it cheaper? The first low hanging fruit that a finance guy trying to reduce their cloud spend would do is things like, you know, buy the instances up front.

So you've paid for the full year and now you've been given a million hours of compute.

That would might, that might cut your bill in half, but if anything that would drive your usage up, you know, you've got a million hours, you are gonna use them.

Chris Adams: Commit to, so you have to commit to then spending a billion. You're like, "oh, great. I have the cost, but now I definitely need to use these." Right?

James Hall: Yeah, exactly. And like, yeah, you say commitments. Like I promise AWS I'm gonna spend $2 million, so I'm gonna do whatever it takes to spend that $2 million. If I don't spend $2 million, I'll actually have to pay the difference. So if I only do a million in compute, I'm gonna have to pay a million and get nothing for it.

So I'm gonna do as much compute as humanly possible to get the most bang for my back. And I think that's where a lot of the issues is with using costs. Like if you tell someone something's cheap, they're not gonna use less, they're gonna be like, "this looks like a great deal." I'm guilty of it myself. I'll buy clothes I don't need 'cause it's on a clearance sale.

You know? And that's kind of how cloud operates. But when you start looking at, when you get a good methodology that really looks at the usage and the nuances between chip sets and storage tiers, you know, there is a big overlap between, you know, cutting the cost from a 2X large to a large that may halve your bill, and it will halve your carbon. And that's the kind of things you need to be looking out for. You need a really nuanced methodology that really looks at the usage more than just trying to use costs.

Chris Adams: Okay, so that's one place where it's not so helpful. And you said a little bit like there are some places where it does help, like literally just having the size of the machine is one of the things you might actually do. Now I've gotta ask, you spoke before about like region shifting and stuff, something you mentioned before.

Is there any incentive to do anything like that when you are looking at buying stuff in this way? Or is there any kind of, what's the word I'm after, opinion that FinOps or GreenOps has around things like that because as far as I can tell, there isn't, there is very rarely a financial incentive to do anything like that.

If anything, it costs, usually costs more to use, maybe say, run something in, say Switzerland for example, compared to running an AWS East, for example. I mean, is that something you've seen, any signs of that where people kind of nudge people towards the greener choice rather than just showing like a green logo on a dashboard for example?

James Hall: Well, I mean, this is where GreenOps comes into its own really, because I could tell everyone to move to France or Switzerland, but when you come to each individual cloud environment, they will have policies and approved regions and data sovereignty things, and this is why all you can do is give them the data and then let the enterprise make the decision. But ultimately, like we are working with a retailer who had a failover for storage and compute, but they had it all failing over to one of the really dirty regions, like I think they were based in the UK and they failed over to Germany, but they did have Sweden as one of the options for failover, and they just weren't using it.

There's no particular reason they weren't using it, but they had just chosen Germany at one point. So why not just make that failover option Sweden? You know, if it's within the limits of your policies and what you're allowed to do. But, the region switching is completely trivial, unfortunately, in the cloud.

So you know, you wouldn't lift and shift your entire environment to another place because there are performance, there are cost implications, but again, it's like how do you add sustainability impact to the trade-off decision? You know, if increasing your cost 10% is worth a 90% carbon reduction for you, great.

Please do it if you know the hours of work are worth it for you. But if cost is the priority, where is the middle ground where you can be like, okay, these two regions are the same, they have the same latency, but this one's 20% less carbon. That is the reason I'm gonna move over there. So it's all about, you've already, you can do the cost benefit analysis quite easily, and many people do.

But how do you enable them to do a carbon benefit analysis as well? And then once they've got all the data in front of them, just start making more informed decisions. And that's why I think the data is more important than, you know, necessarily telling them what the processes are, giving them the, here's the Ultimate Guide to GreenOps. You know, data's just a catalyst for decisions and if you just need to give them trustworthy data. And then how many use cases does trustworthy data have? You know, how many, how long is a piece of string? I've seen many, but every time there's a new customer, there's new use cases.

Chris Adams: Okay, cool. Thank you for that. So, one thing that we spoke before in this kind of pre-call was the fact that, sustainability is becoming somewhat more mainstream. And there's now, within the kind of FinOps foundation or the people who are doing stuff for FinOps are starting to kind of wake up to this and trying to figure out how to incorporate some of this into the way they might kind of operate a team or a cloud or anything like that.

And you. I believe you told me about a thing called FOCUS, which is, this is like something like a standardization project across all the FinOps and then, and now there's a sustainability working group, particularly inside this FOCUS group. For people who are not familiar with this, could you tell me what FOCUS is and what this sustainability working group as well working on?

You know, 'cause working groups are supposed to work on stuff, right?

James Hall: Yeah, so as exactly as you said, FOCUS is a standardization of billing data. So you know, when you get your AWS bill, your Azure bill, they have similar data in them. But they will be completely different column names. Completely different granularities, different column sizes. And so if you're trying to make a master report where you can look at all of your cloud and all of your SaaS bills, you need to do all sorts of data transformations to try and make the columns look the same.

You know, maybe AWS has a column that goes one step more granular than Azure, or you're trying to, you know, do a bill on all your compute, but Azure calls it virtual machines. AWS calls it EC2. So you either need to go and categorize them all yourself to make a, you know, a master category that lets you group by all these different things or, you know, thankfully FOCUS have gone and done that themselves, and it started off as a, like a Python script you could run on your own data set to do the transformation for you, but slowly more cloud providers are adopting the FoCUS framework, which means, you know, when you're exporting your billing data, you can ask AWS give me the original or give me a FOCUS one. So they start giving you the data in a way where it's like, I can easily combine all my data sets. And the reason this is super interesting for carbon is because, you know, carbon is a currency in many ways, in the fact that the, 

Chris Adams: there's price on it in Europe. There's a price on it in the UK. Yeah.

James Hall: There's a price on it, but also like the way Azure will present you, their carbon data could be, you know, the equivalent of yen, AWS could be the equivalent of dollars.

They're all saying CO2 E, so you might think they're equivalent, but actually they're almost completely different currencies. So this effort of standardization is how do we bring it back? Maybe like, don't give us the CO2 E, but how do we go a few steps before that point and like, how do we start getting similar numbers?

So when we wanna make a master report for all the cloud providers, it's apples to apples, not apples to oranges. You know, how do we standardize the data sets to make the reporting, the cross cloud reporting more meaningful for FinOps people?

Chris Adams: Ah, I see. Okay. So I didn't realize that the FOCUS stuff has actually listing, I guess like what the, let's, call them primitives, like, you know, compute and storage. Like they all have different names for that stuff, but FOCUS has a kind of shared idea for what the concept of cloud compute, a virtual machine might be, and likewise for storage.

So that's the thing you are trying, you're trying to apply, attach a carbon value to in these cases, so you can make some meaningful judgment or so you can present that information to people. 

James Hall: Yeah, it's about making the reports at the same, but also how do you make the numbers, the source of the numbers more similar? 'Cause currently, Azure may say a hundred tons in their dashboard. AWS may say one ton in their dashboard. You know, the spend and the real carbon could be identical, but it's just the formula behind it is so vastly different that you're coming out with two different numbers.

Chris Adams: I see. I think you're referring to at this point here. Some places they might share a number, which is what we refer to as a location based figure. So that's like, what was kind of considered on the ground based on the power intensity from the grid in like a particular part of the world.

And then a market based figure might be quite a bit lower. 'Cause you said, well, we've purchased all this green energy, so therefore we are gonna kind of deduct that from what a figure should be. And that's how we'd have a figure of like one versus 100. But if you're not comparing these two together. It's gonna, these are gonna look totally different.

And you, like you said, it's not apples. With apples. It's apples with very, yeah. It's something totally different. Okay. That is helpful.

James Hall: It gets a lot more confusing than that 'cause it's not just market and location based. Like you could have two location based numbers, but Azure are using the grid carbon intensity annual average from 2020 because that's what they've got approved. AWS may be using, you know, Our World in Data 2023 number, you know, and those are just two different sources for grid intensity.

And then what categories are they including? Are they including Scope 3 categories? How many of the scope 2 categories are they including? So when you've got like a hundred different inputs that go into a CO2 number, unless all 100 are the same, you do not have a meaningful comparison between the two.

Even location/market based is just one aspect of what goes into the CO2 number, and then where do they get the kilowatt hour numbers from? Is it a literal telemetry device? Or are they using a spend based property on their side? Because that's not completely alien to cloud providers to ultimately rely on spend at the end of the day.

So does Azure use spend or does AWS use spend? What type of spend are they using? And that's where you need the transparency as well, because if you don't understand where the numbers come from, it could be the most accurate number in the world, but if they don't tell you everything that went into it, how are you meant to know?

Chris Adams: I see. Okay. That's really interesting. 'Cause the Green Web Foundation, the organization I'm part of, there is a gov, there's a UK government group called the Government Digital Sustainability Alliance. And they've been doing these really fascinating lunch and learns and

one thing that showed up was when the UK government was basically saying, look, these are, this is the carbon footprint, you know, on a kind of per department level. Like this is what the Ministry of Justice is, or this is what say the Ministry of Defense might be, for example. And that helps explain why you had figures where you had a bunch of people saying the carbon footprint of all these data centers is really high.

And then you said they, there were people talking about saying, well, we're comparing this to cloud looks great, but 'cause the figures for cloud are way lower. But the thing they, the thing that I was that people had to caveat that with, they basically said, well, we know that this makes cloud look way more efficient here, and it looks like it's much more, much lower carbon, but because we've only got this final kind of market based figure, we know that it's not a like for like comparison, but until we have that information, we're, this is the best we actually have. And this, is an organization which actually has like legally binding targets. They have to reduce emissions by a certain figure, by a certain date. This does seem like it has to be, I can see why you would need this transparency because it seems very difficult to see how you could meaningfully track your progress towards a target if you don't have access to that.

Right?

James Hall: Yeah. Well, 

I always like to use the currency conversion analogy. If you had a dashboard where AWS is all in dollars, Azure, or your on premise is in yen. There's 149 yen in 1 dollar. So, but if you didn't know this one's yen and this one's dollars, you'd be like, "this one's 149 times cheaper. Why aren't we going all in on this one?"

But actually it's just different currencies. And they are the same at the end of the day. Under the hood, they're the same. But, know, just the way they've turned it into an accounting exercise has kind of muddied the water, which is why I love electricity metrics more. You know, they're almost like the, non fungible token of, you know, data centers and cloud.

'Cause you can use that to calculate location-based. You can use calculate market-based. You can use electricity to calculate water cooling and metrics and things like that. So if you can get the electricity, then you're well on your way to meaningful comparisons.

Chris Adams: And that's the one that everyone guards very jealously a lot of the time, right?

James Hall: Exactly. Yeah. Well that's directly related to your cost of running business and that is the proprietary information.

Chris Adams: I see. Okay. Alright, so we spoke, we've done a bit of a deep dive into the GSG protocol, scope 3, supply chain emissions and things like that. If I may, you mentioned, you, referenced this idea of war stories before. Right. And I. It's surprisingly hard to find people with real world stories about okay, making meaningful changes to like cloud emissions in the world.

Do you have any like stories that you've come across in the last four years that you think are particularly worth sharing or that might be worth, I dunno, catch people's attention, for example. Like there's gotta be something that you found that you are allowed to talk about, right.

James Hall: Yeah, I mean, MasterCard, one of our Lighthouse customers, they've spoken about the work we're doing with them a lot in, at various FinOps conferences and things like that. But they're very advanced in their GreenOps goals. They have quite ambitious net zero goals and they take their IT sustainability very seriously.

Yeah, when we first spoke to them. Ultimately the name of the game was to get the cloud measurement up to the point of their on-premise. 'Cause their on-premise was very advanced, daily electricity metrics with pre-approved, CO2 numbers or CO2 carbon coefficients that multiplied the, you multiply the electricity with.

But they were getting, having no luck with cloud, essentially, you know, they spend a lot in the cloud and, but they, they were honestly like, rather than going for just the double wins, which is kind of what most people wanna do, where it's like, I'm gonna use this as a mechanism to save more money.

They honestly wanted to do no more harm and actually start making decisions purely for the sustainability benefits. And we kind of went in there with the FinOps team, worked on their FinOps reporting, combined it with their FinOps recommendations and the accountability, which is their tool of choice.

But then they started having more use cases around. How do they use our carbon data, not our electricity data from the cloud or like, because we have a big list of hourly carbon coefficients. They wanna use that data to start choosing where they put their on-premise data centers as well, and like really making the sustainability impact a huge factor in where they place their regions, which I think is a very interesting one. 'Cause we had only really focused on how do we help people in their public cloud. But they wanted to align their on-premise reporting with their cloud reporting and ultimately start even making decisions. Okay, I know I need to put a data center in this country.

Do I go AWS, Azure, or on-prem for this one? And what is the sustainability impact of all three? And, you know, how do I weigh that against the cost as well? And it's kind of like the golden standard of making sustainability a big part of the trade-off decision. 'Cause they would not go somewhere, even if it saved them 50% of their cost, if it doubled their carbon. They're way beyond that point. So they're a super interesting one. And even in public sector as well, like the departments we are working with are relatively new to FinOps and they didn't really have like a proper accountability structure for their cloud bill. But when you start adding carbon data to it, you are getting a lot more eyes onto the, onto your bills and your usage.

And ultimately we help them create that more of a FinOps function just with the carbon data. 'Cause people find carbon data typically more interesting than spend data. But if you put them on the same dashboard, now it's all about how do you market efficient usage? And I think that's one of the main, use cases of GreenOps is to get more eyes or more usage.

So, 'cause the more ideas you've got piling in, the more use cases you find and.

Chris Adams: Okay. Alright, so we spoke, so you spoke about carbon as one of the main things that people are caring about, right. And we're starting to develop more of an awareness that maybe some data centers might themselves be exposed to kind of climate risks themselves. Because I know they were built on a floodplain, for example.

And you don't want a data center on a floodplain in the middle of a flood, for example. Right. but there's also like the flip side, you know, that's too much water. But there are cases where people worry about not enough water, for example. I mean, is that something that you've seen people talk about more of?

Because there does seem to be a growing awareness about the water footprint of digital infrastructure as well now. Is that something you're seeing people track or even try to like manage right now?

James Hall: Well, 

we find that water metrics are very popular in the US more so than the CO2 metrics, and I think it's because the people there feel the pain of lack of water. You know, you've got the Flint water crisis. In the UK, we've got an energy crisis stopping people from building homes. So what you really wanna do is enable the person who's trying to use this data to drive efficiency, to tell as many different stories as

is possible,. You know, the more metrics and the more choice they have of what to present to the engineers and what to present to leadership, the better outcomes they're gonna get. Water is a key one because data centers and electricity production uses tons of water. And the last thing you wanna do is, you know, go to a water scarce area and put a load of servers in there that are gonna guzzle up loads of water. One, because if that water runs out, your whole data center's gonna collapse. So it's, you're exposing yourself to ESG risk. And also, you know, it doesn't seem like the right thing to do. There are people trying to live there who need to use that water to live.

But you know, you've got data centers sucking that water out, so you know, can't you use this data to again, drive different decisions, could invoke an emotional response that helps people drive different decisions or build more efficiently. And if you're saving cost at the end of that as well, then everyone's happy.

Chris Adams: So maybe this is actually one thing we can talk about because, or just like, drill into before we kind of, move on to the next question and wrap up. So we, people have had incentives to track cost and cash for obvious reasons, carbon, as you're seeing more and more laws actually have opinions about carbon footprint and being able to report that people are getting a bit more aware of it.

Like we've spoken about things like location based figures and market based figures. And we have previous episodes where we've explored and actually kind of helped people define those terms. But I feel comfortable using relatively technical terminology now because I think there is a growing sophistication, at least in certain pockets, for example.

Water still seems to be a really new one, and it seems to be very difficult to actually have, find access to meaningful numbers. Even just the idea of like water in the first place. Like you, when you hear figures about water being used, that might not be the same as water. Kind of.

It's not, it might not be going away, so it can't be used. It might be returned in a way that is maybe more difficult to use or isn't, or is sometimes it's cleaner, sometimes it's dirtier, for example. But this, it seems to be poorly understood despite being quite an emotional topic. Have you, yeah, what's your experience been like when people try to engage with this or when you try to even find some of the numbers to present to people and dashboards and things?

James Hall: Yeah. So yeah, surprisingly, all the cloud providers are able to produce factors. I think it's actually a requirement that when you have a data center, you know what the power usage effectiveness is, so what the overhead electricity is, and you know what the water usage effectiveness is. So you know, what is your cooling system, how much water does it use, how much does it withdraw?

Then how much does it actually consume? So the difference between withdrawal and consumption, is withdrawal is you let you take clean water out, you're able to put clean water back relatively quickly. Consumption is you have either poisoned the water with some kind of, you know, you've diluted it or you know, with some kind of coolant that's not fit for human consumption or you've now evaporated it.

And there is some confusion sometimes around "it's evaporated, but it'll rain. It'll rain back down." But, you know, a lake's evaporation and redeposition processs is ike a delicate balance. If it, you know, evaporates 10,000 liters a day and rains 10,000 liters a day after, like a week of it going into the clouds and coming back down the mountain nearby.

If you then have a data center next to it that will accelerate the evaporation by 30,000 leases a day, you really upset the delicate balance that's in there and that, you know, you talk about are these things sustainable? Like financial sustainability is, do you have enough money and income to last a long time, or will your burn rate run out next month?

And it's the same with, you know, sustainability. I think fresh water is a limiting resource in the same way a company's bank balance is their limiting resource. There's a limited amount of electricity, there's a limited amount of water out there. 

I think it was the cEO of Nvidia. I saw a video of him on LinkedIn that said, right now the limit to your cloud environment is how much money you can spend on it.

But soon it will be how much electricity is there? You know, you could spend a trillion dollars, but if there's no more room for electricity, there's no more electricity to be produced, then you can't build anymore data centers or solar farms. And then water's the other side of that.

I think water's even worse because we need water to even live. And you know what happens when there's no more water because the data centers have it. I think it invokes a much more emotional response. When you have good data that kind of is backed by good sources, you can tell an excellent story of why you need to start reducing.

Chris Adams: Okay, well hopefully we can see more of those numbers because it seems like it's something that is quite difficult to get access to at the moment. Water's it, water in particular. Alright, so we're coming to time now and one thing we spoke about in the prep call was talking about the GSG protocol.

We did a bit but nerd like nerding into this and you spoke a little bit about yes, accuracy is good, but you can't just only focus on accuracy if you want someone to actually use any of the tools or you want people to adopt stuff, and you said that in the GHG protocol, which is like the gold standard for people working out kind of the, you know, carbon footprint of things.

You said that there were these different pillars inside of that matter. And if you just look at accuracy, that's not gonna be enough. So can you maybe expand on that for people who maybe aren't as familiar with the GSG protocol as you? Because I think there is something that, I think, that there, there's something there that's worth, I think, worth exploring.

James Hall: Yeah. So it just as a reminder for those out there, the pillars are accuracy, yes, completeness, consistency, transparency, and relevance. A lot of people worry a lot about the accuracy, but, you know, just to give an example that if you had the most amazing, accurate number for your entire cloud environment, you know, 1,352 tons 0.16 grams, but you are one engineer under one application, running a few resources, the total carbon number is completely

useless to you, to be honest. Like how do you make, use that number to make a decision for your tiny, you know, maybe five tons of information. So really you've got to balance all of these things. You know, the transparency is important because you need to build trust in the data. People need to understand where it comes from.

The relevance is, you know, again, are you filtering on just the resources that are important to me? And the consistency touches on, aWS is one ton versus Azure is 100 tons. You can't decide which cloud provider to go into based on these numbers because you know, they're marking their own homework. They've got a hundred different ways to calculate these things. And then the completeness is around, if you're only doing compute, but 90% is storage, you are missing out on loads of information. You know, you could have a super accurate compute for Azure, but if you've got completely different numbers for AWS and you dunno where they come from, you've not got a good data set, a good GreenOps data set to be able to drive decisions or use as a catalyst.

So you really need to prioritize all five of these pillars in an equal measure and treat them all as a priority rather than just go for full accuracy.

Chris Adams: Brilliant. We'll sure make a point of sharing a link to that in the show notes for anyone else who wants to dive into the world of pillars of sustainability reporting, I suppose. Alright. Okay. Well, James, I think that takes us to time. So just before we wrap up, there's gonna be usual things like where people can find you, but are there any particular projects that are catching your eye right now that you are kind of excited about or you'd like to direct people's attention to? 'Cause we'll share a link to the company you work for, obviously, and possibly yourself on LinkedIn or whatever it is. But is there anything else that you've seen in the last couple of weeks that you find particularly exciting in the world of GreenOps or kind of the wider sustainable software field? 

James Hall: Yeah, I mean, a lot of work being done around AI sustainability is particularly interesting. I recommend people go and look at some of the Hugging Face information around which models are more electrically efficient. And from a Greenpixie side, we've got a newsletter now for people wanting to learn more about GreenOps and in fact, we're building out a GreenOps training and certification that I'd be very interested to get a lot of people's feedback on.

Chris Adams: Cool. Alright, well thank you one more time. If people wanna find you on LinkedIn, they would just look up James Hall Greenpixie, presumably right? Or something like that.

James Hall: Yeah, and go to our website as well.

Chris Adams: Well James, thank you so much for taking me along to this deep dive into the world of GreenOps ,cloud carbon reporting and all the, and the rest. Hope you have a lovely day and yeah. Take care of yourself mate. Cheers.

James Hall: Thanks so much, Chris.  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

Chris Adams: To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Host Chris Adams sits down with James Hall, Head of GreenOps at Greenpixie, to explore the evolving discipline of GreenOps—applying operational practices to reduce the environmental impact of cloud computing. They discuss how Greenpixie helps organizations make informed sustainability decisions using certified carbon data, the challenges of scaling cloud carbon measurement, and why transparency and relevance are just as crucial as accuracy. They also discuss using financial cost as a proxy for carbon, the need for standardization through initiatives like FOCUS, and growing interest in water usage metrics.

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TRANSCRIPT BELOW:


James Hall: We want get the carbon data in front of the right people so they can put climate impact as part of the decision making process. Because ultimately, data in and of itself is a catalyst for change. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello and welcome to Environment Variables where we explore the developing world of sustainable software development. We kicked off this podcast more than two years ago with a discussion about cloud carbon calculators and the open source tool, Cloud Carbon Footprint, and Amazon's cloud carbon calculator.

And since then, the term GreenOps has become a term of art in cloud computing circles when we talk about reducing the environmental impact of cloud computing. But what is GreenOps in the first place? With me today is James Hall, the head of GreenOps at Greenpixie, the cloud computing startup, cloud carbon computing startup,

to help me shed some light on what this term actually means and what it's like to use GreenOps in the trenches. James, we have spoken about this episode as a bit of a intro and I'm wondering if I can ask you a little bit about where this term came from in the first place and how you ended up as the def facto head of GreenOps in your current gig.

Because I've never spoken to a head of GreenOps before, so yeah, maybe I should ask you that.

James Hall: Yeah, well, I've been with Greenpixie right from the start, and we weren't really using the term GreenOps when we originally started. It was cloud sustainability. It was about, you know, changing regions to optimize cloud and right sizing. We didn't know about the FinOps industry either. When we first started, we just knew there was a cloud waste problem and we wanted to do something about it.

You know, luckily when it comes to cloud, there is a big overlap between what saves costs and what saves, what saves carbon. But I think the term GreenOps has existed before we started in the industry. I think it, yeah, actually originally, if you go to Wikipedia, GreenOps, it's actually to do with arthropods and Trilobites from a couple million years ago, funnily enough, I'm not sure when it started becoming, you know, green operations.

But, yeah, it originally had a connotation of like data centers and IT and devices and I think Cloud GreenOps, where Greenpixie specializes, is more of a recent thing because, you know, it used to be about, yeah, well it is about how do you get the right data in front of the right people so they can start making better decisions, ultimately.

And that's kind of what GreenOps means to me. So Greenpixie are a GreenOps data company. We're not here to make decisions for you. We are not a consultancy. 

We want get the carbon data in front of the right people so they can put climate impact as part of the decision making process. Because ultimately, data in and of itself is a catalyst for change.

You know, whether you use this data to reduce carbon or you choose to ignore it, you know, that's up to the organization. But it's all about being more informed, ignoring or, you know, changing your strategy around the carbon data.

Chris Adams: Cool. Thank you for that, James. You mentioning Wikipedia and Greenops being all about Trilobites and Arthropods, it makes me realize we definitely should add that to the show notes and that's the thing I'll quickly just do because I forgot to just do the usual intro folks. Yeah, my name's Chris Adams.

I am one of the policy director, technology and policy director at the Green Web Foundation, and I'm also the chair of the policy working group inside the Green Software Foundation. All the things that James and I'll be talking about, we'll do our best to judiciously add show notes so you can, you too can look up the origins of, well, the etymology of GreenOps and find out all about arthropods and trilobites and other.

And probably a lot more cloud computing as well actually. Okay. Thank you for that James. So you spoke a little and you did a really nice job of actually introducing what Greenpixie does. 'Cause that was something I should have asked you earlier as well. So I have some experience using these tools, like Cloud Carbon Footprint and so on to estimate the environmental impact of digital services. Right. And a lot of the time these things use billing data. So there are tools out there that do already do this stuff. But one thing that I saw that sets Greenpixie apart from some other tools as well, was the actual, the certification process, the fact that you folks have, I think, an ISO 14064 certification.

Now, not all of us read over ISO standards for fun, so can you maybe explain why that matters and what that actually, what that changes at all, or even what that certification means? 'Cause, It sounds kind of impressive and exciting, but I'm not quite sure, and I know there are other standards floating around, like the Software Carbon Intensity standard, for example.

Like yeah, maybe you could just provide an intro, then see how that might be different, for example.

James Hall: Yeah, so ISO 14064 is a kind of set of standards and instructions on how to calculate a carbon number, essentially based on the Greenhouse Gas Protocol. So the process of getting that verification is, you know, you have official auditors who are like certified to give out these certifications, and ultimately they go through all your processes, all your sources, all the inputs of your data, and kind of verify that the outputs and the inputs

make sense. You know, do they align with what the Greenhouse Gas Protocol tells you to do? And, you know, it's quite a, it's a year long process as they get to know absolutely everything about your business and processes, you really gotta show them under the hood. But from a customer perspective, it means you know, that it proves that

the methodology you're using is very rigorous and it gives them confidence that they can use yours. I think if a company that produces carbon data has an ISO badge, then you can probably be sure that when you put this data in your ESG reports or use it to make decisions, the auditors will also agree with it.

'Cause the auditors on the other side, you know, your assurers or from EY and PWC, they'll be using the same set of guidance basically. So it's kind of like getting ahead of the auditing process in the same way, like a security ISO would mean the security that the chief security officer that would need to, you know, check a new vendor that they're about to procure from.

If you've got the ISO already, you know they meet our standards for security, it saves me a job having to go and look through every single data processing agreement that they have.

Chris Adams: Gotcha. Okay. So there's a few different ways that you can kind of establish trust. And so one of the options is have everything entirely open, like say Cloud Carbon Footprint or OpenCost has a bunch of stuff in the open. There's also various other approaches, like we maintain a library called CO2.js, where we try to share our methodologies there and then one of the other options is certification. That's another source of trust. I've gotta ask, is this common? Are there other tools that have this? 'Cause when I think about some of the big cloud calculators, do you know if they have this, let's say I'm using say, a very, one of the big three cloud providers.

Do these have, like today, do you know if they actually have the same certification or is that a thing I should be looking for or I should be asking about if I'm relying on the numbers that I'm seeing from our providers like this.

James Hall: Yeah, they actually don't. Well, technically, Azure. Azure's tool did get one in 2020, but you need to get them renewed and reordered as part of the process. So that one's kind of becoming invalid. And I'm not sure AWS or Google Cloud have actually tried, to be honest, but it's quite a funny thought that, you know, it's arguably because this ISO the, data we give you on GCP and AWS is more accurate than the data, or at least more reliable than the data that comes directly out the cloud providers.

Chris Adams: Okay. Alright. Let's, make sure we don't get sued. So I'm just gonna stop there before we go any further. But that's like one of the things that it provides. Essentially it's an external auditor who's looked through this stuff. So rather than being entirely open, that's one of the other mechanisms that you have.

Okay, cool. So maybe we can talk a little bit more about open source. 'Cause I actually first found out about Greenpixie a few years ago when the Green Software Foundation sent me to Egypt, for COP 27 to try and talk to people about green software. And I won't lie, I mostly got blank looks from most people.

You know, they, the, I, there are, 

people tend to talk about sustainability of tech or sustainability via tech, and people tend not to see them as, most of the time I see people like conflating the two rather than actually realizing no, we're talking about of the technology, not just how it's good for stuff, for example, and he told me, I think one of your colleagues, Rory, was this, yeah.

He was telling me a bit about, that Greenpixie was initially using, when you just first started out, you started looking at some tools like Cloud Carbon Footprint as maybe a starting point, but you've ended up having to make various changes to overcome various technical challenges when you scale the use up to like a large, to well, basically on a larger clients and things like that. Could you maybe talk a little bit about some of the challenges you end up facing when you're trying to implement GreenOps like this? Because it's not something that I have direct experience myself. And it's also a thing that I think a lot of people do reach for some open source tools and they're not quite sure why you might use one over the other or what kind of problems they, that they have to deal with when you start processing that, those levels of like billing and usage data and stuff like that.

James Hall: I think with the, with cloud sustainability methodologies, the two main issues are things like performance and the data volume, and then also the maintenance of it. 'Cause just the very nature of cloud is you know, huge data sets that change rapidly. You know, they get updated on the hour and then you've also got the cloud providers always releasing new services, new instance types, things like that.

So, I mean, like your average enterprises with like a hundred million spend or something? Yeah. Those line items of usage data, if you like, go down to the hour will be billions of rows and terabytes of data. And that is not trivial to process. You know, a lot of the tooling at the moment, including Cloud Carbon Footprint, will try to, you know, use a bunch of SQL queries to truncate it, you know, make it go up to monthly.

So you kind of take out the rows by, you know, a factor of 24 times 30 or whatever that is. It's about 740, I think. Something like that (720). Yeah. Yeah. So, and they'll remove things like, you know, there's certain fields in the usage data that will, that are so unique that when you start removing those and truncating it, you're really reducing the size of the files, but you are really losing a lot of that granularity.

'Cause ultimately this billing data is to be used by engineers and FinOps people. They use all these fields. So when you start removing fields because you can't handle the data, you're losing a lot of the familiarity of the data and a lot of the usability for the people who need to use it to make decisions.

So one of the big challenges is how do you make a processor that can easily handle billions of line items without, you know, falling over. And CCF, one of the issues was the performance really when you start trying to apply it to big data sets. And then on the other side is the maintenance.

You know, arguably it's probably not that difficult to make a methodology of a point in time, but you know, over the six months it takes you to create it, it's way out date. You know, they've released a hundred new instance types across the three providers. There's a new type of storage, there's a brand new services, there's new AI models out there.

And so now, like Greenpixie's main job is how do we make sure the data is more, we have more coverage of all the skews that come out and we can deliver the data faster and customers have more choices of how to ingest it. So if you give customers enough choice and you give it to them quick enough and it's, you know, covering all of their services, then you know, that's what those, lack of those three things is really what's stopping people from doing GreenOps, I think.

Chris Adams: Ah, okay, so one of them was, one of the things you mentioned was just the volume, the fact that you've got, you know, hours multiply the number of different, like a thousand different computers or thousands of computers. That's a lot of data. And then there's a, there's like one of the issues about like the metrics issue, like you, if you wanna provide a simple metric, then you end up losing a lot of data.

So that's one of the things you spoke about. And the other one was just the idea of models themselves not being, there's natural cost associated with having to maintain these models. And as far as I'm aware, there aren't, I mean, are there any kind of open sources of models so that you can say, well this is what the figures probably would be for an Amazon EC, you know, 6XL instance, for example.

That's the stuff you're talking to when you say the models that you, they're hard to actually up to, hard to keep up to date, and you have to do that internally inside the organization. Is that it?

James Hall: Yes, we've got a team dedicated to doing that. But ultimately, like there will always be assumptions in there. 'Cause some of these chip sets you actually can't even get your hands on. So, you know, if Amazon release a new instance type that uses an Intel Xeon 7850C, that is not commercially available.

So how do you get your hands on an Intel Xeon 7850B that is commercially available and you're like, okay, it, these six things are similar in terms of performance in hardware. So we're using this as the proxy for the M5 large or whatever it is. And then once you've got the power consumption of those instance types,

then you can start saying, okay, this is how we, this is how we're mapping instances to real life hardware. And then that's when you've gotta start being really transparent about the assumptions, because ultimately there's no right answer. All you can do is tell people, this is how we do it. Do you like it?

Do you?

And you know, over the four years we've been doing this, you know, there's been a lot of trial and error. Actually, right at the start, one of the questions was, what are my credentials? How did I end up as head of GreenOps? I wouldn't have said four years ago I have any credentials to be, you know, a head of GreenOps.

So it was a while when I was the only head of GreenOps in the world, according to a Sales Navigator. Why me? But I think it's like, you know, they say if you do 10,000 hours of anything, you kind of, you become good at it. And I wouldn't say I'm a master by any means, but I've made more mistakes and probably tried more things than anybody else over the four years.

So, you know, just, from the war stories, I've seen what works. I've seen what doesn't work. And I think that's the kind of, that's the kind of experience people wanna trust. And why Greenpixie made me the head of GreenOps.

Chris Adams: Okay. All right. Thanks for that, James. So maybe this is actually a nice segue to talk about a common starting point that lots of people do actually have. So over the last few years, we've also seen people talk about move from not moved away, not just talking about DevOps, but talking about like FinOps.

This idea that you might apply kind of some financial thinking to how you purchase and consume, say, cloud services for example. And this tends to, as far as I understand, kinda nudge people towards things like serverless or certain kinds of ways of buying it in a way, which is almost is, you know, very much influenced by fi by I guess the financial sector.

And you said before that there's some overlap, but it's not totally over there, it's not, you can't just basically take a bunch of FinOps practices and think it's gonna actually help here. Can we explore that a bit and maybe talk a little bit about what folks get wrong when they try to like map this straight across as if it's the same thing?

Please.

James Hall: Yeah, so one of the big issues is cost proxies, actually. Yeah, a lot of FinOps as well, how do you fix, or how do you optimize from a cost perspective? What already exists? You know, you've already emitted it. How do you now make it cheaper? The first low hanging fruit that a finance guy trying to reduce their cloud spend would do is things like, you know, buy the instances up front.

So you've paid for the full year and now you've been given a million hours of compute.

That would might, that might cut your bill in half, but if anything that would drive your usage up, you know, you've got a million hours, you are gonna use them.

Chris Adams: Commit to, so you have to commit to then spending a billion. You're like, "oh, great. I have the cost, but now I definitely need to use these." Right?

James Hall: Yeah, exactly. And like, yeah, you say commitments. Like I promise AWS I'm gonna spend $2 million, so I'm gonna do whatever it takes to spend that $2 million. If I don't spend $2 million, I'll actually have to pay the difference. So if I only do a million in compute, I'm gonna have to pay a million and get nothing for it.

So I'm gonna do as much compute as humanly possible to get the most bang for my back. And I think that's where a lot of the issues is with using costs. Like if you tell someone something's cheap, they're not gonna use less, they're gonna be like, "this looks like a great deal." I'm guilty of it myself. I'll buy clothes I don't need 'cause it's on a clearance sale.

You know? And that's kind of how cloud operates. But when you start looking at, when you get a good methodology that really looks at the usage and the nuances between chip sets and storage tiers, you know, there is a big overlap between, you know, cutting the cost from a 2X large to a large that may halve your bill, and it will halve your carbon. And that's the kind of things you need to be looking out for. You need a really nuanced methodology that really looks at the usage more than just trying to use costs.

Chris Adams: Okay, so that's one place where it's not so helpful. And you said a little bit like there are some places where it does help, like literally just having the size of the machine is one of the things you might actually do. Now I've gotta ask, you spoke before about like region shifting and stuff, something you mentioned before.

Is there any incentive to do anything like that when you are looking at buying stuff in this way? Or is there any kind of, what's the word I'm after, opinion that FinOps or GreenOps has around things like that because as far as I can tell, there isn't, there is very rarely a financial incentive to do anything like that.

If anything, it costs, usually costs more to use, maybe say, run something in, say Switzerland for example, compared to running an AWS East, for example. I mean, is that something you've seen, any signs of that where people kind of nudge people towards the greener choice rather than just showing like a green logo on a dashboard for example?

James Hall: Well, I mean, this is where GreenOps comes into its own really, because I could tell everyone to move to France or Switzerland, but when you come to each individual cloud environment, they will have policies and approved regions and data sovereignty things, and this is why all you can do is give them the data and then let the enterprise make the decision. But ultimately, like we are working with a retailer who had a failover for storage and compute, but they had it all failing over to one of the really dirty regions, like I think they were based in the UK and they failed over to Germany, but they did have Sweden as one of the options for failover, and they just weren't using it.

There's no particular reason they weren't using it, but they had just chosen Germany at one point. So why not just make that failover option Sweden? You know, if it's within the limits of your policies and what you're allowed to do. But, the region switching is completely trivial, unfortunately, in the cloud.

So you know, you wouldn't lift and shift your entire environment to another place because there are performance, there are cost implications, but again, it's like how do you add sustainability impact to the trade-off decision? You know, if increasing your cost 10% is worth a 90% carbon reduction for you, great.

Please do it if you know the hours of work are worth it for you. But if cost is the priority, where is the middle ground where you can be like, okay, these two regions are the same, they have the same latency, but this one's 20% less carbon. That is the reason I'm gonna move over there. So it's all about, you've already, you can do the cost benefit analysis quite easily, and many people do.

But how do you enable them to do a carbon benefit analysis as well? And then once they've got all the data in front of them, just start making more informed decisions. And that's why I think the data is more important than, you know, necessarily telling them what the processes are, giving them the, here's the Ultimate Guide to GreenOps. You know, data's just a catalyst for decisions and if you just need to give them trustworthy data. And then how many use cases does trustworthy data have? You know, how many, how long is a piece of string? I've seen many, but every time there's a new customer, there's new use cases.

Chris Adams: Okay, cool. Thank you for that. So, one thing that we spoke before in this kind of pre-call was the fact that, sustainability is becoming somewhat more mainstream. And there's now, within the kind of FinOps foundation or the people who are doing stuff for FinOps are starting to kind of wake up to this and trying to figure out how to incorporate some of this into the way they might kind of operate a team or a cloud or anything like that.

And you. I believe you told me about a thing called FOCUS, which is, this is like something like a standardization project across all the FinOps and then, and now there's a sustainability working group, particularly inside this FOCUS group. For people who are not familiar with this, could you tell me what FOCUS is and what this sustainability working group as well working on?

You know, 'cause working groups are supposed to work on stuff, right?

James Hall: Yeah, so as exactly as you said, FOCUS is a standardization of billing data. So you know, when you get your AWS bill, your Azure bill, they have similar data in them. But they will be completely different column names. Completely different granularities, different column sizes. And so if you're trying to make a master report where you can look at all of your cloud and all of your SaaS bills, you need to do all sorts of data transformations to try and make the columns look the same.

You know, maybe AWS has a column that goes one step more granular than Azure, or you're trying to, you know, do a bill on all your compute, but Azure calls it virtual machines. AWS calls it EC2. So you either need to go and categorize them all yourself to make a, you know, a master category that lets you group by all these different things or, you know, thankfully FOCUS have gone and done that themselves, and it started off as a, like a Python script you could run on your own data set to do the transformation for you, but slowly more cloud providers are adopting the FoCUS framework, which means, you know, when you're exporting your billing data, you can ask AWS give me the original or give me a FOCUS one. So they start giving you the data in a way where it's like, I can easily combine all my data sets. And the reason this is super interesting for carbon is because, you know, carbon is a currency in many ways, in the fact that the, 

Chris Adams: there's price on it in Europe. There's a price on it in the UK. Yeah.

James Hall: There's a price on it, but also like the way Azure will present you, their carbon data could be, you know, the equivalent of yen, AWS could be the equivalent of dollars.

They're all saying CO2 E, so you might think they're equivalent, but actually they're almost completely different currencies. So this effort of standardization is how do we bring it back? Maybe like, don't give us the CO2 E, but how do we go a few steps before that point and like, how do we start getting similar numbers?

So when we wanna make a master report for all the cloud providers, it's apples to apples, not apples to oranges. You know, how do we standardize the data sets to make the reporting, the cross cloud reporting more meaningful for FinOps people?

Chris Adams: Ah, I see. Okay. So I didn't realize that the FOCUS stuff has actually listing, I guess like what the, let's, call them primitives, like, you know, compute and storage. Like they all have different names for that stuff, but FOCUS has a kind of shared idea for what the concept of cloud compute, a virtual machine might be, and likewise for storage.

So that's the thing you are trying, you're trying to apply, attach a carbon value to in these cases, so you can make some meaningful judgment or so you can present that information to people. 

James Hall: Yeah, it's about making the reports at the same, but also how do you make the numbers, the source of the numbers more similar? 'Cause currently, Azure may say a hundred tons in their dashboard. AWS may say one ton in their dashboard. You know, the spend and the real carbon could be identical, but it's just the formula behind it is so vastly different that you're coming out with two different numbers.

Chris Adams: I see. I think you're referring to at this point here. Some places they might share a number, which is what we refer to as a location based figure. So that's like, what was kind of considered on the ground based on the power intensity from the grid in like a particular part of the world.

And then a market based figure might be quite a bit lower. 'Cause you said, well, we've purchased all this green energy, so therefore we are gonna kind of deduct that from what a figure should be. And that's how we'd have a figure of like one versus 100. But if you're not comparing these two together. It's gonna, these are gonna look totally different.

And you, like you said, it's not apples. With apples. It's apples with very, yeah. It's something totally different. Okay. That is helpful.

James Hall: It gets a lot more confusing than that 'cause it's not just market and location based. Like you could have two location based numbers, but Azure are using the grid carbon intensity annual average from 2020 because that's what they've got approved. AWS may be using, you know, Our World in Data 2023 number, you know, and those are just two different sources for grid intensity.

And then what categories are they including? Are they including Scope 3 categories? How many of the scope 2 categories are they including? So when you've got like a hundred different inputs that go into a CO2 number, unless all 100 are the same, you do not have a meaningful comparison between the two.

Even location/market based is just one aspect of what goes into the CO2 number, and then where do they get the kilowatt hour numbers from? Is it a literal telemetry device? Or are they using a spend based property on their side? Because that's not completely alien to cloud providers to ultimately rely on spend at the end of the day.

So does Azure use spend or does AWS use spend? What type of spend are they using? And that's where you need the transparency as well, because if you don't understand where the numbers come from, it could be the most accurate number in the world, but if they don't tell you everything that went into it, how are you meant to know?

Chris Adams: I see. Okay. That's really interesting. 'Cause the Green Web Foundation, the organization I'm part of, there is a gov, there's a UK government group called the Government Digital Sustainability Alliance. And they've been doing these really fascinating lunch and learns and

one thing that showed up was when the UK government was basically saying, look, these are, this is the carbon footprint, you know, on a kind of per department level. Like this is what the Ministry of Justice is, or this is what say the Ministry of Defense might be, for example. And that helps explain why you had figures where you had a bunch of people saying the carbon footprint of all these data centers is really high.

And then you said they, there were people talking about saying, well, we're comparing this to cloud looks great, but 'cause the figures for cloud are way lower. But the thing they, the thing that I was that people had to caveat that with, they basically said, well, we know that this makes cloud look way more efficient here, and it looks like it's much more, much lower carbon, but because we've only got this final kind of market based figure, we know that it's not a like for like comparison, but until we have that information, we're, this is the best we actually have. And this, is an organization which actually has like legally binding targets. They have to reduce emissions by a certain figure, by a certain date. This does seem like it has to be, I can see why you would need this transparency because it seems very difficult to see how you could meaningfully track your progress towards a target if you don't have access to that.

Right?

James Hall: Yeah. Well, 

I always like to use the currency conversion analogy. If you had a dashboard where AWS is all in dollars, Azure, or your on premise is in yen. There's 149 yen in 1 dollar. So, but if you didn't know this one's yen and this one's dollars, you'd be like, "this one's 149 times cheaper. Why aren't we going all in on this one?"

But actually it's just different currencies. And they are the same at the end of the day. Under the hood, they're the same. But, know, just the way they've turned it into an accounting exercise has kind of muddied the water, which is why I love electricity metrics more. You know, they're almost like the, non fungible token of, you know, data centers and cloud.

'Cause you can use that to calculate location-based. You can use calculate market-based. You can use electricity to calculate water cooling and metrics and things like that. So if you can get the electricity, then you're well on your way to meaningful comparisons.

Chris Adams: And that's the one that everyone guards very jealously a lot of the time, right?

James Hall: Exactly. Yeah. Well that's directly related to your cost of running business and that is the proprietary information.

Chris Adams: I see. Okay. Alright, so we spoke, we've done a bit of a deep dive into the GSG protocol, scope 3, supply chain emissions and things like that. If I may, you mentioned, you, referenced this idea of war stories before. Right. And I. It's surprisingly hard to find people with real world stories about okay, making meaningful changes to like cloud emissions in the world.

Do you have any like stories that you've come across in the last four years that you think are particularly worth sharing or that might be worth, I dunno, catch people's attention, for example. Like there's gotta be something that you found that you are allowed to talk about, right.

James Hall: Yeah, I mean, MasterCard, one of our Lighthouse customers, they've spoken about the work we're doing with them a lot in, at various FinOps conferences and things like that. But they're very advanced in their GreenOps goals. They have quite ambitious net zero goals and they take their IT sustainability very seriously.

Yeah, when we first spoke to them. Ultimately the name of the game was to get the cloud measurement up to the point of their on-premise. 'Cause their on-premise was very advanced, daily electricity metrics with pre-approved, CO2 numbers or CO2 carbon coefficients that multiplied the, you multiply the electricity with.

But they were getting, having no luck with cloud, essentially, you know, they spend a lot in the cloud and, but they, they were honestly like, rather than going for just the double wins, which is kind of what most people wanna do, where it's like, I'm gonna use this as a mechanism to save more money.

They honestly wanted to do no more harm and actually start making decisions purely for the sustainability benefits. And we kind of went in there with the FinOps team, worked on their FinOps reporting, combined it with their FinOps recommendations and the accountability, which is their tool of choice.

But then they started having more use cases around. How do they use our carbon data, not our electricity data from the cloud or like, because we have a big list of hourly carbon coefficients. They wanna use that data to start choosing where they put their on-premise data centers as well, and like really making the sustainability impact a huge factor in where they place their regions, which I think is a very interesting one. 'Cause we had only really focused on how do we help people in their public cloud. But they wanted to align their on-premise reporting with their cloud reporting and ultimately start even making decisions. Okay, I know I need to put a data center in this country.

Do I go AWS, Azure, or on-prem for this one? And what is the sustainability impact of all three? And, you know, how do I weigh that against the cost as well? And it's kind of like the golden standard of making sustainability a big part of the trade-off decision. 'Cause they would not go somewhere, even if it saved them 50% of their cost, if it doubled their carbon. They're way beyond that point. So they're a super interesting one. And even in public sector as well, like the departments we are working with are relatively new to FinOps and they didn't really have like a proper accountability structure for their cloud bill. But when you start adding carbon data to it, you are getting a lot more eyes onto the, onto your bills and your usage.

And ultimately we help them create that more of a FinOps function just with the carbon data. 'Cause people find carbon data typically more interesting than spend data. But if you put them on the same dashboard, now it's all about how do you market efficient usage? And I think that's one of the main, use cases of GreenOps is to get more eyes or more usage.

So, 'cause the more ideas you've got piling in, the more use cases you find and.

Chris Adams: Okay. Alright, so we spoke, so you spoke about carbon as one of the main things that people are caring about, right. And we're starting to develop more of an awareness that maybe some data centers might themselves be exposed to kind of climate risks themselves. Because I know they were built on a floodplain, for example.

And you don't want a data center on a floodplain in the middle of a flood, for example. Right. but there's also like the flip side, you know, that's too much water. But there are cases where people worry about not enough water, for example. I mean, is that something that you've seen people talk about more of?

Because there does seem to be a growing awareness about the water footprint of digital infrastructure as well now. Is that something you're seeing people track or even try to like manage right now?

James Hall: Well, 

we find that water metrics are very popular in the US more so than the CO2 metrics, and I think it's because the people there feel the pain of lack of water. You know, you've got the Flint water crisis. In the UK, we've got an energy crisis stopping people from building homes. So what you really wanna do is enable the person who's trying to use this data to drive efficiency, to tell as many different stories as

is possible,. You know, the more metrics and the more choice they have of what to present to the engineers and what to present to leadership, the better outcomes they're gonna get. Water is a key one because data centers and electricity production uses tons of water. And the last thing you wanna do is, you know, go to a water scarce area and put a load of servers in there that are gonna guzzle up loads of water. One, because if that water runs out, your whole data center's gonna collapse. So it's, you're exposing yourself to ESG risk. And also, you know, it doesn't seem like the right thing to do. There are people trying to live there who need to use that water to live.

But you know, you've got data centers sucking that water out, so you know, can't you use this data to again, drive different decisions, could invoke an emotional response that helps people drive different decisions or build more efficiently. And if you're saving cost at the end of that as well, then everyone's happy.

Chris Adams: So maybe this is actually one thing we can talk about because, or just like, drill into before we kind of, move on to the next question and wrap up. So we, people have had incentives to track cost and cash for obvious reasons, carbon, as you're seeing more and more laws actually have opinions about carbon footprint and being able to report that people are getting a bit more aware of it.

Like we've spoken about things like location based figures and market based figures. And we have previous episodes where we've explored and actually kind of helped people define those terms. But I feel comfortable using relatively technical terminology now because I think there is a growing sophistication, at least in certain pockets, for example.

Water still seems to be a really new one, and it seems to be very difficult to actually have, find access to meaningful numbers. Even just the idea of like water in the first place. Like you, when you hear figures about water being used, that might not be the same as water. Kind of.

It's not, it might not be going away, so it can't be used. It might be returned in a way that is maybe more difficult to use or isn't, or is sometimes it's cleaner, sometimes it's dirtier, for example. But this, it seems to be poorly understood despite being quite an emotional topic. Have you, yeah, what's your experience been like when people try to engage with this or when you try to even find some of the numbers to present to people and dashboards and things?

James Hall: Yeah. So yeah, surprisingly, all the cloud providers are able to produce factors. I think it's actually a requirement that when you have a data center, you know what the power usage effectiveness is, so what the overhead electricity is, and you know what the water usage effectiveness is. So you know, what is your cooling system, how much water does it use, how much does it withdraw?

Then how much does it actually consume? So the difference between withdrawal and consumption, is withdrawal is you let you take clean water out, you're able to put clean water back relatively quickly. Consumption is you have either poisoned the water with some kind of, you know, you've diluted it or you know, with some kind of coolant that's not fit for human consumption or you've now evaporated it.

And there is some confusion sometimes around "it's evaporated, but it'll rain. It'll rain back down." But, you know, a lake's evaporation and redeposition processs is ike a delicate balance. If it, you know, evaporates 10,000 liters a day and rains 10,000 liters a day after, like a week of it going into the clouds and coming back down the mountain nearby.

If you then have a data center next to it that will accelerate the evaporation by 30,000 leases a day, you really upset the delicate balance that's in there and that, you know, you talk about are these things sustainable? Like financial sustainability is, do you have enough money and income to last a long time, or will your burn rate run out next month?

And it's the same with, you know, sustainability. I think fresh water is a limiting resource in the same way a company's bank balance is their limiting resource. There's a limited amount of electricity, there's a limited amount of water out there. 

I think it was the cEO of Nvidia. I saw a video of him on LinkedIn that said, right now the limit to your cloud environment is how much money you can spend on it.

But soon it will be how much electricity is there? You know, you could spend a trillion dollars, but if there's no more room for electricity, there's no more electricity to be produced, then you can't build anymore data centers or solar farms. And then water's the other side of that.

I think water's even worse because we need water to even live. And you know what happens when there's no more water because the data centers have it. I think it invokes a much more emotional response. When you have good data that kind of is backed by good sources, you can tell an excellent story of why you need to start reducing.

Chris Adams: Okay, well hopefully we can see more of those numbers because it seems like it's something that is quite difficult to get access to at the moment. Water's it, water in particular. Alright, so we're coming to time now and one thing we spoke about in the prep call was talking about the GSG protocol.

We did a bit but nerd like nerding into this and you spoke a little bit about yes, accuracy is good, but you can't just only focus on accuracy if you want someone to actually use any of the tools or you want people to adopt stuff, and you said that in the GHG protocol, which is like the gold standard for people working out kind of the, you know, carbon footprint of things.

You said that there were these different pillars inside of that matter. And if you just look at accuracy, that's not gonna be enough. So can you maybe expand on that for people who maybe aren't as familiar with the GSG protocol as you? Because I think there is something that, I think, that there, there's something there that's worth, I think, worth exploring.

James Hall: Yeah. So it just as a reminder for those out there, the pillars are accuracy, yes, completeness, consistency, transparency, and relevance. A lot of people worry a lot about the accuracy, but, you know, just to give an example that if you had the most amazing, accurate number for your entire cloud environment, you know, 1,352 tons 0.16 grams, but you are one engineer under one application, running a few resources, the total carbon number is completely

useless to you, to be honest. Like how do you make, use that number to make a decision for your tiny, you know, maybe five tons of information. So really you've got to balance all of these things. You know, the transparency is important because you need to build trust in the data. People need to understand where it comes from.

The relevance is, you know, again, are you filtering on just the resources that are important to me? And the consistency touches on, aWS is one ton versus Azure is 100 tons. You can't decide which cloud provider to go into based on these numbers because you know, they're marking their own homework. They've got a hundred different ways to calculate these things. And then the completeness is around, if you're only doing compute, but 90% is storage, you are missing out on loads of information. You know, you could have a super accurate compute for Azure, but if you've got completely different numbers for AWS and you dunno where they come from, you've not got a good data set, a good GreenOps data set to be able to drive decisions or use as a catalyst.

So you really need to prioritize all five of these pillars in an equal measure and treat them all as a priority rather than just go for full accuracy.

Chris Adams: Brilliant. We'll sure make a point of sharing a link to that in the show notes for anyone else who wants to dive into the world of pillars of sustainability reporting, I suppose. Alright. Okay. Well, James, I think that takes us to time. So just before we wrap up, there's gonna be usual things like where people can find you, but are there any particular projects that are catching your eye right now that you are kind of excited about or you'd like to direct people's attention to? 'Cause we'll share a link to the company you work for, obviously, and possibly yourself on LinkedIn or whatever it is. But is there anything else that you've seen in the last couple of weeks that you find particularly exciting in the world of GreenOps or kind of the wider sustainable software field? 

James Hall: Yeah, I mean, a lot of work being done around AI sustainability is particularly interesting. I recommend people go and look at some of the Hugging Face information around which models are more electrically efficient. And from a Greenpixie side, we've got a newsletter now for people wanting to learn more about GreenOps and in fact, we're building out a GreenOps training and certification that I'd be very interested to get a lot of people's feedback on.

Chris Adams: Cool. Alright, well thank you one more time. If people wanna find you on LinkedIn, they would just look up James Hall Greenpixie, presumably right? Or something like that.

James Hall: Yeah, and go to our website as well.

Chris Adams: Well James, thank you so much for taking me along to this deep dive into the world of GreenOps ,cloud carbon reporting and all the, and the rest. Hope you have a lovely day and yeah. Take care of yourself mate. Cheers.

James Hall: Thanks so much, Chris.  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

Chris Adams: To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> The Week in Green Software: Data Centers, AI and the Nuclear Question The Week in Green Software: Data Centers, AI and the Nuclear Question Thu, 03 Apr 2025 07:00:00 GMT 43:49 70y7xjn0 false https://podcasts.castplus.fm/e/x8vqx43n-the-week-in-green-software-data-centers-ai-and-the-nuclear-question 68dc7d0ad5c8a1f9b326f2da 68dc7cef46a2532cdd8d1c23 Host Anne Currie is joined by the seasoned Chris Liljenstolpe to talk about the latest trends shaping sustainable technology. They dive into the energy demands of AI-driven data centers and ask the big question around nuclear power in green computing. Discussing the trajectory of AI and data center technology, they take a look into the past of another great networking technology, the internet, to gain insights into the future of energy-efficient innovation in the tech industry. 102 Host Anne Currie is joined by the seasoned Chris Liljenstolpe to talk about the latest trends shaping sustainable technology. They dive into the energy demands of AI-driven data centers and ask the big question around nuclear power in green computing. Discussing the trajectory of AI and data center technology, they take a look into the past of another great networking technology, the internet, to gain insights into the future of energy-efficient innovation in the tech industry.

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TRNSCRIPT BELOW:

Christopher Liljenstolpe: The US grid's gonna be capped by 2031. We will be out of power in the United States by 2031. Europe will be out first. So something has to give, we have to become more efficient with the way we utilize these resources, the algorithms we build. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello, and welcome to This Week in Software, where we bring you the latest news and insights from the world of sustainable software. This week I'm your guest host Anne Curry. As you know, I'm quite often your guest host, so you're not hearing the dult tones of the usual host, Chris Adams. today we'll be talking to Chris Liljenstolpe.

Christopher Liljenstolpe, a leading expert in data center architecture and sustainability at Cisco Networks. Christopher is also the father of Project Calico and co-founder of Tigera, and he's a super expert in cloud infrastructure in green computing. But before I introduce him, I'm going to make it clear I've known Chris for years because he, and he's worked very closely with my husband, so we know each other very well.

So that might explain why we seem like we know each other quite well. Who knows. What I do know from Chris is that it's impossible to say what we'll be talking about today. We will go all over the place. But Chris, do you wanna introduce yourself?

Christopher Liljenstolpe: We might even cover the topic at hand, although that is an unlikely outcome. But who knows? That might be a first. That would be a first, but it might be an outcome. 

Anne Currie: So introduce yourself. Introduce yourself.

Christopher Liljenstolpe: Sure. So, as Anne said, my name's Christopher Liljenstolpe. I am currently senior director for data Center Architecture, and sustainability here at Cisco, which means, once again, I failed to duck. So I'm the poor sod who's gotten the job of trying to square an interesting circle, which is, how do we build sustainable data centers, and what does a sustainable data center look like?

At the same time, dealing with this oncoming light at the end of the tunnel that is certainly not sunshine and blue birds, but is a locomotive called AI. And it's bringing with it gigawatt data centers. So, you know, put that in perspective. Mintel, two years ago we were talking about a high power data center

might be a 90 kilowatt rack data center, or a 100 kilowatt rack data center, or a 60 kilowatt rack data center. And about two years ago we went to, okay, it might be 150 kilowatt rack data center, and that was up from 30 kilowatts from years ago. Took a very long time to get to 30 kilowatts. That was good. From two years ago to nine months ago.

Nine months ago it went from 150 kilowatts to 250 kilowatts. So it took us decades to get from two kilowatts to 90 kilowatts to 150 kilowatts. And then in a year we went from 150 to 250, maybe 350. Jensen last week just took us to 600 kilowatts a rack. So yeah, that light at the end of the tunnel is not sunshine at the end of the tunnel.

So yeah, how do we do sustainable data centers when you've got racks that need nuclear power plants that need strapped into each and every rack? So, you know, I'm the one who gets to figure out, you know, what does a gigawatt data center look like and how do you make it sustainable? So that's my day job.

And then, and this really becomes a system of systems problem, which is usually what I end up doing throughout most of my career. Put the Lego blocks together, build system of systems, and then figure out what Lego blocks are missing and what we need to build. So, I did that with Anne's husband on a slightly different space, which was how do you build very scalable networks with millions of endpoints for Kubernetes?

And now I'm doing this for data center infrastructure. 

Anne Currie: Which at least is absolutely fascinating. So for listeners, a bit background on me. I'm one of the authors of O'Reilly's new book, Building Green Software. I'm also the CEO of a learning and development company Strategically Green with the husband who used to work with Chris. So, in Building Green Software, Chris was a major contributor to the networking chapter.

So if you are interested in some of the background in this, and the networking chapter is very high level, you don't need to know any super amazing stuff about it, it'll ramp you up on the basics of networking. So take a, have a look, have a read of that. If you want a kind of, a little bit of a lightweight background to what we'll be talking about today.

But actually what we're talking about today is not networking. It is, it was a part of, it is obviously at a key part of any data center, but that's not really where your focus is on the moment. It sounds like, more like energy is what you are caring about at the moment with DCs. Is that true or both? It'll always be both, but... 

Christopher Liljenstolpe: It is, it's both. Energy starts behaving a bit like networking a bit at this level. And it's getting the energy and getting the energy out as well. The cooling is actually a real interesting part of it, but

we really start thinking about the energy as an energy network. You almost have to, when we start thinking about energy flows this size, and controlling them and managing them.

But, then there's other aspects to this as well. Some of the things that are driving this insane, I'll be right out and say it, this insane per rack density. Why do we need 600 kilowatt racks? Do we need 600 kilowatt racks? But let's assume we do need them. Why do we need them? We need to pack as many GPUs as closely together as possible.

That means that we need, and why do we need to do that? We need to get them as closest together as possible because we want them to be network close for very high speed so that they, we have a very high performance cluster or closely bound cluster so that you get your ChatGPT answers very quickly,

and they don't hallucinate. So that means putting lots of GPUs and a very high bandwidth memory very close to one another. And when you do that in networking, you want that to be in copper and you want that to be a very specific kind of networking that really ends up using a whole lot of energy unless you pack it very closely together.

So that 600 kilowatts is actually the low power variant. If we stretched further out, it would be by another order of magnitude, because we'd have to go into fiber. So we pack it very close. And that means we end up packing a lot of stuff very closely together that drives a lot of power into one rack, and it takes a lot of power to get the heat back out of it again.

So it would be worse if we stretched it further out, but it's a networking, it's partially a networking thing that's driving this, actually. So is there one of the things, levers we can try and pull, is there a better way of doing this networking to cluster these things tighter together? So it always comes back to the network, one way or the other. 

Anne Currie: It does indeed always come. So although I live in a networking household, this I'm not so familiar with it, I don't know how this works. Is this that the GPUs have to talk together very fast, so there's almost no transit time elapsed, transit time in messages between the machines.

Is that why the networking is so important? 

Christopher Liljenstolpe: You wanna get as many GPUs talking as closely together as possible. More specifically GPUs and their high bandwidth memory. So the HBM stacks, the high bandwidth memory stacks and the GPUs. The minute that you have, the way, and one good question, if this isn't a good architecture or not.

There are basically in a aI infrastructure, there's three networks that tie the infrastructure together. This what's called the scale up Network, which is the very high speed network that stitches, some number of GPUs together, and that's on the order of, today, anywhere from 3.6 terabits per second, upwards to what's coming down the road,

about 10 terabits a second of what's called non-blocking traffic network between the GPUs in a scale up cluster. And that could be anywhere from eight GPUs up to now within the next year or two, 500 and some odd GPUs in that cluster. So in that realm, you could have up to 500 GPUs all talking to each other at 10 terabits a second, or eight terabits a second depending on the GPU manufacturer, et cetera.

And that's the highest performing part of the network. Then those clusters are talking to other GPUs and other clusters at usually around 800 gigabits a second. So that's a huge step down in performance. And then those GPUs are talking to the outside world, all those GPUs are going to the outside world at the servers, those things are in the server.

Then usually those are packaged for eight GPUs in the server. Those servers driving to the outside world at 800 gigabits a second per server. And that's how they get their data. That's how they get their requests and how they give their answers. so 800 gigabits a second. 

Anne Currie: I'm gonna stop now and ask a stupid question, which, say a very simple question. So stepping back, a network, and I'm not a net network expert, so I might be able to say something totally stupid here. So, networks, there are two, at least two very important things about networks.

One is the bandwidth. The bandwidth is how much enormous, how much data can you get down the pipes from one place to another? And the other is latency. How long does it take to do it? So I think what you are saying there, if I understand it correctly, is AI really needs high bandwidth.

And that's what's driving it. It's not latency, it's bandwidth. 

Christopher Liljenstolpe: It's, yeah, no, you are correct. And people get that wrong. Because there's such high bandwidth, the latency doesn't matter as much, head end latency, because the amount of data being moved is big and the bandwidth is high. There is a little bit of a latency hit, but high performance computing is more latency sensitive.

If you've got a very high bandwidth network, the data packets are actually pretty small. So the latency isn't as big a hit. The third is congestion. Congestion kills an AI network. And this is the problem. So if I can take the whole model that I'm computing against and put it in that scale up domain,

then everything can talk to everything at full bandwidth and there's no congestion. But if you remember those GPUs that are in the high bandwidth domain, there's eight today, or maybe 72 or 36 or 256 or maybe 500 and some odd if Jenssen's build is correct and some of the other things we're working on with some other vendors might be correct.

So that's a lot of bandwidth. If you can't fit it all in that one, then they have to go over that slower 800 gig per GPU versus 10 terabits per GPU to talk to A GPU in another one of those high bandwidth clusters. And all of a sudden you go from 10 terabits or eight terabits, or three terabits even, to 800 gigabits.

So that's all of a sudden a much more contended or congested network. So you go from running down a, you know, a motorway at two o'clock in the morning to a bmo, a b, you know, side road, with lots of people on it. And the GPUs do this.

Anne Currie: Oh yeah.

Christopher Liljenstolpe: And everything slows to a crawl. And all the GPUs go to massive, basically idle.

And that's what people don't want. 'Cause those GPUs are very expensive. There's hundreds of those GPU servers are hundreds of thousands of dollars. They use a lot of power and they're just idling waiting for the GPU on the other side of that slow link to get back with an answer. So you don't want your, model or that you're inferring against or your training to be split across these things.

So you want everything on that high speed link. And if you want everything on that very high speed link, that multiple terabits per second per GPU, and to think about this, that means that if I've got eight GPUs in a server, that means I've got 80 terabits of bandwidth coming into that server. And if I've got 10 servers, let's say, in that cluster, that means I've got 80 terabits of bandwidth between that server and every other server in that cluster.

And you do the math, that's about 10,000 cables running up and down inside that rack. So the cabling becomes interesting. There's all sorts of interesting problems here. so I cram everything in. So this is why I wanna get everything crammed in as tightly as possible so I can get as many things into that rack, it's an easier problem.

And the power to put that on copper that runs maybe one meter in length or a meter and a half is less than a wat per cable. Per what's called cerdes. Put it on fiber, I'm over a watt, at least, maybe over a couple of watts. So I go from a 10th of a watt to a couple of watts and it takes more space on the board and everything else so that we get into physics problems.

That's why I need to pack it in tight. That's why I need more power in a higher density space, 'cause I wanna get everything into that one high bandwidth domain. Now, another practice might be to do away with this concept of scale out and scale up, and there's some architectures that might do that.

But the main model today, the NVIDIA model is scale up and scale out are kept separate. One can argue is that a good model? It is the model in the industry today. 

That means the software developers have to be cogent of that as well. And the scheduler, people who design the schedulers have to be cogent of that as well.

And so this is a design that now ripples through the entire architecture all the way up through the software stack and everything else. 

Anne Currie: So what you're saying is that we, when we talk about AI and we talk about GPUs and all that kind of stuff, and the incredible amount of power that it requires, we tend not to think about the fact that actually it's the networking that requires one hell of a lot of that power. It's, this is not networking going, you know, across the country.

There's not networking outside the data centers. This is networking inside them.

Christopher Liljenstolpe: This is networking the rack. 

Anne Currie: within, 

Christopher Liljenstolpe: This is a one meter diameter, two meter diameter network and it's tens of thousands of cables. 

Anne Currie: So I'm sure that something you've been thinking about a lot recently is the enormous shift that's taken place with DeepSeek coming in. Has that completely, have you got, how much of an effect does that have on the network side of things?

Christopher Liljenstolpe: So the whole idea behind DeepSeek is you don't need to do, from a training perspective, I think of it as the data sort pre-trained. So you don't need to do as much pre-training. You don't need to do as much training, therefore you don't need as many GPUs to sort of prep your data, prep your model.

So that means you don't need as big a scale up cluster to train to get ready to infer. And remember, training doesn't make you any money. If you're in this to make money, training doesn't make you any money. It's inference. It's using the, you know, using the model is what makes the money.

And potentially inference as well might be impacted. But Jensen made an interesting point was, as we start doing reasoned inference, that's gonna require a lot more compute. Now it starts looking more like inference, like training, and you're gonna make, up until recently, inference was always one and done.

You make one pass through inference and you get the result. That's why we used to get some interesting, let's just call them interesting results. We used to call it, you know, hallucinations. But now you take and you make one pass through and then you sort of check it. Does it make sense and do you reason?

Does it look reasonable? And you make another pass through again, another pass through again, and a pass through again, this reasoned inference. That all of a sudden starts using a lot more compute. Looks a little bit more like a training job almost. And that now starts using a lot more GPUs and you need more scale up bandwidth in GPUs.

So it'll be interesting to see if DeepSeek benefits, should benefit that reasoned inference as well. The bigger question is, DeepSeek probably only be as good as the pre-trained data they ingest, right? So this sort of becomes, you know, do we feed our AIs with other AI data? And at some point, do we all become self referenced, right?

Do we take AI data to feed other AI data? And pretty soon we're all, you know, it is like if all the code in GitHub is written by AIs, and then we use, we train coding models for GitHub using AI written code. Is that a good thing or not a good thing? 

Anne Currie: If it's tested code. I mean, if they also write tests and they run the tests and the code works, then, but... 

Christopher Liljenstolpe: Yeah. Yeah. Of course, it's sort of like having the developer write their code too, right? You up with a monoculture.

Anne Currie: Yeah, that is true.

Christopher Liljenstolpe: You end up with a monoculture.

Anne Currie: Yeah, it, yeah,

Christopher Liljenstolpe: Or not. Or not, maybe you don't end up with a monoculture. I don't know. This is, now we're getting into philosophy.

Anne Currie: So it's interesting. I, I do know, 

Christopher Liljenstolpe: And now everyone just watched this went from infrastructure to software design to philosophy, and just went. 

Anne Currie: You know, it's, I, the AI stuff, I do find quite fascinating. I do know somebody who's a Deep Mind engineer and used to work on OpenAI, and I remember them telling me years ago, years and years ago that the big, the massive change, the switch from, you know, it was kind of when AI was starting to get good, I was talking to her nearly 10 years ago.

I was like, suddenly it's got a lot better. Why has it got a lot better? And he said it's randomness. It's, we realized that actually if you injected a load of randomness into, a load more randomness into its decision making, suddenly got vastly better. It was a sea change. So it's not as predictable.

And it's, it, you know, it is odd that AI, something we don't talk about a lot is that AI is based, at its heart, on the injection of randomness, which I find fascinating. And then, yeah. 

Christopher Liljenstolpe: There was, an interesting study. If you train AI on bad data in one domain, it will start giving you, bad results off of other domains as well.

Anne Currie: That's interesting.

Christopher Liljenstolpe: Which was a really sort of, but anyway, yeah, now we're really off the rail. 

Anne Currie: But yeah, we are, and in fact we've only got 10 minutes left, so we should actually go back onto sustainability. 'Cause the question I wanted to ask you, you mentioned in our bit that we were talking about there, about racks, that, you know, racks are becoming, you know, you needed a nuclear power station for every rack these days.

But is that literally the case? Can this only be done through nuclear or can it be done like Texas are making out, are making calls for large, flexible loads for all mega amounts of solar that they're running at. Is it realistic? What do you think, is nuclear and AI, is it a prerequisite? 

Christopher Liljenstolpe: It is not a prerequisite, but it is probably gonna be a base load demand. And that's because the amount, at least at this point, the amount of money you will invest if you're gonna put up anything a hundred megawatts or more of AI compute, that is a serious amount of investment. And let's also be honest, if you're talking about 500 megawatts or a gigawatt facility, you're also,

you're not lifting a substation permit, 'cause there aren't substations for things like that, you are going to jack yourself into a power plant. Because at that point, you know, a gigawatt is a power generation station, right? That is a reactor in a nuclear power station that. Is a, you know, a gas

generator, a gas turbine in a, you know, a co-generation power plant, et cetera. It's a turbine in a major hydro, right? It is a full scale commercial power plant unit. So there's no reason to have a substation because you are consuming a full commercial power plant. So you might as well plant it there. That's not small money. You are gonna have to guarantee a load to a power company to do that. One. Two, the amount you're gonna spend on the GPUs, let alone all the other infrastructure that goes around it, that is a huge capital investment. You are not gonna want that sitting idle for one minute in a year. So that is going to be a base load that will always, your shareholders are gonna string you up, that will always be running, so that's gonna be a base load. So something's gonna have to support that base load. It could be solar, but then you're gonna have to have a very big battery plant. There's one going in, in India.

There's a one gigawatt facility going in for AI, and it's fully built out. It's gonna be held up by a solar plant. That solar plant is gonna be, one third of the ground is going to be solar, and the remainder is gonna be battery to hold the thing for 24x7 so they will be doing solar, but it's going to be solar battery.

But yeah, this will be, you're gonna want this thing running all the time. So we joke about it being nuclear. The funny thing was three years ago we were saying these small modular reactors, a hundred megawatts, that's a perfect size for a data hall. Now we're just saying, you know, go, you know, unshutter your commercial nuclear reactors because the gigawatt size commercial nuclear reactors by now are about the right size, the interesting part to that is, what do you do when you have to refuel the reactor? Because the reactors, most commercial reactors have to be shut down when you refuel. If you're jacked into a reactor, you're, what do you do when they have to shut down the reactor? That's a year process.

What do you do for power? 'Cause you're probably not connected to the grid. You're connected to, like what they did in Pennsylvania. You're connected to the reactor. What do you do for power when they shut down that reactor? I hope the folks have thought about that. Maybe you still do small, like small modulars.

Maybe you do 12 small modulars at a hundred megawatts each, and you sort of have an n+2. Interesting thoughts.

Anne Currie: Well, that is a very interesting thought. So yeah, so you're making two fascinating points there that I have never heard made. One is that we are totally over, we've totally run ahead of SMRs, you know, all that thing we're talking. Totally. We've galloped ahead of that and yet it might actually be worth bringing them back just because of that kind of modern resilience thing of it's better to have 10 than one. You know better to have 10 small ones than one big one.

Christopher Liljenstolpe: Yeah, I've got resilient reactors, and if it's molten salts, you can refuel them by just, topping off the salt tanks as you go. And you can remove the poison out of 'em as you go. So, you know, just, back the salt truck up and dump more salt in. It's a little more than that, but yeah, sort of.

Anne Currie: Yeah.

Christopher Liljenstolpe: If you're interested in bashing your head into the wall and learning about things that you never thought you'd have to learn about, this is a fun time to get into data center infrastructure because you get to do things like, okay, how do I cram a couple hundred terabits per second into a network in a rack? At the same time,

talk about liquid molten salt reactors. I mean, you know, it's sort of a broad spectrum of, you know, and oh, and let's also talk about signal integrity of dielectric fluids. 'Cause we might have to send all this stuff swimming in a tank. It's, you know, you have a lot of interesting conversations in one day. 

Anne Currie: It sounds like you're in a pretty fun area at the moment and we thought it was fun. We thought network cloud networking was fun five years ago. That was nothing as it turns out.

Christopher Liljenstolpe: Yeah, so, and one thing that's sort of interesting now is we took Scalable Sustainable Infrastructure Alliance in the Linux Foundation. We've merged it, as I'm sure you've heard, with Green Software Foundation,

which, so we thought it was probably time to get the hardware guys and the software guys talking, and gals talking together because we realized that we really needed to have these, the stack not have this wall between the hardware and the software.

We really needed to have the same things we were talking about before I alluded to. It's like, okay, the hardware impacts of the horror show that we've got going on. I say that in the nicest possible way to my friends doing the chips, the unique challenges that we have coming, we really need better understanding on the scheduler sides, et cetera, and how we manage that and monitor that and the impacts of that on the software side.

So we decided to take the folks who are working on open hardware designs and making those sustainable, and marrying that to the software side and the green software folks who are working on how we manage and monitor that as well. So we decided to take those two and put them together. And the first project out of that is gonna be something called Project Mycelium, which is going to be actually looking at, how we build software linkages on, how you manage and monitor the hardware infrastructure on the software side. 

Anne Currie: Named after the networks of fungus under the, the way that actually, everything in a forest is more, more connected together than we'd ever realized previously using these incredible mycelium connections, I take it. I'm guessing that's why it's named that way.

Christopher Liljenstolpe: Exactly. Exactly. And a good friend of mine, who used to be the CTO, field CTO at Equinix, is gonna be running that project for me there.

Anne Currie: So, yeah. Utterly fascinating stuff. So yes, I mean, so take, so stepping back from all of this, it's mind blowing amount of new, of complex new thoughts and approaches to things. And what's your view? I mean, you, have a kind of. 30,000, 40,000 foot of view, tend to, on all of these things.

What are you thinking? Where's it all going? What's it gonna, what's gonna happen?

Christopher Liljenstolpe: Well, one of my jokes is yes. AI will kill us all. The question is, will it get smart enough and realize we're the problem and actively kill us, or will it just take so much resources, it will just melt all the ice caps and create a water world before it becomes sentient and just kill us that way? that's it.

There's a joke in every joke. I think right now the path that we're on frankly is not sustainable. You know, we can't, you know, the next logical step from this is we're looking, you know, if we follow that train of 150 know, 60, 100 152, 5600, it's north of a megawatt a rack. That path is unsustainable both from, resources, power, but also economics.

It just, we can't do that. At the going rate, the US grid's gonna be capped by 2031. We will be out of power in the United States by 2031. Europe will be out first. so yeah. So something has to give, we have to become more efficient with the way we utilize these resources, the algorithms we build. We're still brute forcing AI.

We think this is all brilliant software. It's not, we're still brute forcing the heck out of this stuff.

 So something's gotta give there. I think when that does, there'll be a lot of business models that might face some challenges. Because there's a lot of value built that this is going to continue going this way.

But it needs to happen. So we're gonna end up, I think, and there's a lot of fluff as well. There's a lot of pet, the equivalent of pets.com, out there right now. I think we'll end up with a lot more distributed use cases for AI that don't need the same amount of power. We don't need huge inference across it.

But yeah, the, current trend will have to get adjusted, and somebody's gonna figure it out. 

Anne Currie: The old phrase.

Christopher Liljenstolpe: People try it out.

Anne Currie: If something can't go on, it won't, it'll stop, you know? 

know 

Christopher Liljenstolpe: There will be enough economic pressure that it will drive an innovation that will fix it. So I mean, just you looking at it, just

Anne Currie: Yeah, it's the code. 

Christopher Liljenstolpe: I'm not sure how we'll mine enough copper to support this building the power transmission infrastructure. So anyway, that's my doom and gloom part of this.

But I think, it's, what we will end up by the time we're done with it though, is a very efficient computational infrastructure, is it's forcing us to look at everything along the stack. Air is an absolutely horrible heat transfer fluid. We are, everyone's running madly down the road of liquid.

Everyone's running madly down the road of higher voltage. Which again, the way we transmit power in a data center is pretty horrible today. Everyone's ringing all the efficiencies they can outta that because now we have to, it's just economically impossible to do it any other way. So whatever comes outta the back of this is we are gonna have a very efficient data center infrastructure.

Which is all for the better. We're probably gonna end up with a, we will probably end up driving, this will probably fix the grids, because it has to, because we're driving a very different power transmission infrastructure. So we'll fix a bunch of problems along the way. Silver lining. 

Anne Currie: And there is a lot of money behind it. So it's, yeah, it is actually aligned with a lot of good things that we want and it's driving a lot of money in those directions. Yeah. It's interesting. If it doesn't kill us all, which, you know, 

Christopher Liljenstolpe: Yeah, and who knows? It'll probably, it'll probably bring back nuclear, we'll probably, have, be able to have rational conversations about other non-carbon emitting power sources that, 

Anne Currie: Space-based solar power. Well, I'm desperate for it.

Christopher Liljenstolpe: Maybe, yeah, maybe. Might get some countries that just recently shuttered all their nuclear plants go back and put their cooling towers back up.

Not talking about any European countries.

Anne Currie: Well, I'm sure everybody's brain is completely full now, so, and we've had a really interesting discussion that I have utterly enjoyed. So I think we should probably draw the podcast to an end with any final comments that anybody wants to make. So everything we, well, everything we talked about that we can put in the show notes, will be in the show notes at the bottom of the episode.

Do you have any final points that you want to make?

Christopher Liljenstolpe: I mean, it is fun times. And it's not all doom and gloom, but you know, it is right now, there is a bit of a hike and you know, it, 

at this point it seems like it is a train that's gonna keep on going and it will correct. But it is leading to a lot of innovation and that innovation will hang around. Just like when the dot-com bust happened, we will see a correction here and what people thought originally the internet was going to do and what was gonna be delivered by the internet didn't really happen. But it certainly, the things that it is used for, people never, even the people who originally created the ARPANET or the people who invested in the dot-com original late nineties explosion,

what they, the money they put into it, they had, they did not foresee what it is being used for now. But we, the world has been, you know, forever changed by that for good and ill both, by that investment and it's gonna be the same thing here. What we're investing in building now, we think we know what it's gonna be used for,

we're wrong. Everything we think it's gonna be used for, 

5% of it will probably be still what it's being used for 15 years from now. The rest of it, we have no idea. 

And we'll benefit from it and we'll suffer for it. But, we're building a base infrastructure and other people will build, will actually build on that base infrastructure and deliver things that we will have no idea about. 

Anne Currie: Yeah, I, that reminds me of sort of a discussion that we had a few years back about why the internet survived 2020, the beginning of the pandemic, which kept up the west. 'Cause otherwise nobody would, if we hadn't been able to all stay at home and work over, video conferencing, things like that.

And a lot of the infrastructure that was put in place that we relied on there was to support high definition stream tv. So it was like game people put it in so the folk could watch Game of Thrones, then Game of Thrones saved the West. It's like, who would've predicted that? You just don't know what's gonna happen.

Christopher Liljenstolpe: Exactly. Yep. Indeed. And that infrastructure actually, which we didn't talk about, was put in place because, service providers made a horrible choice early on of putting in broadband that was the cheap choice that couldn't do multicast. If they had put in multicast capable infrastructure, they wouldn't have put in the amount of backbone infrastructure that they did. Because they would've had multicast and they wouldn't have had to do the build, that they did, which indeed actually helped us. So it was, you know, that not having multicast out there actually probably saved our bacon. And it pains me to no end. because I was sitting there banging away in the mid nineties, et cetera, as like, "we need to get multicast out there. It's so much more efficient. It will save so much money." And if we had, we probably would've been in much worse shape when the pandemic hit.

Anne Currie: It is interesting that flabbiness, things like inefficient code and inefficient code is what we've been building for the past 20 years. Most of my career, we've been building highly inefficient code, but it does mean there's a lot of untapped potential in there to improve, you know, it's, 

Christopher Liljenstolpe: True.

Anne Currie: Unrealized potential as a result of lazy behavior in the past. We are mining our own past laziness that might save us all.

Christopher Liljenstolpe: Indeed.

Anne Currie: On that note, our laziness and lack of foresight in the past have tended to save us in the future. It might well save us again. On that happy note or that nuanced note,

thank you very much for listening and thank you very much for being my excellent guest today, Chris.

Christopher Liljenstolpe: Thank you for having me on, Anne, and thank you everyone for listening. I hope it was, if not educational, at least entertaining.

Anne Currie: I'm sure it was both. Thank you very much and speak to you on the next time I'm hosting the Environment Variables podcast. Goodbye.

Christopher Liljenstolpe: Bye everyone.  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

Chris Adams: To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Host Anne Currie is joined by the seasoned Chris Liljenstolpe to talk about the latest trends shaping sustainable technology. They dive into the energy demands of AI-driven data centers and ask the big question around nuclear power in green computing. Discussing the trajectory of AI and data center technology, they take a look into the past of another great networking technology, the internet, to gain insights into the future of energy-efficient innovation in the tech industry.

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TRNSCRIPT BELOW:

Christopher Liljenstolpe: The US grid's gonna be capped by 2031. We will be out of power in the United States by 2031. Europe will be out first. So something has to give, we have to become more efficient with the way we utilize these resources, the algorithms we build. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: Hello, and welcome to This Week in Software, where we bring you the latest news and insights from the world of sustainable software. This week I'm your guest host Anne Curry. As you know, I'm quite often your guest host, so you're not hearing the dult tones of the usual host, Chris Adams. today we'll be talking to Chris Liljenstolpe.

Christopher Liljenstolpe, a leading expert in data center architecture and sustainability at Cisco Networks. Christopher is also the father of Project Calico and co-founder of Tigera, and he's a super expert in cloud infrastructure in green computing. But before I introduce him, I'm going to make it clear I've known Chris for years because he, and he's worked very closely with my husband, so we know each other very well.

So that might explain why we seem like we know each other quite well. Who knows. What I do know from Chris is that it's impossible to say what we'll be talking about today. We will go all over the place. But Chris, do you wanna introduce yourself?

Christopher Liljenstolpe: We might even cover the topic at hand, although that is an unlikely outcome. But who knows? That might be a first. That would be a first, but it might be an outcome. 

Anne Currie: So introduce yourself. Introduce yourself.

Christopher Liljenstolpe: Sure. So, as Anne said, my name's Christopher Liljenstolpe. I am currently senior director for data Center Architecture, and sustainability here at Cisco, which means, once again, I failed to duck. So I'm the poor sod who's gotten the job of trying to square an interesting circle, which is, how do we build sustainable data centers, and what does a sustainable data center look like?

At the same time, dealing with this oncoming light at the end of the tunnel that is certainly not sunshine and blue birds, but is a locomotive called AI. And it's bringing with it gigawatt data centers. So, you know, put that in perspective. Mintel, two years ago we were talking about a high power data center

might be a 90 kilowatt rack data center, or a 100 kilowatt rack data center, or a 60 kilowatt rack data center. And about two years ago we went to, okay, it might be 150 kilowatt rack data center, and that was up from 30 kilowatts from years ago. Took a very long time to get to 30 kilowatts. That was good. From two years ago to nine months ago.

Nine months ago it went from 150 kilowatts to 250 kilowatts. So it took us decades to get from two kilowatts to 90 kilowatts to 150 kilowatts. And then in a year we went from 150 to 250, maybe 350. Jensen last week just took us to 600 kilowatts a rack. So yeah, that light at the end of the tunnel is not sunshine at the end of the tunnel.

So yeah, how do we do sustainable data centers when you've got racks that need nuclear power plants that need strapped into each and every rack? So, you know, I'm the one who gets to figure out, you know, what does a gigawatt data center look like and how do you make it sustainable? So that's my day job.

And then, and this really becomes a system of systems problem, which is usually what I end up doing throughout most of my career. Put the Lego blocks together, build system of systems, and then figure out what Lego blocks are missing and what we need to build. So, I did that with Anne's husband on a slightly different space, which was how do you build very scalable networks with millions of endpoints for Kubernetes?

And now I'm doing this for data center infrastructure. 

Anne Currie: Which at least is absolutely fascinating. So for listeners, a bit background on me. I'm one of the authors of O'Reilly's new book, Building Green Software. I'm also the CEO of a learning and development company Strategically Green with the husband who used to work with Chris. So, in Building Green Software, Chris was a major contributor to the networking chapter.

So if you are interested in some of the background in this, and the networking chapter is very high level, you don't need to know any super amazing stuff about it, it'll ramp you up on the basics of networking. So take a, have a look, have a read of that. If you want a kind of, a little bit of a lightweight background to what we'll be talking about today.

But actually what we're talking about today is not networking. It is, it was a part of, it is obviously at a key part of any data center, but that's not really where your focus is on the moment. It sounds like, more like energy is what you are caring about at the moment with DCs. Is that true or both? It'll always be both, but... 

Christopher Liljenstolpe: It is, it's both. Energy starts behaving a bit like networking a bit at this level. And it's getting the energy and getting the energy out as well. The cooling is actually a real interesting part of it, but

we really start thinking about the energy as an energy network. You almost have to, when we start thinking about energy flows this size, and controlling them and managing them.

But, then there's other aspects to this as well. Some of the things that are driving this insane, I'll be right out and say it, this insane per rack density. Why do we need 600 kilowatt racks? Do we need 600 kilowatt racks? But let's assume we do need them. Why do we need them? We need to pack as many GPUs as closely together as possible.

That means that we need, and why do we need to do that? We need to get them as closest together as possible because we want them to be network close for very high speed so that they, we have a very high performance cluster or closely bound cluster so that you get your ChatGPT answers very quickly,

and they don't hallucinate. So that means putting lots of GPUs and a very high bandwidth memory very close to one another. And when you do that in networking, you want that to be in copper and you want that to be a very specific kind of networking that really ends up using a whole lot of energy unless you pack it very closely together.

So that 600 kilowatts is actually the low power variant. If we stretched further out, it would be by another order of magnitude, because we'd have to go into fiber. So we pack it very close. And that means we end up packing a lot of stuff very closely together that drives a lot of power into one rack, and it takes a lot of power to get the heat back out of it again.

So it would be worse if we stretched it further out, but it's a networking, it's partially a networking thing that's driving this, actually. So is there one of the things, levers we can try and pull, is there a better way of doing this networking to cluster these things tighter together? So it always comes back to the network, one way or the other. 

Anne Currie: It does indeed always come. So although I live in a networking household, this I'm not so familiar with it, I don't know how this works. Is this that the GPUs have to talk together very fast, so there's almost no transit time elapsed, transit time in messages between the machines.

Is that why the networking is so important? 

Christopher Liljenstolpe: You wanna get as many GPUs talking as closely together as possible. More specifically GPUs and their high bandwidth memory. So the HBM stacks, the high bandwidth memory stacks and the GPUs. The minute that you have, the way, and one good question, if this isn't a good architecture or not.

There are basically in a aI infrastructure, there's three networks that tie the infrastructure together. This what's called the scale up Network, which is the very high speed network that stitches, some number of GPUs together, and that's on the order of, today, anywhere from 3.6 terabits per second, upwards to what's coming down the road,

about 10 terabits a second of what's called non-blocking traffic network between the GPUs in a scale up cluster. And that could be anywhere from eight GPUs up to now within the next year or two, 500 and some odd GPUs in that cluster. So in that realm, you could have up to 500 GPUs all talking to each other at 10 terabits a second, or eight terabits a second depending on the GPU manufacturer, et cetera.

And that's the highest performing part of the network. Then those clusters are talking to other GPUs and other clusters at usually around 800 gigabits a second. So that's a huge step down in performance. And then those GPUs are talking to the outside world, all those GPUs are going to the outside world at the servers, those things are in the server.

Then usually those are packaged for eight GPUs in the server. Those servers driving to the outside world at 800 gigabits a second per server. And that's how they get their data. That's how they get their requests and how they give their answers. so 800 gigabits a second. 

Anne Currie: I'm gonna stop now and ask a stupid question, which, say a very simple question. So stepping back, a network, and I'm not a net network expert, so I might be able to say something totally stupid here. So, networks, there are two, at least two very important things about networks.

One is the bandwidth. The bandwidth is how much enormous, how much data can you get down the pipes from one place to another? And the other is latency. How long does it take to do it? So I think what you are saying there, if I understand it correctly, is AI really needs high bandwidth.

And that's what's driving it. It's not latency, it's bandwidth. 

Christopher Liljenstolpe: It's, yeah, no, you are correct. And people get that wrong. Because there's such high bandwidth, the latency doesn't matter as much, head end latency, because the amount of data being moved is big and the bandwidth is high. There is a little bit of a latency hit, but high performance computing is more latency sensitive.

If you've got a very high bandwidth network, the data packets are actually pretty small. So the latency isn't as big a hit. The third is congestion. Congestion kills an AI network. And this is the problem. So if I can take the whole model that I'm computing against and put it in that scale up domain,

then everything can talk to everything at full bandwidth and there's no congestion. But if you remember those GPUs that are in the high bandwidth domain, there's eight today, or maybe 72 or 36 or 256 or maybe 500 and some odd if Jenssen's build is correct and some of the other things we're working on with some other vendors might be correct.

So that's a lot of bandwidth. If you can't fit it all in that one, then they have to go over that slower 800 gig per GPU versus 10 terabits per GPU to talk to A GPU in another one of those high bandwidth clusters. And all of a sudden you go from 10 terabits or eight terabits, or three terabits even, to 800 gigabits.

So that's all of a sudden a much more contended or congested network. So you go from running down a, you know, a motorway at two o'clock in the morning to a bmo, a b, you know, side road, with lots of people on it. And the GPUs do this.

Anne Currie: Oh yeah.

Christopher Liljenstolpe: And everything slows to a crawl. And all the GPUs go to massive, basically idle.

And that's what people don't want. 'Cause those GPUs are very expensive. There's hundreds of those GPU servers are hundreds of thousands of dollars. They use a lot of power and they're just idling waiting for the GPU on the other side of that slow link to get back with an answer. So you don't want your, model or that you're inferring against or your training to be split across these things.

So you want everything on that high speed link. And if you want everything on that very high speed link, that multiple terabits per second per GPU, and to think about this, that means that if I've got eight GPUs in a server, that means I've got 80 terabits of bandwidth coming into that server. And if I've got 10 servers, let's say, in that cluster, that means I've got 80 terabits of bandwidth between that server and every other server in that cluster.

And you do the math, that's about 10,000 cables running up and down inside that rack. So the cabling becomes interesting. There's all sorts of interesting problems here. so I cram everything in. So this is why I wanna get everything crammed in as tightly as possible so I can get as many things into that rack, it's an easier problem.

And the power to put that on copper that runs maybe one meter in length or a meter and a half is less than a wat per cable. Per what's called cerdes. Put it on fiber, I'm over a watt, at least, maybe over a couple of watts. So I go from a 10th of a watt to a couple of watts and it takes more space on the board and everything else so that we get into physics problems.

That's why I need to pack it in tight. That's why I need more power in a higher density space, 'cause I wanna get everything into that one high bandwidth domain. Now, another practice might be to do away with this concept of scale out and scale up, and there's some architectures that might do that.

But the main model today, the NVIDIA model is scale up and scale out are kept separate. One can argue is that a good model? It is the model in the industry today. 

That means the software developers have to be cogent of that as well. And the scheduler, people who design the schedulers have to be cogent of that as well.

And so this is a design that now ripples through the entire architecture all the way up through the software stack and everything else. 

Anne Currie: So what you're saying is that we, when we talk about AI and we talk about GPUs and all that kind of stuff, and the incredible amount of power that it requires, we tend not to think about the fact that actually it's the networking that requires one hell of a lot of that power. It's, this is not networking going, you know, across the country.

There's not networking outside the data centers. This is networking inside them.

Christopher Liljenstolpe: This is networking the rack. 

Anne Currie: within, 

Christopher Liljenstolpe: This is a one meter diameter, two meter diameter network and it's tens of thousands of cables. 

Anne Currie: So I'm sure that something you've been thinking about a lot recently is the enormous shift that's taken place with DeepSeek coming in. Has that completely, have you got, how much of an effect does that have on the network side of things?

Christopher Liljenstolpe: So the whole idea behind DeepSeek is you don't need to do, from a training perspective, I think of it as the data sort pre-trained. So you don't need to do as much pre-training. You don't need to do as much training, therefore you don't need as many GPUs to sort of prep your data, prep your model.

So that means you don't need as big a scale up cluster to train to get ready to infer. And remember, training doesn't make you any money. If you're in this to make money, training doesn't make you any money. It's inference. It's using the, you know, using the model is what makes the money.

And potentially inference as well might be impacted. But Jensen made an interesting point was, as we start doing reasoned inference, that's gonna require a lot more compute. Now it starts looking more like inference, like training, and you're gonna make, up until recently, inference was always one and done.

You make one pass through inference and you get the result. That's why we used to get some interesting, let's just call them interesting results. We used to call it, you know, hallucinations. But now you take and you make one pass through and then you sort of check it. Does it make sense and do you reason?

Does it look reasonable? And you make another pass through again, another pass through again, and a pass through again, this reasoned inference. That all of a sudden starts using a lot more compute. Looks a little bit more like a training job almost. And that now starts using a lot more GPUs and you need more scale up bandwidth in GPUs.

So it'll be interesting to see if DeepSeek benefits, should benefit that reasoned inference as well. The bigger question is, DeepSeek probably only be as good as the pre-trained data they ingest, right? So this sort of becomes, you know, do we feed our AIs with other AI data? And at some point, do we all become self referenced, right?

Do we take AI data to feed other AI data? And pretty soon we're all, you know, it is like if all the code in GitHub is written by AIs, and then we use, we train coding models for GitHub using AI written code. Is that a good thing or not a good thing? 

Anne Currie: If it's tested code. I mean, if they also write tests and they run the tests and the code works, then, but... 

Christopher Liljenstolpe: Yeah. Yeah. Of course, it's sort of like having the developer write their code too, right? You up with a monoculture.

Anne Currie: Yeah, that is true.

Christopher Liljenstolpe: You end up with a monoculture.

Anne Currie: Yeah, it, yeah,

Christopher Liljenstolpe: Or not. Or not, maybe you don't end up with a monoculture. I don't know. This is, now we're getting into philosophy.

Anne Currie: So it's interesting. I, I do know, 

Christopher Liljenstolpe: And now everyone just watched this went from infrastructure to software design to philosophy, and just went. 

Anne Currie: You know, it's, I, the AI stuff, I do find quite fascinating. I do know somebody who's a Deep Mind engineer and used to work on OpenAI, and I remember them telling me years ago, years and years ago that the big, the massive change, the switch from, you know, it was kind of when AI was starting to get good, I was talking to her nearly 10 years ago.

I was like, suddenly it's got a lot better. Why has it got a lot better? And he said it's randomness. It's, we realized that actually if you injected a load of randomness into, a load more randomness into its decision making, suddenly got vastly better. It was a sea change. So it's not as predictable.

And it's, it, you know, it is odd that AI, something we don't talk about a lot is that AI is based, at its heart, on the injection of randomness, which I find fascinating. And then, yeah. 

Christopher Liljenstolpe: There was, an interesting study. If you train AI on bad data in one domain, it will start giving you, bad results off of other domains as well.

Anne Currie: That's interesting.

Christopher Liljenstolpe: Which was a really sort of, but anyway, yeah, now we're really off the rail. 

Anne Currie: But yeah, we are, and in fact we've only got 10 minutes left, so we should actually go back onto sustainability. 'Cause the question I wanted to ask you, you mentioned in our bit that we were talking about there, about racks, that, you know, racks are becoming, you know, you needed a nuclear power station for every rack these days.

But is that literally the case? Can this only be done through nuclear or can it be done like Texas are making out, are making calls for large, flexible loads for all mega amounts of solar that they're running at. Is it realistic? What do you think, is nuclear and AI, is it a prerequisite? 

Christopher Liljenstolpe: It is not a prerequisite, but it is probably gonna be a base load demand. And that's because the amount, at least at this point, the amount of money you will invest if you're gonna put up anything a hundred megawatts or more of AI compute, that is a serious amount of investment. And let's also be honest, if you're talking about 500 megawatts or a gigawatt facility, you're also,

you're not lifting a substation permit, 'cause there aren't substations for things like that, you are going to jack yourself into a power plant. Because at that point, you know, a gigawatt is a power generation station, right? That is a reactor in a nuclear power station that. Is a, you know, a gas

generator, a gas turbine in a, you know, a co-generation power plant, et cetera. It's a turbine in a major hydro, right? It is a full scale commercial power plant unit. So there's no reason to have a substation because you are consuming a full commercial power plant. So you might as well plant it there. That's not small money. You are gonna have to guarantee a load to a power company to do that. One. Two, the amount you're gonna spend on the GPUs, let alone all the other infrastructure that goes around it, that is a huge capital investment. You are not gonna want that sitting idle for one minute in a year. So that is going to be a base load that will always, your shareholders are gonna string you up, that will always be running, so that's gonna be a base load. So something's gonna have to support that base load. It could be solar, but then you're gonna have to have a very big battery plant. There's one going in, in India.

There's a one gigawatt facility going in for AI, and it's fully built out. It's gonna be held up by a solar plant. That solar plant is gonna be, one third of the ground is going to be solar, and the remainder is gonna be battery to hold the thing for 24x7 so they will be doing solar, but it's going to be solar battery.

But yeah, this will be, you're gonna want this thing running all the time. So we joke about it being nuclear. The funny thing was three years ago we were saying these small modular reactors, a hundred megawatts, that's a perfect size for a data hall. Now we're just saying, you know, go, you know, unshutter your commercial nuclear reactors because the gigawatt size commercial nuclear reactors by now are about the right size, the interesting part to that is, what do you do when you have to refuel the reactor? Because the reactors, most commercial reactors have to be shut down when you refuel. If you're jacked into a reactor, you're, what do you do when they have to shut down the reactor? That's a year process.

What do you do for power? 'Cause you're probably not connected to the grid. You're connected to, like what they did in Pennsylvania. You're connected to the reactor. What do you do for power when they shut down that reactor? I hope the folks have thought about that. Maybe you still do small, like small modulars.

Maybe you do 12 small modulars at a hundred megawatts each, and you sort of have an n+2. Interesting thoughts.

Anne Currie: Well, that is a very interesting thought. So yeah, so you're making two fascinating points there that I have never heard made. One is that we are totally over, we've totally run ahead of SMRs, you know, all that thing we're talking. Totally. We've galloped ahead of that and yet it might actually be worth bringing them back just because of that kind of modern resilience thing of it's better to have 10 than one. You know better to have 10 small ones than one big one.

Christopher Liljenstolpe: Yeah, I've got resilient reactors, and if it's molten salts, you can refuel them by just, topping off the salt tanks as you go. And you can remove the poison out of 'em as you go. So, you know, just, back the salt truck up and dump more salt in. It's a little more than that, but yeah, sort of.

Anne Currie: Yeah.

Christopher Liljenstolpe: If you're interested in bashing your head into the wall and learning about things that you never thought you'd have to learn about, this is a fun time to get into data center infrastructure because you get to do things like, okay, how do I cram a couple hundred terabits per second into a network in a rack? At the same time,

talk about liquid molten salt reactors. I mean, you know, it's sort of a broad spectrum of, you know, and oh, and let's also talk about signal integrity of dielectric fluids. 'Cause we might have to send all this stuff swimming in a tank. It's, you know, you have a lot of interesting conversations in one day. 

Anne Currie: It sounds like you're in a pretty fun area at the moment and we thought it was fun. We thought network cloud networking was fun five years ago. That was nothing as it turns out.

Christopher Liljenstolpe: Yeah, so, and one thing that's sort of interesting now is we took Scalable Sustainable Infrastructure Alliance in the Linux Foundation. We've merged it, as I'm sure you've heard, with Green Software Foundation,

which, so we thought it was probably time to get the hardware guys and the software guys talking, and gals talking together because we realized that we really needed to have these, the stack not have this wall between the hardware and the software.

We really needed to have the same things we were talking about before I alluded to. It's like, okay, the hardware impacts of the horror show that we've got going on. I say that in the nicest possible way to my friends doing the chips, the unique challenges that we have coming, we really need better understanding on the scheduler sides, et cetera, and how we manage that and monitor that and the impacts of that on the software side.

So we decided to take the folks who are working on open hardware designs and making those sustainable, and marrying that to the software side and the green software folks who are working on how we manage and monitor that as well. So we decided to take those two and put them together. And the first project out of that is gonna be something called Project Mycelium, which is going to be actually looking at, how we build software linkages on, how you manage and monitor the hardware infrastructure on the software side. 

Anne Currie: Named after the networks of fungus under the, the way that actually, everything in a forest is more, more connected together than we'd ever realized previously using these incredible mycelium connections, I take it. I'm guessing that's why it's named that way.

Christopher Liljenstolpe: Exactly. Exactly. And a good friend of mine, who used to be the CTO, field CTO at Equinix, is gonna be running that project for me there.

Anne Currie: So, yeah. Utterly fascinating stuff. So yes, I mean, so take, so stepping back from all of this, it's mind blowing amount of new, of complex new thoughts and approaches to things. And what's your view? I mean, you, have a kind of. 30,000, 40,000 foot of view, tend to, on all of these things.

What are you thinking? Where's it all going? What's it gonna, what's gonna happen?

Christopher Liljenstolpe: Well, one of my jokes is yes. AI will kill us all. The question is, will it get smart enough and realize we're the problem and actively kill us, or will it just take so much resources, it will just melt all the ice caps and create a water world before it becomes sentient and just kill us that way? that's it.

There's a joke in every joke. I think right now the path that we're on frankly is not sustainable. You know, we can't, you know, the next logical step from this is we're looking, you know, if we follow that train of 150 know, 60, 100 152, 5600, it's north of a megawatt a rack. That path is unsustainable both from, resources, power, but also economics.

It just, we can't do that. At the going rate, the US grid's gonna be capped by 2031. We will be out of power in the United States by 2031. Europe will be out first. so yeah. So something has to give, we have to become more efficient with the way we utilize these resources, the algorithms we build. We're still brute forcing AI.

We think this is all brilliant software. It's not, we're still brute forcing the heck out of this stuff.

 So something's gotta give there. I think when that does, there'll be a lot of business models that might face some challenges. Because there's a lot of value built that this is going to continue going this way.

But it needs to happen. So we're gonna end up, I think, and there's a lot of fluff as well. There's a lot of pet, the equivalent of pets.com, out there right now. I think we'll end up with a lot more distributed use cases for AI that don't need the same amount of power. We don't need huge inference across it.

But yeah, the, current trend will have to get adjusted, and somebody's gonna figure it out. 

Anne Currie: The old phrase.

Christopher Liljenstolpe: People try it out.

Anne Currie: If something can't go on, it won't, it'll stop, you know? 

know 

Christopher Liljenstolpe: There will be enough economic pressure that it will drive an innovation that will fix it. So I mean, just you looking at it, just

Anne Currie: Yeah, it's the code. 

Christopher Liljenstolpe: I'm not sure how we'll mine enough copper to support this building the power transmission infrastructure. So anyway, that's my doom and gloom part of this.

But I think, it's, what we will end up by the time we're done with it though, is a very efficient computational infrastructure, is it's forcing us to look at everything along the stack. Air is an absolutely horrible heat transfer fluid. We are, everyone's running madly down the road of liquid.

Everyone's running madly down the road of higher voltage. Which again, the way we transmit power in a data center is pretty horrible today. Everyone's ringing all the efficiencies they can outta that because now we have to, it's just economically impossible to do it any other way. So whatever comes outta the back of this is we are gonna have a very efficient data center infrastructure.

Which is all for the better. We're probably gonna end up with a, we will probably end up driving, this will probably fix the grids, because it has to, because we're driving a very different power transmission infrastructure. So we'll fix a bunch of problems along the way. Silver lining. 

Anne Currie: And there is a lot of money behind it. So it's, yeah, it is actually aligned with a lot of good things that we want and it's driving a lot of money in those directions. Yeah. It's interesting. If it doesn't kill us all, which, you know, 

Christopher Liljenstolpe: Yeah, and who knows? It'll probably, it'll probably bring back nuclear, we'll probably, have, be able to have rational conversations about other non-carbon emitting power sources that, 

Anne Currie: Space-based solar power. Well, I'm desperate for it.

Christopher Liljenstolpe: Maybe, yeah, maybe. Might get some countries that just recently shuttered all their nuclear plants go back and put their cooling towers back up.

Not talking about any European countries.

Anne Currie: Well, I'm sure everybody's brain is completely full now, so, and we've had a really interesting discussion that I have utterly enjoyed. So I think we should probably draw the podcast to an end with any final comments that anybody wants to make. So everything we, well, everything we talked about that we can put in the show notes, will be in the show notes at the bottom of the episode.

Do you have any final points that you want to make?

Christopher Liljenstolpe: I mean, it is fun times. And it's not all doom and gloom, but you know, it is right now, there is a bit of a hike and you know, it, 

at this point it seems like it is a train that's gonna keep on going and it will correct. But it is leading to a lot of innovation and that innovation will hang around. Just like when the dot-com bust happened, we will see a correction here and what people thought originally the internet was going to do and what was gonna be delivered by the internet didn't really happen. But it certainly, the things that it is used for, people never, even the people who originally created the ARPANET or the people who invested in the dot-com original late nineties explosion,

what they, the money they put into it, they had, they did not foresee what it is being used for now. But we, the world has been, you know, forever changed by that for good and ill both, by that investment and it's gonna be the same thing here. What we're investing in building now, we think we know what it's gonna be used for,

we're wrong. Everything we think it's gonna be used for, 

5% of it will probably be still what it's being used for 15 years from now. The rest of it, we have no idea. 

And we'll benefit from it and we'll suffer for it. But, we're building a base infrastructure and other people will build, will actually build on that base infrastructure and deliver things that we will have no idea about. 

Anne Currie: Yeah, I, that reminds me of sort of a discussion that we had a few years back about why the internet survived 2020, the beginning of the pandemic, which kept up the west. 'Cause otherwise nobody would, if we hadn't been able to all stay at home and work over, video conferencing, things like that.

And a lot of the infrastructure that was put in place that we relied on there was to support high definition stream tv. So it was like game people put it in so the folk could watch Game of Thrones, then Game of Thrones saved the West. It's like, who would've predicted that? You just don't know what's gonna happen.

Christopher Liljenstolpe: Exactly. Yep. Indeed. And that infrastructure actually, which we didn't talk about, was put in place because, service providers made a horrible choice early on of putting in broadband that was the cheap choice that couldn't do multicast. If they had put in multicast capable infrastructure, they wouldn't have put in the amount of backbone infrastructure that they did. Because they would've had multicast and they wouldn't have had to do the build, that they did, which indeed actually helped us. So it was, you know, that not having multicast out there actually probably saved our bacon. And it pains me to no end. because I was sitting there banging away in the mid nineties, et cetera, as like, "we need to get multicast out there. It's so much more efficient. It will save so much money." And if we had, we probably would've been in much worse shape when the pandemic hit.

Anne Currie: It is interesting that flabbiness, things like inefficient code and inefficient code is what we've been building for the past 20 years. Most of my career, we've been building highly inefficient code, but it does mean there's a lot of untapped potential in there to improve, you know, it's, 

Christopher Liljenstolpe: True.

Anne Currie: Unrealized potential as a result of lazy behavior in the past. We are mining our own past laziness that might save us all.

Christopher Liljenstolpe: Indeed.

Anne Currie: On that note, our laziness and lack of foresight in the past have tended to save us in the future. It might well save us again. On that happy note or that nuanced note,

thank you very much for listening and thank you very much for being my excellent guest today, Chris.

Christopher Liljenstolpe: Thank you for having me on, Anne, and thank you everyone for listening. I hope it was, if not educational, at least entertaining.

Anne Currie: I'm sure it was both. Thank you very much and speak to you on the next time I'm hosting the Environment Variables podcast. Goodbye.

Christopher Liljenstolpe: Bye everyone.  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

Chris Adams: To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Backstage: Green Software Patterns Backstage: Green Software Patterns Thu, 27 Mar 2025 07:00:00 GMT 11:45 x1lnz531 false https://podcasts.castplus.fm/e/p8lxvqw8-backstage-green-software-patterns 68dc7cfc7be17a7f013561f3 68dc7cef46a2532cdd8d1c23 In this episode, Chris Skipper takes us backstage into the Green Software Patterns Project, an open-source initiative designed to help software practitioners reduce emissions by applying vendor-neutral best practices. Guests Franziska Warncke and Liya Mathew, project leads for the initiative, discuss how organizations like AVEVA and MasterCard have successfully integrated these patterns to enhance software sustainability. They also explore the rigorous review process for new patterns, upcoming advancements such as persona-based approaches, and how developers and researchers can contribute. 101 In this episode, Chris Skipper takes us backstage into the Green Software Patterns Project, an open-source initiative designed to help software practitioners reduce emissions by applying vendor-neutral best practices. Guests Franziska Warncke and Liya Mathew, project leads for the initiative, discuss how organizations like AVEVA and MasterCard have successfully integrated these patterns to enhance software sustainability. They also explore the rigorous review process for new patterns, upcoming advancements such as persona-based approaches, and how developers and researchers can contribute. 

Learn more about our people:

Find out more about the GSF:

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Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Chris Skipper: Welcome to Environment Variables, where we bring you the latest news from the world of sustainable software development. I am the producer of the show, Chris Skipper, and today we're excited to bring you another episode of Backstage, where we uncover the stories, challenges, and innovations driving the future of green software.

In this episode, we're diving into the Green Software Patterns Project, an open source initiative designed to curate and share best practices for reducing software emissions.

The project provides a structured approach for software practitioners to discover, contribute, and apply vendor-neutral green software patterns that can make a tangible impact on sustainability. Joining us today are Franziska Warncke and Liya Mathew, the project leads for the Green Software Patterns Initiative.

They'll walk us through how the project works, its role in advancing sustainable software development, and what the future holds for the Green Software Patterns. Before we get started, a quick reminder that everything we discuss in this episode will be linked in the show notes below. So without further ado, let's dive into our first question about the Green Software Patterns project. My first question is for Liya. The project is designed to help software practitioners reduce emissions in their applications.

What are some real world examples of how these patterns have been successfully applied to lower carbon footprints?

Liya Mathew: Thanks for the question, and yes, I am pretty sure that there are a lot of organizations as well as individuals who have greatly benefited from this project. A key factor behind the success of this project is the impact that these small actions can have on longer runs. For example, AVEVA has been an excellent case of an organization that embraced these patterns.

They created their own scoring system based on Patterns which help them measure and improve their software sustainability. Similarly, MasterCard has also adopted and used these patterns effectively. What's truly inspiring is that both AVEVA and MasterCard were willing to share their learnings with the GSF and the open source community as well.

Their contributions will help others learn and benefit from their experiences, fostering a collaborative environment where everyone can work towards a more sustainable software.

Chris Skipper: Green software patterns must balance general applicability with technical specificity. How do you ensure that these patterns remain actionable and practical across different industries, technologies and software architectures?

Liya Mathew: One of the core and most useful features of patterns is the ability to correlate the software carbon intensity specification. Think of it as a bridge that connects learning and measurement. When we look through existing catalog of patterns, one essential thing that stands out is their adaptability.

Many of these patterns not only align with sustainability, but also coincide with security and reliability best practices. The beauty of this approach is that we don't need to completely rewrite our software architecture to make it more sustainable. Small actions like catching static data or providing a dark mode can make significant difference.

These are simple, yet effective steps that can lead us a long way towards sustainability. Also, we are nearing the graduation of Patterns V1. This milestone marks a significant achievement and we are already looking ahead to the next exciting phase: Patterns V2. In Patterns V2, we are focusing on persona-based and behavioral patterns, which will bring even more tailored and impactful solutions to our community.

These new patterns will help address specific needs and behaviors, making our tools even more adaptable and effective.

Chris Skipper: The review and approval process for new patterns involves multiple stages, including subject matter expert validation and team consensus. Could you walk us through the workflow for submitting and reviewing patterns?

Liya Mathew: Sure. The review and approval process for new patterns involve multiple stages, ensuring that each pattern meets a standard before integration. Initially, when a new pattern is submitted, it undergoes an initial review by our initial reviewers. During this stage, reviewers check if the pattern aligns with the GSF's mission of reducing software emissions, follows the GSF Pattern template, and adheres to proper formatting rules. They also ensure that there is enough detail for the subject matter expert to evaluate the pattern. If any issue arises, the reviewer provides clear and constructive feedback directly in the pull request, and the submitter updates a pattern accordingly.

Once the pattern passes the initial review, it is assigned to an appropriate SME for deeper technical review, which should take no more than a week, barring any lengthy feedback cycles. The SME checks for duplicate patterns validates the content as assesses efficiency and accuracy of the pattern in reducing software remission.

It also ensures that the pattern's level of depth is appropriate. If any areas are missing or incomplete, the SME provides feedback in the pull request. If the patterns meet all the criteria, SME will then remove the SME review label and adds a team consensus label and assigns this pull request back to the initial reviewer.

Then the Principles and Patterns Working Group has two weeks to comment or object to the pattern, requiring a team consensus before the PR can be approved and merged in the development branch. Thus the raw process ensures that each pattern is well vetted and aligned with our goals.

Chris Skipper: For listeners who want to start using green software patterns in their projects, what's the best way to get involved, access the catalog, or submit a new pattern?

Liya Mathew: All the contributions are made via GitHub pull requests. You can start by submitting a pull request on our repository. Additionally, we would love to connect with everyone interested in contributing. Feel free to reach out to us on LinkedIn or any social media handles and express your interest in joining our project's weekly calls.

Also, check if your organization is a member of the Green Software Foundation. We warmly welcome contributions in any capacity. As mentioned earlier, we are setting our sights on a very ambitious goal for this project, and your involvement would be invaluable.

Chris Skipper: Thanks to Liya for those great answers. Next, we had some questions for Franziska. The Green Software Patterns project provides a structured open source database of curated software patterns that help reduce software emissions. Could you give us an overview of how the project started and its core mission? 

Franziska Warncke: Great question. The Green Software Patterns project emerged from a growing recommendation of the environmental impact of software and the urgent need for sustainable software engineering practices. As we've seen the tech industry expand, it became clear that while hardware efficiency has been a focal point for sustainability, software optimization was often overlooked.

 A group of dedicated professionals began investigating existing documentation, including resources like the AWS Well-Architected Framework, and this exploration laid to groundwork for the project. This allows us to create a structured approach to the curating of the patterns that can help reduce software emissions.

We developed a template that outlines how each pattern should be presented, ensuring clarity and consistency. Additionally, we categorize these patterns into the three main areas, cloud, web, and AI. 

Chris Skipper: Building an open source knowledge base and ensuring it remains useful, requires careful curation and validation. What are some of the biggest challenges your team has faced in developing and maintaining the green software patterns database? 

Franziska Warncke: Building and maintaining an open source knowledge base like the Green Software Patterns database, comes with its own set of challenges. One of the biggest hurdles we've encountered is resource constraints. As an open source project, we often operate with limited time personnel, which makes it really, really difficult to prioritize certain tasks over others.

Despite this challenge, we are committed to continuous improvement, collaboration, and community engagement to ensure that the Green Software Patterns database remains a valuable resource for developers looking to adopt more sustainable practices.

Chris Skipper: Looking ahead, what are some upcoming initiatives for the project? Are there any plans to expand the pattern library or introduce new methodologies for evaluating and implementing patterns? 

Franziska Warncke: Yes, we have some exciting initiatives on the horizon. So one of our main focuses is to restructure the patterns catalog to adopt the persona-based approach. This means we want to create tailored patterns for various worlds within the software industry, like developers, project managers, UX designers, and system architects.

By doing this, we aim to make the patents more relevant and accessible to a broader audience. We are also working on improving the visualization of the patterns. We recognize that user-friendly visuals are crucial for helping people understand and adopt these patterns in their own projects, which was really missing before.

In addition to that, we plan to categorize the patterns based on different aspects. Such as persona type, adoptability and effectiveness. This structured approach will help users quickly find the patterns that are most relevant to their roads and their needs, making the entire experience much more streamlined. Moreover, we are actively seeking new contributors to join us.

And we believe that the widest set of voices and perspective will enrich our knowledge base and ensure that our patterns reflect a wide range of experience. So, if anyone is interested, we'd love to hear from you. 

Chris Skipper: The Green Software Patterns Project is open source and community-driven. How can developers, organizations, and researchers contribute to expanding the catalog and improving the quality of the patterns?

Franziska Warncke: Yeah, 

the Green Software Patterns Project is indeed open source and community driven, and we welcome contributions from developers, organizations, and researchers to help expand our catalog and improve the quality of the patterns. We need people to review the existing patterns critically and provide feedback.

This includes helping us categorize them for a specific persona, ensuring that each pattern is tailored to each of various roles in the software industry. Additionally, contributors can assist by adding more information and context to the patterns, making them more comprehensive and useful. Visuals are another key area where we need help.

Creating clear and engaging visuals that illustrate how to implement these patterns can significantly enhance their usability. Therefore, we are looking for experts who can contribute their skills in design and visualization to make the patterns more accessible. So if you're interested, then we would love to have you on board.

Thank you.

Chris Skipper: Thanks to Franziska for those wonderful answers. So we've reached the end of the special backstage episode on the Green Software Patterns Project at the GSF. I hope you enjoyed the podcast. To listen to more podcasts about green software, please visit podcast.greensoftware.foundation. And we'll see you on the next episode.

Bye for now.

​ 



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]]> In this episode, Chris Skipper takes us backstage into the Green Software Patterns Project, an open-source initiative designed to help software practitioners reduce emissions by applying vendor-neutral best practices. Guests Franziska Warncke and Liya Mathew, project leads for the initiative, discuss how organizations like AVEVA and MasterCard have successfully integrated these patterns to enhance software sustainability. They also explore the rigorous review process for new patterns, upcoming advancements such as persona-based approaches, and how developers and researchers can contribute. 

Learn more about our people:

Find out more about the GSF:

Resources:

If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Chris Skipper: Welcome to Environment Variables, where we bring you the latest news from the world of sustainable software development. I am the producer of the show, Chris Skipper, and today we're excited to bring you another episode of Backstage, where we uncover the stories, challenges, and innovations driving the future of green software.

In this episode, we're diving into the Green Software Patterns Project, an open source initiative designed to curate and share best practices for reducing software emissions.

The project provides a structured approach for software practitioners to discover, contribute, and apply vendor-neutral green software patterns that can make a tangible impact on sustainability. Joining us today are Franziska Warncke and Liya Mathew, the project leads for the Green Software Patterns Initiative.

They'll walk us through how the project works, its role in advancing sustainable software development, and what the future holds for the Green Software Patterns. Before we get started, a quick reminder that everything we discuss in this episode will be linked in the show notes below. So without further ado, let's dive into our first question about the Green Software Patterns project. My first question is for Liya. The project is designed to help software practitioners reduce emissions in their applications.

What are some real world examples of how these patterns have been successfully applied to lower carbon footprints?

Liya Mathew: Thanks for the question, and yes, I am pretty sure that there are a lot of organizations as well as individuals who have greatly benefited from this project. A key factor behind the success of this project is the impact that these small actions can have on longer runs. For example, AVEVA has been an excellent case of an organization that embraced these patterns.

They created their own scoring system based on Patterns which help them measure and improve their software sustainability. Similarly, MasterCard has also adopted and used these patterns effectively. What's truly inspiring is that both AVEVA and MasterCard were willing to share their learnings with the GSF and the open source community as well.

Their contributions will help others learn and benefit from their experiences, fostering a collaborative environment where everyone can work towards a more sustainable software.

Chris Skipper: Green software patterns must balance general applicability with technical specificity. How do you ensure that these patterns remain actionable and practical across different industries, technologies and software architectures?

Liya Mathew: One of the core and most useful features of patterns is the ability to correlate the software carbon intensity specification. Think of it as a bridge that connects learning and measurement. When we look through existing catalog of patterns, one essential thing that stands out is their adaptability.

Many of these patterns not only align with sustainability, but also coincide with security and reliability best practices. The beauty of this approach is that we don't need to completely rewrite our software architecture to make it more sustainable. Small actions like catching static data or providing a dark mode can make significant difference.

These are simple, yet effective steps that can lead us a long way towards sustainability. Also, we are nearing the graduation of Patterns V1. This milestone marks a significant achievement and we are already looking ahead to the next exciting phase: Patterns V2. In Patterns V2, we are focusing on persona-based and behavioral patterns, which will bring even more tailored and impactful solutions to our community.

These new patterns will help address specific needs and behaviors, making our tools even more adaptable and effective.

Chris Skipper: The review and approval process for new patterns involves multiple stages, including subject matter expert validation and team consensus. Could you walk us through the workflow for submitting and reviewing patterns?

Liya Mathew: Sure. The review and approval process for new patterns involve multiple stages, ensuring that each pattern meets a standard before integration. Initially, when a new pattern is submitted, it undergoes an initial review by our initial reviewers. During this stage, reviewers check if the pattern aligns with the GSF's mission of reducing software emissions, follows the GSF Pattern template, and adheres to proper formatting rules. They also ensure that there is enough detail for the subject matter expert to evaluate the pattern. If any issue arises, the reviewer provides clear and constructive feedback directly in the pull request, and the submitter updates a pattern accordingly.

Once the pattern passes the initial review, it is assigned to an appropriate SME for deeper technical review, which should take no more than a week, barring any lengthy feedback cycles. The SME checks for duplicate patterns validates the content as assesses efficiency and accuracy of the pattern in reducing software remission.

It also ensures that the pattern's level of depth is appropriate. If any areas are missing or incomplete, the SME provides feedback in the pull request. If the patterns meet all the criteria, SME will then remove the SME review label and adds a team consensus label and assigns this pull request back to the initial reviewer.

Then the Principles and Patterns Working Group has two weeks to comment or object to the pattern, requiring a team consensus before the PR can be approved and merged in the development branch. Thus the raw process ensures that each pattern is well vetted and aligned with our goals.

Chris Skipper: For listeners who want to start using green software patterns in their projects, what's the best way to get involved, access the catalog, or submit a new pattern?

Liya Mathew: All the contributions are made via GitHub pull requests. You can start by submitting a pull request on our repository. Additionally, we would love to connect with everyone interested in contributing. Feel free to reach out to us on LinkedIn or any social media handles and express your interest in joining our project's weekly calls.

Also, check if your organization is a member of the Green Software Foundation. We warmly welcome contributions in any capacity. As mentioned earlier, we are setting our sights on a very ambitious goal for this project, and your involvement would be invaluable.

Chris Skipper: Thanks to Liya for those great answers. Next, we had some questions for Franziska. The Green Software Patterns project provides a structured open source database of curated software patterns that help reduce software emissions. Could you give us an overview of how the project started and its core mission? 

Franziska Warncke: Great question. The Green Software Patterns project emerged from a growing recommendation of the environmental impact of software and the urgent need for sustainable software engineering practices. As we've seen the tech industry expand, it became clear that while hardware efficiency has been a focal point for sustainability, software optimization was often overlooked.

 A group of dedicated professionals began investigating existing documentation, including resources like the AWS Well-Architected Framework, and this exploration laid to groundwork for the project. This allows us to create a structured approach to the curating of the patterns that can help reduce software emissions.

We developed a template that outlines how each pattern should be presented, ensuring clarity and consistency. Additionally, we categorize these patterns into the three main areas, cloud, web, and AI. 

Chris Skipper: Building an open source knowledge base and ensuring it remains useful, requires careful curation and validation. What are some of the biggest challenges your team has faced in developing and maintaining the green software patterns database? 

Franziska Warncke: Building and maintaining an open source knowledge base like the Green Software Patterns database, comes with its own set of challenges. One of the biggest hurdles we've encountered is resource constraints. As an open source project, we often operate with limited time personnel, which makes it really, really difficult to prioritize certain tasks over others.

Despite this challenge, we are committed to continuous improvement, collaboration, and community engagement to ensure that the Green Software Patterns database remains a valuable resource for developers looking to adopt more sustainable practices.

Chris Skipper: Looking ahead, what are some upcoming initiatives for the project? Are there any plans to expand the pattern library or introduce new methodologies for evaluating and implementing patterns? 

Franziska Warncke: Yes, we have some exciting initiatives on the horizon. So one of our main focuses is to restructure the patterns catalog to adopt the persona-based approach. This means we want to create tailored patterns for various worlds within the software industry, like developers, project managers, UX designers, and system architects.

By doing this, we aim to make the patents more relevant and accessible to a broader audience. We are also working on improving the visualization of the patterns. We recognize that user-friendly visuals are crucial for helping people understand and adopt these patterns in their own projects, which was really missing before.

In addition to that, we plan to categorize the patterns based on different aspects. Such as persona type, adoptability and effectiveness. This structured approach will help users quickly find the patterns that are most relevant to their roads and their needs, making the entire experience much more streamlined. Moreover, we are actively seeking new contributors to join us.

And we believe that the widest set of voices and perspective will enrich our knowledge base and ensure that our patterns reflect a wide range of experience. So, if anyone is interested, we'd love to hear from you. 

Chris Skipper: The Green Software Patterns Project is open source and community-driven. How can developers, organizations, and researchers contribute to expanding the catalog and improving the quality of the patterns?

Franziska Warncke: Yeah, 

the Green Software Patterns Project is indeed open source and community driven, and we welcome contributions from developers, organizations, and researchers to help expand our catalog and improve the quality of the patterns. We need people to review the existing patterns critically and provide feedback.

This includes helping us categorize them for a specific persona, ensuring that each pattern is tailored to each of various roles in the software industry. Additionally, contributors can assist by adding more information and context to the patterns, making them more comprehensive and useful. Visuals are another key area where we need help.

Creating clear and engaging visuals that illustrate how to implement these patterns can significantly enhance their usability. Therefore, we are looking for experts who can contribute their skills in design and visualization to make the patterns more accessible. So if you're interested, then we would love to have you on board.

Thank you.

Chris Skipper: Thanks to Franziska for those wonderful answers. So we've reached the end of the special backstage episode on the Green Software Patterns Project at the GSF. I hope you enjoyed the podcast. To listen to more podcasts about green software, please visit podcast.greensoftware.foundation. And we'll see you on the next episode.

Bye for now.

​ 



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]]> The Week in Green Software: Sustainable AI Progress The Week in Green Software: Sustainable AI Progress Thu, 20 Mar 2025 07:00:00 GMT 50:34 j122ryr1 false https://podcasts.castplus.fm/e/2861q0qn-the-week-in-green-software-sustainable-ai-progress 68dc7d0e46a2532cdd8d2637 68dc7cef46a2532cdd8d1c23 100 For this 100th episode of Environment Variables, guest host Anne Currie is joined by Holly Cummins, senior principal engineer at Red Hat, to discuss the intersection of AI, efficiency, and sustainable software practices. They explore the concept of "Lightswitch Ops"—designing systems that can easily be turned off and on to reduce waste—and the importance of eliminating zombie servers. They cover AI’s growing energy demands, the role of optimization in software sustainability, and Microsoft's new shift in cloud investments. They also touch on AI regulation and the evolving strategies for balancing performance, cost, and environmental impact in tech. 

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Find out more about the GSF:

News:

Events:

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If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!


TRANSCRIPT BELOW:

Holly Cummins: Demand for AI is growing, demand for AI will grow indefinitely. But of course, that's not sustainable. Again, you know, it's not sustainable in terms of financially and so at some point there will be that correction. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: So hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software. Now, today you're not hearing the dulcet tones of your usual host, Chris Adams. I am a guest host on this, a common guest, a frequent guest host, Anne Currie. And my guest today is somebody I've known for quite a few years and I'm really looking forward to chatting to, Holly.

So do you want to introduce yourself, Holly?

Holly Cummins: So I'm Holly Cummins. I work for Red Hat. My day job is that, I'm a senior principal engineer and I'm helping to develop Quarkus, which is Java middleware. And I'm looking at the ecosystem of Quarkus, which sounds really sustainability oriented, but actually the day job aspect is I'm more looking at

the contributors and, you know, the extensions and that kind of thing. But one of the other things that I do end up looking a lot at is the ecosystem aspect of Quarkus in terms of sustainability. Because Quarkus is a extremely efficient Java runtime. And so when I joined the team, one of the things we asked well, one of the things I asked was, can we, know this is really efficient. Does that translate into an environmental, you know, benefit? Is it actually benefiting the ecosystem? You know, can we quantify it? And so we did that work and we were able to sort of validate our intuition that it did have a much lower carbon footprint, which was nice.

But some things of what we did actually surprised us as well, which was also good because it's always good to be challenged in your assumptions. And so now part of what I'm doing as well is sort of broadening that focus from, instead of measuring what we've done in the past, thinking about, well, what does a sustainable middleware architecture look like?

What kind of things do we need to be providing?

Anne Currie: Thank you very much indeed. That's a really good overview of what I really primarily want to be talking about today. We will be talking about a couple of articles as usual on AI, but really I want to be focused on what you're doing in your day job because I think it's really interesting and incredibly relevant.

So, as I said, my name is Anne Currie. I am the CEO of a learning and development company called Strategically Green. We do workshops and training around building green software and changing your systems to align with renewables. But I'm also one of the authors of O'Reilly's new book, Building Green Software, and Holly was probably the most, the biggest single reviewer/contributor to that book, and it was in her best interest to do so because, we make, I make tons and tons of reference to a concept that you came up with.

I'm very interested in the backstory to this concept, but perhaps you can tell me a little bit more about it because it is, this is something I've not said to you before, but it is, this comes up in review feedback, for me, for the book, more than any other concept in the book. Lightswitch Ops. People saying, "Oh, we've put in, we've started to do Lightswitch Ops."

If anybody says "I've started to do" anything, it's always Lightswitch Ops. So tell us, what is Lightswitch Ops?

Holly Cummins: So Lightswitch Ops, it's really, it's about architecting your systems so that they can tolerate being turned off and on, which sounds, you know, it sounds sort of obvious, but historically that's not how our systems have worked. And so the first step is architect your system so that they can tolerate being turned off and on.

And then the next part is once you have that, actually turn them off and on. And, it sort of, it came about because I'm working on product development now, and I started my career as a performance engineer, but in between those two, I was a client facing consultant, which was incredibly interesting.

And it was, I mean, there was, so many things that were interesting, but one of the things that I sort of kept seeing was, you know, you sort of work with clients and some of them you're like, "Oh wow, you're, you know, you're really at the top of your game" and some you think, "why are you doing this way when this is clearly, you know, counterproductive" or that kind of thing.

And one of the things that I was really shocked by was how much waste there was just everywhere. And I would see things like organizations where they would be running a batch job and the batch job would only run at the weekends, but the systems that supported it would be up 24/7. Or sometimes we see the opposite as well, where it's a test system for manual testing and people are only in the office, you know, nine to five only in one geo and the systems are up 24 hours.

And the reason for this, again, it's sort of, you know, comes back to that initial thing, it's partly that we just don't think about it and, you know, that we're all a little bit lazy, but it's also that many of us have had quite negative experiences of if you turn your computer off, it will never be the same when it comes back up.

I mean, I still have this with my laptop, actually, you know, I'm really reluctant to turn it off. But now we have, with laptops, we do have the model where you can close the lid and it will go to sleep and you know that it's using very little energy, but then when you bring it back up in the morning, it's the same as it was without having to have the energy penalty of keeping it on overnight. And I think, when you sort of look at the model of how we treat our lights in our house, nobody has ever sort of left a room and said, "I could turn the light off, but if I turn the light off, will the light ever come back on in the same form again?"

Right? Like we just don't do that. We 

have a great deal of confidence that it's reliable to turn a light off and on and that it's low friction to do it. And so we need to get to that point with our computer systems. And you can sort roll with the analogy a bit more as well, which is in our houses, it tends to be quite a manual thing of turning the lights off and on.

You know, I turn the light on when I need it. In institutional buildings, it's usually not a manual process to turn the lights off and on. Instead, what we end up is, we end up with some kind of automation. So, like, often there's a motion sensor. So, you know, I used to have it that if I would stay in our office late at night, at some point if you sat too still because you were coding and deep in thought, the lights around you would go off and then you'd have to, like, wave your arms to make the lights go back on.

And it's that, you know, it's this sort of idea of like we can detect the traffic, we can detect the activity, and not waste the energy. And again, we can do exactly this our computer systems. So we can have it so that it's really easy to turn them off and on. And then we can go one step further and we can automate it and we can say, let's script to turn things off at 5pm because we're only in one geo.

And you know, if we turn them off at 5pm, then we're enforcing quite a strict work life balance. So...

Anne Currie: Nice, nice work.

Holly Cummins: Yeah. Sustainable. Sustainable pace. Yeah. Or we can do sort of, you know, more sophisticated things as well. Or we can say, okay, well, let's just look at the traffic and if there's no traffic to this, let's turn it off.

off 

Anne Currie: Yeah, it is an interestingly simple concept because it's, 

when people come up with something which is like, in some ways, similar analogies, a light bulb moment of, you know, why don't people turn things off? Becasue, so Holly, everybody is an unbelievably good public speaker.

One of the best public speakers out there at the moment. And we first met because you came and gave talks at, in some tracks I was hosting on a variety. Some on high performance code, code efficiency, some on, being green. One of the stories you told was about your Lightswitch moment, the realization that actually this was a thing that needed to happen.

And I thought it was fascinating. It was about how, I know everybody, I've been in the tech industry for a long time, so I've worked with Java a lot over the years and many years ago. And one of the issues with Java in the old days was always, it was very hard to turn things off and turn them back on again.

And that was fine in the old world, but you talked about how that was no longer fine. And that was an issue with the cloud because the cloud, using the cloud well, turning things on and off and things, doing things like auto scaling is utterly key to the idea of the cloud. And therefore it had to become part of Quarkus, part of the future of Java. Am I right in that understanding? 

Holly Cummins: Yeah, absolutely. And the cloud sort of plays into both parts of the story, actually. So definitely we, the things that we need to be cloud native, like being able to support turning off and on again, are very well aligned to what you need to support Lightswitch Ops. And so the, you know, there with those two, we're pulling in the same direction.

The needs of the cloud and the needs of sustainability are both driving us to make systems that, I just saw yesterday, sorry this is a minor digression, but I was looking something up, and we used to talk a lot about the Twelve-Factor App, and you know, at the time we started talking about Twelve-Factor Apps, those characteristics were not at all universal. And then someone came up with the term, the One-Factor App, which was the application that could just tolerate being turned off and on.

And sometimes even that was like too much of a stretch. And so there's the state aspect to it, but then there's also the performance aspect of it and the timeliness aspect of it. And that's really what Quarkus has been looking at that if you want to have any kind of auto scaling or any kind of serverless architecture or anything like that, the way Java has historically worked, which is that it eats a lot of memory and it takes a long time to start up, just isn't going to work.

And the sort of the thing that's interesting about that is quite often when we talk about optimizing things or becoming more efficient or becoming greener, it's all about the trade offs of like, you know, "oh, I could have the thing I really want, or I could save the world. I guess I should save the world." But sometimes what we can do is we can just find things that we were paying for, that we didn't even want anymore. And that's, I think, what Quarkus was able to do. Because a lot of the reason that Java has a big memory footprint and a lot of the reason that Java is slow to start up is it was designed for a different kind of ops.

The cloud didn't exist. CI/CD didn't exist. DevOps didn't exist. And so the way you built your application was you knew you would get a release maybe once a year and deployment was like a really big deal. And you know, you'd all go out and you'd have a party after you successfully deployed because it was so challenging.

And so you wanted to make sure that everything you did was to avoid having to do a deployment and to avoid having to talk to the ops team because they were scary. But of course, even though we had this model where releases happen very rarely, or the big releases happen very rarely, of course, the world still moves on, you know, people still had defects, people, so what you ended up with was something that was really much more optimized towards patching.

So can we take the system and without actually taking, turning it off and on, because that's almost impossible, can we patch it? So everything was about trying to change the engine of the plane while the plane was flying, which is really clever engineering. If you can support that, you know, well done you.

It's so dynamic. And so everything was optimized so that, you know, you could change your dependencies and things would keep working. And, you know, you could even change some fairly important characteristics of your dependencies and everything would sort of adjust and it would ripple back through the system.

But because that dynamism was baked into every aspect of the architecture, it meant that everything just had a little bit of drag, and everything had a little bit of slowdown that came from that indirection. And then now you look at it in the cloud and you think, well, wait a minute. I don't need that. I don't need that indirection.

I don't need to be able to patch because I have a CI/CD pipeline, and if I'm going into my production systems and SSHing in to change my binaries, something has gone horribly wrong with my process. And you know, I need to, I have all sorts of problems. So really what Quarkus was able to do was get rid of a whole bunch of reflection, get rid of a whole bunch of indirection,

do more upfront at build time. And then that gives you much leaner behavior at runtime, which is what you want in a cloud environment.

Anne Currie: Yeah. And what I love about this and love about the story of Quarkus is, it's aligned with something, non functional requirements. It's like, it's an unbelievably boring name, and for something which is a real pain point for companies. But it's also, in many ways, the most important thing and the most difficult thing that we do.

It's like, being secure, being cost effective, being resilient. A lot of people say to me, well, you know, actually all you're doing with green is adding another non functional requirement. We know those are terrible. But I can say, no, we need to not make it another non functional requirements. It's just a good, another motivator for doing the first three well, you know. Also scaling is about resilience. It's about cost saving, and it's about being green. And it's about, and being able to pave rather than patch, I think is, was the term. It's more secure, you know. Actually patching is much less secure than repaving, taking everything down and bringing it back up.

All the modern thinking about being more secure, being faster, being cheaper, being more resilient is aligned or needs to be aligned with being green and it can be, and it should be, and it shouldn't just be about doing less.

Holly Cummins: Absolutely. And, you know, especially for the security aspect, when you look at something like tree shaking, that gives you more performance by getting rid of the code that you weren't using. Of course, it makes you more secure as well because you get rid of all these code paths and all of these entry points and vulnerabilities that had no benefit to you, but were still a vulnerability.

Anne Currie: Yeah, I mean, one of the things that you've talked about Lightswitch Ops being related to is, well, actually not Lightswitch Ops, but the thing that you developed before Lightswitch Ops, the concept of zombie servers. Tell us a little bit about that because that not only is cost saving, it's a really big security improvement.

So tell us about zombie, the precursor to Lightswitch Ops.

Holly Cummins: Yeah, zombie servers are again, one of those things that I sort of, I noticed it when I was working with clients, but I also noticed it a lot in our own development practices that what we would do was we would have a project and we would fire up a server in great excitement and you know, we'd register something on the cloud or whatever.

And then we'd get distracted and then, or then we, you know, sometimes we would develop it but fail to go to production. Sometimes we'd get distracted and not even develop it. And I looked and I think some of these costs became more visible and more obvious when we move to the cloud, because it used to be that when you would provision a server, once it was provisioned, you'd gone through all of the pain of provisioning it and it would just sit there and you would keep it in case you needed it.

But with the cloud, all of a sudden, keeping it until you needed it had a really measurable cost. And I looked and I realized, you know, I was spending, well, I wasn't personally spending, I was costing my company thousands of pounds a month on these cloud servers that I'd provisioned and forgotten about.

And then I looked at how Kubernetes, the sort of the Kubernetes servers were being used and some of the profiles of the Kubernetes servers. And I realized that, again, there's, each company would have many clusters. And I was thinking, are they really using all of those clusters all of the time?

And so I started to look into it and then I realized that there had been a lot of research done on it and it was shocking. So again, you know, the sort of the, I have to say I didn't coin the term zombie servers. I talk about it a lot, but, there was a company called the Antithesis Institute.

And what they did, although actually, see, now I'm struggling with the name of it because I always thought they were called the Antithesis Institute. And I think it's actually a one letter variant of that, which is much less obvious as a word, but much more distinctive. But I've, every time I talked about them, I mistyped it.

And now I can't remember which one is the correct one, but in any case, it's something like the Antithesis Institute. And they did these surveys and they found that, it was something like a third of the servers that they looked at were doing no work at all. Or rather no, no useful work. So they're still consuming energy, but there's no work being done.

And when they say no useful work as well, that sounds like a kind of low bar. Because when I think about my day job, quite a lot of it is doing work that isn't useful. But they had, you know, it wasn't like these servers were serving cat pictures or that kind of thing. You know, these servers were doing nothing at all.

There was no traffic in, there was no traffic out. So you can really, you know, that's just right for automation to say, "well, wait a minute, if nothing's going in and nothing's coming out, we can shut this thing down." And then there was about a further third that had a utilization that was less than 5%.

So again, you know, this thing, it's talking to the outside world every now and then, but barely. So again, you know, it's just right for a sort of a consolidation. But the, I mean, the interesting thing about zombies is as soon as you talk about it, usually, you know, someone in the audience, they'll turn a little bit green and they'll go, "Oh, I've just remembered that server that I provisioned."

And sometimes, you know, I'm the one giving the talk and I'm like, Oh, while preparing this talk, I just realized I forgot a server, because it's so easy to do. And the way we're measured as well, and the way we measure our own productivity is we give a lot more value to creating than to cleaning up.

Anne Currie: Yeah. And in some ways that makes sense because, you know, creating is about growth and cleaning up you know, it's about degrowth. It's about like, you know, it's like you want to tell the story of growth, but I've heard a couple of really interesting, sales on zombie servers since you started, well, yeah, since you started talking about it, you may not have invented it, but you popularized it. One was from, VMware, a cost saving thing. They were, and it's a story I tell all the time about when they were moving data centers in Singapore, setting up a new data center in Singapore.

They decided to do a review of all their machines to see what had to go across. And they realized that 66 percent of their machines did not need to be reproduced in the new data center. You know, they had a, and that was VMware. People who are really good at running data centers. So imagine what that's like.

But moving data centers is a time when it often gets spotted. But I will say, a more, a differently disturbing story from a company that wished to remain nameless. Although I don't think they need to because I think it's just an absolutely bog standard thing. They were doing a kind of thriftathon style thing of reviewing their data center to see if there was stuff that they could save money on, and they found a machine that was running at 95, 100 percent CPU, and they thought, they thought, Oh my God, it's been hacked.

It's been hacked. Somebody's mining Bitcoin on this. It's, you know, or maybe it's attacking us. Who knows? And so they went and they did some searching around internally, and they found out that it was somebody who turned on a load test, and then forgot to turn it off three years previously. And And the, I would say that obviously that came up from the cost, but it also came up from the fact that machine could have been hacked.

You know, it could be, could have been mining Bitcoin. It could have been attacking them. It could have been doing anything. They hadn't noticed because it was a machine that no one was looking at. And I thought it was an excellent example. I thought those two, excellent examples of the cost and the massive security hole that comes from machines that nobody is looking at anymore.

So, you know, non functional requirements, they're really important. And

Holly Cummins: Yeah.

Anne Currie: doing better on them is also green. And also, they're very, non functional requirements are really closely tied together.

Holly Cummins: Yeah. I mean, oh, I love both of those stories. And I've heard the VMware one before, but I hadn't heard the one about the hundred percent, the load test. That is fantastic. One of the reasons I like talking about zombies and I think one of the reasons people like hearing about it I mean, it's partly the saving the world.

But also I think when we look at greenness and sustainability, some of it is not a very cheerful topic, but the zombie servers almost always when you discover the cases of them, they are hilarious. I mean, they're awful, but they're hilarious And you know, it's just this sort of stuff of, "how did this happen?

How did we allow this to happen?" Sometimes it's so easy to do better. And the examples of doing bad are just something that we can all relate to. And, but on the same time, you know, you sort of think, oh, that shouldn't have happened. How did that happen?

Anne Currie: But there's another thing I really like about zombie servers, and I think you've pointed out this yourself, and I plagiarized from your ideas like crazy in Building Green Software, which is one of the reasons why I got you to be a reviewer, so you could complain about it if you wanted to early on. The, 

Holly Cummins: It also means I would agree with you a lot. Yes. Oh This is very, sensible. Very sensible. Yes.

Anne Currie: One of the things that we, that constantly comes up when I'm talking to people about this and when we're writing the book and when we're going out to conferences, is people need a way in. And it's often that, you know, that people think the way into building green software is to rewrite everything in C and then they go, "well, I can't do that.

So that's the end. That's the only way in. And I'm not going to be able to do it. So I can't do anything at all." Operations and zombie servers is a really good way in, because you can just do it, you can, instead of having a hackathon, you can just do a thrift a thon, get everybody to have a little bot running that doesn't need to be running, instantly halve your, you know, it's not uncommon for people to find ways to halve their life.

Yeah. carbon emissions and halve their hosting costs simultaneously in quite a short period of time and it'd be the first thing they do. So I quite like it because it's the first thing they do. What do you think about that? It's, is it the low hanging fruit?

Holly Cummins: Yeah, absolutely, I think, yeah, it's the low hanging fruit, it's easy, it's, kind of entertaining because when you find the problems you can laugh at yourself, and there's, again, there's no downside and several upsides, you know, so it's, you know, it's this double win of I got rid of something I wasn't even using, I have more space in my closet, and I don't have to pay for it.

Anne Currie: Yeah, I just read a book that I really should have read years and years ago, and I don't know why I didn't, because people have been telling me to read it for years, which was the goal. Which is, it's not about tech, but it is about tech. It's kind of the book that was the precursor to the Phoenix Projects, which I think a lot read.

And it was, it's all about TPS, the Toyota Production System. In a kind of like an Americanized version of it, how are the tires production system should be brought to America. And it was written in the 80s and it's all about work in progress and cleaning your environment and getting rid of stuff that gets in your way and just obscures everything.

, you can't see what's going on. Effectively, it was a precursor to lean, which I think is really very well aligned. Green and lean, really well aligned. And, it's something that we don't think about, that cleaning up waste just makes your life much better in ways that are hard to imagine until you've done it.

And zombie, cleaning zombie servers up just makes your systems more secure, cheaper, more resilient, more everything. It's a really good thing to do.

Holly Cummins: Yeah. And there's sort of another way that those align as well, which I think is interesting because I think it's not necessarily intuitive. Which is, sometimes when we talk about zombie servers and server waste, people's first response is, this is terrible. The way I'm going to solve it is I'm going to put in barriers in place so that getting a server is harder.

And that seems really intuitive, right? Because it's like, Oh yes, we need to solve it. But of course, but it has the exact opposite effect. And again it seems so counterintuitive because it seems like if you have a choice between shutting the barn door before the horses left and shutting the barn door after the horses left, you should shut the barn door before the horses left.

But what happens is that if those barriers are in place, once people have a server, if they had to sweat blood to get that server, they are never giving it up. It doesn't matter how many thriftathons you do, they are going to cling to that server because it was so painful to get. So what you need to do is you need to just create these really sort of low friction systems where it's easy come, easy go.

So it's really easy to get the hardware you need. And so you're really willing to give it up and that kind of self service model, that kind of low friction, high automation model is really well aligned again with lean. It's really well aligned with DevOps. It's really well aligned with cloud native.

And so it has a whole bunch of benefits for us as users as well. If it's easier for me to get a server, that means I'm more likely to surrender it, but it also means I didn't have to suffer to get it, which is just a win for me personally. 

Anne Currie: It is. And there's something at the end of the goal in the little bit at the end, which I thought was my goodness, the most amazing, a bit of a lightswitch moment for me, when it was talking to this still about 10 years ago, but it was, it's talking about, ideas about stuff that, basically underpin the cloud, underpin modern computing, underpin factories and also warehouses and because I worked for a long time in companies that had warehouses, so you kind of see that there are enormous analogies and it was talking about how a lot of the good modern practice in this has been known since the 50s.

And, it, even in places like japan, where it's really well known, I mean, Toyota is so, the Toyota production system is so well managed, almost everybody knows it, and everybody wants to, every company in Japan wants to be operating in that way. Still, the penetration of companies that actually achieve it is very low, it's only like 20%.

I thought, it's interesting, why is that? And then I realised that you'd been kind of hinting why it was throughout. And if you look on the Toyota website, they're quite clear about it. They say the Toyota production system is all about trial and error. Doesn't matter, you can't read a book that tells you what we did, and then say, "oh well if I do that, then I will achieve the result."

They say it's all about a culture of trial and error. And then you achieve, then you build something which will be influenced by what we do, and influenced by what other people do, and influenced by a lot of these ideas. But fundamentally, it has to be unique to you because anything complicated is context-specific.

Therefore, you are going to have to learn from it. But one of the, one of the key things for trial and error is not making it so hard to try something and so painful if you make an error that you never do any trial and error. And I think that's very aligned with what you were saying about if you make it too hard, then nobody does any trial and error.

Holly Cummins: Yeah. Absolutely.

Anne Currie: I wrote a new version of it, called the cloud native attitude, which was all about, you know, what are people doing? You know, what's the UK enterprise version of the TPS system, and what are the fundamentals and what are people actually doing?

And what I realized was that everybody was doing things that were quite different, that was specific to them, that used some of the same building blocks and were quite often in the cloud because that reduced their bottlenecks over getting hardware. Because that's always, that's a common bottleneck for everybody.

So they wanted to reduce the bottleneck there of getting the access to hardware. But what they were actually doing was built trial and error wise, depending on their own specific context. And every company is different and has a different context. And, yeah, so you have to be able to, that is why failure is so, can't be a four letter word.

Holly Cummins: Yeah. Technically, it's a seven letter word if you say failure, but...

Anne Currie: And it should be treated that way.

Yeah. 

 I'm very aware that actually our brief for this was to talk about three articles on AI.

Holly Cummins: I have to say, I did have a bit of a panic when I was reviewing the articles because they were very deep into the sort of the intricacies of, you know, AI policy and AI governance, which is not my specialty area.

Anne Currie: No, neither is it mine. All that and when I was reading it, I thought quite a lot about what we've just talked about. It is a new area. It's something that, as far as AI is concerned, I love AI. I have no problem with AI. I think it's fantastic. It's amazing what it can produce.

And if you are not playing around on the free version of ChatGPT, then you are not keeping on top of things because it changes all the time. And it's, very like managing somebody. You get out of it what you put in. If you put in, if you make a very cursory, ask it a couple of cursory questions, you'll get a couple of cursory answers.

If you, you know, leaning back on Toyota again, you almost need to five wise it. You need to No, go, no, but why? Go a little bit deeper. Now go a little bit deeper. Now go a little bit deeper. And then you'll notice that the answers get better and better, like a person, better and better.

So if you, really do, it is worth playing around with it. 

Holly Cummins: Just on that, I was just reading an article from Simon Willison this morning and he, was talking about sort of, you know, a similar idea that, you know, you have to put a lot into it and that to get good, he was talking about it for coding assistance that, you know, to get good outputs, it's not trivial.

And a lot of people will sort of try it and then be disappointed by their first result and go, "Oh, well, it's terrible" and dismiss it. But he was saying that one of the mistakes that people make is to anthropomorphize it. And so when they see it making mistakes that a human would never make, they go, "well, this is terrible" and they don't think about it in terms of, well, this has some weaknesses and this has some strengths and they're not the same weaknesses and strengths as a person would have.

And so I can't just see this one thing that a human would never do and then dismiss it. I, you know, you need to sort of adapt how you use it for its strengths and weaknesses, which I thought was really interesting. The sort of the, you know, it's so tempting to anthropomorphize it because it is so human ish in its outputs because it's trained on human inputs, but it is not, it does not have the same strengths and weaknesses as a person.

Anne Currie: Well, I would say the thing is, it can be used in lots of different ways. There are ways you can use it which, actually, it can react like a person, and therefore does need to be called. I mean, if you ask it to do creative things, it's quite human like. And it will come up with, and it will blag, and it will, you know, it's, you just have to treat it to certainly, certain creative things.

You have to go, "is that true?" Can you double check that? Is that, I appreciate your enthusiasm there, but it might not be right. Can you just double check that? In the same way that you would do for, with a very enthusiastic graduate. And you wouldn't have fired them because they said something that seemed plausible

and, well, unless you'd said, do not tell me anything that seems plausible, then you don't double check. Because to a certain extent, they're always enthused. And that's where ideas come from. Stretching what's saying, well, you know, I don't know if this is happening, but this could happen. You have to be a little bit out there to generate new ideas and have new thoughts. I heard a very interesting podcast yesterday where one of the Reeds, I can never remember if it was Reed Hastings or Reed Hoffman, you know, it's like it was talking about AI, it was AI energy use.

And he was saying, we're not stupid, you know, if there's, basically, there are two things that we know are coming. One is AI and one is climate change. We're not going to build, to try and create an AI industry that's requires the fossil fuel industry because that would be crazy talk, you know, we do all need to remember that climate change is coming and it is a different model for how, and, you know, if you are building an AI system that relies on fossil fuels, then you are an idiot because, the big players are not. You know, it's, I love looking at our world in data and looking at what is growing in the world?

And if you look to a chart that's really interesting to look at, if you ever feel depressed about climate change is to look at the global growth in solar power in solar generated power. It's going up like it's not even exponential. It's, you know, it's, it looks vertically asymptotic.

You know, it's super exponential. It's going faster than exponential, nothing else is developing that way. Except maybe AI, but AI from a from a lower point and, actually I think the AI will, and then you've got things with AI, you've got stuff like DeepSeek that's coming out of field and saying, "do you know?

You just didn't need to write this so inefficiently. You could, you know, you could do this on a lot less, and it'd be a lot cheaper, and you could do things on the edge that you didn't know that you could do." So, yeah, I'm not too worried about AI. I think that DeepSeek surprised me.

Holly Cummins: Yeah, I agree. 

I think we have been seeing this, you know, sort of enormous rise in energy consumption, but that's not sustainable, and it's not sustainable in terms of climate, but it's also not sustainable financially. And so financial corrections tend to come before the climate corrections.

And so what we're seeing now is architectures that are designed to reduce the energy costs because they need to reduce the actual financial costs. So we get things like DeepSeek where there's the sort of fundamental efficiency in the model of the architecture or the architecture of the model rather.

But then we're also seeing things as well, like you know, up until maybe a year ago, the way it worked was that the bigger the model, the better the results. Just, you know, absolutely. And now we're starting to see things where the model gets bigger. And the results get worse and you see this with RAG systems as well, where when you do your RAG experiment and you feed in just two pages of data, it works fantastically well and then you go, "okay, I'm going to proceed."

And then you feed in like 2000 pages of data and your RAG suddenly isn't really working and it's not really giving you correct responses anymore. And so I think we're seeing an architectural shift away from the really big monolithic models to more orchestrated models. Which is kind of bad in a way, right?

Because it means we as engineers have to do more work. We can't just like have one big monolith and say, "solve everything." But on the other hand, what do engineers love? We love engineering. So it means that there's opportunities for us. So, you know, a pattern that we're seeing a lot now is that you have your sort of orchestrator model that takes the query in and triages it.

And it says, "is this something that should go out to the web? Because, actually, like, that's the best place for this news topic. Or is this something that should go to my RAG model? Is this something..." 

You know, and so it'll choose the right model. Those models are smaller, and so they have a much more limited scope.

But, within that scope, they can give you much higher quality answers than the huge supermodel, and they cost much less to run. So you end up with a system, again, it's about the double win, where you have a system which maybe took a little bit more work to architect, but gives you better answers for a lower cost. 

Anne Currie: That is really interesting and more aligned as well with how power is being developed potentially, you know, that there is, that you really want to be doing more stuff at the edge, which that you want, and you want people to be doing stuff at home on their own devices, you know, rather than just always having to go to, as you say, Supermodels are bad.

We all disapprove of supermodels. 

Holly Cummins: Yeah. and in terms of, you know, that aligns with some of the sort of the, you know, the privacy concerns as well, which is, you know, people want to be doing it at home and certainly organizations want to be keeping their data in house. And so then that means that they need the more organization local model to be keeping their, dirty secrets in house.

Anne Currie: Well, it is true. I mean, the thing is you, it is very hard to keep things secure and sometimes just do want to keep things in house, some of your data in house, you don't necessarily even want to stick it on Amazon if you can avoid it. But yes, so that's been a really interesting discussion and we have completely gone off topic and we've hardly talked at all about, the AI regulation.

I think we both agree that AI regulation, it's quite soon to be doing it. It's interesting. I can see why, the Americans have a tendency to take a completely different approach to the EU. If you look at their laws and I have to, I did do some lecturing in AI ethics and legalities and American laws do tend to be like, well, something goes wrong, you know, get your pantsuit off and fix it. EU laws tend to be about, don't even, don't do it. You know, as you said before, close the door before the horse has, you know, has bolted. And the American law is about bringing it back.

But in some ways, that is, that exemplifies why America grows much faster than Europe does. , 

Holly Cummins: Yeah.

I was, when I was looking at some of the announcements that did come out of the AI summit, I think, yeah, I have really mixed feelings about it because I think I generally feel that regulation is good, but I also agree with you that it can have a stifling effect on growth, but one thing that I think is fairly clearly positive that did seem to be emphasized in the announcements as well is the open source aspect.

So, like, we're, I mean, we have, you know, sort of open source models now, but they're not as open source as, you know, open source software in terms of how reproducible they are, how accessible they are for people to see the innards of, but I think I was thinking a little bit again when I was sort of the way the AI summit is

is making these sort of bodies that have like the public private partnerships, which isn't anything new, but you know, we're sort of seeing quite a few governments coming together. So like the current AI announcement, I think had nine governments and dozens of companies, but it reminded me a little bit of the sort of the birth of radio. When we had this resource which was the airwaves, the frequencies that, you know, had, nobody had cared about. And then now all of a sudden it was quite valuable and there was potentially, you know, the sort of wild west of like, okay, who can take this and exploit it commercially? And then government stepped in and said, "actually, no, this is a resource that belongs to all of us.

And so it needs to be managed." Who has access to it and who can just grab it. And I feel a bit like, even though in a technical sense, the data all around us isn't all of ours. It's, you know, a lot of it is copyrighted and that kind of thing. But if you look at the sort of aggregate of like 

all of the data that humanity has produced, that is a collective asset.

And so it should be that how it gets used is for a collective benefit and that regulation, and making sure that it's not just one or two organizations that have the technical potential to leverage that data is a collectively good 

thing.

Anne Currie: Especially at the moment, we don't want everything to be happening in the US, because, maybe the US is not the friendly partner that we would always thought it would be, it's, diversity

Holly Cummins: diversity is good. Diversity of geographic interests.

Anne Currie: Indeed. Yeah, it is. So yeah, it's, but it is early days. I'm not an anti AI person by any stretch. In fact, I love AI. I think it's really is an amazing thing. And we just need to align it with the interests of the rest of the humanity in terms

Holly Cummins: Yes.

Anne Currie: but it is interesting. They're saying that in terms of being green, the big players are not idiots. They know that things need to be aligned. But in terms of data, they certainly will be acting in their best interests. So, yeah, I can see they, yeah, indeed. Very interesting. So, we are now coming to time, we've done quite a lot, we've done quite a lot. There won't be much to edit out from what we've talked about today.

I think it's great, it's very good. But, 

Holly Cummins: Shall we talk about the Microsoft article though? Cause that, I thought that was really interesting.

Anne Currie: oh yeah, go for it, Yes,

Holly Cummins: Yeah, so one of the other articles that we have is, It said that Microsoft had, was reducing its investment in data centers, which was, I was quite shocked to read that because it's the exact opposite of all of the news articles that we normally see, including one I saw this morning that said that, you know, the big three are looking at increasing their investment in nuclear.

But I thought it was sort of interesting because we've, I think we always tend to sort of extrapolate from the current state and extrapolate it indefinitely forward. So we say demand for AI is growing, demand for AI will grow indefinitely, but of course, that's not sustainable. Again you know, it's not sustainable in terms of financially and so at some point there will be that correction and it seems like, Microsoft has perhaps looked at how much they've invested in data centers and said "oh, perhaps this was a little bit much, perhaps let's rollback that investment just a little bit, because now we have an over capacity on data centers."

Anne Currie: Well, I mean, I wonder how much of DeepSeek had an effect on which is that everybody was looking at it and going, the thing is, I mean, Azure is, it's, not, well, I say this is a public story. So I could, because I have it in the book, the story of during the pandemic, the team, the Microsoft Teams folks looking at what they were doing and saying, "could this be more efficient?" And the answer was yes, because had really no effort in whatsoever to make what they were doing efficient. Really basic efficiency stuff they hadn't done. And so there was tons of waste in that system. And the thing is, when you gallop ahead to do things, you do end up with a lot of waste.

DeepSeek was a great example of, you know this AI thing, we can do it on like much cheaper chips and much fewer machines. And you don't have to do it that way. So I'm hoping that this means that Microsoft have decided to start investing in efficiency. It's a shame because they used to have an amazing team who were fantastic at this kind of stuff, who used it, so we, I was saying, Holly spoke at a conference I did last year about code efficiency. And Quarkus being a really good example of a more efficient platform for running Java on. The first person I had on that used to work for Azure. And he used to, was probably the world's expert in actual practical code efficiency. He got made redundant. Yeah. Because, Microsoft at the time were not interested in efficiency. So "who cares? Pfft, go on, out." But he's now working at NVIDIA doing all the efficiency stuff there. Because some people are not, who paying attention to, I, well I think the lesson there is that maybe Microsoft were not paying that much attention to efficiency, the idea that actually you don't need 10 data centers. A little bit of easy, well, very difficult change to make it really efficient. But quite often there's a lot of low hanging fruit in efficiency.

Holly Cummins: Absolutely. And you need to remember to do it as well, because I think that, I think probably it is a reasonable and correct flow to say, innovate first, optimize second. So, you know, you, don't have be looking at that efficiency as you're innovating because that stifles the efficiency and you know, you might be optimizing something that never becomes anything, but you have to then remember once you've got it out there to go back and say, "Oh, look at all of these low hanging fruit. Look how much waste there is here. Let's, sort it out now that we've proven it's a success."

Anne Currie: Yeah. Yeah, it is. Yes. It's like "don't prematurely optimize does" not mean "never optimize."

Holly Cummins: Yes. Yes.

Anne Currie: So, I, my strong suspicion is that Microsoft are kind of waking up to that a little bit. The thing is, if you have limitless money, and you just throw a whole load of money at things, then, it is hard to go and optimize. As you say, it's a bit like that whole thing of going in and turning off those zombie machines.

You know, you have to go and do it know, it's, you have to choose to do it. If you have limitless money, you never do it, because it's a bit boring, it's not as exciting as a new thing. Yeah, but yeah, limitless money has its downsides as well as up.

Holly Cummins: Yes. Who knew?

Anne Currie: Yeah, but so I think we are at the end of our time. Is there anything else you want to say before you, it was an excellent hour.

Holly Cummins: Nope. Nope. This has been absolutely fantastic chatting to you Anne.

Anne Currie: Excellent. It's been very good talking to you as always. And so my final thing is, if anybody who's listening to this podcast has not read building green software from O'Reilly, you absolutely should, because a lot of what we just talked about was covered in the book. Reviewed by Holly.

Holly Cummins: I can recommend the book.

Anne Currie: I think your name is somewhere as a, some nice thing you said about it somewhere on the book cover, but, so thank you very much indeed. And just a reminder to everybody, everything we've talked about all the links in the show notes at the bottom of the episode. And, we will see, I will see you again soon on the Environment Variables podcast.

Goodbye. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> For this 100th episode of Environment Variables, guest host Anne Currie is joined by Holly Cummins, senior principal engineer at Red Hat, to discuss the intersection of AI, efficiency, and sustainable software practices. They explore the concept of "Lightswitch Ops"—designing systems that can easily be turned off and on to reduce waste—and the importance of eliminating zombie servers. They cover AI’s growing energy demands, the role of optimization in software sustainability, and Microsoft's new shift in cloud investments. They also touch on AI regulation and the evolving strategies for balancing performance, cost, and environmental impact in tech. 

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TRANSCRIPT BELOW:

Holly Cummins: Demand for AI is growing, demand for AI will grow indefinitely. But of course, that's not sustainable. Again, you know, it's not sustainable in terms of financially and so at some point there will be that correction. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne Currie: So hello and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software. Now, today you're not hearing the dulcet tones of your usual host, Chris Adams. I am a guest host on this, a common guest, a frequent guest host, Anne Currie. And my guest today is somebody I've known for quite a few years and I'm really looking forward to chatting to, Holly.

So do you want to introduce yourself, Holly?

Holly Cummins: So I'm Holly Cummins. I work for Red Hat. My day job is that, I'm a senior principal engineer and I'm helping to develop Quarkus, which is Java middleware. And I'm looking at the ecosystem of Quarkus, which sounds really sustainability oriented, but actually the day job aspect is I'm more looking at

the contributors and, you know, the extensions and that kind of thing. But one of the other things that I do end up looking a lot at is the ecosystem aspect of Quarkus in terms of sustainability. Because Quarkus is a extremely efficient Java runtime. And so when I joined the team, one of the things we asked well, one of the things I asked was, can we, know this is really efficient. Does that translate into an environmental, you know, benefit? Is it actually benefiting the ecosystem? You know, can we quantify it? And so we did that work and we were able to sort of validate our intuition that it did have a much lower carbon footprint, which was nice.

But some things of what we did actually surprised us as well, which was also good because it's always good to be challenged in your assumptions. And so now part of what I'm doing as well is sort of broadening that focus from, instead of measuring what we've done in the past, thinking about, well, what does a sustainable middleware architecture look like?

What kind of things do we need to be providing?

Anne Currie: Thank you very much indeed. That's a really good overview of what I really primarily want to be talking about today. We will be talking about a couple of articles as usual on AI, but really I want to be focused on what you're doing in your day job because I think it's really interesting and incredibly relevant.

So, as I said, my name is Anne Currie. I am the CEO of a learning and development company called Strategically Green. We do workshops and training around building green software and changing your systems to align with renewables. But I'm also one of the authors of O'Reilly's new book, Building Green Software, and Holly was probably the most, the biggest single reviewer/contributor to that book, and it was in her best interest to do so because, we make, I make tons and tons of reference to a concept that you came up with.

I'm very interested in the backstory to this concept, but perhaps you can tell me a little bit more about it because it is, this is something I've not said to you before, but it is, this comes up in review feedback, for me, for the book, more than any other concept in the book. Lightswitch Ops. People saying, "Oh, we've put in, we've started to do Lightswitch Ops."

If anybody says "I've started to do" anything, it's always Lightswitch Ops. So tell us, what is Lightswitch Ops?

Holly Cummins: So Lightswitch Ops, it's really, it's about architecting your systems so that they can tolerate being turned off and on, which sounds, you know, it sounds sort of obvious, but historically that's not how our systems have worked. And so the first step is architect your system so that they can tolerate being turned off and on.

And then the next part is once you have that, actually turn them off and on. And, it sort of, it came about because I'm working on product development now, and I started my career as a performance engineer, but in between those two, I was a client facing consultant, which was incredibly interesting.

And it was, I mean, there was, so many things that were interesting, but one of the things that I sort of kept seeing was, you know, you sort of work with clients and some of them you're like, "Oh wow, you're, you know, you're really at the top of your game" and some you think, "why are you doing this way when this is clearly, you know, counterproductive" or that kind of thing.

And one of the things that I was really shocked by was how much waste there was just everywhere. And I would see things like organizations where they would be running a batch job and the batch job would only run at the weekends, but the systems that supported it would be up 24/7. Or sometimes we see the opposite as well, where it's a test system for manual testing and people are only in the office, you know, nine to five only in one geo and the systems are up 24 hours.

And the reason for this, again, it's sort of, you know, comes back to that initial thing, it's partly that we just don't think about it and, you know, that we're all a little bit lazy, but it's also that many of us have had quite negative experiences of if you turn your computer off, it will never be the same when it comes back up.

I mean, I still have this with my laptop, actually, you know, I'm really reluctant to turn it off. But now we have, with laptops, we do have the model where you can close the lid and it will go to sleep and you know that it's using very little energy, but then when you bring it back up in the morning, it's the same as it was without having to have the energy penalty of keeping it on overnight. And I think, when you sort of look at the model of how we treat our lights in our house, nobody has ever sort of left a room and said, "I could turn the light off, but if I turn the light off, will the light ever come back on in the same form again?"

Right? Like we just don't do that. We 

have a great deal of confidence that it's reliable to turn a light off and on and that it's low friction to do it. And so we need to get to that point with our computer systems. And you can sort roll with the analogy a bit more as well, which is in our houses, it tends to be quite a manual thing of turning the lights off and on.

You know, I turn the light on when I need it. In institutional buildings, it's usually not a manual process to turn the lights off and on. Instead, what we end up is, we end up with some kind of automation. So, like, often there's a motion sensor. So, you know, I used to have it that if I would stay in our office late at night, at some point if you sat too still because you were coding and deep in thought, the lights around you would go off and then you'd have to, like, wave your arms to make the lights go back on.

And it's that, you know, it's this sort of idea of like we can detect the traffic, we can detect the activity, and not waste the energy. And again, we can do exactly this our computer systems. So we can have it so that it's really easy to turn them off and on. And then we can go one step further and we can automate it and we can say, let's script to turn things off at 5pm because we're only in one geo.

And you know, if we turn them off at 5pm, then we're enforcing quite a strict work life balance. So...

Anne Currie: Nice, nice work.

Holly Cummins: Yeah. Sustainable. Sustainable pace. Yeah. Or we can do sort of, you know, more sophisticated things as well. Or we can say, okay, well, let's just look at the traffic and if there's no traffic to this, let's turn it off.

off 

Anne Currie: Yeah, it is an interestingly simple concept because it's, 

when people come up with something which is like, in some ways, similar analogies, a light bulb moment of, you know, why don't people turn things off? Becasue, so Holly, everybody is an unbelievably good public speaker.

One of the best public speakers out there at the moment. And we first met because you came and gave talks at, in some tracks I was hosting on a variety. Some on high performance code, code efficiency, some on, being green. One of the stories you told was about your Lightswitch moment, the realization that actually this was a thing that needed to happen.

And I thought it was fascinating. It was about how, I know everybody, I've been in the tech industry for a long time, so I've worked with Java a lot over the years and many years ago. And one of the issues with Java in the old days was always, it was very hard to turn things off and turn them back on again.

And that was fine in the old world, but you talked about how that was no longer fine. And that was an issue with the cloud because the cloud, using the cloud well, turning things on and off and things, doing things like auto scaling is utterly key to the idea of the cloud. And therefore it had to become part of Quarkus, part of the future of Java. Am I right in that understanding? 

Holly Cummins: Yeah, absolutely. And the cloud sort of plays into both parts of the story, actually. So definitely we, the things that we need to be cloud native, like being able to support turning off and on again, are very well aligned to what you need to support Lightswitch Ops. And so the, you know, there with those two, we're pulling in the same direction.

The needs of the cloud and the needs of sustainability are both driving us to make systems that, I just saw yesterday, sorry this is a minor digression, but I was looking something up, and we used to talk a lot about the Twelve-Factor App, and you know, at the time we started talking about Twelve-Factor Apps, those characteristics were not at all universal. And then someone came up with the term, the One-Factor App, which was the application that could just tolerate being turned off and on.

And sometimes even that was like too much of a stretch. And so there's the state aspect to it, but then there's also the performance aspect of it and the timeliness aspect of it. And that's really what Quarkus has been looking at that if you want to have any kind of auto scaling or any kind of serverless architecture or anything like that, the way Java has historically worked, which is that it eats a lot of memory and it takes a long time to start up, just isn't going to work.

And the sort of the thing that's interesting about that is quite often when we talk about optimizing things or becoming more efficient or becoming greener, it's all about the trade offs of like, you know, "oh, I could have the thing I really want, or I could save the world. I guess I should save the world." But sometimes what we can do is we can just find things that we were paying for, that we didn't even want anymore. And that's, I think, what Quarkus was able to do. Because a lot of the reason that Java has a big memory footprint and a lot of the reason that Java is slow to start up is it was designed for a different kind of ops.

The cloud didn't exist. CI/CD didn't exist. DevOps didn't exist. And so the way you built your application was you knew you would get a release maybe once a year and deployment was like a really big deal. And you know, you'd all go out and you'd have a party after you successfully deployed because it was so challenging.

And so you wanted to make sure that everything you did was to avoid having to do a deployment and to avoid having to talk to the ops team because they were scary. But of course, even though we had this model where releases happen very rarely, or the big releases happen very rarely, of course, the world still moves on, you know, people still had defects, people, so what you ended up with was something that was really much more optimized towards patching.

So can we take the system and without actually taking, turning it off and on, because that's almost impossible, can we patch it? So everything was about trying to change the engine of the plane while the plane was flying, which is really clever engineering. If you can support that, you know, well done you.

It's so dynamic. And so everything was optimized so that, you know, you could change your dependencies and things would keep working. And, you know, you could even change some fairly important characteristics of your dependencies and everything would sort of adjust and it would ripple back through the system.

But because that dynamism was baked into every aspect of the architecture, it meant that everything just had a little bit of drag, and everything had a little bit of slowdown that came from that indirection. And then now you look at it in the cloud and you think, well, wait a minute. I don't need that. I don't need that indirection.

I don't need to be able to patch because I have a CI/CD pipeline, and if I'm going into my production systems and SSHing in to change my binaries, something has gone horribly wrong with my process. And you know, I need to, I have all sorts of problems. So really what Quarkus was able to do was get rid of a whole bunch of reflection, get rid of a whole bunch of indirection,

do more upfront at build time. And then that gives you much leaner behavior at runtime, which is what you want in a cloud environment.

Anne Currie: Yeah. And what I love about this and love about the story of Quarkus is, it's aligned with something, non functional requirements. It's like, it's an unbelievably boring name, and for something which is a real pain point for companies. But it's also, in many ways, the most important thing and the most difficult thing that we do.

It's like, being secure, being cost effective, being resilient. A lot of people say to me, well, you know, actually all you're doing with green is adding another non functional requirement. We know those are terrible. But I can say, no, we need to not make it another non functional requirements. It's just a good, another motivator for doing the first three well, you know. Also scaling is about resilience. It's about cost saving, and it's about being green. And it's about, and being able to pave rather than patch, I think is, was the term. It's more secure, you know. Actually patching is much less secure than repaving, taking everything down and bringing it back up.

All the modern thinking about being more secure, being faster, being cheaper, being more resilient is aligned or needs to be aligned with being green and it can be, and it should be, and it shouldn't just be about doing less.

Holly Cummins: Absolutely. And, you know, especially for the security aspect, when you look at something like tree shaking, that gives you more performance by getting rid of the code that you weren't using. Of course, it makes you more secure as well because you get rid of all these code paths and all of these entry points and vulnerabilities that had no benefit to you, but were still a vulnerability.

Anne Currie: Yeah, I mean, one of the things that you've talked about Lightswitch Ops being related to is, well, actually not Lightswitch Ops, but the thing that you developed before Lightswitch Ops, the concept of zombie servers. Tell us a little bit about that because that not only is cost saving, it's a really big security improvement.

So tell us about zombie, the precursor to Lightswitch Ops.

Holly Cummins: Yeah, zombie servers are again, one of those things that I sort of, I noticed it when I was working with clients, but I also noticed it a lot in our own development practices that what we would do was we would have a project and we would fire up a server in great excitement and you know, we'd register something on the cloud or whatever.

And then we'd get distracted and then, or then we, you know, sometimes we would develop it but fail to go to production. Sometimes we'd get distracted and not even develop it. And I looked and I think some of these costs became more visible and more obvious when we move to the cloud, because it used to be that when you would provision a server, once it was provisioned, you'd gone through all of the pain of provisioning it and it would just sit there and you would keep it in case you needed it.

But with the cloud, all of a sudden, keeping it until you needed it had a really measurable cost. And I looked and I realized, you know, I was spending, well, I wasn't personally spending, I was costing my company thousands of pounds a month on these cloud servers that I'd provisioned and forgotten about.

And then I looked at how Kubernetes, the sort of the Kubernetes servers were being used and some of the profiles of the Kubernetes servers. And I realized that, again, there's, each company would have many clusters. And I was thinking, are they really using all of those clusters all of the time?

And so I started to look into it and then I realized that there had been a lot of research done on it and it was shocking. So again, you know, the sort of the, I have to say I didn't coin the term zombie servers. I talk about it a lot, but, there was a company called the Antithesis Institute.

And what they did, although actually, see, now I'm struggling with the name of it because I always thought they were called the Antithesis Institute. And I think it's actually a one letter variant of that, which is much less obvious as a word, but much more distinctive. But I've, every time I talked about them, I mistyped it.

And now I can't remember which one is the correct one, but in any case, it's something like the Antithesis Institute. And they did these surveys and they found that, it was something like a third of the servers that they looked at were doing no work at all. Or rather no, no useful work. So they're still consuming energy, but there's no work being done.

And when they say no useful work as well, that sounds like a kind of low bar. Because when I think about my day job, quite a lot of it is doing work that isn't useful. But they had, you know, it wasn't like these servers were serving cat pictures or that kind of thing. You know, these servers were doing nothing at all.

There was no traffic in, there was no traffic out. So you can really, you know, that's just right for automation to say, "well, wait a minute, if nothing's going in and nothing's coming out, we can shut this thing down." And then there was about a further third that had a utilization that was less than 5%.

So again, you know, this thing, it's talking to the outside world every now and then, but barely. So again, you know, it's just right for a sort of a consolidation. But the, I mean, the interesting thing about zombies is as soon as you talk about it, usually, you know, someone in the audience, they'll turn a little bit green and they'll go, "Oh, I've just remembered that server that I provisioned."

And sometimes, you know, I'm the one giving the talk and I'm like, Oh, while preparing this talk, I just realized I forgot a server, because it's so easy to do. And the way we're measured as well, and the way we measure our own productivity is we give a lot more value to creating than to cleaning up.

Anne Currie: Yeah. And in some ways that makes sense because, you know, creating is about growth and cleaning up you know, it's about degrowth. It's about like, you know, it's like you want to tell the story of growth, but I've heard a couple of really interesting, sales on zombie servers since you started, well, yeah, since you started talking about it, you may not have invented it, but you popularized it. One was from, VMware, a cost saving thing. They were, and it's a story I tell all the time about when they were moving data centers in Singapore, setting up a new data center in Singapore.

They decided to do a review of all their machines to see what had to go across. And they realized that 66 percent of their machines did not need to be reproduced in the new data center. You know, they had a, and that was VMware. People who are really good at running data centers. So imagine what that's like.

But moving data centers is a time when it often gets spotted. But I will say, a more, a differently disturbing story from a company that wished to remain nameless. Although I don't think they need to because I think it's just an absolutely bog standard thing. They were doing a kind of thriftathon style thing of reviewing their data center to see if there was stuff that they could save money on, and they found a machine that was running at 95, 100 percent CPU, and they thought, they thought, Oh my God, it's been hacked.

It's been hacked. Somebody's mining Bitcoin on this. It's, you know, or maybe it's attacking us. Who knows? And so they went and they did some searching around internally, and they found out that it was somebody who turned on a load test, and then forgot to turn it off three years previously. And And the, I would say that obviously that came up from the cost, but it also came up from the fact that machine could have been hacked.

You know, it could be, could have been mining Bitcoin. It could have been attacking them. It could have been doing anything. They hadn't noticed because it was a machine that no one was looking at. And I thought it was an excellent example. I thought those two, excellent examples of the cost and the massive security hole that comes from machines that nobody is looking at anymore.

So, you know, non functional requirements, they're really important. And

Holly Cummins: Yeah.

Anne Currie: doing better on them is also green. And also, they're very, non functional requirements are really closely tied together.

Holly Cummins: Yeah. I mean, oh, I love both of those stories. And I've heard the VMware one before, but I hadn't heard the one about the hundred percent, the load test. That is fantastic. One of the reasons I like talking about zombies and I think one of the reasons people like hearing about it I mean, it's partly the saving the world.

But also I think when we look at greenness and sustainability, some of it is not a very cheerful topic, but the zombie servers almost always when you discover the cases of them, they are hilarious. I mean, they're awful, but they're hilarious And you know, it's just this sort of stuff of, "how did this happen?

How did we allow this to happen?" Sometimes it's so easy to do better. And the examples of doing bad are just something that we can all relate to. And, but on the same time, you know, you sort of think, oh, that shouldn't have happened. How did that happen?

Anne Currie: But there's another thing I really like about zombie servers, and I think you've pointed out this yourself, and I plagiarized from your ideas like crazy in Building Green Software, which is one of the reasons why I got you to be a reviewer, so you could complain about it if you wanted to early on. The, 

Holly Cummins: It also means I would agree with you a lot. Yes. Oh This is very, sensible. Very sensible. Yes.

Anne Currie: One of the things that we, that constantly comes up when I'm talking to people about this and when we're writing the book and when we're going out to conferences, is people need a way in. And it's often that, you know, that people think the way into building green software is to rewrite everything in C and then they go, "well, I can't do that.

So that's the end. That's the only way in. And I'm not going to be able to do it. So I can't do anything at all." Operations and zombie servers is a really good way in, because you can just do it, you can, instead of having a hackathon, you can just do a thrift a thon, get everybody to have a little bot running that doesn't need to be running, instantly halve your, you know, it's not uncommon for people to find ways to halve their life.

Yeah. carbon emissions and halve their hosting costs simultaneously in quite a short period of time and it'd be the first thing they do. So I quite like it because it's the first thing they do. What do you think about that? It's, is it the low hanging fruit?

Holly Cummins: Yeah, absolutely, I think, yeah, it's the low hanging fruit, it's easy, it's, kind of entertaining because when you find the problems you can laugh at yourself, and there's, again, there's no downside and several upsides, you know, so it's, you know, it's this double win of I got rid of something I wasn't even using, I have more space in my closet, and I don't have to pay for it.

Anne Currie: Yeah, I just read a book that I really should have read years and years ago, and I don't know why I didn't, because people have been telling me to read it for years, which was the goal. Which is, it's not about tech, but it is about tech. It's kind of the book that was the precursor to the Phoenix Projects, which I think a lot read.

And it was, it's all about TPS, the Toyota Production System. In a kind of like an Americanized version of it, how are the tires production system should be brought to America. And it was written in the 80s and it's all about work in progress and cleaning your environment and getting rid of stuff that gets in your way and just obscures everything.

, you can't see what's going on. Effectively, it was a precursor to lean, which I think is really very well aligned. Green and lean, really well aligned. And, it's something that we don't think about, that cleaning up waste just makes your life much better in ways that are hard to imagine until you've done it.

And zombie, cleaning zombie servers up just makes your systems more secure, cheaper, more resilient, more everything. It's a really good thing to do.

Holly Cummins: Yeah. And there's sort of another way that those align as well, which I think is interesting because I think it's not necessarily intuitive. Which is, sometimes when we talk about zombie servers and server waste, people's first response is, this is terrible. The way I'm going to solve it is I'm going to put in barriers in place so that getting a server is harder.

And that seems really intuitive, right? Because it's like, Oh yes, we need to solve it. But of course, but it has the exact opposite effect. And again it seems so counterintuitive because it seems like if you have a choice between shutting the barn door before the horses left and shutting the barn door after the horses left, you should shut the barn door before the horses left.

But what happens is that if those barriers are in place, once people have a server, if they had to sweat blood to get that server, they are never giving it up. It doesn't matter how many thriftathons you do, they are going to cling to that server because it was so painful to get. So what you need to do is you need to just create these really sort of low friction systems where it's easy come, easy go.

So it's really easy to get the hardware you need. And so you're really willing to give it up and that kind of self service model, that kind of low friction, high automation model is really well aligned again with lean. It's really well aligned with DevOps. It's really well aligned with cloud native.

And so it has a whole bunch of benefits for us as users as well. If it's easier for me to get a server, that means I'm more likely to surrender it, but it also means I didn't have to suffer to get it, which is just a win for me personally. 

Anne Currie: It is. And there's something at the end of the goal in the little bit at the end, which I thought was my goodness, the most amazing, a bit of a lightswitch moment for me, when it was talking to this still about 10 years ago, but it was, it's talking about, ideas about stuff that, basically underpin the cloud, underpin modern computing, underpin factories and also warehouses and because I worked for a long time in companies that had warehouses, so you kind of see that there are enormous analogies and it was talking about how a lot of the good modern practice in this has been known since the 50s.

And, it, even in places like japan, where it's really well known, I mean, Toyota is so, the Toyota production system is so well managed, almost everybody knows it, and everybody wants to, every company in Japan wants to be operating in that way. Still, the penetration of companies that actually achieve it is very low, it's only like 20%.

I thought, it's interesting, why is that? And then I realised that you'd been kind of hinting why it was throughout. And if you look on the Toyota website, they're quite clear about it. They say the Toyota production system is all about trial and error. Doesn't matter, you can't read a book that tells you what we did, and then say, "oh well if I do that, then I will achieve the result."

They say it's all about a culture of trial and error. And then you achieve, then you build something which will be influenced by what we do, and influenced by what other people do, and influenced by a lot of these ideas. But fundamentally, it has to be unique to you because anything complicated is context-specific.

Therefore, you are going to have to learn from it. But one of the, one of the key things for trial and error is not making it so hard to try something and so painful if you make an error that you never do any trial and error. And I think that's very aligned with what you were saying about if you make it too hard, then nobody does any trial and error.

Holly Cummins: Yeah. Absolutely.

Anne Currie: I wrote a new version of it, called the cloud native attitude, which was all about, you know, what are people doing? You know, what's the UK enterprise version of the TPS system, and what are the fundamentals and what are people actually doing?

And what I realized was that everybody was doing things that were quite different, that was specific to them, that used some of the same building blocks and were quite often in the cloud because that reduced their bottlenecks over getting hardware. Because that's always, that's a common bottleneck for everybody.

So they wanted to reduce the bottleneck there of getting the access to hardware. But what they were actually doing was built trial and error wise, depending on their own specific context. And every company is different and has a different context. And, yeah, so you have to be able to, that is why failure is so, can't be a four letter word.

Holly Cummins: Yeah. Technically, it's a seven letter word if you say failure, but...

Anne Currie: And it should be treated that way.

Yeah. 

 I'm very aware that actually our brief for this was to talk about three articles on AI.

Holly Cummins: I have to say, I did have a bit of a panic when I was reviewing the articles because they were very deep into the sort of the intricacies of, you know, AI policy and AI governance, which is not my specialty area.

Anne Currie: No, neither is it mine. All that and when I was reading it, I thought quite a lot about what we've just talked about. It is a new area. It's something that, as far as AI is concerned, I love AI. I have no problem with AI. I think it's fantastic. It's amazing what it can produce.

And if you are not playing around on the free version of ChatGPT, then you are not keeping on top of things because it changes all the time. And it's, very like managing somebody. You get out of it what you put in. If you put in, if you make a very cursory, ask it a couple of cursory questions, you'll get a couple of cursory answers.

If you, you know, leaning back on Toyota again, you almost need to five wise it. You need to No, go, no, but why? Go a little bit deeper. Now go a little bit deeper. Now go a little bit deeper. And then you'll notice that the answers get better and better, like a person, better and better.

So if you, really do, it is worth playing around with it. 

Holly Cummins: Just on that, I was just reading an article from Simon Willison this morning and he, was talking about sort of, you know, a similar idea that, you know, you have to put a lot into it and that to get good, he was talking about it for coding assistance that, you know, to get good outputs, it's not trivial.

And a lot of people will sort of try it and then be disappointed by their first result and go, "Oh, well, it's terrible" and dismiss it. But he was saying that one of the mistakes that people make is to anthropomorphize it. And so when they see it making mistakes that a human would never make, they go, "well, this is terrible" and they don't think about it in terms of, well, this has some weaknesses and this has some strengths and they're not the same weaknesses and strengths as a person would have.

And so I can't just see this one thing that a human would never do and then dismiss it. I, you know, you need to sort of adapt how you use it for its strengths and weaknesses, which I thought was really interesting. The sort of the, you know, it's so tempting to anthropomorphize it because it is so human ish in its outputs because it's trained on human inputs, but it is not, it does not have the same strengths and weaknesses as a person.

Anne Currie: Well, I would say the thing is, it can be used in lots of different ways. There are ways you can use it which, actually, it can react like a person, and therefore does need to be called. I mean, if you ask it to do creative things, it's quite human like. And it will come up with, and it will blag, and it will, you know, it's, you just have to treat it to certainly, certain creative things.

You have to go, "is that true?" Can you double check that? Is that, I appreciate your enthusiasm there, but it might not be right. Can you just double check that? In the same way that you would do for, with a very enthusiastic graduate. And you wouldn't have fired them because they said something that seemed plausible

and, well, unless you'd said, do not tell me anything that seems plausible, then you don't double check. Because to a certain extent, they're always enthused. And that's where ideas come from. Stretching what's saying, well, you know, I don't know if this is happening, but this could happen. You have to be a little bit out there to generate new ideas and have new thoughts. I heard a very interesting podcast yesterday where one of the Reeds, I can never remember if it was Reed Hastings or Reed Hoffman, you know, it's like it was talking about AI, it was AI energy use.

And he was saying, we're not stupid, you know, if there's, basically, there are two things that we know are coming. One is AI and one is climate change. We're not going to build, to try and create an AI industry that's requires the fossil fuel industry because that would be crazy talk, you know, we do all need to remember that climate change is coming and it is a different model for how, and, you know, if you are building an AI system that relies on fossil fuels, then you are an idiot because, the big players are not. You know, it's, I love looking at our world in data and looking at what is growing in the world?

And if you look to a chart that's really interesting to look at, if you ever feel depressed about climate change is to look at the global growth in solar power in solar generated power. It's going up like it's not even exponential. It's, you know, it's, it looks vertically asymptotic.

You know, it's super exponential. It's going faster than exponential, nothing else is developing that way. Except maybe AI, but AI from a from a lower point and, actually I think the AI will, and then you've got things with AI, you've got stuff like DeepSeek that's coming out of field and saying, "do you know?

You just didn't need to write this so inefficiently. You could, you know, you could do this on a lot less, and it'd be a lot cheaper, and you could do things on the edge that you didn't know that you could do." So, yeah, I'm not too worried about AI. I think that DeepSeek surprised me.

Holly Cummins: Yeah, I agree. 

I think we have been seeing this, you know, sort of enormous rise in energy consumption, but that's not sustainable, and it's not sustainable in terms of climate, but it's also not sustainable financially. And so financial corrections tend to come before the climate corrections.

And so what we're seeing now is architectures that are designed to reduce the energy costs because they need to reduce the actual financial costs. So we get things like DeepSeek where there's the sort of fundamental efficiency in the model of the architecture or the architecture of the model rather.

But then we're also seeing things as well, like you know, up until maybe a year ago, the way it worked was that the bigger the model, the better the results. Just, you know, absolutely. And now we're starting to see things where the model gets bigger. And the results get worse and you see this with RAG systems as well, where when you do your RAG experiment and you feed in just two pages of data, it works fantastically well and then you go, "okay, I'm going to proceed."

And then you feed in like 2000 pages of data and your RAG suddenly isn't really working and it's not really giving you correct responses anymore. And so I think we're seeing an architectural shift away from the really big monolithic models to more orchestrated models. Which is kind of bad in a way, right?

Because it means we as engineers have to do more work. We can't just like have one big monolith and say, "solve everything." But on the other hand, what do engineers love? We love engineering. So it means that there's opportunities for us. So, you know, a pattern that we're seeing a lot now is that you have your sort of orchestrator model that takes the query in and triages it.

And it says, "is this something that should go out to the web? Because, actually, like, that's the best place for this news topic. Or is this something that should go to my RAG model? Is this something..." 

You know, and so it'll choose the right model. Those models are smaller, and so they have a much more limited scope.

But, within that scope, they can give you much higher quality answers than the huge supermodel, and they cost much less to run. So you end up with a system, again, it's about the double win, where you have a system which maybe took a little bit more work to architect, but gives you better answers for a lower cost. 

Anne Currie: That is really interesting and more aligned as well with how power is being developed potentially, you know, that there is, that you really want to be doing more stuff at the edge, which that you want, and you want people to be doing stuff at home on their own devices, you know, rather than just always having to go to, as you say, Supermodels are bad.

We all disapprove of supermodels. 

Holly Cummins: Yeah. and in terms of, you know, that aligns with some of the sort of the, you know, the privacy concerns as well, which is, you know, people want to be doing it at home and certainly organizations want to be keeping their data in house. And so then that means that they need the more organization local model to be keeping their, dirty secrets in house.

Anne Currie: Well, it is true. I mean, the thing is you, it is very hard to keep things secure and sometimes just do want to keep things in house, some of your data in house, you don't necessarily even want to stick it on Amazon if you can avoid it. But yes, so that's been a really interesting discussion and we have completely gone off topic and we've hardly talked at all about, the AI regulation.

I think we both agree that AI regulation, it's quite soon to be doing it. It's interesting. I can see why, the Americans have a tendency to take a completely different approach to the EU. If you look at their laws and I have to, I did do some lecturing in AI ethics and legalities and American laws do tend to be like, well, something goes wrong, you know, get your pantsuit off and fix it. EU laws tend to be about, don't even, don't do it. You know, as you said before, close the door before the horse has, you know, has bolted. And the American law is about bringing it back.

But in some ways, that is, that exemplifies why America grows much faster than Europe does. , 

Holly Cummins: Yeah.

I was, when I was looking at some of the announcements that did come out of the AI summit, I think, yeah, I have really mixed feelings about it because I think I generally feel that regulation is good, but I also agree with you that it can have a stifling effect on growth, but one thing that I think is fairly clearly positive that did seem to be emphasized in the announcements as well is the open source aspect.

So, like, we're, I mean, we have, you know, sort of open source models now, but they're not as open source as, you know, open source software in terms of how reproducible they are, how accessible they are for people to see the innards of, but I think I was thinking a little bit again when I was sort of the way the AI summit is

is making these sort of bodies that have like the public private partnerships, which isn't anything new, but you know, we're sort of seeing quite a few governments coming together. So like the current AI announcement, I think had nine governments and dozens of companies, but it reminded me a little bit of the sort of the birth of radio. When we had this resource which was the airwaves, the frequencies that, you know, had, nobody had cared about. And then now all of a sudden it was quite valuable and there was potentially, you know, the sort of wild west of like, okay, who can take this and exploit it commercially? And then government stepped in and said, "actually, no, this is a resource that belongs to all of us.

And so it needs to be managed." Who has access to it and who can just grab it. And I feel a bit like, even though in a technical sense, the data all around us isn't all of ours. It's, you know, a lot of it is copyrighted and that kind of thing. But if you look at the sort of aggregate of like 

all of the data that humanity has produced, that is a collective asset.

And so it should be that how it gets used is for a collective benefit and that regulation, and making sure that it's not just one or two organizations that have the technical potential to leverage that data is a collectively good 

thing.

Anne Currie: Especially at the moment, we don't want everything to be happening in the US, because, maybe the US is not the friendly partner that we would always thought it would be, it's, diversity

Holly Cummins: diversity is good. Diversity of geographic interests.

Anne Currie: Indeed. Yeah, it is. So yeah, it's, but it is early days. I'm not an anti AI person by any stretch. In fact, I love AI. I think it's really is an amazing thing. And we just need to align it with the interests of the rest of the humanity in terms

Holly Cummins: Yes.

Anne Currie: but it is interesting. They're saying that in terms of being green, the big players are not idiots. They know that things need to be aligned. But in terms of data, they certainly will be acting in their best interests. So, yeah, I can see they, yeah, indeed. Very interesting. So, we are now coming to time, we've done quite a lot, we've done quite a lot. There won't be much to edit out from what we've talked about today.

I think it's great, it's very good. But, 

Holly Cummins: Shall we talk about the Microsoft article though? Cause that, I thought that was really interesting.

Anne Currie: oh yeah, go for it, Yes,

Holly Cummins: Yeah, so one of the other articles that we have is, It said that Microsoft had, was reducing its investment in data centers, which was, I was quite shocked to read that because it's the exact opposite of all of the news articles that we normally see, including one I saw this morning that said that, you know, the big three are looking at increasing their investment in nuclear.

But I thought it was sort of interesting because we've, I think we always tend to sort of extrapolate from the current state and extrapolate it indefinitely forward. So we say demand for AI is growing, demand for AI will grow indefinitely, but of course, that's not sustainable. Again you know, it's not sustainable in terms of financially and so at some point there will be that correction and it seems like, Microsoft has perhaps looked at how much they've invested in data centers and said "oh, perhaps this was a little bit much, perhaps let's rollback that investment just a little bit, because now we have an over capacity on data centers."

Anne Currie: Well, I mean, I wonder how much of DeepSeek had an effect on which is that everybody was looking at it and going, the thing is, I mean, Azure is, it's, not, well, I say this is a public story. So I could, because I have it in the book, the story of during the pandemic, the team, the Microsoft Teams folks looking at what they were doing and saying, "could this be more efficient?" And the answer was yes, because had really no effort in whatsoever to make what they were doing efficient. Really basic efficiency stuff they hadn't done. And so there was tons of waste in that system. And the thing is, when you gallop ahead to do things, you do end up with a lot of waste.

DeepSeek was a great example of, you know this AI thing, we can do it on like much cheaper chips and much fewer machines. And you don't have to do it that way. So I'm hoping that this means that Microsoft have decided to start investing in efficiency. It's a shame because they used to have an amazing team who were fantastic at this kind of stuff, who used it, so we, I was saying, Holly spoke at a conference I did last year about code efficiency. And Quarkus being a really good example of a more efficient platform for running Java on. The first person I had on that used to work for Azure. And he used to, was probably the world's expert in actual practical code efficiency. He got made redundant. Yeah. Because, Microsoft at the time were not interested in efficiency. So "who cares? Pfft, go on, out." But he's now working at NVIDIA doing all the efficiency stuff there. Because some people are not, who paying attention to, I, well I think the lesson there is that maybe Microsoft were not paying that much attention to efficiency, the idea that actually you don't need 10 data centers. A little bit of easy, well, very difficult change to make it really efficient. But quite often there's a lot of low hanging fruit in efficiency.

Holly Cummins: Absolutely. And you need to remember to do it as well, because I think that, I think probably it is a reasonable and correct flow to say, innovate first, optimize second. So, you know, you, don't have be looking at that efficiency as you're innovating because that stifles the efficiency and you know, you might be optimizing something that never becomes anything, but you have to then remember once you've got it out there to go back and say, "Oh, look at all of these low hanging fruit. Look how much waste there is here. Let's, sort it out now that we've proven it's a success."

Anne Currie: Yeah. Yeah, it is. Yes. It's like "don't prematurely optimize does" not mean "never optimize."

Holly Cummins: Yes. Yes.

Anne Currie: So, I, my strong suspicion is that Microsoft are kind of waking up to that a little bit. The thing is, if you have limitless money, and you just throw a whole load of money at things, then, it is hard to go and optimize. As you say, it's a bit like that whole thing of going in and turning off those zombie machines.

You know, you have to go and do it know, it's, you have to choose to do it. If you have limitless money, you never do it, because it's a bit boring, it's not as exciting as a new thing. Yeah, but yeah, limitless money has its downsides as well as up.

Holly Cummins: Yes. Who knew?

Anne Currie: Yeah, but so I think we are at the end of our time. Is there anything else you want to say before you, it was an excellent hour.

Holly Cummins: Nope. Nope. This has been absolutely fantastic chatting to you Anne.

Anne Currie: Excellent. It's been very good talking to you as always. And so my final thing is, if anybody who's listening to this podcast has not read building green software from O'Reilly, you absolutely should, because a lot of what we just talked about was covered in the book. Reviewed by Holly.

Holly Cummins: I can recommend the book.

Anne Currie: I think your name is somewhere as a, some nice thing you said about it somewhere on the book cover, but, so thank you very much indeed. And just a reminder to everybody, everything we've talked about all the links in the show notes at the bottom of the episode. And, we will see, I will see you again soon on the Environment Variables podcast.

Goodbye. 

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> AI Energy Measurement for Beginners AI Energy Measurement for Beginners Thu, 06 Mar 2025 07:00:00 GMT 56:58 209q9k80 false https://podcasts.castplus.fm/e/1820rl98-ai-energy-measurement-for-beginners 68dc7d19597bc7d53fb83b48 68dc7cef46a2532cdd8d1c23 99 Host Chris Adams is joined by Charles Tripp and Dawn Nafus to explore the complexities of measuring AI's environmental impact from a novice’s starting point. They discuss their research paper, A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning, breaking down key insights on how energy efficiency in AI systems is often misunderstood. They discuss practical strategies for optimizing energy use, the challenges of accurate measurement, and the broader implications of AI’s energy demands. They also highlight initiatives like Hugging Face’s Energy Score Alliance, discuss how transparency and better metrics can drive more sustainable AI development and how they both have a commonality with eagle(s)!

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TRANSCRIPT BELOW:

Charles Tripp: But now it's starting to be like, well, we can't build that data center because we can't get the energy to it that we need to do the things we want to do with it. we haven't taken that incremental cost into account over time, we just kind of ignored it. And now we hit like the barrier, right? 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host Chris Adams. If you follow a strict media diet, you switch off the Wi-Fi on your house and you throw your phone into the ocean, you might be able to avoid the constant stream of stories about AI in the tech industry. For the rest of us, though, it's basically unavoidable. So having an understanding of the environmental impact of AI is increasingly important if you want to be a responsible practitioner navigating the world of AI, generative AI, machine learning models, DeepSeek, and the rest. Earlier this year, I had a paper shared with me with the intriguing title A Beginner's Guide to Power and Energy Measurement, an Estimation for Computing and Machine Learning. And it turned out to be one of the most useful resources I've since come across for making sense of the environmental footprint of AI. So I was over the moon when I found out that two of the authors were both willing and able to come on to discuss this subject today. So joining me today are Dawn Nafus and Charles Tripp, who worked on the paper and did all this research. And well, instead of me introducing them, well, they're right here. I might as well let them do the honors themselves, actually. So, I'm just going to work in alphabetical order. Charles, I think you're slightly ahead of Dawn. So, if I, can I just give you the room to, like, introduce yourself?

Charles Tripp: Sure. I'm a machine learning researcher and 

Stanford 

algorithms researcher, and I've been programming pretty much my whole life since I was a little kid, and I love computers. I researched machine learning and reinforcement learning in particular at Stanford, started my own company, but kind of got burnt out on it.

And then I went to the National Renewable Energy Lab where I applied machine learning techniques to energy efficiency and renewable energy problems there. And while I was there, I started to realize that computing energy efficiency was a risingly, like, an increasingly important area of study on its own.

So I had the opportunity to sort of lead an effort there to create a program of research around that topic. And it was through that work that I started working on this paper, made these connections with Dawn. And I worked there for six years and just recently changed jobs to be a machine learning engineer at Zazzle.

I'm continuing to do this research. And, yeah. 

Chris Adams: Brilliant. Thank you, Charles. Okay, so national, that's NREL that some people refer

Charles Tripp: That's right. It's one of the national labs. 

Chris Adams: Okay. Brillinat. And Dawn, I guess I should give you the space to introduce yourself, and welcome back again, actually. 

Dawn Nafus: Thank you. Great to be here. My name is Dawn Nafus. I'm a principal engineer now in Intel Labs. I also run the Socio Technical Systems Lab. And I also sit on Intel's Responsible AI Advisory Council, where we look after what kinds of machine learning tools and products do we want to put out the door. 

Chris Adams: Brilliant, thank you, Dawn. And if you're new to this podcast, I mentioned my name was Chris Adams at the beginning of the podcast. I work at the Green Web Foundation. I'm the director of technology and policy there. I'm one of the authors of a report all about the environmental impact of AI last year, so I have like some background on this. I also work as the policy chair in the Green Software Foundation Policy Working Group as well. So that's another thing that I do. And if you, if there, we'll do our best to make sure that we link to every single paper and project on this, so if there are any particular things you find interesting, please do follow, look for the show notes. Okay, Dawn, I'm, let's, shall we start? I think you're both sitting comfortably, right? Shall I begin?

Okay, good. So, Dawn, I'm really glad you actually had a chance to both work on this paper and share and let me know about it in the first place. And I can tell when I read through it, there was quite an effort to, like, do all the research for this.

So, can I ask, like, what was the motivation for doing this in the first place? And, like, was there any particular people you feel really should read it?

Dawn Nafus: Yeah, absolutely. We primarily wrote this for ourselves. In a way. And I'll explain what I mean by that. So, oddly, it actually started life in my role in Responsible AI, where I had recently advocated that Intel should adopt a Protect the Environment principle alongside our suite of other Responsible AI principles, right?

Bias and inclusion, transparency, human oversight, all the rest of it. And so, the first thing that comes up when you advocate for a principle, and they did actually implement it, is "what are you going to do about it?" And so, we had a lot of conversation about exactly that, and really started to hone in on energy transparency, in part because, you know, from a governance perspective, that's an easy thing to at least conceptualize, right? You can get a number.

Chris Adams: Mmm. 

Dawn Nafus: You know, it's the place where people's heads first go to. And of course it's the biggest part of, or a very large part of the problem in the first place. Something that you can actually control at a development level. And so, but once we started poking at it, it was, "what do we actually mean by measuring? And for what? And for whom?" So as an example, if we measured, say, the last training run, that'll give you a nice guesstimate for your next training run, but that's not a carbon footprint, right? A footprint is everything that you've done before that, which folks might not have kept track of, right?

So, you know, we're really starting to wrestle with this. And then in parallel, in labs, we were doing some socio technical work on, carbon awareness. And there too, we had to start with measuring. Right? You had to start somewhere. And so that's exactly what the team did. And they found interestingly, or painfully depending on your point of view, look, this stuff ain't so simple, right?

If what you're doing is running a giant training run, you stick CodeCarbon in or whatever it is, sure, you can get absolutely a reasonable number. If you're trying to do something a little bit more granular, a little bit trickier, it turns out you actually have to know what you're looking at inside a data center, and frankly, we didn't, as machine learning people primarily. And so, we hit a lot of barriers and what we wanted to do was to say, okay, there are plenty of other people who are going to find the same stuff we did, so, and they shouldn't have to find out the hard way. So that was the motivation.

Chris Adams: Well, I'm glad that you did because this was actually the thing that we found as well, when we were looking into this, it looks simple on the outside, and then it turned, it feels a bit like a kind of fractal of complexity, and there's various layers that you need to be thinking about. And this is one thing I really appreciated in the paper that we actually, that, that was kind of broken out like that.

So you can at least have a model to think about it. And Charles, maybe this is actually one thing I can, like, hand over to you because I spoke about this kind of hierarchy of things you might do, like there's

stuff you might do at a data facility level or right all the way down to a, like, a node level, for example.

Can you take me through some of the ideas there? Because I know for people who haven't read the paper yet, that seemed to be one of the key ideas behind this, that there are different places where you might make an intervention. And this is actually a key thing to take away if you're trying to kind of interrogate this for the first time.

Charles Tripp: Yeah, I think it's, both interventions and measurement, or I should, it's really more estimation at any level. And it also depends on your goals and perspective. So it, like, if you are operating a data center, right? You're probably concerned with the entire data center, right? Like the cooling systems, the idle power draw, the, converting power to different levels, right?

Like transformer efficiency, things like that. Maybe even the transmission line losses and all of these things. And you may not really care too much about, like, the code level, right? So the types of measurements you might take there or estimates you might make are going to be different. They're gonna be at, like, the system level.

Like, how much is my cooling system using in different conditions, different operating conditions, environmental conditions? From a user's perspective, you might care a lot more about, like, how much energy, how much carbon is this job using? And that's gonna depend on those data center variables. But there's also a degree of like, well, the data center is going to be running whether or not I run my job.

Right? So I really care about my jobs impact more. And then I might be caring about much shorter term, more local estimates, like ones that, might be from measuring the nodes that I'm running on's power or which was what we did it at NREL or, much higher frequency, but less accurate measurements that come from the hardware itself.

Most modern computing hardware has a way to get these hardware estimates of the current power consumption. And you could log those. And there's also difficulties. Once you start doing that is the measurement itself can cause energy consumption. Right? And also potentially interfere with your software and cause it to run more slowly and potentially use more energy.

And so, like, there's difficulties there at that level. Yeah, but there's a whole suite of tools that are appropriate for different uses and purposes, right? Like measuring the power at the wall, going into the data center may be useful at the data center or multiple data center level. Still doesn't tell you all the story, right?

Like the losses in the transmission lines and where did that power come from are still not accounted for, right? But it also doesn't give you a sense for, like, what happens that I take interventions at the user level? It's very hard to see that from that high level, right? Because there's many things running on the system, different conditions there. From the user's point of view, they might only care about, like, you know, this one key piece of my software that's running, you know, like the kernel of this deep learning network.

How much energy is that taking? How much additional energy is that taking? And that's like a very different thing that very different measurements are appropriate for and interventions, right?

Like changing that little, you know, optimizing a little piece of code versus like, maybe we need to change the way our cooling system works on the whole data center or the way that we schedule jobs. Yeah, and the paper goes through many of these levels of granularity.

Chris Adams: Yeah, so this is one thing that really kind of struck out at me because when you, it started at the kind of facility level, which is looking at an entire building where you mentioned things like say, you know, power coming into the entire facility. And then I believe you went down to looking at say the, within that facility, there might be one or more data centers, then you're going down to things like a rack level and then you're going down to

kind of at a node level and then you're all even going all the way down to like a particularly tight loop or the equivalent for that. And when you're looking at things like this, there are questions about like what you what... 

if you would make something particularly efficient at, say, the bottom level, the node level, that doesn't necessarily impact, it might not have an impact higher up, for example, because that capacity might be just reallocated to someone else.

For example, it might just be that there's a certain kind of minimum amount of power draw that you aren't able to have much of an impact on. I mean, like, this is, these are some of the things

I was surprised by, or not surprised by, but I really appreciated breaking some of that, these out, because one thing that seemed to, one thing that was, I guess, counterintuitive when I was looking at this was that things you might do at one level can actually be counter, can hinder steps further down, for example, and vice versa.

Charles Tripp: Yeah, that's right. I mean, I think, two important sort of findings are, yeah, like battle scars that we got from doing these measurements. And one data set we produced is called BUTTER-E, which is like a really large scale measurement of energy consumption of training and testing neural networks and how the architecture impacts it.

And we were trying to get reasonable measurements while doing this. And, of the difficulties is in comparing measurements between runs on different systems, even if they're identically configured, can be tricky because different systems based on, you know, manufacturing variances, the heat, you know, like how warm is that system at that time?

Anything that might be happening in the background or over the network, anything that might be just a little different about its environment can have, real measurable impacts on the energy consumed. So, like comparing energy consumption between runs on different nodes, even with identical configurations, we had to account for biases and they're like, oh, this node draws a little bit more power than this one at idle.

And we have to like, adjust for that in order to make a clear comparison of what the difference was. And this problem gets bigger when you have different system configurations or even same configuration, but running in like a totally different data center. So that was like one tricky finding. And I think two other little ones I can mention, maybe we could go into more detail later. But, 

another one, like you mentioned, is the overall system utilization and how that's impacted by a particular piece of software running a particular job running is going to vary a lot on what those other users of the system are doing and how that system is scheduled.

So, you can definitely get in the situations where, yeah, I reduced my energy consumption, but that total system is just going to, that energy is going to be used some other time, especially if the energy consumption savings I get are from shortening the amount of time I'm using a resource and then someone else.

But it does mean that the computing is being done more efficiently, right? Like, if everyone does that, then more computing can be done within the same amount of energy. But it's hard to quantify that. Like, what is my impact? It's hard to say, right?

Chris Adams: I see, yeah, and Dawn, go on, I can, see you nodding, so I want you to come in now.

Dawn Nafus: If I can jump in a bit, I mean, I think that speaks to one of the things we're trying to bring out, maybe not literally, but make possible, is this. Those things could actually be better aligned in a certain way, right? Like, the energy that is, you know, for example, when there is idle time, right?

I mean, there are things that data center operators can do to reduce that, right? you know, you can bring things into lower power states, all the rest of it, right? So, in a way, kind of, but at the same time, the developer can't control it, but if they don't actually know that's going on, and it's just like, well, it's there anyway, there's nothing for me to do, right, that's also a problem, right?

So in a way, you've got two different kinds of actors looking at it in very different perspectives. And the clearer we can get about roles and responsibilities, right, you can start to do things like reduce your power when things are idling. Yes, you do have that problem of somebody else is going to jump in. But Charles, I think as your work shows, you know, there's still some idling going on, even though you wouldn't think, so maybe you could talk a little bit about that.

Charles Tripp: Yeah, so one really interesting thing that I didn't expect going into doing these measurements in this type of analysis was, well, first, I thought, "oh great, we can just measure the power on each node, run things and compare them." And we ran into problems immediately. Like, you couldn't compare the energy consumption from two identically configured systems directly, especially if you're collecting a lot of data, because one is just going to use like slightly more than the other because of the different variables I mentioned.

And then when you compare them, you're like, well, that run used way more energy, but it's not because of anything about how the job was configured. It's just, that system used a little bit more. So if I switch them, I'd get the opposite result. So that was one thing. But then, as we got into it and we were trying to figure out, okay, well, now that we figured out a way to account for these variations, let's see what the impact is of running different software with different configurations, especially like neural networks, different configurations on energy consumption and our initial hypothesis was that it was based on mainly the size of the neural network and, you know, like how many parameters basically, like how many calculations, these sorts of things.

And if you look in the research, A lot of the research out there about making neural networks and largely algorithms in general more efficient focuses on how many operations, how many flops does this take, you know? And look, we reduced it by a huge amount. So that means that we get the same energy consumption reductions.

We kind of thought that was probably true for the most part. But as we took measurements, we found that had almost no connection to how much energy was consumed. And the reason was that the amount of energy consumed had way more to do with how much data was moved around on the computer. So how much data was loaded from the network?

How much data was loaded from disc? How much data was loaded from disc into memory, into GPU RAM for using the GPU, into the different caching levels and red, even the registers? So if we computed like how much data got moved in and out of like level two cache on the CPU, we could see that had a huge correlation, like almost direct correlation with energy consumption. Not the number of calculations.

Now, you could get in a situation where, like, basically no data is leaving cache, and I'm doing a ton of computing on that data. In that case, probably a number of calculations does matter, but in most cases, especially in deep learning, has almost no connections, the amount of data moved. So then we thought, okay, well, it's amount of data moved.

It's the data moving. The data has a certain cost. But then we look deeper, and we saw that actually. The amount of data moved is not really what's causing the energy to be consumed. It's the stalls while the system is waiting to load the data. It's waiting for the data to come from, you know, system memory into level three cache.

It needs to do some calculations on that data. So it's pulling it out while it's sitting there waiting. It's that idle power draw. Just it could be for like a millisecond or even a nanosecond or something, right? But it adds up if you have, you know, billions of accesses. Each of those little stalls is drawing some power, and it adds up to be quite a significant amount of power.

So we found that actually the driver of the energy consumption, the primary driver by far in what we were studying in deep learning was 

the idle power draw while waiting for data to move around the system. And this was like really surprising because we started with number of calculations, it turns out almost irrelevant.

Right. And then we're like, well, is it the amount of data moved around? It's actually not quite the amount of data moved around, but that does like cause the stalls whenever I need to access the data, but it's really that idle power draw. And and I think that's probably true for a lot of software.

Chris Adams: Yes. I think that does sound about right.

I'm just gonna try if I follow that, because there was, I think there was a few quite key important ideas there. But there's also, if you aren't familiar with how computers are designed, you it might, there. I'll try to paraphrase it. So we've had this idea that the main thing is like, the number of calculations being done. That's like what we thought was the key idea.

But, 

Charles Tripp: How much work, you know.

Chris Adams: Yeah, exactly. And, what we actually, what we know about is inside a computer you have like multiple layers of, let's call them say, caches or multiple layers at where you might store data so it's easy and fast to access, but that starts quite small and then gets larger and larger, which a little bit slower over time.

So you might have, like you said, L2 cache, for example, and that's going to be smaller, much, much faster, but smaller than, say, the RAM on your system, and then if you go a bit further down, you've got like a disk, which is going to be way, what larger, and then that's going to be somewhat slower still, so moving between these stages so that you can process, that was actually one of the things that you were looking at, and then it turned out that actually, the thing that, well, there is some correlation there, one of the key drivers actually is the chips kind of in a ready state, ready to actually waiting for that stuff to come in.

They can't really be asleep because they know the data is going to have to come in, have to process it. They have to be almost like anticipating at all these levels. And that's one of the things we, that's one of the big drivers of actually the resource use and

the energy use. 

Charles Tripp: I mean, so, like, what we saw was, we actually estimated how much energy it took, like, per byte to move data from, like, system RAM to level three cache to level two to level one to a register at each level. And at some cases, it was so small, we couldn't even really estimate it. But in most cases, we were able to get an estimate for the For that, but a much larger cost was initiating the transfer, and even bigger than that was just the idle power draw during the time that the program executed and how long it executed for. And by combining those, we were able to estimate that most of that power consumption, like 99 percent in most cases was from that idle time, even those little micro stalls waiting for the data to move around. And that's because moving the data while it does take some energy doesn't take that much in comparison to the amount of energy of like keeping the ram on and the data is just like alive in the ram or keeping the CPU active, right?

Like CPUs can go into lower power states, but generally, at least part of that system has to shut down. So like doing it like at a very, fine grain scale is not really feasible. 

Many systems can change power state at a like a faster rate than you might imagine, but it's still a lot slower than like out of, you know, per instruction per byte level of, like, I need to load this data.

Like, okay, shut down the system and wait a second, right? Like, that's, it just, not a second, like a few nanoseconds. It's just not practical to do that. And it's so it's just keeping everything on during that time. That's sucking up most of the power. the So one strategy, simple strategy, but it's difficult to implement in some cases is to initiate that load that transfer earlier.

So if you can prefetch the data into the higher levels of memory before you hit the stall where you're waiting to actually use it,

you can probably significantly reduce this power consumption, due to that idle wait. But it's difficult to figure out how to properly do that prefetching. 

Chris Adams: Ah, I see. Thanks, charles. So it sounds like, okay, they, we might kind of approach this and there might be some things which feel kind of intuitive but it turns out there's quite a few counterintuitive things.

And like, Dawn, I can see you nodding away sagely here and I suspect there's a few things that you might have to add on this. Because this is, I mean, can I give you a bit of space, Dawn, to kind of talk about some of this too, because I know that this is something that you've shared with me before, is that yeah, there are maybe some rules of thumb you might use, but it's never that simple, basically, or you realise actually that there's quite a bit more to it than that, for example.

Dawn Nafus: Exactly. Well, I think what I really learned out of this effort is that measurement can actually recalibrate your rules of thumbs, right? So you don't actually have to be measuring all the time for all reasons, but even just that the simple, I mean, not so simple story that Charles told like, okay, you know, so I spent a lot of time talking with developers and trying to understand how they work and at a developer perception level, right?

What do they feel like? What's palpable to them, right? Send the stuff off, go have a cup of coffee, whatever it is, right? So they're not seeing all that, you know, and, you know, when I talk to them, most of them aren't thinking about the kinds of things that were just raised, right? Like how much data are you looking at a time?

You can actually set and tweak that. 

And that's the kind of, you know, Folks develop an idea about that, and they don't think too hard about it usually, right. So, with measuring, you can start to actually recalibrate the things you do see, right? I think this also gets back to, you know, why is it counterintuitive that, you know, some of these mechanisms and how you actually are training, as opposed to how many flops you're doing, how many parameters, why is that counterintuitive?

Well, at a certain level, you know, the number of flops do actually matter, right? If we do actually have a gigantic, you know, I'm gonna call myself a foundation model type size stuff, I'm gonna build out an entire data center for it, it does matter. But as you get, you know, down and down and more specific, it's a, different ball game.

And there are these tricks of scale that are sort of throughout this stuff, right? Like the fact that, yes, you can make a credible claim, that foundation model will always be more energy intensive than, you know, something so small you can run on a laptop, right? That's always going to be true, right? No measurement necessary, right? You keep going down and down, and you're like, okay, let's get more specific. You can get to actually where this, where our frustration really started was, you, if you try to go to the extreme, right, try to chase every single electron through a data center, you're not going to do it. It feels like physics, it feels objective, it feels true, but at minimum you start to hit the observer effect, right, that, you know, which is what we did.

We were, my colleague Nicole Beckage was trying to measure at an epoch level, right, sort of essentially round, you know, mini round of training. And what she found was that, you know, she was trying to sample so often that she's pulling energy out of the processing and it just, it messed up the numbers, right? So you can try to get down, you know, into that, you know, what feels like more accuracy and then all of a sudden you're in a different ballpark. So these, tricks of like aggregation and scale and what can you say credibly at what level, I think are fascinating, but you kind of got to get a feel for it in the same way that you can get a feel for, "yep, if I'm sending my job off, I know I have at least, you know, however many hours or however many days," right?

Charles Tripp: There's also so much variation that's out of your control, right? Like one run to another one system to another, even different times where you ran on the same system can cause measureable and in some cases significant variations in the energy consumption.

So it's more about, I think about understanding what's causing the energy consumption.

I think that's the more valuable thing to do. But it's easy to like, be like, "I already understand it." And

 I think there's a, there's like a historical bias towards number of operations because in old computers without much caching or anything like this, right? Like I restore old computers and, like an old 386 or IBM XT, right?

Like it's running, it has registers in the CPU and then it has main memory. And it, and almost everything is basically how many operations I'm doing is going to closely correlate with how fast the thing runs and

probably how much energy it uses, because most of the energy consumption on those systems Is just basically constant, no matter what I'm doing, right?

It's just it doesn't like idle down the processor while it's not working, right? And there's a historical bias. It's built up over time that, like, was focused on the, you know, and it's also at the programmer level. Like, I'm thinking about what is the computer doing? 

Chris Adams: What do I have controll over?

Charles Tripp: But it's only through it's only through actually measuring it that you gain a clearer picture of, like, what is actually using energy.

And I think if you get that picture, then you'll gain an understanding more of

how can I make this software or the data center or anything in between like job allocation more energy efficient, but it's only through actually measuring that we can get that clear picture. Because if we guess, especially using kind of our biases from how we learn to use computers, how we learn about how computers work, we're actually very likely to get an incorrect understanding, incorrect picture of what's driving the energy consumption.

It's much less intuitive than people think.

Chris Adams: Ah, okay, there's a couple of things I'd like to comment on, and then Dawn, i might give you a bit of space on this, because, you said, so there's one, so we're just talking about like flops as a thing that people, okay, are used to looking at, and are like, it's literally written into the AI Act, like, things above a certain number of flops are considered, you know, foundational models, for example, so, you know, that's a really good example of what this actually might be.

And I guess the other thing that I wanted to kind of like touch on is that, I work in the kind of web land, and like, I mean, the Green Web Foundation is a clue in our organization's name. We've had exactly the same thing, where we've been struggling to understand the impact of, say, moving data around, and whether, how much credence you should give to that versus things happening inside a browser, for example.

It looks like you've got some similar kinds of issues and things to be wrestling, with here. But Dawn, I wanted to give you a bit of space because both of you alluded to this, about this idea of having an understanding of what you can and what you can't control and, how you might have a bias for doing one thing without, and then miss something really much larger elsewhere, for example.

Can I maybe give you a bit of space to talk about this idea of, okay, well, which things do you, should you be focusing on, and also understanding of what's within your sphere of influence? What can you control? What can't you control, for example?

Dawn Nafus: Exactly. I think it's in a sense you've captured the main point, which is, you know, that measurements are most helpful when they are relevant to the thing you can control, right? So as a very simple example, you know, there are plenty of AI developers who have a choice in what data centers they can use.

There are plenty who don't, right? You know, when Charles works or worked at NREL, right. The supercomputer was there. That was it. You're not moving, right? 

So, if you can move, you know, that overall data center efficiency number that really matters because you can say, alright, "I'm putting my stuff here and not there." If you can't move, like, there's no need to mess with. It it is what it is, right? At the same, and this gets us into this interesting problem, again, a tension between what you might look at it from a policy perspective versus what a developer might look at. We had a lot of kind of, you know, can I say, come to Jesus?

We had a little moment

where we, is that on a podcast? I think I can. Where there was this question of, 

are we giving people a bum steer by focusing at, you know, granular developer level stuff, right? Where it's so much actually is on how you run the data center, right? So you, again, you talk about tricks of scale. On the one hand, you know, the amount of energy that you might be directly saving just by, you know, not using or not using, by the time all of those things move through the grid and you're talking about coming, you know, energy coming off of the transmissions cables, right, in aggregate might not actually be directly that big. It might be, but it might not be. And then you flip that around and you think about what aggregate demand looks like and the fact that so much of AI demand is, you know, that's what's putting pressure on our electricity grid.

Right? Then that's the most effective thing you could do, is actually get these, you know, very specific individual jobs down and down, right? So, again, it's all about what you can control, but there are these, whatever perspective you take is just going to flip your, you know, your understanding of the issue around.

Chris Adams: So this was actually one thing I quite appreciated from the paper. There were a few things saying, and it does touch on this idea, that yeah, you, might be focusing on the thing that you feel that you're able to control, but just because you're able to, like, Make very efficient part of this spot here that doesn't necessarily translate into a saving higher up in the system. Simply because if it's, if you don't, if higher up in the system isn't set to actually take advantage of that, then you might never achieve some of these savings It's a little bit like when you're working in cloud, for example, people tell you do all these things to kind of optimize your cloud savings. But if people are not turning data centers off, at best, you might be slowing the growth of infrastructure rollout in future, and like these are, and these are much, much harder things to kind of claim responsibility for, or say that, "yeah, it was definitely, if it weren't for me doing those things, we wouldn't have had that happen."

This is one of the things that I appreciated the paper just making some allusions to and saying, look, yeah, this is, you know, this is why I mean, to be honest, when I was reading this, I was like, wow, there is, there was obviously some stuff for, beginners, but there's actually quite a lot here, which is quite meaty for people who are thinking of it as a much larger systemic level.

So there's definitely things like experts could take away from this as well. So, I just want to check, are there any particular takeaways the two of you would like to kind of draw people's attention to beyond what we've been discussing so far? Because I quite enjoyed the paper and there's a few kind of nice ideas from this. Charles, if I just give you a bit of space to, kind of, come in. 

Charles Tripp: Yeah. I've got, kind of two topics that I think build on what we talked about before, but could be really useful for people to be aware of. So one is, sort of one of the outcomes of our studying of the impact of different architectures, data sets, hyper parameter settings on deep neural network energy consumption was that the most efficient networks, most energy efficient networks, and largely that correlates with most time efficient as well, but not always, the most efficient ones were not the smallest ones, and they were not the biggest ones, right?

The biggest ones were just required so much data movement. They were slow. The smallest ones, they took a lot more iterations, right? It took a lot more for them to learn the same thing. And the most efficient ones were the ones where the working sets, where the amount of data that was moved around, matched the different cache sizes.

So as you made the network bigger, it got more efficient because it learned faster. Then when it got so big that the data in like between layers, the communication between layers, for example, started to spill out of a cache level. Then it became much less energy efficient, because of that data movement stall happening.

So we found that like there is like an optimum point there. And for most algorithms, this is probably true where if the working set is sized appropriately for the memory hierarchy, you gain the most efficiency, right? Because generally, like, as I can use more data at a time, I can get my software to work better, right, more efficiently. But there's a point where it falls out of the cache and that becomes less efficient. Exactly what point is going to depend on the software. But I think focusing on that working set size and how it matches to the hardware is a really key piece for almost anyone looking to optimize software for energy efficiency is to think about that. How much data am I moving around and how does that map to the cache? So that's like a practical thing.

Chris Adams: Can I stop you Because I find that quite interesting, in that a lot of the time as developers we're kind of taught to kind of abstract away from

the underlying hardware, and that seems to be going the other way. That's saying, "no, you do need to be thinking about this.

You can't.

There, you know, there's no magic trick." 

Charles Tripp: Right? And so, like, for neural networks, that could mean sizing my layers so that those working sets match the cache hierarchy, which is something that no one even considers. It's not even close in most architectures. Like, no one has even thought about this. The other thing is on your point about data center operations and kind of the different perspectives,

one thing that we started to think about as we were doing some of this work was it might make sense to allocate time or in the case of like commercial data center, commercial cloud operator, even like charge field based on at least partly the energy rather than the time, as to incentivize them to use less energy, right?

Like make things more energy efficient. Those can be correlated, but not always right. And another piece of it that I want to touch on of that same puzzle is, from a lot of data center operators perspective, they want to show their systems fully utilized, right? Like there's demand for the system, so we should build an even bigger system and a better system. When it comes to energy consumption.

That's probably not the best way to go, because that means that those systems are sitting there probably doing inefficient things. Maybe even idling a lot of time, right? Like a user allocated the node, but it's just sitting there doing nothing, right? It may be more useful instead of thinking about, like, how much is the system always being utilized?

But think about how much, how much computation or how many jobs or whatever your, like, utilization metric is, do I get, like, per unit energy, right? And you may think about how much, or per unit carbon, right? And you may also think about, like, how much energy savings can I get by doing things like shutting down nodes when they're unlikely to be utilized and more about like having a dynamic capacity, right?

Like full tilt. I can use I can do how many flops or whatever, right? But I can also scale that down to reduce my idle power draw by, you know, 50 percent in low demand conditions. And if you have that dynamic capacity, you may actually be able to get even more throughput. But it's with less energy because when there's no demand, I'm like shutting,

I'm like scaling down my data center, right? And then when there's demand, I'm scaling it up. But these are things that are requiring cultural changes in data center operations to happen.

Chris Adams: I'm glad you mentioned this thing here because, Dawn, I know that you had some notes about, it sounds like in order for you to do that, you need, you probably need different metrics exposed or different kinds of transparency to what we have right now.

Probably more actually. Dawn, can I give you a bit of space to talk about this? Because this is one thing that you told me about before and it's something that is actually touched on in the paper quite a few times actually.

Dawn Nafus: Yeah, I mean, I think we can notice a real gap in a way between the kinds of things that Charles brings his attention to, and the kinds of things that show up in policy environments, in responsible AI circles, right, where I'm a bit closer, we can be a bit vague, and I think we are at the stage where, at least my read on the situation, is that, you know, there's, regardless of where you sit in the debates, and there are rip roaring debates about what to do about the AI energy situation, but I think transparency is probably the one thing we can get the most consensus on, but then, like, just back to that, what the heck does that mean? And I think we need a little, like a, more beats than are currently given to actually where, what work are those measurements doing?

You know, some of the feedback we've gotten is, you know, "well, can't you just come up with a standard?" Like, what's the right standard? It's like, well, no, actually, if data centers aren't standard, and there are many different ways to build a model, then, yes, you can have a standard as a way of having a conversation across a number of different parties to do a very specific thing, like for example, Charles's example, you know, suggested that if we're charging on a per energy basis, that changes a whole lot. Right? But what you can't do is to say, this is the standard that is the right way to do it, and then that meets the requirement, because that's, you know, what we found is that clearly the world is far more, you know, complicated and specific than that.

So, I, you know, I would really encourage the responsible AI community to start to get very specific very quickly, which I don't yet see happening, but I think it's just on the horizon. 

Chris Adams: Okay. Well I'm glad you mentioned about maybe taking this a little bit wider 'cause we've dived quite spent a lot of time talking about this paper, but there's other things happening in the world of AI actually, and I wanna give you folks a bit of space to kind of talk about anything that like, or things that you are, that you would like to kind of direct some attention to or you've seen that really you found particularly interesting.

Charles, can I give you some space first and then give Dawn the same, to like say it to like I know, either shout out or point to some particular things that, if they've found this conversation interesting so far, what they might want to be looking at. More data.

Charles Tripp: Yeah. I mean, I think, both in like computer program, computer science at large and especially in machine learning, we've kind of had an attitude, especially within deep learning within machine learning, an attitude of throwing more compute at the problem, right? And more data. The more data that we put through a model and the bigger, the more complicated the model is, the more capable it can be.

But this brute force approach is one of the main things that's driving this increasing computing energy consumption. Right? And I think that it is high time that we start taking a look at making the algorithms we use more energy efficient instead of just throwing more compute. It's easy to throw more compute at it, which is why it's been done.

And also because there hasn't been a significant like material incremental cost of like, Oh, you know, now we need. Twice made GPUs. I don't big deal. But now we're starting to hit constraints because we haven't thought about that incremental energy costs. We haven't had to, as an industry at large, right?

Like, but 

now it's starting to be like, well, we can't build that data center because we can't get the energy to it that we need to do the things we want to do with it because we haven't taken that incremental cost into account over time, we just kind of ignored it. And now we hit like the barrier, right? 

And so I think thinking about, the energy costs and probably this means investing in more finding more efficient algorithms, more efficient approaches as well as more efficient ways to run data centers and run jobs. That's gonna become increasingly important, even as our compute capacity continues to increase.

The energy costs are likely to increase along with that as we use more and more, and we need create more generation capacity, right? Like, it's expensive at some point where we're really driving that energy production, and that's going to be increasingly an important cost as well as it is now, like, starting to be a constraint to what kind of computing we can do.

So I think investing in more efficient approaches is going to be really key in the future. 

Chris Adams: There's one thing that I, that I think Dawn might come in on this actually, is that, you're talking about, it seems that you're talking about having more of a focus on surfacing some of the kind of efficiency or the fact that resource efficiency is actually going to be something that we probably need to value or sharpen, I mean, because as I understand it so far, it's not particularly visible in benchmarks or anything like that right now, like, and if you have benchmarks deciding, what counts as a good model or a good use of this until that's included. You're not going to have anything like this. Is that the kind of stuff you're kind of suggesting we should probably have? Like, some more recognition of, like, or even like, you're taking at the energy efficiency of something and being that thing that you draw attention to or you include in counting something as good or not, essentially.

Dawn Nafus: You know, I have a particular view of efficiency. I suspect many of your listeners might, as well. You know, I think it's notable that at the moment when we're seeing the, you know, the the model of the month, apparently, or the set of models of DeepSeek has come onto the scene and immediately we're starting to see, for the first time, you know, a Jevons paradox showing up in the public discourse.

So this is the paradox that when you make things more efficient, you can also end up stimulating so much demand... 

Chris Adams: Absolute use grows even though it gets individually more efficient.

Dawn Nafus: Yeah, exactly. Again, this is like this topsy turvy world that we're in. And so, you know, now the Jevons paradoxes is front page news, you know, my view is that yes, you know, again, we need to be particular about what sorts of efficiencies are we looking for where and not, you know, sort of willy nilly, you know, create an environment where, which I'm not saying you're doing Charles, but you know, what we don't want to do is create an environment where if you can just say it's more efficient, then, somehow, you know, we're all good, right. Which is, you know, what some of the social science of Energy Star has actually suggested that, that stuff is going on. 

With that said, right, 

I am a big fan of the Hugging Face Energy Star initiative. That looks incredibly promising. And I think one of the things that's really promising about it, so this is, you know, you know, leaderboards when, you know, people put their models up on Hugging Face. There's some energy measurement that happens, some carbon measurement, and then, you know, leaderboards are created and all the rest of it. And I think one of the things that's really good at, right, I can imagine issues as well, but you're A, you know, creating a way to give some people credit for actually looking. B, you're creating a way of distinguishing between two models very clearly, right? So in that context, do you have to be perfect about how many kilowatts or watts or whatever it is? No, actually, right? Right? You know, you're looking at more or less in comparable models. But C, it also interjects this kind of path dependence. Like, who is the next person who uses it? Right?

That really matters. If you're setting up something early on, yes, they'll do something a little bit different. They might not just run inference on it. But you're, changing how models evolve over time and kind of steering it towards even, you know, having energy presence at all. So that's pretty cool to my mind.

So I'm looking forward to... 

Chris Adams: Cool. We'll share a link to the Hugging Face. I think they, I think, do you know what they were called? I think it's the, you might be, I think it's, it was initially called the Energy Star Alliance, and then I think they've been told that they need to change the name to the Energy Score Alliance from this, because I

think it, Energy Star turned out to be a trademark, but we can definitely add a link to that in the show notes, because, these, this actually, I think it's something that is officially visible now. It's something that people have been working on late last year, and now there is, we'll share a link to the actual GitHub repo, to the code on GitHub to kind of run this, because this works for both closed source models and open source models. So it does give some of that visibility. Also in France, there is the Frugal LLM challenge, which also sounds similar to what you're talking about, this idea of essentially trying to emphasize more than just the, you know, like to pay a bit more attention to the energy efficiency aspect of this and I'm glad you mentioned the DeepSeek thing as well because suddenly everyone in the world is an armchair expert on William Stanley Jevons paradox stuff.

Everybody knows! Yeah. 

Dawn Nafus: Actually, if I could just add one small thing, since you mentioned the Frugal effort in France, there's a whole computer science community, sort of almost at a step's length from the AI development community that's really into just saying, "look, what, you know, what is the purpose of the thing that I'm building, period."

And even, and that, you know, frugal computing, computing within limits, all of that world really about how do we get, you know, just something that somebody is going to actually value, as opposed to, you getting to the next, you know, score on a benchmark leaderboard somewhere. so I think that's kind of also lurking in the background here.

Chris Adams: I'm glad you mentioned this, what we'll do, we'll add a we'll add links to both of those and, you immediately make me think of, there is this actual, so we're technologists mostly, the three of us, we're talking about this and I work in a civil society organization and, just this week, there was a big announcement, like a kind of set of demands from civil society about AI that's being shared at the AI Action Summit, this big summit where all the great and good are meeting in Paris, as you alluded to, next week to talk about what should we do about this? And, they, it's literally called Within Bounds, and we'll share a link to that. And it does talk about this, like, well, you know, if we're going to be using things like AI, what do, we need to have a discussion about what they're for. And that's the first thing I've seen which actually has discussions about saying, well, we should be actually having some concrete limits on the amount of energy for this, because we've seen that if this is a constraint, it doesn't stop engineers.

It doesn't stop innovation. People are able to build new things. What we should also do is we should share a link to, I believe, Vlad Coraoma. he did an interview with him all about Jevons paradox a few, I think, late last year, and that's a really nice deep dive for people who want to basically sound knowledgeable in these conversations on LinkedIn or social media right now, it's a really useful one there as well. 

Okay, so we spoke a little bit about these ones here. Charles, are there any particular projects you'd like to kind of like name check before we start to wrap up? Because I think we're coming up to the hour now, actually.

Charles Tripp: I don't know, not particular, but I did mention earlier, you know, we published this BUTTER-E data set and a paper along with it, as well as a larger one without energy measurements called BUTTER. Those are available online. You can just search for it and you'll find it right away. I think, if that's of interest to anyone hearing this, you know, there's a lot of measurements and analysis in there, including, you know, all the details of analysis that I mentioned where we, had this journey from number of compute cycles to, like, amount of stall, in terms of what drives energy consumption. 

Chris Adams: Ah, it's visible so people can see it. Oh, that's really cool. I didn't realize about that. Also, while you're still here, Charles, while I have access to you, before we did this interview, you mentioned, there's a whole discussion about wind turbines killing birds, and you were telling me this awesome story about how you were able to model the path of golden eagles to essentially avoid these kind of bird strike stuff happening.

Is that in the public domain? Is something, can we link to that? That sounded super cool. 

Charles Tripp: There's several, papers. I'll have to dig up the links, but there's several papers we published and some software also to create these models. 

But yeah, I worked on a project where we looked at, we took, eagle biologists and computational fluid dynamics experts and machine learning experts.

And we got together and we created some models based off of real data, real telemetry of tracking, golden eagle flight paths through, well, in many locations, including at wind sites, and match that up with the atmospheric conditions, the flow field, like, or graphic updrafts, which is where the wind hits, you know, like a mountain or a hill and it, some of it blows up.

Right. And golden eagles take advantage of this as well as thermal updrafts caused by heating at the ground. Right. Causing the air to rise to fly. Golden eagles don't really like flapping. They like gliding. And because of that, golden eagles and other soaring birds, their flight paths are fairly easy to predict, right?

Like, you may not know, like, oh, are they going to take a left turn here or right turn there, but generally they're going to fly in the places where there's strong updrafts and using actual data and knowledge from the eagle biologists and simulations of the flow patterns, we were able to create a model that allows wind turbines to be cited and also operate, right?

Like, what, under what conditions, like, what wind conditions in particular and what time of year, which also affects the eagles' behavior, should I perhaps reduce my usage of certain turbines to reduce bird strikes? And in fact, we showed that it could be done without significantly, or even at all, impacting the energy production of a wind site.

You could significantly reduce the chances of colliding with a bird.

Chris Adams: And it's probably good for the birds too, as well, isn't it? Yeah.

Alright, we definitely need to find some links for that. That's, going to be absolute catnip for the nerdy listeners who put, who are into this. Dawn, can I just give you the last word? Are there any particular things that you'd like to, I mean actually I should ask like, we'll add links to like you and Charles online, but if there's anything that you would draw people's attention to before we wrap up, what would you pay, what would you plug here? 

Dawn Nafus: I actually did want to just give a shout out to National Renewable Energy Lab, period. One of the things that are amazing about them, speaking of eagles, a different eagle is, they have a supercomputer called Eagle. I believe they've got another one now. It is lovingly instrumented with all sorts of energy measurements, basically anything you can think to measure.

I think you can do it in there. There's another data set from another one of our co authors, Hilary Egan, that has some sort of jobs data. You can dig in and explore like what a real world data center job, you know, situation looks like. 

So I just want to give all the credit in the world to National Renewable Energy Lab and the stuff they do on the computing side.

It's just phenomenal.

Chris Adams: Yes, I think that's a really, I would echo that very much. I'm a big fan of NREL and the output for them. It's a really like a national treasure Folks, I'm really, thank you so much for taking me through all of this work and diving in as deeply as we did and referring to things that soar as well, actually, Charles. I hope we could do this again sometime soon, but otherwise, have a lovely day, and thank you once again for joining us. Lovely seeing you two again.

Charles Tripp: Good seeing you.

Chris Adams: Okay, ciao!  

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]]> Host Chris Adams is joined by Charles Tripp and Dawn Nafus to explore the complexities of measuring AI's environmental impact from a novice’s starting point. They discuss their research paper, A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning, breaking down key insights on how energy efficiency in AI systems is often misunderstood. They discuss practical strategies for optimizing energy use, the challenges of accurate measurement, and the broader implications of AI’s energy demands. They also highlight initiatives like Hugging Face’s Energy Score Alliance, discuss how transparency and better metrics can drive more sustainable AI development and how they both have a commonality with eagle(s)!

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TRANSCRIPT BELOW:

Charles Tripp: But now it's starting to be like, well, we can't build that data center because we can't get the energy to it that we need to do the things we want to do with it. we haven't taken that incremental cost into account over time, we just kind of ignored it. And now we hit like the barrier, right? 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host Chris Adams. If you follow a strict media diet, you switch off the Wi-Fi on your house and you throw your phone into the ocean, you might be able to avoid the constant stream of stories about AI in the tech industry. For the rest of us, though, it's basically unavoidable. So having an understanding of the environmental impact of AI is increasingly important if you want to be a responsible practitioner navigating the world of AI, generative AI, machine learning models, DeepSeek, and the rest. Earlier this year, I had a paper shared with me with the intriguing title A Beginner's Guide to Power and Energy Measurement, an Estimation for Computing and Machine Learning. And it turned out to be one of the most useful resources I've since come across for making sense of the environmental footprint of AI. So I was over the moon when I found out that two of the authors were both willing and able to come on to discuss this subject today. So joining me today are Dawn Nafus and Charles Tripp, who worked on the paper and did all this research. And well, instead of me introducing them, well, they're right here. I might as well let them do the honors themselves, actually. So, I'm just going to work in alphabetical order. Charles, I think you're slightly ahead of Dawn. So, if I, can I just give you the room to, like, introduce yourself?

Charles Tripp: Sure. I'm a machine learning researcher and 

Stanford 

algorithms researcher, and I've been programming pretty much my whole life since I was a little kid, and I love computers. I researched machine learning and reinforcement learning in particular at Stanford, started my own company, but kind of got burnt out on it.

And then I went to the National Renewable Energy Lab where I applied machine learning techniques to energy efficiency and renewable energy problems there. And while I was there, I started to realize that computing energy efficiency was a risingly, like, an increasingly important area of study on its own.

So I had the opportunity to sort of lead an effort there to create a program of research around that topic. And it was through that work that I started working on this paper, made these connections with Dawn. And I worked there for six years and just recently changed jobs to be a machine learning engineer at Zazzle.

I'm continuing to do this research. And, yeah. 

Chris Adams: Brilliant. Thank you, Charles. Okay, so national, that's NREL that some people refer

Charles Tripp: That's right. It's one of the national labs. 

Chris Adams: Okay. Brillinat. And Dawn, I guess I should give you the space to introduce yourself, and welcome back again, actually. 

Dawn Nafus: Thank you. Great to be here. My name is Dawn Nafus. I'm a principal engineer now in Intel Labs. I also run the Socio Technical Systems Lab. And I also sit on Intel's Responsible AI Advisory Council, where we look after what kinds of machine learning tools and products do we want to put out the door. 

Chris Adams: Brilliant, thank you, Dawn. And if you're new to this podcast, I mentioned my name was Chris Adams at the beginning of the podcast. I work at the Green Web Foundation. I'm the director of technology and policy there. I'm one of the authors of a report all about the environmental impact of AI last year, so I have like some background on this. I also work as the policy chair in the Green Software Foundation Policy Working Group as well. So that's another thing that I do. And if you, if there, we'll do our best to make sure that we link to every single paper and project on this, so if there are any particular things you find interesting, please do follow, look for the show notes. Okay, Dawn, I'm, let's, shall we start? I think you're both sitting comfortably, right? Shall I begin?

Okay, good. So, Dawn, I'm really glad you actually had a chance to both work on this paper and share and let me know about it in the first place. And I can tell when I read through it, there was quite an effort to, like, do all the research for this.

So, can I ask, like, what was the motivation for doing this in the first place? And, like, was there any particular people you feel really should read it?

Dawn Nafus: Yeah, absolutely. We primarily wrote this for ourselves. In a way. And I'll explain what I mean by that. So, oddly, it actually started life in my role in Responsible AI, where I had recently advocated that Intel should adopt a Protect the Environment principle alongside our suite of other Responsible AI principles, right?

Bias and inclusion, transparency, human oversight, all the rest of it. And so, the first thing that comes up when you advocate for a principle, and they did actually implement it, is "what are you going to do about it?" And so, we had a lot of conversation about exactly that, and really started to hone in on energy transparency, in part because, you know, from a governance perspective, that's an easy thing to at least conceptualize, right? You can get a number.

Chris Adams: Mmm. 

Dawn Nafus: You know, it's the place where people's heads first go to. And of course it's the biggest part of, or a very large part of the problem in the first place. Something that you can actually control at a development level. And so, but once we started poking at it, it was, "what do we actually mean by measuring? And for what? And for whom?" So as an example, if we measured, say, the last training run, that'll give you a nice guesstimate for your next training run, but that's not a carbon footprint, right? A footprint is everything that you've done before that, which folks might not have kept track of, right?

So, you know, we're really starting to wrestle with this. And then in parallel, in labs, we were doing some socio technical work on, carbon awareness. And there too, we had to start with measuring. Right? You had to start somewhere. And so that's exactly what the team did. And they found interestingly, or painfully depending on your point of view, look, this stuff ain't so simple, right?

If what you're doing is running a giant training run, you stick CodeCarbon in or whatever it is, sure, you can get absolutely a reasonable number. If you're trying to do something a little bit more granular, a little bit trickier, it turns out you actually have to know what you're looking at inside a data center, and frankly, we didn't, as machine learning people primarily. And so, we hit a lot of barriers and what we wanted to do was to say, okay, there are plenty of other people who are going to find the same stuff we did, so, and they shouldn't have to find out the hard way. So that was the motivation.

Chris Adams: Well, I'm glad that you did because this was actually the thing that we found as well, when we were looking into this, it looks simple on the outside, and then it turned, it feels a bit like a kind of fractal of complexity, and there's various layers that you need to be thinking about. And this is one thing I really appreciated in the paper that we actually, that, that was kind of broken out like that.

So you can at least have a model to think about it. And Charles, maybe this is actually one thing I can, like, hand over to you because I spoke about this kind of hierarchy of things you might do, like there's

stuff you might do at a data facility level or right all the way down to a, like, a node level, for example.

Can you take me through some of the ideas there? Because I know for people who haven't read the paper yet, that seemed to be one of the key ideas behind this, that there are different places where you might make an intervention. And this is actually a key thing to take away if you're trying to kind of interrogate this for the first time.

Charles Tripp: Yeah, I think it's, both interventions and measurement, or I should, it's really more estimation at any level. And it also depends on your goals and perspective. So it, like, if you are operating a data center, right? You're probably concerned with the entire data center, right? Like the cooling systems, the idle power draw, the, converting power to different levels, right?

Like transformer efficiency, things like that. Maybe even the transmission line losses and all of these things. And you may not really care too much about, like, the code level, right? So the types of measurements you might take there or estimates you might make are going to be different. They're gonna be at, like, the system level.

Like, how much is my cooling system using in different conditions, different operating conditions, environmental conditions? From a user's perspective, you might care a lot more about, like, how much energy, how much carbon is this job using? And that's gonna depend on those data center variables. But there's also a degree of like, well, the data center is going to be running whether or not I run my job.

Right? So I really care about my jobs impact more. And then I might be caring about much shorter term, more local estimates, like ones that, might be from measuring the nodes that I'm running on's power or which was what we did it at NREL or, much higher frequency, but less accurate measurements that come from the hardware itself.

Most modern computing hardware has a way to get these hardware estimates of the current power consumption. And you could log those. And there's also difficulties. Once you start doing that is the measurement itself can cause energy consumption. Right? And also potentially interfere with your software and cause it to run more slowly and potentially use more energy.

And so, like, there's difficulties there at that level. Yeah, but there's a whole suite of tools that are appropriate for different uses and purposes, right? Like measuring the power at the wall, going into the data center may be useful at the data center or multiple data center level. Still doesn't tell you all the story, right?

Like the losses in the transmission lines and where did that power come from are still not accounted for, right? But it also doesn't give you a sense for, like, what happens that I take interventions at the user level? It's very hard to see that from that high level, right? Because there's many things running on the system, different conditions there. From the user's point of view, they might only care about, like, you know, this one key piece of my software that's running, you know, like the kernel of this deep learning network.

How much energy is that taking? How much additional energy is that taking? And that's like a very different thing that very different measurements are appropriate for and interventions, right?

Like changing that little, you know, optimizing a little piece of code versus like, maybe we need to change the way our cooling system works on the whole data center or the way that we schedule jobs. Yeah, and the paper goes through many of these levels of granularity.

Chris Adams: Yeah, so this is one thing that really kind of struck out at me because when you, it started at the kind of facility level, which is looking at an entire building where you mentioned things like say, you know, power coming into the entire facility. And then I believe you went down to looking at say the, within that facility, there might be one or more data centers, then you're going down to things like a rack level and then you're going down to

kind of at a node level and then you're all even going all the way down to like a particularly tight loop or the equivalent for that. And when you're looking at things like this, there are questions about like what you what... 

if you would make something particularly efficient at, say, the bottom level, the node level, that doesn't necessarily impact, it might not have an impact higher up, for example, because that capacity might be just reallocated to someone else.

For example, it might just be that there's a certain kind of minimum amount of power draw that you aren't able to have much of an impact on. I mean, like, this is, these are some of the things

I was surprised by, or not surprised by, but I really appreciated breaking some of that, these out, because one thing that seemed to, one thing that was, I guess, counterintuitive when I was looking at this was that things you might do at one level can actually be counter, can hinder steps further down, for example, and vice versa.

Charles Tripp: Yeah, that's right. I mean, I think, two important sort of findings are, yeah, like battle scars that we got from doing these measurements. And one data set we produced is called BUTTER-E, which is like a really large scale measurement of energy consumption of training and testing neural networks and how the architecture impacts it.

And we were trying to get reasonable measurements while doing this. And, of the difficulties is in comparing measurements between runs on different systems, even if they're identically configured, can be tricky because different systems based on, you know, manufacturing variances, the heat, you know, like how warm is that system at that time?

Anything that might be happening in the background or over the network, anything that might be just a little different about its environment can have, real measurable impacts on the energy consumed. So, like comparing energy consumption between runs on different nodes, even with identical configurations, we had to account for biases and they're like, oh, this node draws a little bit more power than this one at idle.

And we have to like, adjust for that in order to make a clear comparison of what the difference was. And this problem gets bigger when you have different system configurations or even same configuration, but running in like a totally different data center. So that was like one tricky finding. And I think two other little ones I can mention, maybe we could go into more detail later. But, 

another one, like you mentioned, is the overall system utilization and how that's impacted by a particular piece of software running a particular job running is going to vary a lot on what those other users of the system are doing and how that system is scheduled.

So, you can definitely get in the situations where, yeah, I reduced my energy consumption, but that total system is just going to, that energy is going to be used some other time, especially if the energy consumption savings I get are from shortening the amount of time I'm using a resource and then someone else.

But it does mean that the computing is being done more efficiently, right? Like, if everyone does that, then more computing can be done within the same amount of energy. But it's hard to quantify that. Like, what is my impact? It's hard to say, right?

Chris Adams: I see, yeah, and Dawn, go on, I can, see you nodding, so I want you to come in now.

Dawn Nafus: If I can jump in a bit, I mean, I think that speaks to one of the things we're trying to bring out, maybe not literally, but make possible, is this. Those things could actually be better aligned in a certain way, right? Like, the energy that is, you know, for example, when there is idle time, right?

I mean, there are things that data center operators can do to reduce that, right? you know, you can bring things into lower power states, all the rest of it, right? So, in a way, kind of, but at the same time, the developer can't control it, but if they don't actually know that's going on, and it's just like, well, it's there anyway, there's nothing for me to do, right, that's also a problem, right?

So in a way, you've got two different kinds of actors looking at it in very different perspectives. And the clearer we can get about roles and responsibilities, right, you can start to do things like reduce your power when things are idling. Yes, you do have that problem of somebody else is going to jump in. But Charles, I think as your work shows, you know, there's still some idling going on, even though you wouldn't think, so maybe you could talk a little bit about that.

Charles Tripp: Yeah, so one really interesting thing that I didn't expect going into doing these measurements in this type of analysis was, well, first, I thought, "oh great, we can just measure the power on each node, run things and compare them." And we ran into problems immediately. Like, you couldn't compare the energy consumption from two identically configured systems directly, especially if you're collecting a lot of data, because one is just going to use like slightly more than the other because of the different variables I mentioned.

And then when you compare them, you're like, well, that run used way more energy, but it's not because of anything about how the job was configured. It's just, that system used a little bit more. So if I switch them, I'd get the opposite result. So that was one thing. But then, as we got into it and we were trying to figure out, okay, well, now that we figured out a way to account for these variations, let's see what the impact is of running different software with different configurations, especially like neural networks, different configurations on energy consumption and our initial hypothesis was that it was based on mainly the size of the neural network and, you know, like how many parameters basically, like how many calculations, these sorts of things.

And if you look in the research, A lot of the research out there about making neural networks and largely algorithms in general more efficient focuses on how many operations, how many flops does this take, you know? And look, we reduced it by a huge amount. So that means that we get the same energy consumption reductions.

We kind of thought that was probably true for the most part. But as we took measurements, we found that had almost no connection to how much energy was consumed. And the reason was that the amount of energy consumed had way more to do with how much data was moved around on the computer. So how much data was loaded from the network?

How much data was loaded from disc? How much data was loaded from disc into memory, into GPU RAM for using the GPU, into the different caching levels and red, even the registers? So if we computed like how much data got moved in and out of like level two cache on the CPU, we could see that had a huge correlation, like almost direct correlation with energy consumption. Not the number of calculations.

Now, you could get in a situation where, like, basically no data is leaving cache, and I'm doing a ton of computing on that data. In that case, probably a number of calculations does matter, but in most cases, especially in deep learning, has almost no connections, the amount of data moved. So then we thought, okay, well, it's amount of data moved.

It's the data moving. The data has a certain cost. But then we look deeper, and we saw that actually. The amount of data moved is not really what's causing the energy to be consumed. It's the stalls while the system is waiting to load the data. It's waiting for the data to come from, you know, system memory into level three cache.

It needs to do some calculations on that data. So it's pulling it out while it's sitting there waiting. It's that idle power draw. Just it could be for like a millisecond or even a nanosecond or something, right? But it adds up if you have, you know, billions of accesses. Each of those little stalls is drawing some power, and it adds up to be quite a significant amount of power.

So we found that actually the driver of the energy consumption, the primary driver by far in what we were studying in deep learning was 

the idle power draw while waiting for data to move around the system. And this was like really surprising because we started with number of calculations, it turns out almost irrelevant.

Right. And then we're like, well, is it the amount of data moved around? It's actually not quite the amount of data moved around, but that does like cause the stalls whenever I need to access the data, but it's really that idle power draw. And and I think that's probably true for a lot of software.

Chris Adams: Yes. I think that does sound about right.

I'm just gonna try if I follow that, because there was, I think there was a few quite key important ideas there. But there's also, if you aren't familiar with how computers are designed, you it might, there. I'll try to paraphrase it. So we've had this idea that the main thing is like, the number of calculations being done. That's like what we thought was the key idea.

But, 

Charles Tripp: How much work, you know.

Chris Adams: Yeah, exactly. And, what we actually, what we know about is inside a computer you have like multiple layers of, let's call them say, caches or multiple layers at where you might store data so it's easy and fast to access, but that starts quite small and then gets larger and larger, which a little bit slower over time.

So you might have, like you said, L2 cache, for example, and that's going to be smaller, much, much faster, but smaller than, say, the RAM on your system, and then if you go a bit further down, you've got like a disk, which is going to be way, what larger, and then that's going to be somewhat slower still, so moving between these stages so that you can process, that was actually one of the things that you were looking at, and then it turned out that actually, the thing that, well, there is some correlation there, one of the key drivers actually is the chips kind of in a ready state, ready to actually waiting for that stuff to come in.

They can't really be asleep because they know the data is going to have to come in, have to process it. They have to be almost like anticipating at all these levels. And that's one of the things we, that's one of the big drivers of actually the resource use and

the energy use. 

Charles Tripp: I mean, so, like, what we saw was, we actually estimated how much energy it took, like, per byte to move data from, like, system RAM to level three cache to level two to level one to a register at each level. And at some cases, it was so small, we couldn't even really estimate it. But in most cases, we were able to get an estimate for the For that, but a much larger cost was initiating the transfer, and even bigger than that was just the idle power draw during the time that the program executed and how long it executed for. And by combining those, we were able to estimate that most of that power consumption, like 99 percent in most cases was from that idle time, even those little micro stalls waiting for the data to move around. And that's because moving the data while it does take some energy doesn't take that much in comparison to the amount of energy of like keeping the ram on and the data is just like alive in the ram or keeping the CPU active, right?

Like CPUs can go into lower power states, but generally, at least part of that system has to shut down. So like doing it like at a very, fine grain scale is not really feasible. 

Many systems can change power state at a like a faster rate than you might imagine, but it's still a lot slower than like out of, you know, per instruction per byte level of, like, I need to load this data.

Like, okay, shut down the system and wait a second, right? Like, that's, it just, not a second, like a few nanoseconds. It's just not practical to do that. And it's so it's just keeping everything on during that time. That's sucking up most of the power. the So one strategy, simple strategy, but it's difficult to implement in some cases is to initiate that load that transfer earlier.

So if you can prefetch the data into the higher levels of memory before you hit the stall where you're waiting to actually use it,

you can probably significantly reduce this power consumption, due to that idle wait. But it's difficult to figure out how to properly do that prefetching. 

Chris Adams: Ah, I see. Thanks, charles. So it sounds like, okay, they, we might kind of approach this and there might be some things which feel kind of intuitive but it turns out there's quite a few counterintuitive things.

And like, Dawn, I can see you nodding away sagely here and I suspect there's a few things that you might have to add on this. Because this is, I mean, can I give you a bit of space, Dawn, to kind of talk about some of this too, because I know that this is something that you've shared with me before, is that yeah, there are maybe some rules of thumb you might use, but it's never that simple, basically, or you realise actually that there's quite a bit more to it than that, for example.

Dawn Nafus: Exactly. Well, I think what I really learned out of this effort is that measurement can actually recalibrate your rules of thumbs, right? So you don't actually have to be measuring all the time for all reasons, but even just that the simple, I mean, not so simple story that Charles told like, okay, you know, so I spent a lot of time talking with developers and trying to understand how they work and at a developer perception level, right?

What do they feel like? What's palpable to them, right? Send the stuff off, go have a cup of coffee, whatever it is, right? So they're not seeing all that, you know, and, you know, when I talk to them, most of them aren't thinking about the kinds of things that were just raised, right? Like how much data are you looking at a time?

You can actually set and tweak that. 

And that's the kind of, you know, Folks develop an idea about that, and they don't think too hard about it usually, right. So, with measuring, you can start to actually recalibrate the things you do see, right? I think this also gets back to, you know, why is it counterintuitive that, you know, some of these mechanisms and how you actually are training, as opposed to how many flops you're doing, how many parameters, why is that counterintuitive?

Well, at a certain level, you know, the number of flops do actually matter, right? If we do actually have a gigantic, you know, I'm gonna call myself a foundation model type size stuff, I'm gonna build out an entire data center for it, it does matter. But as you get, you know, down and down and more specific, it's a, different ball game.

And there are these tricks of scale that are sort of throughout this stuff, right? Like the fact that, yes, you can make a credible claim, that foundation model will always be more energy intensive than, you know, something so small you can run on a laptop, right? That's always going to be true, right? No measurement necessary, right? You keep going down and down, and you're like, okay, let's get more specific. You can get to actually where this, where our frustration really started was, you, if you try to go to the extreme, right, try to chase every single electron through a data center, you're not going to do it. It feels like physics, it feels objective, it feels true, but at minimum you start to hit the observer effect, right, that, you know, which is what we did.

We were, my colleague Nicole Beckage was trying to measure at an epoch level, right, sort of essentially round, you know, mini round of training. And what she found was that, you know, she was trying to sample so often that she's pulling energy out of the processing and it just, it messed up the numbers, right? So you can try to get down, you know, into that, you know, what feels like more accuracy and then all of a sudden you're in a different ballpark. So these, tricks of like aggregation and scale and what can you say credibly at what level, I think are fascinating, but you kind of got to get a feel for it in the same way that you can get a feel for, "yep, if I'm sending my job off, I know I have at least, you know, however many hours or however many days," right?

Charles Tripp: There's also so much variation that's out of your control, right? Like one run to another one system to another, even different times where you ran on the same system can cause measureable and in some cases significant variations in the energy consumption.

So it's more about, I think about understanding what's causing the energy consumption.

I think that's the more valuable thing to do. But it's easy to like, be like, "I already understand it." And

 I think there's a, there's like a historical bias towards number of operations because in old computers without much caching or anything like this, right? Like I restore old computers and, like an old 386 or IBM XT, right?

Like it's running, it has registers in the CPU and then it has main memory. And it, and almost everything is basically how many operations I'm doing is going to closely correlate with how fast the thing runs and

probably how much energy it uses, because most of the energy consumption on those systems Is just basically constant, no matter what I'm doing, right?

It's just it doesn't like idle down the processor while it's not working, right? And there's a historical bias. It's built up over time that, like, was focused on the, you know, and it's also at the programmer level. Like, I'm thinking about what is the computer doing? 

Chris Adams: What do I have controll over?

Charles Tripp: But it's only through it's only through actually measuring it that you gain a clearer picture of, like, what is actually using energy.

And I think if you get that picture, then you'll gain an understanding more of

how can I make this software or the data center or anything in between like job allocation more energy efficient, but it's only through actually measuring that we can get that clear picture. Because if we guess, especially using kind of our biases from how we learn to use computers, how we learn about how computers work, we're actually very likely to get an incorrect understanding, incorrect picture of what's driving the energy consumption.

It's much less intuitive than people think.

Chris Adams: Ah, okay, there's a couple of things I'd like to comment on, and then Dawn, i might give you a bit of space on this, because, you said, so there's one, so we're just talking about like flops as a thing that people, okay, are used to looking at, and are like, it's literally written into the AI Act, like, things above a certain number of flops are considered, you know, foundational models, for example, so, you know, that's a really good example of what this actually might be.

And I guess the other thing that I wanted to kind of like touch on is that, I work in the kind of web land, and like, I mean, the Green Web Foundation is a clue in our organization's name. We've had exactly the same thing, where we've been struggling to understand the impact of, say, moving data around, and whether, how much credence you should give to that versus things happening inside a browser, for example.

It looks like you've got some similar kinds of issues and things to be wrestling, with here. But Dawn, I wanted to give you a bit of space because both of you alluded to this, about this idea of having an understanding of what you can and what you can't control and, how you might have a bias for doing one thing without, and then miss something really much larger elsewhere, for example.

Can I maybe give you a bit of space to talk about this idea of, okay, well, which things do you, should you be focusing on, and also understanding of what's within your sphere of influence? What can you control? What can't you control, for example?

Dawn Nafus: Exactly. I think it's in a sense you've captured the main point, which is, you know, that measurements are most helpful when they are relevant to the thing you can control, right? So as a very simple example, you know, there are plenty of AI developers who have a choice in what data centers they can use.

There are plenty who don't, right? You know, when Charles works or worked at NREL, right. The supercomputer was there. That was it. You're not moving, right? 

So, if you can move, you know, that overall data center efficiency number that really matters because you can say, alright, "I'm putting my stuff here and not there." If you can't move, like, there's no need to mess with. It it is what it is, right? At the same, and this gets us into this interesting problem, again, a tension between what you might look at it from a policy perspective versus what a developer might look at. We had a lot of kind of, you know, can I say, come to Jesus?

We had a little moment

where we, is that on a podcast? I think I can. Where there was this question of, 

are we giving people a bum steer by focusing at, you know, granular developer level stuff, right? Where it's so much actually is on how you run the data center, right? So you, again, you talk about tricks of scale. On the one hand, you know, the amount of energy that you might be directly saving just by, you know, not using or not using, by the time all of those things move through the grid and you're talking about coming, you know, energy coming off of the transmissions cables, right, in aggregate might not actually be directly that big. It might be, but it might not be. And then you flip that around and you think about what aggregate demand looks like and the fact that so much of AI demand is, you know, that's what's putting pressure on our electricity grid.

Right? Then that's the most effective thing you could do, is actually get these, you know, very specific individual jobs down and down, right? So, again, it's all about what you can control, but there are these, whatever perspective you take is just going to flip your, you know, your understanding of the issue around.

Chris Adams: So this was actually one thing I quite appreciated from the paper. There were a few things saying, and it does touch on this idea, that yeah, you, might be focusing on the thing that you feel that you're able to control, but just because you're able to, like, Make very efficient part of this spot here that doesn't necessarily translate into a saving higher up in the system. Simply because if it's, if you don't, if higher up in the system isn't set to actually take advantage of that, then you might never achieve some of these savings It's a little bit like when you're working in cloud, for example, people tell you do all these things to kind of optimize your cloud savings. But if people are not turning data centers off, at best, you might be slowing the growth of infrastructure rollout in future, and like these are, and these are much, much harder things to kind of claim responsibility for, or say that, "yeah, it was definitely, if it weren't for me doing those things, we wouldn't have had that happen."

This is one of the things that I appreciated the paper just making some allusions to and saying, look, yeah, this is, you know, this is why I mean, to be honest, when I was reading this, I was like, wow, there is, there was obviously some stuff for, beginners, but there's actually quite a lot here, which is quite meaty for people who are thinking of it as a much larger systemic level.

So there's definitely things like experts could take away from this as well. So, I just want to check, are there any particular takeaways the two of you would like to kind of draw people's attention to beyond what we've been discussing so far? Because I quite enjoyed the paper and there's a few kind of nice ideas from this. Charles, if I just give you a bit of space to, kind of, come in. 

Charles Tripp: Yeah. I've got, kind of two topics that I think build on what we talked about before, but could be really useful for people to be aware of. So one is, sort of one of the outcomes of our studying of the impact of different architectures, data sets, hyper parameter settings on deep neural network energy consumption was that the most efficient networks, most energy efficient networks, and largely that correlates with most time efficient as well, but not always, the most efficient ones were not the smallest ones, and they were not the biggest ones, right?

The biggest ones were just required so much data movement. They were slow. The smallest ones, they took a lot more iterations, right? It took a lot more for them to learn the same thing. And the most efficient ones were the ones where the working sets, where the amount of data that was moved around, matched the different cache sizes.

So as you made the network bigger, it got more efficient because it learned faster. Then when it got so big that the data in like between layers, the communication between layers, for example, started to spill out of a cache level. Then it became much less energy efficient, because of that data movement stall happening.

So we found that like there is like an optimum point there. And for most algorithms, this is probably true where if the working set is sized appropriately for the memory hierarchy, you gain the most efficiency, right? Because generally, like, as I can use more data at a time, I can get my software to work better, right, more efficiently. But there's a point where it falls out of the cache and that becomes less efficient. Exactly what point is going to depend on the software. But I think focusing on that working set size and how it matches to the hardware is a really key piece for almost anyone looking to optimize software for energy efficiency is to think about that. How much data am I moving around and how does that map to the cache? So that's like a practical thing.

Chris Adams: Can I stop you Because I find that quite interesting, in that a lot of the time as developers we're kind of taught to kind of abstract away from

the underlying hardware, and that seems to be going the other way. That's saying, "no, you do need to be thinking about this.

You can't.

There, you know, there's no magic trick." 

Charles Tripp: Right? And so, like, for neural networks, that could mean sizing my layers so that those working sets match the cache hierarchy, which is something that no one even considers. It's not even close in most architectures. Like, no one has even thought about this. The other thing is on your point about data center operations and kind of the different perspectives,

one thing that we started to think about as we were doing some of this work was it might make sense to allocate time or in the case of like commercial data center, commercial cloud operator, even like charge field based on at least partly the energy rather than the time, as to incentivize them to use less energy, right?

Like make things more energy efficient. Those can be correlated, but not always right. And another piece of it that I want to touch on of that same puzzle is, from a lot of data center operators perspective, they want to show their systems fully utilized, right? Like there's demand for the system, so we should build an even bigger system and a better system. When it comes to energy consumption.

That's probably not the best way to go, because that means that those systems are sitting there probably doing inefficient things. Maybe even idling a lot of time, right? Like a user allocated the node, but it's just sitting there doing nothing, right? It may be more useful instead of thinking about, like, how much is the system always being utilized?

But think about how much, how much computation or how many jobs or whatever your, like, utilization metric is, do I get, like, per unit energy, right? And you may think about how much, or per unit carbon, right? And you may also think about, like, how much energy savings can I get by doing things like shutting down nodes when they're unlikely to be utilized and more about like having a dynamic capacity, right?

Like full tilt. I can use I can do how many flops or whatever, right? But I can also scale that down to reduce my idle power draw by, you know, 50 percent in low demand conditions. And if you have that dynamic capacity, you may actually be able to get even more throughput. But it's with less energy because when there's no demand, I'm like shutting,

I'm like scaling down my data center, right? And then when there's demand, I'm scaling it up. But these are things that are requiring cultural changes in data center operations to happen.

Chris Adams: I'm glad you mentioned this thing here because, Dawn, I know that you had some notes about, it sounds like in order for you to do that, you need, you probably need different metrics exposed or different kinds of transparency to what we have right now.

Probably more actually. Dawn, can I give you a bit of space to talk about this? Because this is one thing that you told me about before and it's something that is actually touched on in the paper quite a few times actually.

Dawn Nafus: Yeah, I mean, I think we can notice a real gap in a way between the kinds of things that Charles brings his attention to, and the kinds of things that show up in policy environments, in responsible AI circles, right, where I'm a bit closer, we can be a bit vague, and I think we are at the stage where, at least my read on the situation, is that, you know, there's, regardless of where you sit in the debates, and there are rip roaring debates about what to do about the AI energy situation, but I think transparency is probably the one thing we can get the most consensus on, but then, like, just back to that, what the heck does that mean? And I think we need a little, like a, more beats than are currently given to actually where, what work are those measurements doing?

You know, some of the feedback we've gotten is, you know, "well, can't you just come up with a standard?" Like, what's the right standard? It's like, well, no, actually, if data centers aren't standard, and there are many different ways to build a model, then, yes, you can have a standard as a way of having a conversation across a number of different parties to do a very specific thing, like for example, Charles's example, you know, suggested that if we're charging on a per energy basis, that changes a whole lot. Right? But what you can't do is to say, this is the standard that is the right way to do it, and then that meets the requirement, because that's, you know, what we found is that clearly the world is far more, you know, complicated and specific than that.

So, I, you know, I would really encourage the responsible AI community to start to get very specific very quickly, which I don't yet see happening, but I think it's just on the horizon. 

Chris Adams: Okay. Well I'm glad you mentioned about maybe taking this a little bit wider 'cause we've dived quite spent a lot of time talking about this paper, but there's other things happening in the world of AI actually, and I wanna give you folks a bit of space to kind of talk about anything that like, or things that you are, that you would like to kind of direct some attention to or you've seen that really you found particularly interesting.

Charles, can I give you some space first and then give Dawn the same, to like say it to like I know, either shout out or point to some particular things that, if they've found this conversation interesting so far, what they might want to be looking at. More data.

Charles Tripp: Yeah. I mean, I think, both in like computer program, computer science at large and especially in machine learning, we've kind of had an attitude, especially within deep learning within machine learning, an attitude of throwing more compute at the problem, right? And more data. The more data that we put through a model and the bigger, the more complicated the model is, the more capable it can be.

But this brute force approach is one of the main things that's driving this increasing computing energy consumption. Right? And I think that it is high time that we start taking a look at making the algorithms we use more energy efficient instead of just throwing more compute. It's easy to throw more compute at it, which is why it's been done.

And also because there hasn't been a significant like material incremental cost of like, Oh, you know, now we need. Twice made GPUs. I don't big deal. But now we're starting to hit constraints because we haven't thought about that incremental energy costs. We haven't had to, as an industry at large, right?

Like, but 

now it's starting to be like, well, we can't build that data center because we can't get the energy to it that we need to do the things we want to do with it because we haven't taken that incremental cost into account over time, we just kind of ignored it. And now we hit like the barrier, right? 

And so I think thinking about, the energy costs and probably this means investing in more finding more efficient algorithms, more efficient approaches as well as more efficient ways to run data centers and run jobs. That's gonna become increasingly important, even as our compute capacity continues to increase.

The energy costs are likely to increase along with that as we use more and more, and we need create more generation capacity, right? Like, it's expensive at some point where we're really driving that energy production, and that's going to be increasingly an important cost as well as it is now, like, starting to be a constraint to what kind of computing we can do.

So I think investing in more efficient approaches is going to be really key in the future. 

Chris Adams: There's one thing that I, that I think Dawn might come in on this actually, is that, you're talking about, it seems that you're talking about having more of a focus on surfacing some of the kind of efficiency or the fact that resource efficiency is actually going to be something that we probably need to value or sharpen, I mean, because as I understand it so far, it's not particularly visible in benchmarks or anything like that right now, like, and if you have benchmarks deciding, what counts as a good model or a good use of this until that's included. You're not going to have anything like this. Is that the kind of stuff you're kind of suggesting we should probably have? Like, some more recognition of, like, or even like, you're taking at the energy efficiency of something and being that thing that you draw attention to or you include in counting something as good or not, essentially.

Dawn Nafus: You know, I have a particular view of efficiency. I suspect many of your listeners might, as well. You know, I think it's notable that at the moment when we're seeing the, you know, the the model of the month, apparently, or the set of models of DeepSeek has come onto the scene and immediately we're starting to see, for the first time, you know, a Jevons paradox showing up in the public discourse.

So this is the paradox that when you make things more efficient, you can also end up stimulating so much demand... 

Chris Adams: Absolute use grows even though it gets individually more efficient.

Dawn Nafus: Yeah, exactly. Again, this is like this topsy turvy world that we're in. And so, you know, now the Jevons paradoxes is front page news, you know, my view is that yes, you know, again, we need to be particular about what sorts of efficiencies are we looking for where and not, you know, sort of willy nilly, you know, create an environment where, which I'm not saying you're doing Charles, but you know, what we don't want to do is create an environment where if you can just say it's more efficient, then, somehow, you know, we're all good, right. Which is, you know, what some of the social science of Energy Star has actually suggested that, that stuff is going on. 

With that said, right, 

I am a big fan of the Hugging Face Energy Star initiative. That looks incredibly promising. And I think one of the things that's really promising about it, so this is, you know, you know, leaderboards when, you know, people put their models up on Hugging Face. There's some energy measurement that happens, some carbon measurement, and then, you know, leaderboards are created and all the rest of it. And I think one of the things that's really good at, right, I can imagine issues as well, but you're A, you know, creating a way to give some people credit for actually looking. B, you're creating a way of distinguishing between two models very clearly, right? So in that context, do you have to be perfect about how many kilowatts or watts or whatever it is? No, actually, right? Right? You know, you're looking at more or less in comparable models. But C, it also interjects this kind of path dependence. Like, who is the next person who uses it? Right?

That really matters. If you're setting up something early on, yes, they'll do something a little bit different. They might not just run inference on it. But you're, changing how models evolve over time and kind of steering it towards even, you know, having energy presence at all. So that's pretty cool to my mind.

So I'm looking forward to... 

Chris Adams: Cool. We'll share a link to the Hugging Face. I think they, I think, do you know what they were called? I think it's the, you might be, I think it's, it was initially called the Energy Star Alliance, and then I think they've been told that they need to change the name to the Energy Score Alliance from this, because I

think it, Energy Star turned out to be a trademark, but we can definitely add a link to that in the show notes, because, these, this actually, I think it's something that is officially visible now. It's something that people have been working on late last year, and now there is, we'll share a link to the actual GitHub repo, to the code on GitHub to kind of run this, because this works for both closed source models and open source models. So it does give some of that visibility. Also in France, there is the Frugal LLM challenge, which also sounds similar to what you're talking about, this idea of essentially trying to emphasize more than just the, you know, like to pay a bit more attention to the energy efficiency aspect of this and I'm glad you mentioned the DeepSeek thing as well because suddenly everyone in the world is an armchair expert on William Stanley Jevons paradox stuff.

Everybody knows! Yeah. 

Dawn Nafus: Actually, if I could just add one small thing, since you mentioned the Frugal effort in France, there's a whole computer science community, sort of almost at a step's length from the AI development community that's really into just saying, "look, what, you know, what is the purpose of the thing that I'm building, period."

And even, and that, you know, frugal computing, computing within limits, all of that world really about how do we get, you know, just something that somebody is going to actually value, as opposed to, you getting to the next, you know, score on a benchmark leaderboard somewhere. so I think that's kind of also lurking in the background here.

Chris Adams: I'm glad you mentioned this, what we'll do, we'll add a we'll add links to both of those and, you immediately make me think of, there is this actual, so we're technologists mostly, the three of us, we're talking about this and I work in a civil society organization and, just this week, there was a big announcement, like a kind of set of demands from civil society about AI that's being shared at the AI Action Summit, this big summit where all the great and good are meeting in Paris, as you alluded to, next week to talk about what should we do about this? And, they, it's literally called Within Bounds, and we'll share a link to that. And it does talk about this, like, well, you know, if we're going to be using things like AI, what do, we need to have a discussion about what they're for. And that's the first thing I've seen which actually has discussions about saying, well, we should be actually having some concrete limits on the amount of energy for this, because we've seen that if this is a constraint, it doesn't stop engineers.

It doesn't stop innovation. People are able to build new things. What we should also do is we should share a link to, I believe, Vlad Coraoma. he did an interview with him all about Jevons paradox a few, I think, late last year, and that's a really nice deep dive for people who want to basically sound knowledgeable in these conversations on LinkedIn or social media right now, it's a really useful one there as well. 

Okay, so we spoke a little bit about these ones here. Charles, are there any particular projects you'd like to kind of like name check before we start to wrap up? Because I think we're coming up to the hour now, actually.

Charles Tripp: I don't know, not particular, but I did mention earlier, you know, we published this BUTTER-E data set and a paper along with it, as well as a larger one without energy measurements called BUTTER. Those are available online. You can just search for it and you'll find it right away. I think, if that's of interest to anyone hearing this, you know, there's a lot of measurements and analysis in there, including, you know, all the details of analysis that I mentioned where we, had this journey from number of compute cycles to, like, amount of stall, in terms of what drives energy consumption. 

Chris Adams: Ah, it's visible so people can see it. Oh, that's really cool. I didn't realize about that. Also, while you're still here, Charles, while I have access to you, before we did this interview, you mentioned, there's a whole discussion about wind turbines killing birds, and you were telling me this awesome story about how you were able to model the path of golden eagles to essentially avoid these kind of bird strike stuff happening.

Is that in the public domain? Is something, can we link to that? That sounded super cool. 

Charles Tripp: There's several, papers. I'll have to dig up the links, but there's several papers we published and some software also to create these models. 

But yeah, I worked on a project where we looked at, we took, eagle biologists and computational fluid dynamics experts and machine learning experts.

And we got together and we created some models based off of real data, real telemetry of tracking, golden eagle flight paths through, well, in many locations, including at wind sites, and match that up with the atmospheric conditions, the flow field, like, or graphic updrafts, which is where the wind hits, you know, like a mountain or a hill and it, some of it blows up.

Right. And golden eagles take advantage of this as well as thermal updrafts caused by heating at the ground. Right. Causing the air to rise to fly. Golden eagles don't really like flapping. They like gliding. And because of that, golden eagles and other soaring birds, their flight paths are fairly easy to predict, right?

Like, you may not know, like, oh, are they going to take a left turn here or right turn there, but generally they're going to fly in the places where there's strong updrafts and using actual data and knowledge from the eagle biologists and simulations of the flow patterns, we were able to create a model that allows wind turbines to be cited and also operate, right?

Like, what, under what conditions, like, what wind conditions in particular and what time of year, which also affects the eagles' behavior, should I perhaps reduce my usage of certain turbines to reduce bird strikes? And in fact, we showed that it could be done without significantly, or even at all, impacting the energy production of a wind site.

You could significantly reduce the chances of colliding with a bird.

Chris Adams: And it's probably good for the birds too, as well, isn't it? Yeah.

Alright, we definitely need to find some links for that. That's, going to be absolute catnip for the nerdy listeners who put, who are into this. Dawn, can I just give you the last word? Are there any particular things that you'd like to, I mean actually I should ask like, we'll add links to like you and Charles online, but if there's anything that you would draw people's attention to before we wrap up, what would you pay, what would you plug here? 

Dawn Nafus: I actually did want to just give a shout out to National Renewable Energy Lab, period. One of the things that are amazing about them, speaking of eagles, a different eagle is, they have a supercomputer called Eagle. I believe they've got another one now. It is lovingly instrumented with all sorts of energy measurements, basically anything you can think to measure.

I think you can do it in there. There's another data set from another one of our co authors, Hilary Egan, that has some sort of jobs data. You can dig in and explore like what a real world data center job, you know, situation looks like. 

So I just want to give all the credit in the world to National Renewable Energy Lab and the stuff they do on the computing side.

It's just phenomenal.

Chris Adams: Yes, I think that's a really, I would echo that very much. I'm a big fan of NREL and the output for them. It's a really like a national treasure Folks, I'm really, thank you so much for taking me through all of this work and diving in as deeply as we did and referring to things that soar as well, actually, Charles. I hope we could do this again sometime soon, but otherwise, have a lovely day, and thank you once again for joining us. Lovely seeing you two again.

Charles Tripp: Good seeing you.

Chris Adams: Okay, ciao!  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.  



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]]> The Week in Green Software: Transparency in Emissions Reporting The Week in Green Software: Transparency in Emissions Reporting Thu, 27 Feb 2025 07:00:00 GMT 53:17 x06lrpv0 false https://podcasts.castplus.fm/e/mn4xlrwn-the-week-in-green-software-transparency-in-emissions-reporting 68dc7d1709b1c365e49ab8f6 68dc7cef46a2532cdd8d1c23 98 For this episode of TWiGS, Chris and Asim discuss the latest developments in emissions reporting, AI energy efficiency, and green software initiatives. They explore the AI Energy Score project by Hugging Face, which aims to provide an efficiency benchmark for AI models, and compare it with other emissions measurement approaches, including the Software Carbon Intensity (SCI) for AI. The conversation also touches on key policy shifts, such as the U.S. executive order on AI data center energy sourcing, and the growing debate on regulating the data center industry. Plus, they dive into the Beginner's Guide to Power and Energy Measurement for Computing and Machine Learning, a must-read for anyone looking to understand energy efficiency in AI. 

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TRANSCRIPT BELOW:

Asim Hussain: There's this assumption out there that we're trying to hunt for the right, true essentialist value of measurement, and it really isn't like that 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello, and welcome to another edition of This Week in Green Software. I'm your host, Chris Adams. Today, we're tackling an ongoing conversation in software today, predicting, measuring, and accurately reporting emissions data, particularly in AI. And as AI adoption skyrockets, so does its energy footprint.

Putting pressure on data infrastructure and sustainability goals. So today we'll be looking at a few new reports, what's going on, and generally doing a kind of roundup of the news and recent events along this. Because it's not all doom and gloom, although there is some. I'm also joined today by my friend and frequent collaborator, Asim Hussain.

Asim, can I give you some space to introduce yourself before we do our weekly, well, semi weekly, news roundup?

Asim Hussain: Not so weakly, anymore. Yeah. Hi. I'm Asim Hussain. I'm the Executive Director of the Green Software Foundation. So we are a standards organization and our mission is a future where software has zero harmful environmental impacts. And you might not be surprised to hear that we believe one of the best paths forwards is developing standards through consensus of multiple organizations.

Because through setting those standards, you can direct billions of dollars into the right places. And if you do it wrong, you can direct billions of dollars into the wrong places. So let's do it right.

Chris Adams: Okay. Thank you for that Asim. If you're new to this podcast, my name is Chris. I'm the director of technology and policy now at the Green Web Foundation, which is not the same as the Green Software Foundation. It's a small Dutch nonprofit, although we are members, founding members of the Green Software Foundation, along with a number of other much, much larger technology giants.

And I'm the host of this podcast and I'll also be doing my best to compile all the links and stories that we have so that if there's anything that has caught your interest as you listen to this, possibly whilst you're washing your dishes, you've got something to follow up with later. Alright!

Asim Hussain: Is it time for my yearly apology for naming it the Green Software Foundation and causing this constant confusion? 

Chris Adams: I think it might be, but sometimes it works in our favor as well, because when people speak to us, like a scrappy startup, a scrappy kind of wacky little non profit, then they say, "oh, we've heard a bunch about you folks. Oh, we thought you were bigger," you know, so it's, we do have, it opens interesting doors. We sometimes do, I have had the odd conversation where people thought I was the Green Web Foundation.

Yeah. So this is,

Asim Hussain: Yeah, let's wear the hats that benefit us at any given moment. 

Chris Adams: Pretty much, yeah. So this is what we're going to have and I think that we are doomed to have this mix up and the fact that we are speaking to each other on a regular basis probably doesn't help us, actually. Maybe we should, I don't know, have some big dramatic fallout or something.

Asim Hussain: Oh yeah, let's do like a fake fallout on the internet, yeah.

Chris Adams: We're not that keen for engagement, are we, mate? Let's not do that, alright? Okay. So, I was going to ask if you're sitting, comfortably, Asim, but I can see that you're on a standing desk, so I think you're now standing comfortably, presumably, right?

Asim Hussain: At attention.

Chris Adams: All right, well in that case, shall we start and look at the first story and then see where we go from there?

All right, so the first story that's kind of shown up on the radar is the AI Energy Score from Hugging Space. Sorry, Hugging Face, not hugging space, god. Yeah, so this is, this is actually essentially a project that is being spearheaded by folks at Hugging Face, but with also involvement from companies you've heard of like Salesforce and so on, to essentially work out something that might be a little bit like an Energy Star for AI.

Now, you probably, it's probably not called Energy Star because Energy Star is a trademark, but the general idea is, essentially, if we're going to have various AI models and things, then we should be thinking about them being efficient, and there are tools available to make this possible, actually. Asim, I know you had a chance to look at some of this, and you've had quite a few conversations with Boris Gamazaychikov the at Salesforce.

They're the kind of one of the AI leads. I'm mentioning Boris because he's quite involved in the GSF. There are lots of other people involved with the Hugging Face project, but Boris is the person who we know, so that's why we've got that named.

Asim Hussain: He's not, he's, so just to be clear like he's not a member Salesforce is not a member of the green software foundation. But yeah, I've just been chatting to boris obviously because we want to, one of the things we try and do is chat to everybody who's doing something in the AI measurement space so that we can at least try and coordinate and have like a common voice.

That's kind of one of the one of the things that we've been doing. Yeah

Chris Adams: Cool, and if I understand it correctly, we'll share a link to both the GitHub.io, the kind of public facing site with all this information about how the Energy Score project is working, plus the leaderboard, which has various closed and open source models. It's actually showing how efficient they are at performing particular tasks.

We'll also share a link to the GitHub repo, which actually shows how it's made because it's using tools that you may have heard of if you've ever messed around with AI models yourself. So it's using Code Carbon, which is pretty much the default tool that people use to work out the environmental footprint of a training run or anything like that.

And I believe the set, the other tool is Optimum or optimal Benchmark. I can never remember, but these two

Asim Hussain: that the actual benchmark tool? That's the thing that actually runs the benchmark, yeah.

Chris Adams: Exactly. So this is not like wacky stuff. This is stuff that you probably should have heard of or you are likely to come across, to see. And there is actually a Docker container for people who aren't able to publish their entire open models, with the idea being that you can run some of this

behind the file, as it were, so you can then share some of the numbers back And, Asim, I can't, while I've got you, I wanted to ask you about this because I know that the, I've been kind of tracking the AI Energy Score project for a few months, but I know there was some work inside the GSF to create a Software Carbon Intensity for AI

Asim Hussain: Oh yeah.

Chris Adams: these aren't competing, but they do overlap and maybe you could actually share a little bit more to explain what these two things are or even what is this SEI for AI in this context.

Asim Hussain: And there's also others as well. So we're talking Sir Joseph, the head of R&D is also sitting in with meetings at the ITU, International Telecoms Union, and so they're working on work themselves. There's EcoLogits from Samuel Rice. There's, there's other ones as well. And I probably just want to preface this by saying something, and I'm going to try and put some words to these thoughts.

I've internalized a lot of how I think about measurement and through conversations with others, I just want to make sure, 

I want to try and get my point across, which is there isn't one true way of measuring everything. It's not like there's one winner and one loser. 

What it is, is that different measurement systems have different trade offs. They incentivize certain things, they disincentivize other things, they have broader scopes and narrower scopes. And one of the things I've realized is you, it's almost impossible to create a measurement system which ticks every single box. Like it's almost impossible to have a measurement system which has the ability to measure like a broad spectrum of stuff and yet still also be consistent and repeatable and all these other areas, all these trade offs.

So yeah, I love AI Energy Score but there's also other ones as well. I just want to preface it by saying every single measure is designed for a particular audience and a particular problem. And I think that's kind of like one of the one of the ways I like to talk to people about it because I do get concerned that people they're always, 

there's this assumption out there that we're trying to hunt for the right true essentialist value of measurement and it really isn't like that

 so take all of my feedback on everything just with that, you know that context in mind yeah, so I think and I think that's kind of like one of the one of the ways that we look at it.

So what's really good about the AI, do you want me to talk about it? You know a

Chris Adams: please do. Yeah, 

Asim Hussain: I mean

Chris Adams: I'm listening to more because I'm, I've got some things to share, but I'm, I haven't heard that much about this. And I haven't been, and I know that the GSF had these workshops going on where people have been exploring this stuff. And I haven't been in those, but I suspect I know you've been in beside them.

And I suspect there've been some good, interesting conversations as a 

can't 

result.

Asim Hussain: I can't

dive too much deep into it because we're still in progress and we had the agreement not to, you know, give too much information about in-progress stuff.

So if someone has a crazy idea, we're not going to publish it and We'll allow people to have these private conversations But I think there's some stuff I can share that one of the things that's come out from our conversations is there's a really, almost one of the most strongest feelings from the group is for a measure that really has a broad scope for a lot of different AI systems, but also for the breadth of the AI life cycle as well.

So, you know, not just inference and also not just training, but also like, the model as it's deployed in an infrastructure. So it's an end to end computation that includes everything across the chain from edge devices all the way over to data preparation. And so there's various scores, so for instance, there's something called the green, the Green AI Index from the Responsible AI Institute, which is also another measure, and that kind of focuses on a pretty broad spectrum.

There's AI Energy Score, which is excellent because it is focusing on just the model itself. And so when you think of the life cycle, it's not like, it's not gonna, it's just focusing on the model. And they've made, they've done a great job of making it a type of measure, which is consistent and repeatable. And they've done that by, you know, you've got your model. Here's our, here is the benchmark you run. You have no, you've got to run this benchmark. Yeah. you also have to run it on this particular hardware because you can't just get a better score by just running on a better hardware. You want to try and measure the model.

Like you've got to, you've got to, you've got to turn variables into constants to kind of get some sort of measure from that perspective and it's really interesting related to the next thing I'm going to talk about the beginner's guide to, it's a report that's coming out because they, I think they did a really good job they're trying to summarize different types of measurements and I think they put it as a system measurement was kind of very big picture It's kind of what one of the things I think maybe where the SCI for AI is going to be talking about. Then they're kind of job/application specific measurements where you kind of make more of those variables constants.

And then there's kind of what we call a code measurements, which are I want to measure, you know, the emissions of this piece of code. In order to do that, you really need to turn a lot of other variables into constants, so you can know that if you turned a for loop into a while loop, what the actual, like, impact would be.

And where I'd say AI Engine Scores is in terms of that taxonomy, it lands more on the code one. But I'm not saying that's a, I'm saying that is the only way you can get something that is consistent where you can actually have a model that, that, you can really give a score to. And it does incentivize a lot of things.

It incentivizes a lot of the almost code based

patterns to improve model efficiency. But it, because of the way it's worked, it won't incentivize other things. Like, it won't incentivize running compute in cleaner regions. Yeah, cause,

Chris Adams: different kinds of energy, or different cooling, for example, you're only looking at the, just the code part specifically.

Asim Hussain: And that's fine.

Yeah, that's fine. Because if you included that, then you wouldn't be able to have a measure that is going to tell you, okay, is Llama better than DeepSeek? They kind of just want to know that from a, you need to turn these things into variables. So, it's very good from that perspective. And I think it's one of the most advanced ones. It's the best one that does it's job. It does do it's job by being a, by, and they admit this, by having like a narrow bandwidth. 

Chris Adams: There's one card it uses, I think it's an NVIDIA H100. I'm, I believe it's that, but I'm not sure I would know an NVIDIA H100 if it was dropped on my feet, so I need to be very clear that I'm at the limits of my expertise when it comes to hardware there. Okay, and the other thing we should probably mention, though, that this was one of the projects that was announced at the AI Action Summit in Paris that happened earlier on, I believe this month, actually, which has all kinds of announcements, so, in Europe, there is a, I think two, I think it's a 200 billion, yeah, a 200 billion euro fund specifically for rolling out AI across Europe.

There was a something that was kind of like a European take on this whole ridiculous Stargate thing. A ginormous French data center thing. 

Asim Hussain: Yeah. 

 

Chris Adams: That was Macron giving him some me too. And there was even actually for civil society, there was 400 million euro fund to kind of try and get an idea of the unintended consequences or talk about how you might reign in some of the worst excesses of this new technology that's being kind of deployed in all these places, sometimes where you're asking for it, sometimes where you might not be asking for it.

Asim Hussain: So 0.2 percent of the 200 billion is for

Chris Adams: Yeah. It's 

Asim Hussain: the question of whether this is a

Chris Adams: It does speak volumes about our priorities, about who are we serving here, basically, I suppose, or whose needs are being prioritized when you have something like that. But yes, this is, this is some of the kind of ongoing conversations we, I guess, we actually have. there's just two things I just want to check because you used to, you mentioned a couple of projects that people might not be aware of that may be relevant for this conversation.

So you spoke about Ecologits, as I understand it, this is if you're using AI right now and you don't have a model, for example, I mean, you don't have like a whole training setup, you can use something like Ecologics to get an idea of inference. So that's, is that the case?

Asim Hussain: Yeah, I think, it does have a methodology as well. So you can actually just take their methodology and, I think he actually asked us to use the word estimate, but like, cause it's all not, none of this is direct measurements, right? So estimate the emissions of a model, but they also have like an API.

So if you have a named model you can call the API and it will kind of give you information about the, I do believe it's only carbon, it might be carbon and water, I can't quite remember, but it kind of gives you

Chris Adams: French, they have, there's like five specific impact, kind of impact factors. There's like water, ADP, like abiotic depletion, something like that. There's basically five things, and one of them is carbon, and one of them is energy, I believe. And this, you don't need to be, like, if you're already using Claude, or you're already using AI, OpenAI, this is just like a one Python package that essentially wraps the function calls you make to, to that API to get some of the numbers back.

So,

Asim Hussain: I don't think, I don't, I think Ecologist is just for models itself, I don't think it's for, 

Chris Adams: Oh no, it is for inference. because we, we put a funding bid to the European AI Act Implementation Fund, where they were basically looking for this stuff. And the thing we realized was that if you are, if you're not doing any training, but you're just doing inference, this is one of the Python packages that will give you an idea about the numbers.

But it is very much, 

Asim Hussain: inference only, 

Chris Adams: yeah, exactly, inference

Asim Hussain: That's one of the conversations, yeah. Like the biggest conversation we're having in this side for AI right now is to include training or not to include training. And like one of the things the AI Energy Score and Ecologits is that it doesn't include training. The Green AI Index does include training. And, you know, that's it's a very, It's a very, oh god, it's such a hard question, it's like so much nuance to it.

Chris Adams: Well, yeah, because if you're including training, then whose training are you including, right? So if I'm using, say, Llama, should I be saying, should some of Llama's footprint, which was training, and we know, should that be allocated to me, or should it not be? And like, we can point to existing protocols that like say maybe you should, but in this case maybe that isn't.

So yeah, this is an open question right now.

Asim Hussain: Well if you, this is where my brain is so stuck in this area. Because if like, if you include open sources. I want open sources models in yours. It doesn't incentivize the reuse of models. If you don't include an open sources, if you're saying it's open source, I'm not going to include it. You can be a company that just goes, "I open sourced this model so I don't have any emissions." So there's like so many different ways it can be. This is a very, hard question that we need to solve. I also think it's very interesting because it's a, I think it's, I think it's, the training question is.

I, I suspect us figuring out or getting consensus on the training question, a very nuanced discussion and conclusion to the training question will actually help in many, other areas of like, how do you actually measure software? Because I think it's, it's, such a difficult question to answer.

I think the solution will inform so many other areas as well, which are kind of slightly simpler.

Chris Adams: It's almost as if using generally accepted accounting practices first developed hundreds of years ago might not be all that useful for thinking about how you use open source models and open weight models in

Asim Hussain: yeah, advanced technology systems. 

Chris Adams: Okay,

Asim Hussain: it's something to do with cloning. Like, if you can clone something, a click of a button, you can't clone a chip. I don't know. I haven't got fully refined thoughts on this yet. So, let's move on.

Chris Adams: We'll wait with bated breath for these, the outputs from the workshops as you do them. All right. So. that gave us a lot of time to chat about that stuff. The other thing I'll just quickly name check for the AI Action Summit was there was a statement called within, the Within Bounds Statement.

I'll share a link to that. This was something that, actually my organization worked with or the organization I'm part of. So, Michelle Thorne, who's my colleague and normally sits next to me, she was working with 120 different civil society groups to basically lay out a set of demands to say, look, if we're talking about AI and we're allocating literally hundreds of billions of euros or dollars to this stuff, can we talk about what it's for and who's benefiting from this stuff?

We'll share a link to that because it's actually, in my view, quite well written and it does a very good job of actually talking about some of the issues that we might not be talking about all the time as people in industry to see how the rest of the world is actually like having to respond to some of this, I suppose.

So we'll share a link to that. But the juicy one now, Asim, is the one that you wanted to talk about, and that we both were nerding out a lot, was A Beginner's Guide to Power and Energy Measurement, an Estimation for Computing and Machine Learning. This is the next story that we have inside it, and I believe you've shared a link to the archive, the archive link for this pre print, because it's a really cool looking paper, and it's publicly available for everyone right now, but it might, I think it's going to be going to some journal, but I'm not quite sure, and figured But 

Asim Hussain: I thought it got published in the, in an NREL journal. I don't know. Maybe it's not maybe it's not in a real journal or maybe now that I understand how journals what journals are maybe doesn't really matter 

Chris Adams: So NREL here being the National Renewable Energy Labs of the United States of America. That's what NREL was in this case here. We've shared a link to it and, you did talk a little bit about why you like this, but can I give you a bit more space to talk about why you've enjoyed this? Because you don't need to be a beginner to actually appreciate this as far as I understand it, right?

Asim Hussain: No, it goes into a lot of detail. I mean, it says beginner, I'd say it goes a beginner's guide. Probably a little bit of imposter syndrome there, because I'd actually call it, like, it's very well written, so a beginner could start it, but I think it goes into very advanced topics that not many people know at all.

So, I think it goes from beginner to advanced. Yeah, I'm quite proud, Akshaya is the lead author of it, and Dawn Nafus is there, these are two people I worked very closely with at Intel. Very proud of this piece of work from them and the people, people over there. I share this with my team, so we're all working on kind of like thinking about how to measure energy.

And it's just exciting to see, just see how her and everybody else kind of rationalize this all into a very easy to understand, you know, set of concepts. As I said before, like they, they, you know, the first thing they go through to try and come up with this taxonomy, you know, are you measuring for a system?

Are you measuring for a job or are you measuring for code? And I think they've done a really good job of trying to like explain the difference they talk about are you measuring directly versus are you measuring versus proxies? I love the fact that she even goes down and said, there's this idea that we have is there's I always say like everything's a model like you can't, there's actually no such thing as direct measurements.

There's just a very advanced model. and she even goes down into, you know, even if you're using a watt meter and not against a wall, you've actually really got to consider like many of the rare areas because you've got to calibrate it. If you don't calibrate it, it's not going to really go, you calibrate a model, right?

It's not going to like, you know, actually turn out the right numbers and gives you a lot of cautionary tales, you know, where, what to think through. And it really just goes into just a lot of these. I don't know if it's worthwhile going into all of it, but there's just a lot of detail about the things to consider, you know, idle power draw, you know, not only that, but like when you run things, when you run, we always knew that like, it was challenging to measure when you're on shared infrastructure, but then they go into like other details, which is like, it gets even more challenging because the, like, the information you're getting from the socket might actually contain information from the energy draw from the memory and it's hard to, like, disambiguate all of this stuff.

There's ways in which, if you're accessing memory, it increases the idle power of a CPU. There is so much great information here, and a lot of little tips as well.

Chris Adams: Yeah, I think I would agree. It's if you are a beginner, there is some stuff that you can take away, but there is a lot of depth inside this. It's, I actually really enjoyed it too. I enjoyed reading it so much that, actually Dawn sent me, she emailed, I think, emailed me at the beginning of this year, actually, saying, "hey Chris, Check out this cool paper" and I really enjoyed reading it and we were going to do an interview.

We've actually got an interview lined up with Dawn Nafus and one of the other authors, Charles Tripp, who was writing for this. And I believe was at NREL and then has left NREL because, 

Asim Hussain: because? 

Chris Adams: of yeah, basically, this was the way that we could actually get some people speaking about it.

Because since we've had a change in administration, if you're a federal employee it's much, much more for you, difficult for you to talk about anything relating to, well, sustainability and technology, which is a real shame, especially when, like, it's useful to be able to draw upon expertise for people who do this kind of stuff, right?

So, maybe that's a question we should ask ourselves, like, are we okay with the people we're asking of these questions to not be able to talk to the public about this kind of stuff? But, what we do have, but to go back to the actual paper. I agree with you. I found it really, useful and this hierarchy of interventions was really useful because one of the key things that it kind of highlighted was basically where you have some control and where you don't have some control and give you a real chance to actually say, well, if I'm not able to do this, what, and what are my options?

If I'm still trying to make a meaningful and measurable, yeah, change. Because in many cases, you do have to think about some of the trade offs. The things you might do at a data center level to make some parts maybe slightly more energy efficient or maybe more carbon efficient can have knock on effects elsewhere, for example, further down the kind of, the list,

like further down the chain, basically. And this is what they do talk about. It's a really fun read if you're interested in AI. There's so much depth and the nice thing is the thing that one thing that's really quite nice about NREL specifically is that they've shared all the data to back up a bunch of this stuff.

So in the podcast interview that we have where we dive into this a bit more, we'll be showing there are some links to all the data sets that NREL was using when they were doing all these constant training runs to figure out what their, what the footprint of x might be and everything like that. So it's probably one of the most useful when open data sets we've seen for people who are trying to get

an idea about what the environmental footprint of using, I mean, AI directly, what the direct footprint of this might actually be.

Asim Hussain: I'd argue this is like a seminal piece, and you know, if there's like, I imagine this is going to be like essential reading for Green Software courses around the world. If you really want to like major software, you should this paper. 

Chris Adams: Awesome work. I don't work with Akshaya, but I guess, awesome work Akshaya and friends, for that, but probably not just for beginners. So please do not be turned off by the beginners part. It's definitely not just for beginners. There's loads there.

Asim Hussain: They probably put beginners in to make sure the beginners read it but advanced people might think "I already know" so I already know tdp so I don't need to know this.

Chris Adams: Yes, by TDP, you're referring to the Thermal density. Oh, what does it stand for? But that's

Asim Hussain: I thought it's thermal design power

Chris Adams: I think it might be actually you're right. This is the amount of power that gets used at certain amounts of utilization, right? So if I'm using the chip at maximum output, it's going to use this much power. But if it's only using half it's going to be something like that.

Asim Hussain: Yeah, but it's also like Akshaya that kind of opened my eyes to understanding kind of how these power curves, she goes into detail here like how those, you know, we hear about these power curves which tell you 10% utilization is this, that's 30% is this. If you, i'm not going to go into details if you read the paper and you realize how those power curves are made they are very rough estimates of what it like looks like, you know, like you don't really know you don't really, you just, there's no register which is telling you I'm 50% percent like, you're just seeing how much throughput, you're just seeing how much you, basically... 

Should I go into it? You basically chuck like a benchmark at it and you keep on hitting you keep on going like okay, dude, it was a website benchmark. Okay, do one hit per second.

Okay, it's fine. You keep on doing it until the benchmark can't go any higher and it's now like 500,000 page views a second. "Okay, I can't seem to do more than 500,000. I must be at 100 percent utilization." That's how that calculation works. And then you think to yourself, "Okay, what does 90% utilization mean?"

If I did 500,000, I'm just going to do 450,000 requests. And that's like the approximated idea of what 90% utilization means. But, what it really kind of ends up meaning is that it, the, it depends on the benchmark because an AI benchmark will have a different energy consumption, your pseudo 90% than a database benchmark, than this benchmark.

When you actually look at the big benchmark providers like, Esper, CERT and all these other ones, they're collections of different types of applications. And the power curve is the average of those. Which is why, like, if you know you're running, and that's why if you're using like a power curve based over, that's what I think it's saying, if you're using a power curve based off of a CERT benchmark, and you're saying that's what your AI consumption is, it might not be.

You really want a power curve which has been generated just by running a, an AI workload. Because the AI workload might just trigger different parts of the chip in different ways. It's very complicated. Yeah, and it, so, it's one of the things we were like, talking about, It's actually one of the reasons I kind of really like the way Kepler works.

Because Kepler, 

Chris Adams: Sorry, I'm going to you there. before you go on this, the reason it's, I'm actually, the reason I'm quite happy to give some space for this, is that people who have listened to this might not know that you were literally working at Intel trying to figure this stuff out when you were doing a bunch of the green software stuff, so it's okay, listen, you know, I, like, you do have some prior art in this stuff, right?

Asim Hussain: Yeah. Yeah, we're basically diving into all this stuff. And I kind of learned so much while I was over there. How Kepler works is quite interesting. Is, So Kepler is this kubernetes based system which does a whole bunch of things but one really intelligent thing it does is it tries to figure out what your energy consumption is from the actual stuff that's running on the chips that you're running on. So it has like a machine learning model that, I think it's got, I think it's got some, if you start off Kepler with nothing and it doesn't know anything it will tell you energy numbers but it kind of learns and improves and fine tunes itself based upon A, your actual chips, B, how your chips were configured, C, what you're actually running on your chips.

So you kind of get a more accurate power reading from Kepler. One of the things I think would be great for them to do is to kind of just take that out of Kubernetes. And, because that doesn't necessarily need to be a Kubernetes piece, but it's baked into that infrastructure. Because that would be generally useful everywhere. Yeah.

Chris Adams: We will share links to both of those, and Asim, you're able to find a link for some of this power curve nerdery, that would be very, helpful, because I do know...

Asim Hussain: This paper's got it, yeah.

Chris Adams: Well, okay, in that case, we'll use that, because I do know that, well, some of the work I'm doing outside of being on podcasts with you, for example, I'm aware of, like, there are people putting together procurement guidelines where they speak specifically about this kind of stuff like please tell us what the figures are going to be for this power curve based on these ideas here and being able to refer to some of the actual literature is actually very helpful for people to understand why a government buyer might be asking for this stuff and why that's being used as one way to figure out some of the environmental footprints of the use of digital services.

All right, we'll add some links to that one and then we'll see what we're doing for time. Can I share one? I want to share a story from me. So this one, this is actually, it's not so much about, it kind of is about technology. This is actually an executive order from the USA called Advancing United States Leadership in Artificial Intelligence Infrastructure.

We've shared a link to this and the reason I shared this is because I think it's actually because I work in the policy working group inside the GSF and because we speak a lot about the carbon intensity of power and stuff like that. It's often quite rare to find really good, quite well written and detailed examples of kind of policy.

And this is one that, for a short, beautiful short period of days, was actually publicly available. So this was, I think,

Asim Hussain: I see the link is, oh, no, it's a real link. No, it is way back machine.

Chris Adams: It's webarchive.org, whitehouse.gov, briefing room, presidential actions, on the 14th of January. Just before the new guy came in, there was an executive order all about essentially, deploying AI, and this was specifically about if you're going to deploy AI on public land, what, and in the US

there's lots and lots of federally owned public land, what kind of criteria do you actually want to require as condition of people being able to put things on your land like this? So just the same way that people who have private land, they can say, you can run a datacenter here, as long as you do X, Y, and Z.

This pretty much lays out, okay, here's what you should be looking for. And this stuff includes a bunch of really, in my view, interesting and like very insightful and incisive policy, pieces of policy inside this. So when we talk about the carbon intensity of power, we've spoken before on this podcast multiple times about how in the hydrogen sector, we already have a very rigorous way of talking about how energy can really be green.

And done a recent podcast interview with Killian Daly from EnergyTag talking about this idea, like three pillars, the idea that energy has to be timely. So you can't have power at night being greened with like solar because they're two separate times a day. Deliverable, like you need to be able to have the generation on the same grid as you're consuming from because otherwise it's not very convincing that it's really powering it. And additional, you need to have new power coming in. This literally is name checking every single one of these inside this. Like the actual wording they use

Asim Hussain: in terms of power, in terms of more generally applying that

Chris Adams: this is specifically for data centers. So if all data centers are like, I'll read some of the kind of quotes from this. Basically, like, as part of ongoing work, the Secretary of Defense and Secretary of Energy shall, Blah, blah, blah, blah, blah, will require concurrent like any AI data centers on a federal site will have procured sufficient new clean power generation with capacity value to meet the data centers needs.

And they've, literally explicitly said "has to be deliverable and has to be matched on an hourly basis." So those are the three things right there. They've actually been more explicit about additional elsewhere. So this is like the three things that already in place in other industries, for the first time, really laid out for how the, how you should be doing this for AI data centers.

So if you're a policymaker outside the USA, just copy this link. This is probably some of the best stuff of particularly relating to policy, to energy policy. When

Asim Hussain: it, but does it say, by the way, shall, you know, you know, the shall means, so just everyone who is listing, shall is a very important term. Shall in the standard space. I presume the policy

Chris Adams: You don't get not do to Basically what they're saying.

Asim Hussain: You gotta

Chris Adams: is mandatory if you want to things on federal land. Elsewhere, yeah,

Asim Hussain: should is different. The, the, so just to, the reason you're talking about, as I presume it's the what's mandated is clean energy. Or is what's mandated,

Chris Adams: yeah, sufficient new clean energy power generation is they use, they, and later on, they actually talk about what counts as clean energy in this because there's a bunch of stuff, it's quite a long executive order, and we've had this new guy come in power, who's basically, who's rescinded every other executive order, apart from this one, even though it's not visible, so there's some stuff inside this,

Asim Hussain: into this one. There's something which benefits, benefits something else.

Chris Adams: So there is the whole thing here about, for example, this does say, well, if we're going to have clean energy, we're going to call it carbon free, and we're going to talk about not just renewable, like wind and solar, they talk about, say, the deployment of nuclear, which America, in America, people tend to be more receptive to, or in some places at least. So there's a part, there's a part there. But they even talk about, say, if you're going to have fossil generation, it needs to be 90 percent carbon capture, right? Now, this is a very high bar to hit, because there, right now, there's basically nowhere in any kind of at scale operation which is hitting 90 percent capture of this.

So if you were to have gas and you were to have this is probably about as rigorous as you can reasonably ask. And if anyone is actually, in the year 2025, when we know all the science available to us, you're not saying something like this, got to ask, okay, who's captured, who is captured here?

Because that is a really, like, there, there is just, it's, you need to have this if you're going to be talking about the use of fossil fuels inside this. And really, you probably shouldn't be using fossil fuels at all anyway. But like, this is examples of, yeah, this is what policy does look like.

If you're going to do this, do this properly.

Asim Hussain: Yeah, but at the same time I think what we're seeing is, I mean, it's interesting that the up, I don't know if I've got time to go into it, but the uptime report talks about the, 

the increasing demands is forcing organizations to, you know, like you utility, there's so much demand from data centers.

It's not really a question of, you know, you've got to use clean energy. It's like, you don't have the energy or you now have to be a good place. You go to demand response. 

But there's also then driving up pressure for those organizations. They're kind of walking back a lot of the stuff previously and there's a lot of fossil fuel generation being thrown out.

Asim Hussain: I have not verified this at all, but today I saw something on my feed. Which said that, I don't like, anyway, which, which said that, Elon's, 

Chris Adams: You might be about the x.ai datacenter, the one in Memphis, running

Asim Hussain: in Memphis, there's gonna be, there's, like 15

Chris Adams: yes!

Asim Hussain: to power it. Which, you know, probably is because the utility said to him, "You're not putting an unbelievable load on our grid. We do not have the capacity for you." And he probably went, "ah, I'll build my own gas generators without asking anybody."

Chris Adams: There is a bit of a story behind this. So essentially, the, there was a datacenter, the x.ai datacenter was built very quickly by datacenter standards. And

usually, if you want to have power for a data center, you're going to have to wait some time if it isn't already available.

And, the, basically the approach that was taken was to essentially deploy a bunch of mobile gas turbines to provide the extra megawatts of power such that you could power that. Now the problem is these are really bad for local air quality. So you're shortening the lives of all the people who live around there, for a start, for the sake of this.

And, the other thing that, one of the reasons you're able to do this is because, they count as a mobile generators, they're not covered by the same clean air laws. So you wouldn't able to, yeah, exactly. So essentially this is stuff which has a real human cost, right? This is an already marginalized and kind of racialized community that it already has very bad air and has like elevated cases of asthma and all the stuff like that.

So there is a real human cost being paid here. And the decision has been made. "We're going to use this because we've decided that's more important than the lives of people around here." So, like, that's essentially what coming down to.

Asim Hussain: But also, I mean that, I'm guessing from the fact that this was an active executive order as a, you know, a few months ago that, that wasn't on federal land and therefore, or something like that must be, or

Chris Adams: This is somewhat separate. I mean, for a start, this, the, for the things, for the, xAI case in particular,

 you don't, any of the local air guidelines or the local air kind of, laws about air, about air quality, don't apply to mobile providers.

Asim Hussain: Oh, 

Chris Adams: providers.

Yeah. Yeah.

Asim Hussain: Even with this executive order, you can always get around it by just playing on mobile?

Chris Adams: So this was, this executive order came later. So we've had this things in xAI. That's been something that we saw last summer. All right. This was only published in January and they, and then it was literally on the White House website for seven days before the new guy came in and it down while pointing to the previous one.

And it's also worth bearing in mind that executive orders are not law. So even though someone can say they need to do this, that doesn't mean that it overrules existing law, example. So absent any other law, this is what you can ask for. And this is why they're able to say for federal law, this is the things we'd be doing.

There's actually a bunch of other really good stuff inside this, in particular, the air quality stuff. So the, as a contrast to saying, "It's okay to use this stuff. Who cares whose lives are shortened?" On the environmental justice, there's a whole piece in this about saying you, if you're going to deploy data centers in public land, then you need to have constant monitoring, all this visit, and have this visible everyone else to see as well.

So like these are the things that I think we don't see that you could totally take as examples away from this. And, they've also literally said. If you're going to deploy, you can't deploy in places which have had traditionally poor air quality below this, this air toxic, AirTox Screening. So basically, places which have already been harmed already, you don't get to deploy them in these places anymore.

And like, this is why I think this is actually quite well written stuff, because it does take into account all these things which we've had, which have been coming up again and again. So if you were trying to come up with some policy for deciding how you deploy, there is so much you can lift from this yourself, for your own corporate policies or anything like that.

Asim Hussain: There's very few benefits to a local community for having a data center built near you, there's very few jobs. There's like very, like, there's a couple of people walking around this giant warehouse and there's all, they've sucked all your electricity, and they, and there's, and you know. I don't know. The data center industry needs to, I was, it was fascinating to me when I was chatting, 

I was at an infrastructure conference last year and I was chatting to a gentleman, won't name his name, from the utility sector, and he was saying to me something very interesting. He was saying to me, he believes the data center industry, this is before, he who shall not be named apparently, entered office.

So, this is before that happened, but he was saying he thinks the data center industry is headed right towards full regulation the same way utilities are regulated. So if you want to do a power plant, you can't just go "Oh, it's gonna make me a lot of money. I'm gonna build a power plant here." You have to go through so many checks and balances.

Your profit is limited. Everything is limited. And he was saying based upon the conversations that are happening, you know, you're claiming that this technology is so fundamental to life and existence that it therefore is a commodity, therefore it's something that's you know similar to energy. Energy utilities can't just say "ah we're going to rack up our prices 40, 43 percent because everybody wants it." You've got to, they'd be regulated for that.

So he was really putting a very convincing argument to me that if the data center industry is not careful It's going to get regulated that way and then they don't want to get regulated that way. It's not fun, apparently. And so I think things like this really matter.

Chris Adams: Yeah.

Asim Hussain: Really do matter.

Yeah, you to think about it. If you're with a data center, you can't be, you can't not think about the impacts of the region that you're in. You've got to really put effort into where you need to be a positive net benefit to the place you're being installed, you know, locally as well.

Chris Adams: So this is actually one thing that, so what I think you're, the argument you're making is that if you can, if you're going to present yourself as a utility, something which is what foundational to everything running on, then you probably, maybe there, then you should expect utility style profits rather than SaaS style profits, Because the margins that you might see, when you're from certain, tech giant companies is like 30 percent for example. That's not the same as utilities might be looking at like around 10 to 15 percent for example. And you have different kinds of oversight being introduced.

So yes, this is a conversation that we might have. I suspect it might be longer than we have given the time we have available, but yes, this is something we might point to. Just following on from this, there's a, you did mention this, uptime report, Uptime Institute Report. We'll share a link to that as well.

And I think there is, we might be in a situation where we have a bit of a fight on our hands, or we might be seeing a fight taking place because we do see like in Europe, for example, where, which is probably, arguably, the place where you see fights around data center deployment the strongest. We've just seen new laws be published about what criteria you need to actually have if you're going to connect to the data centers.

This was published, I think, last week, and we'll share a link to that. Where, in contrast to what we've just talked about here, where the US policy was very clear and was very good, we now see, essentially, a guideline saying you can connect data centers to the grid, but you need to have your own generation and you need to integrate nicely with the grid, but there's no mention of climate change, no mention of local environment or anything like that.

This is literally going to likely incentivize even more on site fossil based generation for this, absent no other criteria being in place. So we might see this being challenged, but I think I agree with you. We currently do have this case where, yes, you got all this new technology being deployed,but there is the kind of, we have a fight where there's almost zero regulation and it doesn't feel like it's going to last. It, I can't see how

Asim Hussain: You don't think, you don't think the absence of regulation is gonna last?

Chris Adams: I think what's going to happen is that if you continue to go through this stuff, you're, what's probably going to happen is that you will end up with so much pushback that you will end up with much, much more heavy handed regular legislative responses to this. Because, right now, there's been this push to kind of, essentially, neuter any kind of meaningful science based or data informed discussion around this.

All that does is play into the hands of a much, much more, a much, much more dramatic response later on. So I think it's, if you want to deploy stuff, then this does feel kind of long term, not very helpful for them. But then again, there's a question about, do we need this, how much do we actually need to be deployed?

There's probably a democratic discussion to actually have about that.

Asim Hussain: Well, we haven't even spoken about DeepSeek and its impact on this whole question and kinda how it has, if I'm going back to that conversation, how that person utilities that their big question was. Because 

the data center providers, everybody's telling them we need a lot more energy in the future.

And they're going, "well, my God, do "we actually put the effort in to try and roll out this new capacity? And then only to find out on the day in the two years later like "ah, we got it wrong I'm sorry, we won't need that." They're asking the and I'm just a, they're asking the "is it BS?" question because they need really to figure it out and I was thinking okay, they might have just been convinced. Then DeepSeek comes along and Now you know everybody's asking the question "huh, will we need this capacity upgrade?" And now, as soon as DeepSeek came along, everybody said, "yeah, that's great. Now we are gonna do even more AI. We do, we definitely need the capacity, but now we can do more with it." And you're like, well, hang on. Because there is oftentimes a thing that goes on in, you have to create the hype to get the funding. You have to create the hype to get the funding. If you want to convince like investors to invest in your organization, if you want to convince them, you have to create the hype. And what DeepSeek's done is it's just popped it and I don't know how much it's popped it because only the investors know how much it's been popped.

But it's popped it and I wonder if it's really popped it quite significantly and whether we are going to see like a significant pullback. Is Stargate really going to happen or does it really not, really matter? They just want to hand money out to, it's just a reason to hand out 500 billion because you know, why not?

Chris Adams: We can share a link to, there's a good paper from Sasha Luccioni talking a little bit, and friends, about Jevons Paradox. I've actually written a blog post about this as well, particularly for DeepSeek, to kind of make this accessible for people who are trying to understand. Does this, is this going to reduce the footprint or is it going to increase the footprint?

Because there's a few different criteria you want to take into account. Just saying,

Asim Hussain: pops the bubble, it will decrease the footprint, I think.

Chris Adams: That's, this is the thing we can look into and decide. Because the flip side is that if this makes it more likely that they'll, you'll, if this lowers the barrier so that more people are able to use it in more places, that can lead to an absolute increase.

So there are different, there are two different, there are different ways and different takes on this and it's very much case of, okay, this is, yeah, this is one thing we'll show a link to. Asim, I think we've gone down a bit of rabbit hole so we should probably look at the events there's anything particularly we have here.

Asim Hussain: So, there's a couple events coming up.

There is the Practical Advice for Responsible AI on February the 27th at 6pm in London. , it's a UK event. And it's gonna be held in person in the Adaptavist offices, and it's gonna talk about green AI with Charles Humble and AI governance Team with Jovita Tam. There's the GSF Oslo meetup happening on again, February 27th at 5:00 PM. It is in person in the Accenture offices from 5 to 8 PM.

And they're going to talk about how to leverage data and technology to drive sustainability initiatives and enhance security measures, dive into green AI, obviously. There's going to be talks from Abhishek Dewangan and Jonny Mauland. I do apologize.

Chris Adams: Sorry,

Asim Hussain: Read them. I'm sorry, Johnny.

Sorry, Abhishek. Details in the podcast notes. And think that's it. I think I'll pass over to you Chris.

Chris Adams: Yeah. Okay, then. I think that takes us to the end of what we have for this. I assume if there's a particular free resource you would point people to right now on green software as a final thing, what would you point people to as a parting?

Asim Hussain: Oh, honestly, it's that beginner's guide. I don't know if it's I don't know if it's, it is very good, I read the Beginner's Guide to Power and Energy Measurement and Estimation for Computing and the last word.

Chris Adams: Wow, Akshaya better be getting a promotion after this, man. This is just like, this is, so yes, this, I agree. It was a really fun read. If you want to basically sound knowledgeable about AI, this is probably the most useful thing to read. And that's as someone who's written a report all about the environmental impact of AI ourselves, where we work.

All right, Asim, it's really lovely to see you again, mate. Thank you so much coming on. I hope the people who did listen to this were able to stay with us and we didn't go get too self indulgent. And if we did, please do tell us and we'll make sure that we don't do it too much next time. And otherwise I'll see you in one of the future episodes of This Week in Green Software.

Thanks, mate.

Asim Hussain: See you later, mate.

Chris Adams: Toodle oo!  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



Hosted on Acast. See acast.com/privacy for more information.

]]> For this episode of TWiGS, Chris and Asim discuss the latest developments in emissions reporting, AI energy efficiency, and green software initiatives. They explore the AI Energy Score project by Hugging Face, which aims to provide an efficiency benchmark for AI models, and compare it with other emissions measurement approaches, including the Software Carbon Intensity (SCI) for AI. The conversation also touches on key policy shifts, such as the U.S. executive order on AI data center energy sourcing, and the growing debate on regulating the data center industry. Plus, they dive into the Beginner's Guide to Power and Energy Measurement for Computing and Machine Learning, a must-read for anyone looking to understand energy efficiency in AI. 

Learn more about our people:

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TRANSCRIPT BELOW:

Asim Hussain: There's this assumption out there that we're trying to hunt for the right, true essentialist value of measurement, and it really isn't like that 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello, and welcome to another edition of This Week in Green Software. I'm your host, Chris Adams. Today, we're tackling an ongoing conversation in software today, predicting, measuring, and accurately reporting emissions data, particularly in AI. And as AI adoption skyrockets, so does its energy footprint.

Putting pressure on data infrastructure and sustainability goals. So today we'll be looking at a few new reports, what's going on, and generally doing a kind of roundup of the news and recent events along this. Because it's not all doom and gloom, although there is some. I'm also joined today by my friend and frequent collaborator, Asim Hussain.

Asim, can I give you some space to introduce yourself before we do our weekly, well, semi weekly, news roundup?

Asim Hussain: Not so weakly, anymore. Yeah. Hi. I'm Asim Hussain. I'm the Executive Director of the Green Software Foundation. So we are a standards organization and our mission is a future where software has zero harmful environmental impacts. And you might not be surprised to hear that we believe one of the best paths forwards is developing standards through consensus of multiple organizations.

Because through setting those standards, you can direct billions of dollars into the right places. And if you do it wrong, you can direct billions of dollars into the wrong places. So let's do it right.

Chris Adams: Okay. Thank you for that Asim. If you're new to this podcast, my name is Chris. I'm the director of technology and policy now at the Green Web Foundation, which is not the same as the Green Software Foundation. It's a small Dutch nonprofit, although we are members, founding members of the Green Software Foundation, along with a number of other much, much larger technology giants.

And I'm the host of this podcast and I'll also be doing my best to compile all the links and stories that we have so that if there's anything that has caught your interest as you listen to this, possibly whilst you're washing your dishes, you've got something to follow up with later. Alright!

Asim Hussain: Is it time for my yearly apology for naming it the Green Software Foundation and causing this constant confusion? 

Chris Adams: I think it might be, but sometimes it works in our favor as well, because when people speak to us, like a scrappy startup, a scrappy kind of wacky little non profit, then they say, "oh, we've heard a bunch about you folks. Oh, we thought you were bigger," you know, so it's, we do have, it opens interesting doors. We sometimes do, I have had the odd conversation where people thought I was the Green Web Foundation.

Yeah. So this is,

Asim Hussain: Yeah, let's wear the hats that benefit us at any given moment. 

Chris Adams: Pretty much, yeah. So this is what we're going to have and I think that we are doomed to have this mix up and the fact that we are speaking to each other on a regular basis probably doesn't help us, actually. Maybe we should, I don't know, have some big dramatic fallout or something.

Asim Hussain: Oh yeah, let's do like a fake fallout on the internet, yeah.

Chris Adams: We're not that keen for engagement, are we, mate? Let's not do that, alright? Okay. So, I was going to ask if you're sitting, comfortably, Asim, but I can see that you're on a standing desk, so I think you're now standing comfortably, presumably, right?

Asim Hussain: At attention.

Chris Adams: All right, well in that case, shall we start and look at the first story and then see where we go from there?

All right, so the first story that's kind of shown up on the radar is the AI Energy Score from Hugging Space. Sorry, Hugging Face, not hugging space, god. Yeah, so this is, this is actually essentially a project that is being spearheaded by folks at Hugging Face, but with also involvement from companies you've heard of like Salesforce and so on, to essentially work out something that might be a little bit like an Energy Star for AI.

Now, you probably, it's probably not called Energy Star because Energy Star is a trademark, but the general idea is, essentially, if we're going to have various AI models and things, then we should be thinking about them being efficient, and there are tools available to make this possible, actually. Asim, I know you had a chance to look at some of this, and you've had quite a few conversations with Boris Gamazaychikov the at Salesforce.

They're the kind of one of the AI leads. I'm mentioning Boris because he's quite involved in the GSF. There are lots of other people involved with the Hugging Face project, but Boris is the person who we know, so that's why we've got that named.

Asim Hussain: He's not, he's, so just to be clear like he's not a member Salesforce is not a member of the green software foundation. But yeah, I've just been chatting to boris obviously because we want to, one of the things we try and do is chat to everybody who's doing something in the AI measurement space so that we can at least try and coordinate and have like a common voice.

That's kind of one of the one of the things that we've been doing. Yeah

Chris Adams: Cool, and if I understand it correctly, we'll share a link to both the GitHub.io, the kind of public facing site with all this information about how the Energy Score project is working, plus the leaderboard, which has various closed and open source models. It's actually showing how efficient they are at performing particular tasks.

We'll also share a link to the GitHub repo, which actually shows how it's made because it's using tools that you may have heard of if you've ever messed around with AI models yourself. So it's using Code Carbon, which is pretty much the default tool that people use to work out the environmental footprint of a training run or anything like that.

And I believe the set, the other tool is Optimum or optimal Benchmark. I can never remember, but these two

Asim Hussain: that the actual benchmark tool? That's the thing that actually runs the benchmark, yeah.

Chris Adams: Exactly. So this is not like wacky stuff. This is stuff that you probably should have heard of or you are likely to come across, to see. And there is actually a Docker container for people who aren't able to publish their entire open models, with the idea being that you can run some of this

behind the file, as it were, so you can then share some of the numbers back And, Asim, I can't, while I've got you, I wanted to ask you about this because I know that the, I've been kind of tracking the AI Energy Score project for a few months, but I know there was some work inside the GSF to create a Software Carbon Intensity for AI

Asim Hussain: Oh yeah.

Chris Adams: these aren't competing, but they do overlap and maybe you could actually share a little bit more to explain what these two things are or even what is this SEI for AI in this context.

Asim Hussain: And there's also others as well. So we're talking Sir Joseph, the head of R&D is also sitting in with meetings at the ITU, International Telecoms Union, and so they're working on work themselves. There's EcoLogits from Samuel Rice. There's, there's other ones as well. And I probably just want to preface this by saying something, and I'm going to try and put some words to these thoughts.

I've internalized a lot of how I think about measurement and through conversations with others, I just want to make sure, 

I want to try and get my point across, which is there isn't one true way of measuring everything. It's not like there's one winner and one loser. 

What it is, is that different measurement systems have different trade offs. They incentivize certain things, they disincentivize other things, they have broader scopes and narrower scopes. And one of the things I've realized is you, it's almost impossible to create a measurement system which ticks every single box. Like it's almost impossible to have a measurement system which has the ability to measure like a broad spectrum of stuff and yet still also be consistent and repeatable and all these other areas, all these trade offs.

So yeah, I love AI Energy Score but there's also other ones as well. I just want to preface it by saying every single measure is designed for a particular audience and a particular problem. And I think that's kind of like one of the one of the ways I like to talk to people about it because I do get concerned that people they're always, 

there's this assumption out there that we're trying to hunt for the right true essentialist value of measurement and it really isn't like that

 so take all of my feedback on everything just with that, you know that context in mind yeah, so I think and I think that's kind of like one of the one of the ways that we look at it.

So what's really good about the AI, do you want me to talk about it? You know a

Chris Adams: please do. Yeah, 

Asim Hussain: I mean

Chris Adams: I'm listening to more because I'm, I've got some things to share, but I'm, I haven't heard that much about this. And I haven't been, and I know that the GSF had these workshops going on where people have been exploring this stuff. And I haven't been in those, but I suspect I know you've been in beside them.

And I suspect there've been some good, interesting conversations as a 

can't 

result.

Asim Hussain: I can't

dive too much deep into it because we're still in progress and we had the agreement not to, you know, give too much information about in-progress stuff.

So if someone has a crazy idea, we're not going to publish it and We'll allow people to have these private conversations But I think there's some stuff I can share that one of the things that's come out from our conversations is there's a really, almost one of the most strongest feelings from the group is for a measure that really has a broad scope for a lot of different AI systems, but also for the breadth of the AI life cycle as well.

So, you know, not just inference and also not just training, but also like, the model as it's deployed in an infrastructure. So it's an end to end computation that includes everything across the chain from edge devices all the way over to data preparation. And so there's various scores, so for instance, there's something called the green, the Green AI Index from the Responsible AI Institute, which is also another measure, and that kind of focuses on a pretty broad spectrum.

There's AI Energy Score, which is excellent because it is focusing on just the model itself. And so when you think of the life cycle, it's not like, it's not gonna, it's just focusing on the model. And they've made, they've done a great job of making it a type of measure, which is consistent and repeatable. And they've done that by, you know, you've got your model. Here's our, here is the benchmark you run. You have no, you've got to run this benchmark. Yeah. you also have to run it on this particular hardware because you can't just get a better score by just running on a better hardware. You want to try and measure the model.

Like you've got to, you've got to, you've got to turn variables into constants to kind of get some sort of measure from that perspective and it's really interesting related to the next thing I'm going to talk about the beginner's guide to, it's a report that's coming out because they, I think they did a really good job they're trying to summarize different types of measurements and I think they put it as a system measurement was kind of very big picture It's kind of what one of the things I think maybe where the SCI for AI is going to be talking about. Then they're kind of job/application specific measurements where you kind of make more of those variables constants.

And then there's kind of what we call a code measurements, which are I want to measure, you know, the emissions of this piece of code. In order to do that, you really need to turn a lot of other variables into constants, so you can know that if you turned a for loop into a while loop, what the actual, like, impact would be.

And where I'd say AI Engine Scores is in terms of that taxonomy, it lands more on the code one. But I'm not saying that's a, I'm saying that is the only way you can get something that is consistent where you can actually have a model that, that, you can really give a score to. And it does incentivize a lot of things.

It incentivizes a lot of the almost code based

patterns to improve model efficiency. But it, because of the way it's worked, it won't incentivize other things. Like, it won't incentivize running compute in cleaner regions. Yeah, cause,

Chris Adams: different kinds of energy, or different cooling, for example, you're only looking at the, just the code part specifically.

Asim Hussain: And that's fine.

Yeah, that's fine. Because if you included that, then you wouldn't be able to have a measure that is going to tell you, okay, is Llama better than DeepSeek? They kind of just want to know that from a, you need to turn these things into variables. So, it's very good from that perspective. And I think it's one of the most advanced ones. It's the best one that does it's job. It does do it's job by being a, by, and they admit this, by having like a narrow bandwidth. 

Chris Adams: There's one card it uses, I think it's an NVIDIA H100. I'm, I believe it's that, but I'm not sure I would know an NVIDIA H100 if it was dropped on my feet, so I need to be very clear that I'm at the limits of my expertise when it comes to hardware there. Okay, and the other thing we should probably mention, though, that this was one of the projects that was announced at the AI Action Summit in Paris that happened earlier on, I believe this month, actually, which has all kinds of announcements, so, in Europe, there is a, I think two, I think it's a 200 billion, yeah, a 200 billion euro fund specifically for rolling out AI across Europe.

There was a something that was kind of like a European take on this whole ridiculous Stargate thing. A ginormous French data center thing. 

Asim Hussain: Yeah. 

 

Chris Adams: That was Macron giving him some me too. And there was even actually for civil society, there was 400 million euro fund to kind of try and get an idea of the unintended consequences or talk about how you might reign in some of the worst excesses of this new technology that's being kind of deployed in all these places, sometimes where you're asking for it, sometimes where you might not be asking for it.

Asim Hussain: So 0.2 percent of the 200 billion is for

Chris Adams: Yeah. It's 

Asim Hussain: the question of whether this is a

Chris Adams: It does speak volumes about our priorities, about who are we serving here, basically, I suppose, or whose needs are being prioritized when you have something like that. But yes, this is, this is some of the kind of ongoing conversations we, I guess, we actually have. there's just two things I just want to check because you used to, you mentioned a couple of projects that people might not be aware of that may be relevant for this conversation.

So you spoke about Ecologits, as I understand it, this is if you're using AI right now and you don't have a model, for example, I mean, you don't have like a whole training setup, you can use something like Ecologics to get an idea of inference. So that's, is that the case?

Asim Hussain: Yeah, I think, it does have a methodology as well. So you can actually just take their methodology and, I think he actually asked us to use the word estimate, but like, cause it's all not, none of this is direct measurements, right? So estimate the emissions of a model, but they also have like an API.

So if you have a named model you can call the API and it will kind of give you information about the, I do believe it's only carbon, it might be carbon and water, I can't quite remember, but it kind of gives you

Chris Adams: French, they have, there's like five specific impact, kind of impact factors. There's like water, ADP, like abiotic depletion, something like that. There's basically five things, and one of them is carbon, and one of them is energy, I believe. And this, you don't need to be, like, if you're already using Claude, or you're already using AI, OpenAI, this is just like a one Python package that essentially wraps the function calls you make to, to that API to get some of the numbers back.

So,

Asim Hussain: I don't think, I don't, I think Ecologist is just for models itself, I don't think it's for, 

Chris Adams: Oh no, it is for inference. because we, we put a funding bid to the European AI Act Implementation Fund, where they were basically looking for this stuff. And the thing we realized was that if you are, if you're not doing any training, but you're just doing inference, this is one of the Python packages that will give you an idea about the numbers.

But it is very much, 

Asim Hussain: inference only, 

Chris Adams: yeah, exactly, inference

Asim Hussain: That's one of the conversations, yeah. Like the biggest conversation we're having in this side for AI right now is to include training or not to include training. And like one of the things the AI Energy Score and Ecologits is that it doesn't include training. The Green AI Index does include training. And, you know, that's it's a very, It's a very, oh god, it's such a hard question, it's like so much nuance to it.

Chris Adams: Well, yeah, because if you're including training, then whose training are you including, right? So if I'm using, say, Llama, should I be saying, should some of Llama's footprint, which was training, and we know, should that be allocated to me, or should it not be? And like, we can point to existing protocols that like say maybe you should, but in this case maybe that isn't.

So yeah, this is an open question right now.

Asim Hussain: Well if you, this is where my brain is so stuck in this area. Because if like, if you include open sources. I want open sources models in yours. It doesn't incentivize the reuse of models. If you don't include an open sources, if you're saying it's open source, I'm not going to include it. You can be a company that just goes, "I open sourced this model so I don't have any emissions." So there's like so many different ways it can be. This is a very, hard question that we need to solve. I also think it's very interesting because it's a, I think it's, I think it's, the training question is.

I, I suspect us figuring out or getting consensus on the training question, a very nuanced discussion and conclusion to the training question will actually help in many, other areas of like, how do you actually measure software? Because I think it's, it's, such a difficult question to answer.

I think the solution will inform so many other areas as well, which are kind of slightly simpler.

Chris Adams: It's almost as if using generally accepted accounting practices first developed hundreds of years ago might not be all that useful for thinking about how you use open source models and open weight models in

Asim Hussain: yeah, advanced technology systems. 

Chris Adams: Okay,

Asim Hussain: it's something to do with cloning. Like, if you can clone something, a click of a button, you can't clone a chip. I don't know. I haven't got fully refined thoughts on this yet. So, let's move on.

Chris Adams: We'll wait with bated breath for these, the outputs from the workshops as you do them. All right. So. that gave us a lot of time to chat about that stuff. The other thing I'll just quickly name check for the AI Action Summit was there was a statement called within, the Within Bounds Statement.

I'll share a link to that. This was something that, actually my organization worked with or the organization I'm part of. So, Michelle Thorne, who's my colleague and normally sits next to me, she was working with 120 different civil society groups to basically lay out a set of demands to say, look, if we're talking about AI and we're allocating literally hundreds of billions of euros or dollars to this stuff, can we talk about what it's for and who's benefiting from this stuff?

We'll share a link to that because it's actually, in my view, quite well written and it does a very good job of actually talking about some of the issues that we might not be talking about all the time as people in industry to see how the rest of the world is actually like having to respond to some of this, I suppose.

So we'll share a link to that. But the juicy one now, Asim, is the one that you wanted to talk about, and that we both were nerding out a lot, was A Beginner's Guide to Power and Energy Measurement, an Estimation for Computing and Machine Learning. This is the next story that we have inside it, and I believe you've shared a link to the archive, the archive link for this pre print, because it's a really cool looking paper, and it's publicly available for everyone right now, but it might, I think it's going to be going to some journal, but I'm not quite sure, and figured But 

Asim Hussain: I thought it got published in the, in an NREL journal. I don't know. Maybe it's not maybe it's not in a real journal or maybe now that I understand how journals what journals are maybe doesn't really matter 

Chris Adams: So NREL here being the National Renewable Energy Labs of the United States of America. That's what NREL was in this case here. We've shared a link to it and, you did talk a little bit about why you like this, but can I give you a bit more space to talk about why you've enjoyed this? Because you don't need to be a beginner to actually appreciate this as far as I understand it, right?

Asim Hussain: No, it goes into a lot of detail. I mean, it says beginner, I'd say it goes a beginner's guide. Probably a little bit of imposter syndrome there, because I'd actually call it, like, it's very well written, so a beginner could start it, but I think it goes into very advanced topics that not many people know at all.

So, I think it goes from beginner to advanced. Yeah, I'm quite proud, Akshaya is the lead author of it, and Dawn Nafus is there, these are two people I worked very closely with at Intel. Very proud of this piece of work from them and the people, people over there. I share this with my team, so we're all working on kind of like thinking about how to measure energy.

And it's just exciting to see, just see how her and everybody else kind of rationalize this all into a very easy to understand, you know, set of concepts. As I said before, like they, they, you know, the first thing they go through to try and come up with this taxonomy, you know, are you measuring for a system?

Are you measuring for a job or are you measuring for code? And I think they've done a really good job of trying to like explain the difference they talk about are you measuring directly versus are you measuring versus proxies? I love the fact that she even goes down and said, there's this idea that we have is there's I always say like everything's a model like you can't, there's actually no such thing as direct measurements.

There's just a very advanced model. and she even goes down into, you know, even if you're using a watt meter and not against a wall, you've actually really got to consider like many of the rare areas because you've got to calibrate it. If you don't calibrate it, it's not going to really go, you calibrate a model, right?

It's not going to like, you know, actually turn out the right numbers and gives you a lot of cautionary tales, you know, where, what to think through. And it really just goes into just a lot of these. I don't know if it's worthwhile going into all of it, but there's just a lot of detail about the things to consider, you know, idle power draw, you know, not only that, but like when you run things, when you run, we always knew that like, it was challenging to measure when you're on shared infrastructure, but then they go into like other details, which is like, it gets even more challenging because the, like, the information you're getting from the socket might actually contain information from the energy draw from the memory and it's hard to, like, disambiguate all of this stuff.

There's ways in which, if you're accessing memory, it increases the idle power of a CPU. There is so much great information here, and a lot of little tips as well.

Chris Adams: Yeah, I think I would agree. It's if you are a beginner, there is some stuff that you can take away, but there is a lot of depth inside this. It's, I actually really enjoyed it too. I enjoyed reading it so much that, actually Dawn sent me, she emailed, I think, emailed me at the beginning of this year, actually, saying, "hey Chris, Check out this cool paper" and I really enjoyed reading it and we were going to do an interview.

We've actually got an interview lined up with Dawn Nafus and one of the other authors, Charles Tripp, who was writing for this. And I believe was at NREL and then has left NREL because, 

Asim Hussain: because? 

Chris Adams: of yeah, basically, this was the way that we could actually get some people speaking about it.

Because since we've had a change in administration, if you're a federal employee it's much, much more for you, difficult for you to talk about anything relating to, well, sustainability and technology, which is a real shame, especially when, like, it's useful to be able to draw upon expertise for people who do this kind of stuff, right?

So, maybe that's a question we should ask ourselves, like, are we okay with the people we're asking of these questions to not be able to talk to the public about this kind of stuff? But, what we do have, but to go back to the actual paper. I agree with you. I found it really, useful and this hierarchy of interventions was really useful because one of the key things that it kind of highlighted was basically where you have some control and where you don't have some control and give you a real chance to actually say, well, if I'm not able to do this, what, and what are my options?

If I'm still trying to make a meaningful and measurable, yeah, change. Because in many cases, you do have to think about some of the trade offs. The things you might do at a data center level to make some parts maybe slightly more energy efficient or maybe more carbon efficient can have knock on effects elsewhere, for example, further down the kind of, the list,

like further down the chain, basically. And this is what they do talk about. It's a really fun read if you're interested in AI. There's so much depth and the nice thing is the thing that one thing that's really quite nice about NREL specifically is that they've shared all the data to back up a bunch of this stuff.

So in the podcast interview that we have where we dive into this a bit more, we'll be showing there are some links to all the data sets that NREL was using when they were doing all these constant training runs to figure out what their, what the footprint of x might be and everything like that. So it's probably one of the most useful when open data sets we've seen for people who are trying to get

an idea about what the environmental footprint of using, I mean, AI directly, what the direct footprint of this might actually be.

Asim Hussain: I'd argue this is like a seminal piece, and you know, if there's like, I imagine this is going to be like essential reading for Green Software courses around the world. If you really want to like major software, you should this paper. 

Chris Adams: Awesome work. I don't work with Akshaya, but I guess, awesome work Akshaya and friends, for that, but probably not just for beginners. So please do not be turned off by the beginners part. It's definitely not just for beginners. There's loads there.

Asim Hussain: They probably put beginners in to make sure the beginners read it but advanced people might think "I already know" so I already know tdp so I don't need to know this.

Chris Adams: Yes, by TDP, you're referring to the Thermal density. Oh, what does it stand for? But that's

Asim Hussain: I thought it's thermal design power

Chris Adams: I think it might be actually you're right. This is the amount of power that gets used at certain amounts of utilization, right? So if I'm using the chip at maximum output, it's going to use this much power. But if it's only using half it's going to be something like that.

Asim Hussain: Yeah, but it's also like Akshaya that kind of opened my eyes to understanding kind of how these power curves, she goes into detail here like how those, you know, we hear about these power curves which tell you 10% utilization is this, that's 30% is this. If you, i'm not going to go into details if you read the paper and you realize how those power curves are made they are very rough estimates of what it like looks like, you know, like you don't really know you don't really, you just, there's no register which is telling you I'm 50% percent like, you're just seeing how much throughput, you're just seeing how much you, basically... 

Should I go into it? You basically chuck like a benchmark at it and you keep on hitting you keep on going like okay, dude, it was a website benchmark. Okay, do one hit per second.

Okay, it's fine. You keep on doing it until the benchmark can't go any higher and it's now like 500,000 page views a second. "Okay, I can't seem to do more than 500,000. I must be at 100 percent utilization." That's how that calculation works. And then you think to yourself, "Okay, what does 90% utilization mean?"

If I did 500,000, I'm just going to do 450,000 requests. And that's like the approximated idea of what 90% utilization means. But, what it really kind of ends up meaning is that it, the, it depends on the benchmark because an AI benchmark will have a different energy consumption, your pseudo 90% than a database benchmark, than this benchmark.

When you actually look at the big benchmark providers like, Esper, CERT and all these other ones, they're collections of different types of applications. And the power curve is the average of those. Which is why, like, if you know you're running, and that's why if you're using like a power curve based over, that's what I think it's saying, if you're using a power curve based off of a CERT benchmark, and you're saying that's what your AI consumption is, it might not be.

You really want a power curve which has been generated just by running a, an AI workload. Because the AI workload might just trigger different parts of the chip in different ways. It's very complicated. Yeah, and it, so, it's one of the things we were like, talking about, It's actually one of the reasons I kind of really like the way Kepler works.

Because Kepler, 

Chris Adams: Sorry, I'm going to you there. before you go on this, the reason it's, I'm actually, the reason I'm quite happy to give some space for this, is that people who have listened to this might not know that you were literally working at Intel trying to figure this stuff out when you were doing a bunch of the green software stuff, so it's okay, listen, you know, I, like, you do have some prior art in this stuff, right?

Asim Hussain: Yeah. Yeah, we're basically diving into all this stuff. And I kind of learned so much while I was over there. How Kepler works is quite interesting. Is, So Kepler is this kubernetes based system which does a whole bunch of things but one really intelligent thing it does is it tries to figure out what your energy consumption is from the actual stuff that's running on the chips that you're running on. So it has like a machine learning model that, I think it's got, I think it's got some, if you start off Kepler with nothing and it doesn't know anything it will tell you energy numbers but it kind of learns and improves and fine tunes itself based upon A, your actual chips, B, how your chips were configured, C, what you're actually running on your chips.

So you kind of get a more accurate power reading from Kepler. One of the things I think would be great for them to do is to kind of just take that out of Kubernetes. And, because that doesn't necessarily need to be a Kubernetes piece, but it's baked into that infrastructure. Because that would be generally useful everywhere. Yeah.

Chris Adams: We will share links to both of those, and Asim, you're able to find a link for some of this power curve nerdery, that would be very, helpful, because I do know...

Asim Hussain: This paper's got it, yeah.

Chris Adams: Well, okay, in that case, we'll use that, because I do know that, well, some of the work I'm doing outside of being on podcasts with you, for example, I'm aware of, like, there are people putting together procurement guidelines where they speak specifically about this kind of stuff like please tell us what the figures are going to be for this power curve based on these ideas here and being able to refer to some of the actual literature is actually very helpful for people to understand why a government buyer might be asking for this stuff and why that's being used as one way to figure out some of the environmental footprints of the use of digital services.

All right, we'll add some links to that one and then we'll see what we're doing for time. Can I share one? I want to share a story from me. So this one, this is actually, it's not so much about, it kind of is about technology. This is actually an executive order from the USA called Advancing United States Leadership in Artificial Intelligence Infrastructure.

We've shared a link to this and the reason I shared this is because I think it's actually because I work in the policy working group inside the GSF and because we speak a lot about the carbon intensity of power and stuff like that. It's often quite rare to find really good, quite well written and detailed examples of kind of policy.

And this is one that, for a short, beautiful short period of days, was actually publicly available. So this was, I think,

Asim Hussain: I see the link is, oh, no, it's a real link. No, it is way back machine.

Chris Adams: It's webarchive.org, whitehouse.gov, briefing room, presidential actions, on the 14th of January. Just before the new guy came in, there was an executive order all about essentially, deploying AI, and this was specifically about if you're going to deploy AI on public land, what, and in the US

there's lots and lots of federally owned public land, what kind of criteria do you actually want to require as condition of people being able to put things on your land like this? So just the same way that people who have private land, they can say, you can run a datacenter here, as long as you do X, Y, and Z.

This pretty much lays out, okay, here's what you should be looking for. And this stuff includes a bunch of really, in my view, interesting and like very insightful and incisive policy, pieces of policy inside this. So when we talk about the carbon intensity of power, we've spoken before on this podcast multiple times about how in the hydrogen sector, we already have a very rigorous way of talking about how energy can really be green.

And done a recent podcast interview with Killian Daly from EnergyTag talking about this idea, like three pillars, the idea that energy has to be timely. So you can't have power at night being greened with like solar because they're two separate times a day. Deliverable, like you need to be able to have the generation on the same grid as you're consuming from because otherwise it's not very convincing that it's really powering it. And additional, you need to have new power coming in. This literally is name checking every single one of these inside this. Like the actual wording they use

Asim Hussain: in terms of power, in terms of more generally applying that

Chris Adams: this is specifically for data centers. So if all data centers are like, I'll read some of the kind of quotes from this. Basically, like, as part of ongoing work, the Secretary of Defense and Secretary of Energy shall, Blah, blah, blah, blah, blah, will require concurrent like any AI data centers on a federal site will have procured sufficient new clean power generation with capacity value to meet the data centers needs.

And they've, literally explicitly said "has to be deliverable and has to be matched on an hourly basis." So those are the three things right there. They've actually been more explicit about additional elsewhere. So this is like the three things that already in place in other industries, for the first time, really laid out for how the, how you should be doing this for AI data centers.

So if you're a policymaker outside the USA, just copy this link. This is probably some of the best stuff of particularly relating to policy, to energy policy. When

Asim Hussain: it, but does it say, by the way, shall, you know, you know, the shall means, so just everyone who is listing, shall is a very important term. Shall in the standard space. I presume the policy

Chris Adams: You don't get not do to Basically what they're saying.

Asim Hussain: You gotta

Chris Adams: is mandatory if you want to things on federal land. Elsewhere, yeah,

Asim Hussain: should is different. The, the, so just to, the reason you're talking about, as I presume it's the what's mandated is clean energy. Or is what's mandated,

Chris Adams: yeah, sufficient new clean energy power generation is they use, they, and later on, they actually talk about what counts as clean energy in this because there's a bunch of stuff, it's quite a long executive order, and we've had this new guy come in power, who's basically, who's rescinded every other executive order, apart from this one, even though it's not visible, so there's some stuff inside this,

Asim Hussain: into this one. There's something which benefits, benefits something else.

Chris Adams: So there is the whole thing here about, for example, this does say, well, if we're going to have clean energy, we're going to call it carbon free, and we're going to talk about not just renewable, like wind and solar, they talk about, say, the deployment of nuclear, which America, in America, people tend to be more receptive to, or in some places at least. So there's a part, there's a part there. But they even talk about, say, if you're going to have fossil generation, it needs to be 90 percent carbon capture, right? Now, this is a very high bar to hit, because there, right now, there's basically nowhere in any kind of at scale operation which is hitting 90 percent capture of this.

So if you were to have gas and you were to have this is probably about as rigorous as you can reasonably ask. And if anyone is actually, in the year 2025, when we know all the science available to us, you're not saying something like this, got to ask, okay, who's captured, who is captured here?

Because that is a really, like, there, there is just, it's, you need to have this if you're going to be talking about the use of fossil fuels inside this. And really, you probably shouldn't be using fossil fuels at all anyway. But like, this is examples of, yeah, this is what policy does look like.

If you're going to do this, do this properly.

Asim Hussain: Yeah, but at the same time I think what we're seeing is, I mean, it's interesting that the up, I don't know if I've got time to go into it, but the uptime report talks about the, 

the increasing demands is forcing organizations to, you know, like you utility, there's so much demand from data centers.

It's not really a question of, you know, you've got to use clean energy. It's like, you don't have the energy or you now have to be a good place. You go to demand response. 

But there's also then driving up pressure for those organizations. They're kind of walking back a lot of the stuff previously and there's a lot of fossil fuel generation being thrown out.

Asim Hussain: I have not verified this at all, but today I saw something on my feed. Which said that, I don't like, anyway, which, which said that, Elon's, 

Chris Adams: You might be about the x.ai datacenter, the one in Memphis, running

Asim Hussain: in Memphis, there's gonna be, there's, like 15

Chris Adams: yes!

Asim Hussain: to power it. Which, you know, probably is because the utility said to him, "You're not putting an unbelievable load on our grid. We do not have the capacity for you." And he probably went, "ah, I'll build my own gas generators without asking anybody."

Chris Adams: There is a bit of a story behind this. So essentially, the, there was a datacenter, the x.ai datacenter was built very quickly by datacenter standards. And

usually, if you want to have power for a data center, you're going to have to wait some time if it isn't already available.

And, the, basically the approach that was taken was to essentially deploy a bunch of mobile gas turbines to provide the extra megawatts of power such that you could power that. Now the problem is these are really bad for local air quality. So you're shortening the lives of all the people who live around there, for a start, for the sake of this.

And, the other thing that, one of the reasons you're able to do this is because, they count as a mobile generators, they're not covered by the same clean air laws. So you wouldn't able to, yeah, exactly. So essentially this is stuff which has a real human cost, right? This is an already marginalized and kind of racialized community that it already has very bad air and has like elevated cases of asthma and all the stuff like that.

So there is a real human cost being paid here. And the decision has been made. "We're going to use this because we've decided that's more important than the lives of people around here." So, like, that's essentially what coming down to.

Asim Hussain: But also, I mean that, I'm guessing from the fact that this was an active executive order as a, you know, a few months ago that, that wasn't on federal land and therefore, or something like that must be, or

Chris Adams: This is somewhat separate. I mean, for a start, this, the, for the things, for the, xAI case in particular,

 you don't, any of the local air guidelines or the local air kind of, laws about air, about air quality, don't apply to mobile providers.

Asim Hussain: Oh, 

Chris Adams: providers.

Yeah. Yeah.

Asim Hussain: Even with this executive order, you can always get around it by just playing on mobile?

Chris Adams: So this was, this executive order came later. So we've had this things in xAI. That's been something that we saw last summer. All right. This was only published in January and they, and then it was literally on the White House website for seven days before the new guy came in and it down while pointing to the previous one.

And it's also worth bearing in mind that executive orders are not law. So even though someone can say they need to do this, that doesn't mean that it overrules existing law, example. So absent any other law, this is what you can ask for. And this is why they're able to say for federal law, this is the things we'd be doing.

There's actually a bunch of other really good stuff inside this, in particular, the air quality stuff. So the, as a contrast to saying, "It's okay to use this stuff. Who cares whose lives are shortened?" On the environmental justice, there's a whole piece in this about saying you, if you're going to deploy data centers in public land, then you need to have constant monitoring, all this visit, and have this visible everyone else to see as well.

So like these are the things that I think we don't see that you could totally take as examples away from this. And, they've also literally said. If you're going to deploy, you can't deploy in places which have had traditionally poor air quality below this, this air toxic, AirTox Screening. So basically, places which have already been harmed already, you don't get to deploy them in these places anymore.

And like, this is why I think this is actually quite well written stuff, because it does take into account all these things which we've had, which have been coming up again and again. So if you were trying to come up with some policy for deciding how you deploy, there is so much you can lift from this yourself, for your own corporate policies or anything like that.

Asim Hussain: There's very few benefits to a local community for having a data center built near you, there's very few jobs. There's like very, like, there's a couple of people walking around this giant warehouse and there's all, they've sucked all your electricity, and they, and there's, and you know. I don't know. The data center industry needs to, I was, it was fascinating to me when I was chatting, 

I was at an infrastructure conference last year and I was chatting to a gentleman, won't name his name, from the utility sector, and he was saying to me something very interesting. He was saying to me, he believes the data center industry, this is before, he who shall not be named apparently, entered office.

So, this is before that happened, but he was saying he thinks the data center industry is headed right towards full regulation the same way utilities are regulated. So if you want to do a power plant, you can't just go "Oh, it's gonna make me a lot of money. I'm gonna build a power plant here." You have to go through so many checks and balances.

Your profit is limited. Everything is limited. And he was saying based upon the conversations that are happening, you know, you're claiming that this technology is so fundamental to life and existence that it therefore is a commodity, therefore it's something that's you know similar to energy. Energy utilities can't just say "ah we're going to rack up our prices 40, 43 percent because everybody wants it." You've got to, they'd be regulated for that.

So he was really putting a very convincing argument to me that if the data center industry is not careful It's going to get regulated that way and then they don't want to get regulated that way. It's not fun, apparently. And so I think things like this really matter.

Chris Adams: Yeah.

Asim Hussain: Really do matter.

Yeah, you to think about it. If you're with a data center, you can't be, you can't not think about the impacts of the region that you're in. You've got to really put effort into where you need to be a positive net benefit to the place you're being installed, you know, locally as well.

Chris Adams: So this is actually one thing that, so what I think you're, the argument you're making is that if you can, if you're going to present yourself as a utility, something which is what foundational to everything running on, then you probably, maybe there, then you should expect utility style profits rather than SaaS style profits, Because the margins that you might see, when you're from certain, tech giant companies is like 30 percent for example. That's not the same as utilities might be looking at like around 10 to 15 percent for example. And you have different kinds of oversight being introduced.

So yes, this is a conversation that we might have. I suspect it might be longer than we have given the time we have available, but yes, this is something we might point to. Just following on from this, there's a, you did mention this, uptime report, Uptime Institute Report. We'll share a link to that as well.

And I think there is, we might be in a situation where we have a bit of a fight on our hands, or we might be seeing a fight taking place because we do see like in Europe, for example, where, which is probably, arguably, the place where you see fights around data center deployment the strongest. We've just seen new laws be published about what criteria you need to actually have if you're going to connect to the data centers.

This was published, I think, last week, and we'll share a link to that. Where, in contrast to what we've just talked about here, where the US policy was very clear and was very good, we now see, essentially, a guideline saying you can connect data centers to the grid, but you need to have your own generation and you need to integrate nicely with the grid, but there's no mention of climate change, no mention of local environment or anything like that.

This is literally going to likely incentivize even more on site fossil based generation for this, absent no other criteria being in place. So we might see this being challenged, but I think I agree with you. We currently do have this case where, yes, you got all this new technology being deployed,but there is the kind of, we have a fight where there's almost zero regulation and it doesn't feel like it's going to last. It, I can't see how

Asim Hussain: You don't think, you don't think the absence of regulation is gonna last?

Chris Adams: I think what's going to happen is that if you continue to go through this stuff, you're, what's probably going to happen is that you will end up with so much pushback that you will end up with much, much more heavy handed regular legislative responses to this. Because, right now, there's been this push to kind of, essentially, neuter any kind of meaningful science based or data informed discussion around this.

All that does is play into the hands of a much, much more, a much, much more dramatic response later on. So I think it's, if you want to deploy stuff, then this does feel kind of long term, not very helpful for them. But then again, there's a question about, do we need this, how much do we actually need to be deployed?

There's probably a democratic discussion to actually have about that.

Asim Hussain: Well, we haven't even spoken about DeepSeek and its impact on this whole question and kinda how it has, if I'm going back to that conversation, how that person utilities that their big question was. Because 

the data center providers, everybody's telling them we need a lot more energy in the future.

And they're going, "well, my God, do "we actually put the effort in to try and roll out this new capacity? And then only to find out on the day in the two years later like "ah, we got it wrong I'm sorry, we won't need that." They're asking the and I'm just a, they're asking the "is it BS?" question because they need really to figure it out and I was thinking okay, they might have just been convinced. Then DeepSeek comes along and Now you know everybody's asking the question "huh, will we need this capacity upgrade?" And now, as soon as DeepSeek came along, everybody said, "yeah, that's great. Now we are gonna do even more AI. We do, we definitely need the capacity, but now we can do more with it." And you're like, well, hang on. Because there is oftentimes a thing that goes on in, you have to create the hype to get the funding. You have to create the hype to get the funding. If you want to convince like investors to invest in your organization, if you want to convince them, you have to create the hype. And what DeepSeek's done is it's just popped it and I don't know how much it's popped it because only the investors know how much it's been popped.

But it's popped it and I wonder if it's really popped it quite significantly and whether we are going to see like a significant pullback. Is Stargate really going to happen or does it really not, really matter? They just want to hand money out to, it's just a reason to hand out 500 billion because you know, why not?

Chris Adams: We can share a link to, there's a good paper from Sasha Luccioni talking a little bit, and friends, about Jevons Paradox. I've actually written a blog post about this as well, particularly for DeepSeek, to kind of make this accessible for people who are trying to understand. Does this, is this going to reduce the footprint or is it going to increase the footprint?

Because there's a few different criteria you want to take into account. Just saying,

Asim Hussain: pops the bubble, it will decrease the footprint, I think.

Chris Adams: That's, this is the thing we can look into and decide. Because the flip side is that if this makes it more likely that they'll, you'll, if this lowers the barrier so that more people are able to use it in more places, that can lead to an absolute increase.

So there are different, there are two different, there are different ways and different takes on this and it's very much case of, okay, this is, yeah, this is one thing we'll show a link to. Asim, I think we've gone down a bit of rabbit hole so we should probably look at the events there's anything particularly we have here.

Asim Hussain: So, there's a couple events coming up.

There is the Practical Advice for Responsible AI on February the 27th at 6pm in London. , it's a UK event. And it's gonna be held in person in the Adaptavist offices, and it's gonna talk about green AI with Charles Humble and AI governance Team with Jovita Tam. There's the GSF Oslo meetup happening on again, February 27th at 5:00 PM. It is in person in the Accenture offices from 5 to 8 PM.

And they're going to talk about how to leverage data and technology to drive sustainability initiatives and enhance security measures, dive into green AI, obviously. There's going to be talks from Abhishek Dewangan and Jonny Mauland. I do apologize.

Chris Adams: Sorry,

Asim Hussain: Read them. I'm sorry, Johnny.

Sorry, Abhishek. Details in the podcast notes. And think that's it. I think I'll pass over to you Chris.

Chris Adams: Yeah. Okay, then. I think that takes us to the end of what we have for this. I assume if there's a particular free resource you would point people to right now on green software as a final thing, what would you point people to as a parting?

Asim Hussain: Oh, honestly, it's that beginner's guide. I don't know if it's I don't know if it's, it is very good, I read the Beginner's Guide to Power and Energy Measurement and Estimation for Computing and the last word.

Chris Adams: Wow, Akshaya better be getting a promotion after this, man. This is just like, this is, so yes, this, I agree. It was a really fun read. If you want to basically sound knowledgeable about AI, this is probably the most useful thing to read. And that's as someone who's written a report all about the environmental impact of AI ourselves, where we work.

All right, Asim, it's really lovely to see you again, mate. Thank you so much coming on. I hope the people who did listen to this were able to stay with us and we didn't go get too self indulgent. And if we did, please do tell us and we'll make sure that we don't do it too much next time. And otherwise I'll see you in one of the future episodes of This Week in Green Software.

Thanks, mate.

Asim Hussain: See you later, mate.

Chris Adams: Toodle oo!  

Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> How to Tell When Energy is Green with Killian Daly How to Tell When Energy is Green with Killian Daly Thu, 20 Feb 2025 07:00:00 GMT 1:00:40 x1ln6z61 false https://podcasts.castplus.fm/e/p8lxlvl8-how-to-tell-when-energy-is-green-with-killian-daly 68dc7d1046a2532cdd8d267e 68dc7cef46a2532cdd8d1c23 In this episode, host Chris Adams is joined by Killian Daly, Executive Director of EnergyTag, to explore the complexities of green energy tracking and carbon accounting. They discuss the challenges of accurately measuring and claiming green energy use, including the flaws in current carbon accounting methods and how EnergyTag is working to improve transparency through time-based and location-based energy tracking. Killian shares insights from his experience managing large-scale energy procurement and highlights the growing adoption of 24/7 clean energy practices by major tech companies and policymakers. They also discuss the impact of green energy policies on industries like hydrogen production and data centers, emphasizing the need for accurate, accountable energy sourcing and we find out just how tubular Ireland can actually be! 97 In this episode, host Chris Adams is joined by Killian Daly, Executive Director of EnergyTag, to explore the complexities of green energy tracking and carbon accounting. They discuss the challenges of accurately measuring and claiming green energy use, including the flaws in current carbon accounting methods and how EnergyTag is working to improve transparency through time-based and location-based energy tracking. Killian shares insights from his experience managing large-scale energy procurement and highlights the growing adoption of 24/7 clean energy practices by major tech companies and policymakers. They also discuss the impact of green energy policies on industries like hydrogen production and data centers, emphasizing the need for accurate, accountable energy sourcing and we find out just how tubular Ireland can actually be!

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TRANSCRIPT BELOW:

Killian Daly: We need to think about this kind of properly and do the accounting correctly.

And unfortunately, we don't do the accounting very well today. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello, and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. When we write software, there are some things we can control directly. For example, we might be able to code in a tight loop ourselves, or design a system that scales to zero when it's not in use.

And if we're buying from a cloud vendor, like many of us do now, we're often buying digital resources, like gigabytes of RAM and disk, or maybe virtual CPUs, rather than physical servers. It's a little bit less direct, but we still know we have a lot of scope for the decisions, to control the impact of their decisions and what kind of environmental consequences come about from that.

However, if we look one level further down the stack, like how the energy powering our kit is sourced, our control is even more indirect. We rarely, if ever, directly choose the kind of generation that powers data centers that our code runs in. But we know it still has an impact. So if we want to source energy responsibly, how do we do it?

If you want to know this, it's a really good idea to talk to someone whose literal job for years has been buying lots and lots of clean energy and is intimately familiar with the standards involved in doing so and who has spent a lot of time thinking about how to make sure you can tell when the energy you're buying really is green.

Fortunately, today I'm joined by just that person, Killian Daly, the Executive Director of the standards organization, EnergyTag. Killian, it's really, nice to have you on the pod. Thanks for coming on.

Killian Daly: Yeah, thanks. Thanks very much for having me, Chris. great to be on the pod and, an avid listener, also. So it's always nice to contribute.

Chris Adams: Thank you very much. Killian, I'm going to give you a bit of space to introduce yourself, and I've just mentioned that you're involved in EnergyTag, and we'll talk a little bit about what EnergyTag does. Because I know you and because, well, I met you maybe three years ago, I figured it might just be, it might be worth just talking a little bit about our lives outside of green software and sustainability.

So, we were in this accelerator with the Green Web Foundation talking about a fossil free internet, and you were talking about EnergyTag and why it's important to track the provenance of energy. I remember you telling, we were asked about our passions. And, you told me about surfing and I never ever thought about Ireland as a place where you would surf because I didn't think it was all that warm. So can you maybe tell me a little bit like enlighten me here because it's not the first country I think of when I think of surfing and when you said that I was like he's" having a joke, right?"

Killian Daly: Yeah. Well, I do like to joke, but this is not actually one of the jokes, Well, it doesn't need to be warm to surf. You just need to have waves, I suppose. So, yeah, it's something since I was really very young. I've always gone to the west coast of Ireland. Beautiful County Clare near the Cliffs of Moher.

Maybe people know of them. And so we go every year. And my cousins, since a very young age, started surfing. We just, you know, solve these big waves and there's other people out there, surfing, bodyboarding and we're like, "Hey, let's try that out. That looks really cool." So, yeah, since I don't know, 6 or 7 years old, I've been going there every year, in summer, also in winter, me and my cousins also go, yeah.

We go at New Year's get into the frigid cold Atlantic. And, yeah, it's magic, really. If you have the right, if you have the right wetsuit, you can kind of, you can get through anything, 

Chris Adams: So there's no such thing as cold was it bad weather, just bad clothing that also applies to wetsuits.

Killian Daly: Yeah. Yeah. Yeah. It couldn't apply. Couldn't apply anymore. And obviously, in winter, you get the biggest swells, right? so actually, people probably don't know it, but Ireland has some of the biggest waves in the world. Now, on the west coast of Ireland, you have, yeah, really massive 50, 60 foot waves.

Yeah, really all you can get some sort of a, all time surf there. So, so yeah, it's one of one of our better kept secrets.

Chris Adams: I was not expecting to learn how to go totally tubular on this podcast.

Killian Daly: Yeah, 

Chris Adams: Wow, that's, yeah, that's...

Killian Daly: It's not, not for the faint of heart, but yeah, I would definitely recommend it.

Chris Adams: Actually, now that you mention that, and now that we talk about, going back to the world of energy, now that people talk about Ireland as, the Saudi Arabia of wind, and it being windy AF, Then I can kind of see where you're coming from with it, actually. It doesn't make a bit more sense. So yeah, thank you for that little segue, actually, Killian.

Okay, so we've started to talk a little bit about energy. And, I know that your, the organization you work for right now is called EnergyTag. But previously, as I understood it, you didn't, you worked in other organizations before. And, you've been working as a kind of buyer of energy, so you know a fair amount about actually sourcing electricity and how to kind of do that in a kind of responsible way.

And I think when I heard you, we spoke about this before, you mentioned that, "yeah, I'm used to buying significant amounts of power" in your kind of previous life. Could I just like, could you maybe talk, provide a bit of a kind of background there, and so we can talk a little bit about context and size, because that might be helpful for us talking about the relative size that tech giants might buy and so on, and how much of that is applicable.

Killian Daly: Yeah, sure. Yeah, so, I've been thinking about energy for a long time, even before my professional career studied energy and electrical engineering since I was 18 years old and did a master's in that, also. And then obviously in my working life as well. I've been basically always in the energy sector.

So before EnergyTag, I was basically overseeing the global electricity portfolio, and the procurement of electricity for a company called Air Liquide, which is basically a large French multinational that produces, liquid air. So, oxygen, nitrogen, all the different parts of air which are, essential, feedstocks into various industries, and they consume a lot of electricity.

So, the portfolio my team oversaw was about 35 to 40 terawatt hours of electricity consumption.

Chris Adams: Okay.

Killian Daly: Yeah, it's a lot, it's more than my home country, Ireland. It's about the same as Google and Microsoft

Chris Adams: put together, yeah. Okay, so, wow. And

Killian Daly: So, it's pretty big stuff. And obviously, when you're working on something like that globally, looking at various electricity markets operating in 80 countries in these huge volumes, I suppose you, kind of learn a lot about what it means to buy power.

Chris Adams: I guess if you're looking at something which is basically as much power as an entire country, then there's going to be like country sized carbon emissions, depending on what you choose to power this from. And I guess that's probably why you, I mean, we, have ways of tracking power. I mean, tracking the carbon emissions from various things like this, I mean, called like the GHG protocol, which is a kind of like the kind of gold standard for talking about some of that stuff.

And this is something that I think you have some exposure to and I remember when you spoke to me, I remember us sitting down one time and you were telling me about There's a thing called scope 1 and there's a thing called scope 2, and that scope 2 was actually a kind of relatively new Idea where this came into this. Can you maybe tell me a little bit like maybe you could explain to someone who is Who's heard of, carbon footprinting, and they know there's a thing called scopes.

Why would anyone care about scope 2 in the first place? And how does it come about in the first place? Because it seems like it's not intuitive for most people when they first, when they start thinking about carbon footprints and stuff like that. 

Killian Daly: Yeah. I think the obvious, first thing you need to take into account when you think of like a company's emissions is, well, what are they burning themselves on site? do they have gas boilers burning gas? Are they burning coal to produce electricity? So that's, I think, very intuitive and obvious. But actually that is not the end of the story. And there's actually like a, a very funny anecdote. I put a true anecdote from the legendary Laurent Segalen, who does the Redefining Energy podcast and general energy guru. And he was actually involved in the kind of creation of a lot of the carbon accounting standards that are used today, this Greenhouse Gas Protocol standard, which is basically used by over 90 percent of companies now to report their carbon emissions.

It is the Bible of how carbon accounting works, right? and so 20 years back, he basically was, down in Australia and visiting an aluminum smelter. On site, they were explaining, "this is very low carbon product. we hardly burn any fossil fuels on site. This is incredibly, clean production." 

Chris Adams: The aluminium here, right? big chunks of aluminium. Okay, right.

Killian Daly: Aluminum, aluminum smelting. So like one of the, biggest metallic commodities that we have, very energy intensive. and so, he was there on site and just saw these big overhead wires coming in from yonder, from somewhere, right? And he said, hang on, what are the, what are those big cables above? and they were like, "oh, yeah, that's the electricity," obviously driving the smelter because aluminium, it's all about electricity. That's what power is an aluminium production facility. And so he said, well, hang on, where is that coming from?

They're like, "oh, no, don't, don't worry about that. That's not our responsibility." Well, it absolutely is, right? so you need to think about where is that electricity coming from? How is that being produced? And in that case, it was coming from a very large multi gigawatt coal power plant right next door. 

Chris Adams: Okay. All right. So I thought you were gonna say, oh, it's maybe a, something clean, like a hydro power station, but no, just a big, fat, dirty, great coal fired power station was the thing generating all the power for it. And that's where

Killian Daly: Absolutely. So, that's kind of the, just a bit of an anecdote is that's why it's so important to think about what we call scope to emissions, the emissions of electricity that I'm consuming, because especially as we electrify the economy, right, more and more emissions are going to become scope 2 emissions.

They're going to be related to someone else either burning fossil fuels to produce electricity and to give to a consumer or ideally, using clean energy sources to generate that electricity without carbon emissions. 

we need to think about this kind of properly and do the accounting correctly.

And unfortunately, we don't do the accounting very well today.

Chris Adams: Alright, so previously, before we even had that, there wasn't even this notion of scope 2 in the . , you might have just had direct, and then maybe this kind of bucket of indirect stuff, which is really hard to measure, so you're not going to really try to measure it. And okay, so, I remember actually reading about some of this myself, and I always wondered, like, where do some of these figures come, where do, where does even the notion of a protocol like this come from? And one of the things I realized was, particularly with the GHG one, was that they're like, when I listened to Laurent Segalen speaking about some of this, he was basically saying, yeah, this was essentially like Shell, the oil company, who basically said, "we have a way of tracking our own emissions."

And, why not use that as a starting point for talking about how we do carbon accounting? And then, scope 2 was a new concept. That was one of the things that they were kind of pushing for. But I suppose this kind of speaks to the idea of, who's in those rooms for those working groups to kind of, that is going to totally change the framing of how we talk about some of this.

And I guess that's probably why this, is this a little bit like why you started talking and getting involved with things like EnergyTags so you could take part in those discussions? Because it feels if this is what we're going to use to define how we do this or how we do that just like you have people talking about okay BP had an impact of changing how we think about carbon footprints from, from an individual point of view.

But you do need people involved in that conversation to say, "actually, no, that's possibly not the best way to think about this, and there are other ways to take this into account." I mean, is this why you got involved in the EnergyTag stuff?

Killian Daly: Yeah, it's one of the main reasons, because I used to do, so, work for one of the world's largest electricity consumers. And so I was responsible for calculating all of the electricity emissions for that company, right? Like doing the scope 2. And so I read the Greenhouse Gas Protocol back to front.

That was how the, all the calculations were done. That's what qualified clean and not clean, right? And I remember thinking, "this is an insanely influential document," right? It's kind of in the weeds. It's kind of stayed maybe, to some people, but I was

Chris Adams: of tedium around it, here. 

Killian Daly: Yeah. But the more I've gotten involved in things like regulation and conversations like that, that is where, it's in the annexes, it's in the details that the big decisions are made often. So I remember thinking back then, this is insanely influential and some of the ways that we're allowed to claim to consume clean energy are, frankly, disconnected from reality in a way that is just not okay, right?

As in this is far too weak. And definitely, I thought, someday I'd love an opportunity to be able to, say, "hang on, can we,

we fix this please? can we do this differently? Can we start to respect some sort of basic realities here?" So, yeah, it was definitely one of the drivers why I joined EnergyTag, which is obviously like a nonprofit that is, has as its mission to clean up accounting, right? And to clean up the way we think about electricity accounting. So, yeah, obviously it's a great honor, I suppose, to be part of those ongoing discussions in the Greenhouse Gas Protocol update process.

Chris Adams: So, We spoke before about how there, before there was even no scope 2, right? So that was like, the bar was on the floor. Right, and then we introduced the idea that, oh, maybe we should think about the emissions from the electricity. So that was kind of a bit of a leap forward by one person pushing for this, that otherwise wouldn't have been in the standard at all, right?

And I just realized actually now that you mentioned that, we spoke about oil firms being very involved in this and being very organized in this, and I remember people talking about Shell, that's what you use, and how much, and I'm just realising, oh Christ, Shell's in the Green Software Foundation as well.

We should, that's something I didn't really think so much about, but they're also there too. So they are organized. Wow. So let's move on. So maybe we could talk a little bit about scope 2 here. The thing I want to kind of get my head around is I'm like, can you maybe talk me through some examples of where this doesn't, this falls down a little bit, where might be a little, stretching your, you spoke about the physicality, the physical reality. where does it need a bit of work, or need some improvement that you're looking to do, looking to address in EnergyTag, for example? 

Killian Daly: Yeah, so basically, one way of doing scope 2 accounting is basically looking at the energy contracts or the electricity supply, contracts that companies have and saying, well, where are you buying your energy from? How are you contracting for your power? Right? And there's a kind of a number of fundamental issues.

One of them is around the temporal correlation, or between when you're consuming electricity and when the electricity you're claiming to consume is being produced. And today, right, we actually allow an annual matching window between production and consumption. And put in simple terms, what that means is that you can be basically solar powered all night long, right. You can take solar energy attributes from the daytime and use them at nighttime, or you could take them from the daytime in March and use them at nighttime in November. At any other time of year. And this just does not make sense, right?

Chris Adams: Not physically how the science works for a start. Maybe if I can just dive into that a little bit in a bit more detail because you've mentioned this idea of certificates for example or like claiming like that and as I understand it if I am running a solar farm right I'm generating two separate things. I'm generating power but I'm generating the kind of greenness so these are two independently sellable things which will sometimes be bundled together. That's how I might buy green energy. But under certain rules, they're not. They can be separated. So it's like the greenness that I'm moving or I'm buying and kind of slapping onto something else to make it green. Is that? And if it's at the same time, it's kind of okay. If it's from totally separate times of day, you do like you mentioned where you're saying this thing running at night runs at solar, is running on the greenness from a solar farm, which is stretching the, well, our imagination, I suppose, and your credulity, I suppose.

Okay, so that's one example of this is something that you wanted to get, wanted to get fixed. Are there any other ones, or things that you'd point people to, because

Killian Daly: I think you know the. The other, the other aspect, I think that's pretty, problematic in today's standards is so we've talked about time and the other big one is space, right? Today we allow consumers to claim to use green energy or clean energy over vast geographical boundaries that really don't respect the physical limits of the grid.

So, for example, the whole U. S. is considered to be one region, right? So you can buy green energy attributes produced in Texas and say that you're using them in New York. So you could be 100 percent power by Texas solar in New York. Or if you're in Europe, Europe is considered of one region. So you have really absurd cases where you can be powered by Icelandic hydro in Germany, and Iceland has never exported any electricity to anyone. There's no cables leaving Iceland. So, that just doesn't make sense. And this has real consequences because what we're trying to do is obviously drive consumers to buy green energy. If they're doing it in this way, then they're kind of, in some cases, pretending to buy green energy rather than actually going and buying green energy and incentivizing more production of green energy and clean flexibility that's needed to integrate that solar and wind, at every hour of the day.

So, that time and space kind of paradigm is maybe a good way of thinking about, some of the fundamental issues here. There are other ones. I don't know how far we want to go into the rabbit hole, but that's two very high level, and hopefully very kind of understandable examples of the problems we have with today's carbon accounting.

Chris Adams: Yeah, I think I understand why that would be something we would address, and so presumably this is the thing that EnergyTag's looking to do now. You're basically saying, well, the current system is asking you to make quite spectacular leaps of faith. And there are certain places where you do want to do leaps of faith and be super creative, but accounting might not be where you want to be super creative or super jumpy. That's not always where you want to have your innovation.

So that's, this is, so you're saying, well, let's actually be, make this more reflective of what's really happening in the world. So that we've got like some kind of solid foundation to be working on. So,

Exactly. 

Killian Daly: And just maybe on that point, this is not what we advocate for is not, it's not anything radically new, to be honest, because the way electricity markets work today, the way electricity utilities deliver power to customers, just you know, let's say pure gray electricity on electricity markets.

It is based on fundamental concepts of time matching. Power markets work on a 16, sorry, a 60, 30 or 15 minute, like balancing period. In Australia, it's 5 minutes. In Europe, there's things called bidding zones. So that's the area over which you can buy and sell electricity. And all of this is to kind of capture these fundamental physical limits of the power system.

You have to balance it in real time. And there's only a certain amount of grid capacity. And so you need to realize areas over which it's reasonable to trade power or not. So all we're saying is, make the green energy market much more like the real power market. So we're actually, if anything, trying to make it a bit more common sense,

whereas today, we're, quite detached from some of those basic limits that

Chris Adams: Ah, I see. Okay. So in fact, in some ways, there are some kind of comparisons where you could plausibly make where people there's a push right now for people to talk about treating environmental data with some of the same seriousness as financial data and apply some of the same constraints it sounds like something a little bit like that so if people are going to have basically take into account the physical constraints when they're purchasing the actual power part, they should think about applying their same ideas when they're thinking about the greenness of it as well. You can't kind of cheat, even if it makes it a bit easier, for example.

Killian Daly: Yeah, well, exactly. And, ultimately, what are we trying to do here? Is the purpose so that certain consumers can say that they have no emissions, or is the purpose to set up an incentive system so that when those consumers actually. Do you say they have no emissions that they've gone through all of the challenges of grid decarbonization?

So they've bought renewables. So they've invested in storage. So, fine, you can consume solar power at nighttime if you put it in a battery during the daytime. They're thinking about, demand flexibility. Are they consuming a bit less when there's less wind and sun? They're hard challenges, right?

We need to do a lot more of those type of things, and a proper accounting framework will make sure that in getting to zero that you have to think about and take all of those boxes. Whereas today, you can just be 100 percent solar powered and obviously that's just not going to lead to the grid decarbonization in the real world that we want to see.

Chris Adams: Maybe if you're in space it might work, but mostly no. Okay.

Killian Daly: Mostly, no. Yeah, 

Chris Adams: Okay, so we spoke a little bit about why there are some problems with the existing process, and like you, we've spoke a little bit, hinted at some kind of ways you could plausibly fix this. So do you, could you mind just talking me through some of the key things that EnergyTag is pushing for in that case?

Because it doesn't sound like you're trying to do something totally wacky, like, say you're never allowed, sorry, you're, it's not like you're asking for something like a significant change, like you're not allowed to split the greenness from power and or stuff like that. It sounds like you're still working inside the current ways that people are used to buying power and do all that stuff at the moment, right?

Maybe you could tell me about how it's supposed to work on the newer schemes that you're working with.

Killian Daly: Yeah. So basically what we're advocating for is that, if you're gonna claim to use green energy based on how you contract for power, then, well, you have to temporally match, right? So you can only claim to use green energy produced in the same hour as your consumption. Not in the same year, Okay. number 1. Number 2 is we need to think about the deliverability constraints, right,

and this geographical matching issue. And what we're saying is that, for example, in Europe, Europe is not a perfectly interconnected grid. And so you shouldn't be able to claim you're consuming green energy from anywhere else in Europe, you should be doing it, in the same bidding zone or, at least at a

Chris Adams: There needs to be some physical deliverable, physical connection to make it possible. Okay.

Killian Daly: Or fine, you can go across border, but you have to show that actually the power actually did come across border and that you're not violating like fun. You're not importing, 10 times more certificates than you are real power between 2 countries, right? So we need to have those, limits put in place.

And another thing that we think is important is that there needs to be some sort of controls on individual consumers just buying a load of certificates, for example, from very old assets. And I'm totally relying on those to be 100 percent green. For example, if I'm in Germany, right, and I just sign a deal with a hydro power plant, that has existed for 100 years and I'm time matched and I'm also within Germany, spatially matched, and I'm claiming to be 100 percent renewable

Chris Adams: it's not speedytransition if it's a hundred years old, that feels like that's stretching the definition of being an agent of that. Okay.

Killian Daly: that's another thing to kind of, you know, having this 3 pillar framework.

Sometimes we call about, and that is very important. I think for an existing consumer, it is legitimate to claim a certain amount of that existing power, but that must have a limit, right? You can't just be resource shuffling and "well I'm the one who's taking all the green energy" and everyone else is left with the, fossil that needs to be controlled also.

Chris Adams: All right. I think I follow that. So basically, so timely has to be more or less the same time, right? Deliverable, like you need to be able to demonstrate that the power could actually be delivered to that place. So deliverable there. And this other one was like, additional, like we need to transition, so you can't look at something which is 100 years old or 50 years old and say "I'm using that, I'm fine." There is this notion of bringing new supply stream to kind of presumably displace or move us away from our current fossil based default, which is not great from a climate point of view, right?

Killian Daly: Exactly. And I think one way, there's a really, a good friend of mine, who's in the Rocky Mountain Institute, Nathan Iyer, smart guy. We've worked a lot on US federal policy topics, and he actually has a really, good analogy about this stuff. BYOB, right?

So, yeah, of these 3 pillars. So, like, when you're going to a party, you need to bring your beer to the party on time. You can't bring it yesterday, you need to bring it when the party is happening. You need to bring it to the party, not to another party. And it needs to also be your own beer.

You can't just be taking someone else's. And it's it's kind of like a bit simplified, but it's a good analogy, I think for what we're trying to get out here. It's if we get everyone to start like thinking that way and acting on those kind of fundamental principles, obviously, we're going to end up being much more effective in deeply decarbonizing our power systems.

Chris Adams: So, decarbonization of the grid communicated through the power of carbonated beverages, basically. Wow!

Killian Daly: What could be better?

Chris Adams: I think it's, well, it's topical, at least it's still talking about CO2, just on slightly different scales, actually. I quite like that, actually. I might borrow that one myself, actually. Okay. So, there's one thing that you mentioned then. So this notion of, we spoke a little bit before about there's this idea of greenness that could be split, you're still keeping that, so you're not, saying, there's no ban on saying you're not allowed to sell power, that is unbundled from that, there is, that is still a kind of key idea of flexibility, could you maybe, I mean, cause from someone who isn't familiar with it, they might say, "why do we even have this, idea of being able to have separate these in the first place.

Doesn't this make things much more complicated?" I mean, I might be going down into the weeds, but is there a reason for that? is it just because that's how it's such a big change there that, or it's really hard to make that, to get people to shift to a new way of doing things or, what was that, what's the thinking around that part there?

Killian Daly: Well, basically, right, anytime you want to claim or have a contract, whether that be an unbundled or a bundle PPA contract, 

Chris Adams: Power Purchase Agreement, right?

Killian Daly: Yeah, a power, like a long term power purchase agreement, for example, right? so anytime you have a contract for a specific type of electricity, you need an accounting mechanism or a tracking mechanism that kind of sits on top of the grid and allocates generation to consumption, because

obviously, the way that the grid actually works, is that electrons are just oscillating around the place. there's not really a methodology to physically trace this individual electron started here and went there, right? And so, much like power markets do, and they have mechanisms for contractually allocating power between different buyers and sellers, as long as it's matched in time and space, that's a fundamental premise of our power markets work, we're basically borrowing that concept, but attaching the greenness attribute,

Chris Adams: Ah,

Killian Daly: and saying "provided that this system, of detaching greenness from the power is respecting temporal and geographical matching requirements, deliverability requirements, sufficiently, then that should be the basis of legitimate green claims and that essentially creates a market mechanism for financing renewables.

If you don't do that, then you cannot have a green power market basically, right? You,= don't have a way of differentiating buyers who are contracted for green power and those who are not doing anything. So, yeah, for example, a few years ago in Air Liquide, we only did this, we didn't look at what contracts we were sourcing.

We just did this location based accounting where you take an average of all the generation in the grid. Which is another way of looking at electricity emissions and a very valid way of doing it. But obviously one disadvantage that has is that it basically leaves all consumers passive.

They have no incentive to do anything in terms of driving electricity decarbonization. So that's why we need these, these mechanisms of essentially having tracking 

Chris Adams: systems. 

Oh, okay, I see. So, if you, if there's no recognition, if I'm working at a large company, why would I, why would I choose to buy something green if I can't be recognized for me doing something, doing that green step? And, so the downside of the location based approach is that yes, it gives you one single answer, but it takes away this idea that organizations which have honestly massive amounts of resources can influence or speed up a transition.

That's what it seems to be a kind of it's trying to respect that reality or at least acknowledge that this is what we expect of organizations if they're that powerful.

Killian Daly: And one person, I know you've had Olivier Corradi from Electricity Maps on before they've done, some very good blog series on this topic. They're obviously have insanely deep knowledge of grid emissions is really no one better that I've come across.

And they did a very kind of simplified explanation of this stuff. And you have the location based method, which is like maximizing physical accuracy and then you have the market based method, which is trying to maximize incentives and financing. And what this 24/7 accounting framework that we're advocating is basically trying to make those things meet in the middle, right? Today we have a market based system that is too much focused on, I would say, flexibility, making it easy for people to say they're green. and so has led to very valid criticism. And what we're trying to do now is bring that market based mechanism back closer to the physical realities of the grid,

Chris Adams: Oh, I see.

Killian Daly: But keeping the, incentive system, because if you don't have that, then, well, I don't really see the point in even doing the exercise.

Chris Adams: Okay. So there's two things that I wanted to kind of just see if I could maybe dive into a little bit on that then. So it sounds like this whole notion of not having this stuff tied to each other is to reflect the fact that people have all these complicated ways to purchase power in the first place.

So in my world as a cloud, as like someone working as a cloud engineer, right, I might buy computing by the hour, but I might also buy it, in advance for three years, for example, for a lower price, and that, that provides a bit of stability for whoever's running my server, but this kind of, this is an example of me having multiple different ways of being able to buy something, and essentially, some of that unbundling there is actually trying to capture the fact that there is, there are all these complicated ways to arrange to pay for something, and this is one way that we can use to value some of the Flexibility and stuff you said before.

So for example, you spoke about you can't run something on solar power, right? But if you had a battery, you can capture that and then use a battery bit like a time machine to kind of run at night almost right so but therefore you're trying to but that's more expensive than just making some claims.

So you need to have some way to recognize the fact that it takes a battery and a bunch of extra smarts to run something at night from that. That's what you're trying to go for with that, right?

Killian Daly: Yeah, exactly. And again, basing things on how power markets contractual, they have ways of already have contracted with allocating power between generators and consumers. I think the biggest issue with unbundling, so, selling the energy attributes and the power to different people. Actually, I think what the fundamental problem is the lack of time matching and deliverability requirements. That's where unbundling has gone wrong. Because it's, it said, "we're going to take the green attribute from this energy in Norway, and we're going to allow it to be used at any time of year, anywhere in Europe."

That's insane. That's where it starts to get completely insane. I don't have any particular problem with you producing it in one hydro plant, and selling the power into a power pool. and then that being consumed in Norway in the same hour. That's literally how power markets work on a short term power market.

Everyone bids into a common pool. And why not just put the attributes into the same pool and well, they, all have the same properties anyway. So it doesn't make a difference. It's the only way you're ever going to have liquidity, right? so I don't see any fundamental issue with, that.

The fundamental issue is with the annual matching and the

Chris Adams: the physics beyond breaking point, essentially.

Killian Daly: And that's, I think that's why I'm bundling, it's got such a bad name, right? And I think that's actually been fair, but I do think that it's not that bundling around bundling or necessarily the issue is, kind of the

Chris Adams: like those three pillars you mentioned. Okay, gotcha. Thank you for indulging me as I went down that thing, because I didn't know the answer to that, and I've always been wondering. Okay, so, we spoke about this thing called EnergyTag. We've spoke a little bit about how it's supposed to work and how it's basically an improvement on some of the approaches before.

And, maybe we could talk a little bit about who's using it? Is anyone, adopting it? maybe we could go from there, because this sounds like a cool idea, but there are many, cool ideas. That no one is paying attention to. And I suspect that would be quite a demoralizing conversation if that was the case.

So, yeah, I mean, who's using this and where, are there any kind of big name adopters you might point people to or anything like that?

Killian Daly: Yeah, so, yeah, two of the leading ones that kind of come to mind immediately, obviously, especially for software folks like yourselves or Google and Microsoft, they have 24/7 clean energy targets by 2030. Basically, they're committing to buying clean power for every hour, their data centers are consuming electricity, everywhere in, in which they're operating.

So they're two of the most, I would say, advanced, ambitious, corporate climate commitments in terms of scope 2 electricity procurement, at least. And they're obviously two major buyers. And they've been signing some really interesting deals as well. So there's, gigawatts now already of these 24/7 or close to 24/7 PPAs signed, 80, 90 percent firmed, portfolios of renewables, and that's game changing, right?

that's something we've seen emerge in the last few years where traditionally, the way of buying renewables has been "I'm going to buy a solar contract, and I'm going to blend that into whatever I'm buying elsewhere." And that's fine, right? But it's only giving you maybe 20, 20 percent of your electricity on an annual basis.

Now, we're seeing new contract structures that are blending together. Solar, wind, batteries, and getting maybe 80, 90 percent like of a flattened,

Chris Adams: so that's what I mean by firmed then, so firmed is this idea that it's basically it's when you say, so if it's not firmed, it's like I'm gonna buy the same amount totally without thinking about when it's matched, but if it's firmed then I am trying to think, I'm taking the steps necessary so that I can make a much more credible claim that the power I'm using is coming from generation or from stored amounts of power or something like that.

Ah,

Killian Daly: And that's, as I said, there's gigawatts of deals done already to date. Are there people doing this hourly matching stuff? Yes, absolutely. Check out our website. There's 30 projects there, with millions of megawatt hours of hourly matching being done.

So, this is not 40 organizations or something doing it 5 continents. So, This is not rocket science, right? This is literally taking meter data. That's very common, hourly production and gen data. You could do it on an Excel file with three columns if you wanted, and matching those things together and seeing where we're at. So it's absolutely demonstrated and leaders are doing it. Is everyone doing this? Is this now the status quo way of doing it? No, absolutely not. And that's what we work every day to try change, right? so we're still, I would say, relatively in the early days of this transition, but, as far as I'm concerned, it's kind of inevitable for credibility reasons, transparency reasons also for pretty fundamental economic reasons. Companies going out there and committing to buy loads of energy that is unmatched to their consumption profile.

They're leaving themselves open to a lot of risks. So, what if you say, okay, I'm just going to buy a load of solar. That has no connection to how I actually consume electricity. You're leaving yourself open to a lot of volatility that we're seeing electricity markets today. A lot of super high prices in the evening.

For example, when you're, when your solar contract is not delivering you anything, then what do you do? Right? you have all this gas volatility and exposure. So it's not just about decarbonization. It's also about things like electricity price hedging. So there's kind of various, I think, fundamentals that mean that.

We are going to move in this direction.

Chris Adams: okay, so So if I understand that final point that you've basically made is if I want to do this kind of matched thing for example, or if I want to, if I want to be buying some power, one of the advantages of doing like a longer term deal is that there's a degree of stability. So let's say, I don't know, a one country decides to invade another country and then totally make gas prices go through the roof.

I'm somewhat insulated from all that stuff so that it's not gonna massively destroy, it's not gonna destroy the, make impossible to kind of pay my own bills, for example. And like we've seen those of examples of that over the last few years, for example. So there's a bit of insulation from that kind of stuff.

Yeah.

Killian Daly: Exactly. So now we do get into kind of contracting mechanisms here. It's a little bit similar to what basically, if you're committing to a fixed price, for example, for a number of years, if you sign like one of these PPAs and you commit, let's say, to a 10 year fixed price for power. And if you're committing to like a affirmed profile, let's say 90 percent matched,

that has a very significant hedging value. So it means that basically you fixed like a lot of your power price. So no matter what happens, if, there's a massive spike in gas prices and power prices go through the roof. You're protected against that. We actually worked on a really interesting study on this a couple of years back or 18 months ago that said.

With Pexapark, who are like PPA analysts, and they basically showed that like a 10 megawatt consumer in Germany could save over 10 million euro, in the best of cases, and at least millions of euro in a given year by signing these 24/7, or close to 24/7 power purchase agreements with clean electricity assets, because one thing that clean energy has as an advantage in an ever more uncertain world is that the costs are basically known up front. You know how much money you need to build a wind turbine to build a battery up front.

It's all capex heavy. And that means that renewables can basically Give you a fixed price up front where honestly, gas cannot, because, most of their costs are operational. It's about buying the gas when you need it to.

Chris Adams: And there's a constant flow is not 

Okay, I guess with the sun, I mean, there's maybe a scenario where, I mean, it's not like there's a Mr burns style blackout of the sun kind of thing, right? if you're relying on something where no one has control over, no one can, kind of blockade the wind or blockade the sun.

That's where some of the stability is coming from, right?

Killian Daly: Yeah, exactly. Right. so you have those things, and you know that those fuel sources basically don't cost anything. Right? so you're all your costs are in construction, materials, all things you basically know, largely upfront, and that does enable you to provide long term contracts, typically way beyond the terms that fossil fuel generators can offer.

And so it can protect you for, the consumers willing to take that long term price risk. It can really offer really significant hedging benefits. not above alternatives.

Chris Adams: Buy that on like the spot market as it were or buying something just like on the regular market. Okay. All right. So, so you mentioned a few large companies doing that stuff and outside of technology, I know that 

I think it's the federal government. They've, it sounds like you said one or two things, which are quite interesting.

There is this idea that 100 percent is obviously really, good. Right. And that's what you want to head towards. But given there are some places where aren't, they're not going, they're not shooting for 100 percent straight away, for example, they might be going for 50 percent or 60 percent or something like that.

This is something that is kind of okay to do, or that's okay to start at. Cause I think I heard about the government, the US government had a plan for something about this by 2030 or something.

Killian Daly: Yeah. So basically, what we, we started the conversation talking about accounting. So I think the first thing you need to do is get, the accounting right. So that when you say 50, it means 50 or when you say 100, it means 100 because if you're just saying 100 and it means 50, then well, you're screwed, right?

You have a bad system. So, I think, actually being at 70 percent renewable, but saying that out loud

Chris Adams: 70%. Yeah.

Killian Daly: and addressing the, the basic fact that you're only there that's much better than kind of saying I'm 100 percent renewable on some annualized basis and kind of like misleading people about where you're at with, decarbonization.

Chris Adams: So it's better to be a real 70 than a fake 100, basically, yeah? 

Killian Daly: Yeah.

And, so, you have, electricity, like suppliers, for example, who are, there's like Good Energy in the UK, Octopus Energy in the UK, most of the electricity suppliers now in the UK, in fact, are, offering these like hourly tariffs.

And, I don't think any of

Chris Adams: it was only one or two that did that. Whoa. That's

Killian Daly: Now, I think this year it'll become more, a kind of a norm, where they will offer this alongside their a hundred percent renewable tariff. And none of those hourly tariffs are gonna start off being a hundred percent renewable, but it's bringing that extra bit of transparency, which I think is great.

And, the likes of good energy, they're already offering to thousands of customers, right? This is not just the Googles and the Microsofts who have their long term targets on this. This is already being offered to thousands of customers around the world because electricity suppliers are basically taking.

They're doing all the work. They're just giving the consumer the number on some dashboard saying, this is how much matching you have. if you look at the Octopus Energy example, it's quite interesting. They have a tariff called Electric Match for some of their B2B customers and they're actually basically reducing your price of power. when you're more matched, so that's quite cool, yeah, they're charging you less the more that your demand is matched to their generation. Right? And I think that's quite a cool gamification of this. They're saying basically try to consume when there's more wind and sun in the UK, you'll be more matched and we'll cut, we'll cut your rates because obviously it's sort of, it costs them less to deliver that in the first place.

So that's. That's the type of cool mechanism.

Chris Adams: So, I swear, every single time I speak to energy people, and they say, "oh yeah, the price is totally changing." Then I think of one level up, when we're like paying for cloud, and it's the same price all the time. Someone's making a bunch of money off us doing all the kind of carbon aware computing stuff, because if the price is going, low, I would expect to see those numbers go low.

This feels like something we might want to have a conversation about inside the tech industry then, if they are, if there's savings being made here, because it feels like it would be nice if those were passed on, I suppose. So, all right, let's speak, go on,

Killian Daly: I think just very importantly, of the, the more I think one fundamental truth that we're going to see,

it's already the case in some parts of the world, but this is going to be an essential truth of the transition. The more renewables you have, the more volatility you're going to have in power prices. And the more flexible you can be in your consumption. It is going to be very rewarding economically, if you can consume, at the times of day when there's loads of wind and sun, power prices are going to be very low and you're going to get rewarded for that. If you can't, if you can only be base load, then that is going to cost you.

Chris Adams: Ah, okay, alright. Okay. Alright, that's it, that's a useful thing to take into account. And so, we spoke before about, scope 2 and stuff like that, and you spoke about this idea that you're defining this standard. Now, EnergyTag is a standard in its own right, but, as I understand it, it's not like you're stepping outside of this.

You are still engaging with the protocols and all the stuff like that right now, yeah?

Killian Daly: Basically, so yeah, 

EnergyTag is a nonprofit. we do a couple of different things. we're obviously focused on this area of electricity accounting, electricity markets and better green energy claims and all that. And so yeah one of the things that we do is we have a voluntary standard for hourly energy tracking because one of the kind of blocking points we have today, is that the way we do this tracking with these energy certificates, it tends to be on a monthly or even an annual basis globally.

And sometimes we don't have the information on the certificates to do this hourly matching. So we're trying to un debottleneck that particular technical issue. Think about how do we track through storage, like doing some novel things there. So we have a standard for that, but that's only one of the building blocks, I would say of this much larger question of, like, how do companies do electricity accounting or how do they do carbon accounting more generally? Our standard is there to work on that specific topic, but actually a lot if not most of what we do today is like working on policy advocacy around the world, working on global standards and basically advocating for those to change because ultimately it's the meta-levers, regulations,

standards. Once they change, then we're just there to help technically put that all together with some voluntary standards as long as they're needed.

But it's not our aim to be the world's next greenhouse gas protocol. That's really not in our wheelhouse. What we want to do is make sure that global standards and regulations are as good as possible.

Chris Adams: Oh, I see. Okay, so that, so if we go for a concrete example of this. So, in Europe, if you want to do a hydrogen project, which is, in some ways, kind of a bit like an AI project in that it's like a building that uses loads and loads and loads of power in one place, right?

Really dense. If you're going to make, green hydrogen, for example, you're taking water, adding loads of electricity to split that, and that's incredibly energy intensive. So you've probably want that, if you want the green hydrogen to be green, probably only use green energy. And one of the things you told me about before was, yes, we won that fight so that any, and if people want to get any of the subsidies from the government to kind of do this green energy thing, they need to have those three pillars style approach, right?

That's what, that's an example of your strategy, yeah?

Killian Daly: Yeah, so this is actually the reason I what really brought me into EnergyTag, it was a Greenhouse Gas Protocol thing, but basically are the key to one of the world's largest hydrogen producers. Right? And so I got put onto this topic a few years ago, which I found incredibly important and fascinating and, maybe not well enough understood.

It's like, when we're going to produce hydrogen using electricity, we need to really make sure that the electricity is squeaky clean, because of the efficiency issues and losses that you just inherently have with electrolysis. And so, just to give a quick example, Jesse Jenkins lab in Princeton University, a guy called Wilson Ricks, who is a rock star of power system modeling, they model this right?

And they show that in the US, if you basically use today's carbon accounting rules, this annual matching stuff, and you built out a hydrogen sector based on those rules, you will have hydrogen that is twice, maybe even three times as bad as today's fossil fuel hydrogen production. and you'd be calling it clean and subsidizing that production. Totally insane, just literally wasting money.

And so it's actually really, important. Billions of dollars of subsidy are going to go into hydrogen in Europe and in the United States. And so we worked a lot with NGOs, advanced companies and other partners to advocate for these strong requirements on green electricity sourcing for hydrogen, both in the US and also in Europe, and we won on both fronts, which has

Chris Adams: Oh, the US way as well!

Killian Daly: Yeah. Yeah. Yeah. Yeah. And it hasn't been, so both of those are legislation in 

Chris Adams: place.

They're in! Yay science!

Killian Daly: Yeah, that's the legal way now to qualify for the tax credit in the US. In Europe, there's a phase in period on the hourly part to 2030. So, in 5 years or whatever.

But anyway, projects built now, they have to be designed to comply with that. And so, 

Chris Adams: if you know,

it's going to be in the law of five, you're just going to make sure you 

Killian Daly: going to start doing it now, right? more or less. yeah, so that's, yeah, obviously, this is kind of like hundreds of millions of tons of CO2 per year on the line between good and bad rules and that, that's kind of a concrete example of, why these things matter. Right? Like accounting sounds boring sometimes. I definitely thought it was boring before I realized like, "Oh my God, I'm working for a huge power consumer and this is changing everything." So yeah, it's definitely super, super important that we get this stuff right.

Right.

Chris Adams: Okay, so we spoke about, it sounds like you've done the work with Air Liquide and you've kind of essentially laid the groundwork to move from a fossil based hydrogen thing to hopefully a greener way of making hydrogen, which ends up being used in all these places. And now it seems like you've got the, okay, you said Google and Microsoft, same power usage as Air Liquide in a single year.

Maybe they might've changed, but back then, there's, so it looks like we're seeing some promising signs. For that over here. So maybe, I mean, if we, want to see that, what do we need to see at a policy level? Do you need to have, government saying, "if you want to have green energy for data centers, you need to be at least as good as the hydrogen, industry."

Is it something like that you need to do? Because what you've described for the hydrogen thing sounds awesome, but I'm not aware of that in the, kind of IT sector yet. That's something that I haven't seen people doing yet. 

Killian Daly: That is also coming, right? So hydrogen has just been the first battleground or the first palce, I think. 

Interestingly, actually, on the 14th of January, just before the inauguration of Donald Trump, as US president, so the Biden administration issued an executive order, which hasn't yet been rescinded.

Basically on data centers on federal lands and in that they do require these 3 pillars. So they do have a 3 pillar requirement on electricity sourcing, which is very interesting. Right? I think that's quite a good template. And I think, we definitely need to think about, okay, if you're going to start building loads of data centers in Ireland, for example, Ireland, 20 percent 25 percent of electricity consumption in Ireland is from data centers. That's way more than anywhere else in the world in relative terms. Yeah, there's a big conversation at the moment in Ireland about "okay, well, how do we make sure this is clean?" How do we think about

procurement requirements for building a new data center? That's a piece of legislation. That's being written at the moment. And how do we also require these data centers to do reporting of their emissions once they're operational? So, the Irish government, is also putting together a reporting framework for data centers and the energy agency.

So the Sustainable Energy Authority of Ireland, SEAI they published a report a couple of weeks ago saying, yeah, they, you know what, they need to do this hourly reporting based on contracts bought in Ireland. So I think we're seeing already promising signs of legislation coming down the road in other sectors outside of hydrogen.

And I think data centers is, probably an obvious one.

Chris Adams: So people are starting to win. Wow, I didn't realize that. I knew somewhat about that there was an executive order that there was a bit of buzz about. But I didn't realize that, set the precedent. So, yeah, we should do what that massive industry over there is doing because that's now the new baseline that, that's where the bar should be.

We should do that as well, basically.

Killian Daly: Exactly, because that those hydrogen rules, it's actually what it actually is. Well, actually, the whole debate was about is what is clean electricity procurement? What does that mean? What does it mean to use clean electricity? And that has been defined now in hydrogen rules and that can be copy and pasted to any large new load.

Well, if you want it to be clean, we already know the answer. It's in legislation,

Chris Adams: It's how to tell when energy is green,

Killian Daly: MIT, IEA, the who's who of energy experts have all modeled this and they've all found that this is the way to do it. So, there's a template there, right? And it's, if you're going to go against that, it, yeah, well, obviously, then you're, obviously sacrificing the integrity of your accounting schemes.

Chris Adams: Wow! That was, we spoke about how to tell when energy is green, and you've, We seem to be ending on a high, I didn't realise we'd actually got to that. That's really, awesome. You've really made my day for that, Killian. Thank you so much for coming on and diving into the minutiae of carbon accounting for electricity, but also ending it with a slightly less depressing piece of news, which I'll take in this current political climate,

Killian Daly: just to interject before I say goodbye, there's one, one really, it's good to end on a positive note, I suppose, in this mad world we live in. 

There was a project announced recently. I think people should go check it out in the Middle East in UAE, where basically for the first time, they're going to deliver basically, around the clock solar power. So 1 gigawatt of solar, all night long because they're basically, building a massive battery and a huge solar farm, and basically all year round is going to deliver, green electricity at under 70 us dollars per megawatt hour, which is extremely competitive.

So, I think solar and storage, what they're going to do together is going to be, is going to change the world. Right? I really think that is going to happen faster than people think. They're going to start to kill gas. So, yeah, I think green energy economics, despite what politicians will want to do with their culture wars,

I think will at the end of the day, hopefully, answer some of the questions we're trying to solve here. So, yeah, thanks so much for having me on. It's been a real pleasure.

Chris Adams: Brilliant, thank you so much for that mate, and may the fossil age end. That's really, that's so, so cool to actually see that, I totally forgot about the Masdar thing, which is the city. Yeah, and we'll share a link to that so people can read about that, because if you care about, I don't know, continued existence on this planet, then yeah, it's probably one to, good one to read about.

Killian, this has been loads of fun, thanks a lot mate, and next time I'm in Brussels I'll let you know, and maybe we can catch up for, have a shoof or something like that. Take care

Killian Daly: Yeah. A hundred percent. Thanks. Bye.  

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> In this episode, host Chris Adams is joined by Killian Daly, Executive Director of EnergyTag, to explore the complexities of green energy tracking and carbon accounting. They discuss the challenges of accurately measuring and claiming green energy use, including the flaws in current carbon accounting methods and how EnergyTag is working to improve transparency through time-based and location-based energy tracking. Killian shares insights from his experience managing large-scale energy procurement and highlights the growing adoption of 24/7 clean energy practices by major tech companies and policymakers. They also discuss the impact of green energy policies on industries like hydrogen production and data centers, emphasizing the need for accurate, accountable energy sourcing and we find out just how tubular Ireland can actually be!

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TRANSCRIPT BELOW:

Killian Daly: We need to think about this kind of properly and do the accounting correctly.

And unfortunately, we don't do the accounting very well today. 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Hello, and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. When we write software, there are some things we can control directly. For example, we might be able to code in a tight loop ourselves, or design a system that scales to zero when it's not in use.

And if we're buying from a cloud vendor, like many of us do now, we're often buying digital resources, like gigabytes of RAM and disk, or maybe virtual CPUs, rather than physical servers. It's a little bit less direct, but we still know we have a lot of scope for the decisions, to control the impact of their decisions and what kind of environmental consequences come about from that.

However, if we look one level further down the stack, like how the energy powering our kit is sourced, our control is even more indirect. We rarely, if ever, directly choose the kind of generation that powers data centers that our code runs in. But we know it still has an impact. So if we want to source energy responsibly, how do we do it?

If you want to know this, it's a really good idea to talk to someone whose literal job for years has been buying lots and lots of clean energy and is intimately familiar with the standards involved in doing so and who has spent a lot of time thinking about how to make sure you can tell when the energy you're buying really is green.

Fortunately, today I'm joined by just that person, Killian Daly, the Executive Director of the standards organization, EnergyTag. Killian, it's really, nice to have you on the pod. Thanks for coming on.

Killian Daly: Yeah, thanks. Thanks very much for having me, Chris. great to be on the pod and, an avid listener, also. So it's always nice to contribute.

Chris Adams: Thank you very much. Killian, I'm going to give you a bit of space to introduce yourself, and I've just mentioned that you're involved in EnergyTag, and we'll talk a little bit about what EnergyTag does. Because I know you and because, well, I met you maybe three years ago, I figured it might just be, it might be worth just talking a little bit about our lives outside of green software and sustainability.

So, we were in this accelerator with the Green Web Foundation talking about a fossil free internet, and you were talking about EnergyTag and why it's important to track the provenance of energy. I remember you telling, we were asked about our passions. And, you told me about surfing and I never ever thought about Ireland as a place where you would surf because I didn't think it was all that warm. So can you maybe tell me a little bit like enlighten me here because it's not the first country I think of when I think of surfing and when you said that I was like he's" having a joke, right?"

Killian Daly: Yeah. Well, I do like to joke, but this is not actually one of the jokes, Well, it doesn't need to be warm to surf. You just need to have waves, I suppose. So, yeah, it's something since I was really very young. I've always gone to the west coast of Ireland. Beautiful County Clare near the Cliffs of Moher.

Maybe people know of them. And so we go every year. And my cousins, since a very young age, started surfing. We just, you know, solve these big waves and there's other people out there, surfing, bodyboarding and we're like, "Hey, let's try that out. That looks really cool." So, yeah, since I don't know, 6 or 7 years old, I've been going there every year, in summer, also in winter, me and my cousins also go, yeah.

We go at New Year's get into the frigid cold Atlantic. And, yeah, it's magic, really. If you have the right, if you have the right wetsuit, you can kind of, you can get through anything, 

Chris Adams: So there's no such thing as cold was it bad weather, just bad clothing that also applies to wetsuits.

Killian Daly: Yeah. Yeah. Yeah. It couldn't apply. Couldn't apply anymore. And obviously, in winter, you get the biggest swells, right? so actually, people probably don't know it, but Ireland has some of the biggest waves in the world. Now, on the west coast of Ireland, you have, yeah, really massive 50, 60 foot waves.

Yeah, really all you can get some sort of a, all time surf there. So, so yeah, it's one of one of our better kept secrets.

Chris Adams: I was not expecting to learn how to go totally tubular on this podcast.

Killian Daly: Yeah, 

Chris Adams: Wow, that's, yeah, that's...

Killian Daly: It's not, not for the faint of heart, but yeah, I would definitely recommend it.

Chris Adams: Actually, now that you mention that, and now that we talk about, going back to the world of energy, now that people talk about Ireland as, the Saudi Arabia of wind, and it being windy AF, Then I can kind of see where you're coming from with it, actually. It doesn't make a bit more sense. So yeah, thank you for that little segue, actually, Killian.

Okay, so we've started to talk a little bit about energy. And, I know that your, the organization you work for right now is called EnergyTag. But previously, as I understood it, you didn't, you worked in other organizations before. And, you've been working as a kind of buyer of energy, so you know a fair amount about actually sourcing electricity and how to kind of do that in a kind of responsible way.

And I think when I heard you, we spoke about this before, you mentioned that, "yeah, I'm used to buying significant amounts of power" in your kind of previous life. Could I just like, could you maybe talk, provide a bit of a kind of background there, and so we can talk a little bit about context and size, because that might be helpful for us talking about the relative size that tech giants might buy and so on, and how much of that is applicable.

Killian Daly: Yeah, sure. Yeah, so, I've been thinking about energy for a long time, even before my professional career studied energy and electrical engineering since I was 18 years old and did a master's in that, also. And then obviously in my working life as well. I've been basically always in the energy sector.

So before EnergyTag, I was basically overseeing the global electricity portfolio, and the procurement of electricity for a company called Air Liquide, which is basically a large French multinational that produces, liquid air. So, oxygen, nitrogen, all the different parts of air which are, essential, feedstocks into various industries, and they consume a lot of electricity.

So, the portfolio my team oversaw was about 35 to 40 terawatt hours of electricity consumption.

Chris Adams: Okay.

Killian Daly: Yeah, it's a lot, it's more than my home country, Ireland. It's about the same as Google and Microsoft

Chris Adams: put together, yeah. Okay, so, wow. And

Killian Daly: So, it's pretty big stuff. And obviously, when you're working on something like that globally, looking at various electricity markets operating in 80 countries in these huge volumes, I suppose you, kind of learn a lot about what it means to buy power.

Chris Adams: I guess if you're looking at something which is basically as much power as an entire country, then there's going to be like country sized carbon emissions, depending on what you choose to power this from. And I guess that's probably why you, I mean, we, have ways of tracking power. I mean, tracking the carbon emissions from various things like this, I mean, called like the GHG protocol, which is a kind of like the kind of gold standard for talking about some of that stuff.

And this is something that I think you have some exposure to and I remember when you spoke to me, I remember us sitting down one time and you were telling me about There's a thing called scope 1 and there's a thing called scope 2, and that scope 2 was actually a kind of relatively new Idea where this came into this. Can you maybe tell me a little bit like maybe you could explain to someone who is Who's heard of, carbon footprinting, and they know there's a thing called scopes.

Why would anyone care about scope 2 in the first place? And how does it come about in the first place? Because it seems like it's not intuitive for most people when they first, when they start thinking about carbon footprints and stuff like that. 

Killian Daly: Yeah. I think the obvious, first thing you need to take into account when you think of like a company's emissions is, well, what are they burning themselves on site? do they have gas boilers burning gas? Are they burning coal to produce electricity? So that's, I think, very intuitive and obvious. But actually that is not the end of the story. And there's actually like a, a very funny anecdote. I put a true anecdote from the legendary Laurent Segalen, who does the Redefining Energy podcast and general energy guru. And he was actually involved in the kind of creation of a lot of the carbon accounting standards that are used today, this Greenhouse Gas Protocol standard, which is basically used by over 90 percent of companies now to report their carbon emissions.

It is the Bible of how carbon accounting works, right? and so 20 years back, he basically was, down in Australia and visiting an aluminum smelter. On site, they were explaining, "this is very low carbon product. we hardly burn any fossil fuels on site. This is incredibly, clean production." 

Chris Adams: The aluminium here, right? big chunks of aluminium. Okay, right.

Killian Daly: Aluminum, aluminum smelting. So like one of the, biggest metallic commodities that we have, very energy intensive. and so, he was there on site and just saw these big overhead wires coming in from yonder, from somewhere, right? And he said, hang on, what are the, what are those big cables above? and they were like, "oh, yeah, that's the electricity," obviously driving the smelter because aluminium, it's all about electricity. That's what power is an aluminium production facility. And so he said, well, hang on, where is that coming from?

They're like, "oh, no, don't, don't worry about that. That's not our responsibility." Well, it absolutely is, right? so you need to think about where is that electricity coming from? How is that being produced? And in that case, it was coming from a very large multi gigawatt coal power plant right next door. 

Chris Adams: Okay. All right. So I thought you were gonna say, oh, it's maybe a, something clean, like a hydro power station, but no, just a big, fat, dirty, great coal fired power station was the thing generating all the power for it. And that's where

Killian Daly: Absolutely. So, that's kind of the, just a bit of an anecdote is that's why it's so important to think about what we call scope to emissions, the emissions of electricity that I'm consuming, because especially as we electrify the economy, right, more and more emissions are going to become scope 2 emissions.

They're going to be related to someone else either burning fossil fuels to produce electricity and to give to a consumer or ideally, using clean energy sources to generate that electricity without carbon emissions. 

we need to think about this kind of properly and do the accounting correctly.

And unfortunately, we don't do the accounting very well today.

Chris Adams: Alright, so previously, before we even had that, there wasn't even this notion of scope 2 in the . , you might have just had direct, and then maybe this kind of bucket of indirect stuff, which is really hard to measure, so you're not going to really try to measure it. And okay, so, I remember actually reading about some of this myself, and I always wondered, like, where do some of these figures come, where do, where does even the notion of a protocol like this come from? And one of the things I realized was, particularly with the GHG one, was that they're like, when I listened to Laurent Segalen speaking about some of this, he was basically saying, yeah, this was essentially like Shell, the oil company, who basically said, "we have a way of tracking our own emissions."

And, why not use that as a starting point for talking about how we do carbon accounting? And then, scope 2 was a new concept. That was one of the things that they were kind of pushing for. But I suppose this kind of speaks to the idea of, who's in those rooms for those working groups to kind of, that is going to totally change the framing of how we talk about some of this.

And I guess that's probably why this, is this a little bit like why you started talking and getting involved with things like EnergyTags so you could take part in those discussions? Because it feels if this is what we're going to use to define how we do this or how we do that just like you have people talking about okay BP had an impact of changing how we think about carbon footprints from, from an individual point of view.

But you do need people involved in that conversation to say, "actually, no, that's possibly not the best way to think about this, and there are other ways to take this into account." I mean, is this why you got involved in the EnergyTag stuff?

Killian Daly: Yeah, it's one of the main reasons, because I used to do, so, work for one of the world's largest electricity consumers. And so I was responsible for calculating all of the electricity emissions for that company, right? Like doing the scope 2. And so I read the Greenhouse Gas Protocol back to front.

That was how the, all the calculations were done. That's what qualified clean and not clean, right? And I remember thinking, "this is an insanely influential document," right? It's kind of in the weeds. It's kind of stayed maybe, to some people, but I was

Chris Adams: of tedium around it, here. 

Killian Daly: Yeah. But the more I've gotten involved in things like regulation and conversations like that, that is where, it's in the annexes, it's in the details that the big decisions are made often. So I remember thinking back then, this is insanely influential and some of the ways that we're allowed to claim to consume clean energy are, frankly, disconnected from reality in a way that is just not okay, right?

As in this is far too weak. And definitely, I thought, someday I'd love an opportunity to be able to, say, "hang on, can we,

we fix this please? can we do this differently? Can we start to respect some sort of basic realities here?" So, yeah, it was definitely one of the drivers why I joined EnergyTag, which is obviously like a nonprofit that is, has as its mission to clean up accounting, right? And to clean up the way we think about electricity accounting. So, yeah, obviously it's a great honor, I suppose, to be part of those ongoing discussions in the Greenhouse Gas Protocol update process.

Chris Adams: So, We spoke before about how there, before there was even no scope 2, right? So that was like, the bar was on the floor. Right, and then we introduced the idea that, oh, maybe we should think about the emissions from the electricity. So that was kind of a bit of a leap forward by one person pushing for this, that otherwise wouldn't have been in the standard at all, right?

And I just realized actually now that you mentioned that, we spoke about oil firms being very involved in this and being very organized in this, and I remember people talking about Shell, that's what you use, and how much, and I'm just realising, oh Christ, Shell's in the Green Software Foundation as well.

We should, that's something I didn't really think so much about, but they're also there too. So they are organized. Wow. So let's move on. So maybe we could talk a little bit about scope 2 here. The thing I want to kind of get my head around is I'm like, can you maybe talk me through some examples of where this doesn't, this falls down a little bit, where might be a little, stretching your, you spoke about the physicality, the physical reality. where does it need a bit of work, or need some improvement that you're looking to do, looking to address in EnergyTag, for example? 

Killian Daly: Yeah, so basically, one way of doing scope 2 accounting is basically looking at the energy contracts or the electricity supply, contracts that companies have and saying, well, where are you buying your energy from? How are you contracting for your power? Right? And there's a kind of a number of fundamental issues.

One of them is around the temporal correlation, or between when you're consuming electricity and when the electricity you're claiming to consume is being produced. And today, right, we actually allow an annual matching window between production and consumption. And put in simple terms, what that means is that you can be basically solar powered all night long, right. You can take solar energy attributes from the daytime and use them at nighttime, or you could take them from the daytime in March and use them at nighttime in November. At any other time of year. And this just does not make sense, right?

Chris Adams: Not physically how the science works for a start. Maybe if I can just dive into that a little bit in a bit more detail because you've mentioned this idea of certificates for example or like claiming like that and as I understand it if I am running a solar farm right I'm generating two separate things. I'm generating power but I'm generating the kind of greenness so these are two independently sellable things which will sometimes be bundled together. That's how I might buy green energy. But under certain rules, they're not. They can be separated. So it's like the greenness that I'm moving or I'm buying and kind of slapping onto something else to make it green. Is that? And if it's at the same time, it's kind of okay. If it's from totally separate times of day, you do like you mentioned where you're saying this thing running at night runs at solar, is running on the greenness from a solar farm, which is stretching the, well, our imagination, I suppose, and your credulity, I suppose.

Okay, so that's one example of this is something that you wanted to get, wanted to get fixed. Are there any other ones, or things that you'd point people to, because

Killian Daly: I think you know the. The other, the other aspect, I think that's pretty, problematic in today's standards is so we've talked about time and the other big one is space, right? Today we allow consumers to claim to use green energy or clean energy over vast geographical boundaries that really don't respect the physical limits of the grid.

So, for example, the whole U. S. is considered to be one region, right? So you can buy green energy attributes produced in Texas and say that you're using them in New York. So you could be 100 percent power by Texas solar in New York. Or if you're in Europe, Europe is considered of one region. So you have really absurd cases where you can be powered by Icelandic hydro in Germany, and Iceland has never exported any electricity to anyone. There's no cables leaving Iceland. So, that just doesn't make sense. And this has real consequences because what we're trying to do is obviously drive consumers to buy green energy. If they're doing it in this way, then they're kind of, in some cases, pretending to buy green energy rather than actually going and buying green energy and incentivizing more production of green energy and clean flexibility that's needed to integrate that solar and wind, at every hour of the day.

So, that time and space kind of paradigm is maybe a good way of thinking about, some of the fundamental issues here. There are other ones. I don't know how far we want to go into the rabbit hole, but that's two very high level, and hopefully very kind of understandable examples of the problems we have with today's carbon accounting.

Chris Adams: Yeah, I think I understand why that would be something we would address, and so presumably this is the thing that EnergyTag's looking to do now. You're basically saying, well, the current system is asking you to make quite spectacular leaps of faith. And there are certain places where you do want to do leaps of faith and be super creative, but accounting might not be where you want to be super creative or super jumpy. That's not always where you want to have your innovation.

So that's, this is, so you're saying, well, let's actually be, make this more reflective of what's really happening in the world. So that we've got like some kind of solid foundation to be working on. So,

Exactly. 

Killian Daly: And just maybe on that point, this is not what we advocate for is not, it's not anything radically new, to be honest, because the way electricity markets work today, the way electricity utilities deliver power to customers, just you know, let's say pure gray electricity on electricity markets.

It is based on fundamental concepts of time matching. Power markets work on a 16, sorry, a 60, 30 or 15 minute, like balancing period. In Australia, it's 5 minutes. In Europe, there's things called bidding zones. So that's the area over which you can buy and sell electricity. And all of this is to kind of capture these fundamental physical limits of the power system.

You have to balance it in real time. And there's only a certain amount of grid capacity. And so you need to realize areas over which it's reasonable to trade power or not. So all we're saying is, make the green energy market much more like the real power market. So we're actually, if anything, trying to make it a bit more common sense,

whereas today, we're, quite detached from some of those basic limits that

Chris Adams: Ah, I see. Okay. So in fact, in some ways, there are some kind of comparisons where you could plausibly make where people there's a push right now for people to talk about treating environmental data with some of the same seriousness as financial data and apply some of the same constraints it sounds like something a little bit like that so if people are going to have basically take into account the physical constraints when they're purchasing the actual power part, they should think about applying their same ideas when they're thinking about the greenness of it as well. You can't kind of cheat, even if it makes it a bit easier, for example.

Killian Daly: Yeah, well, exactly. And, ultimately, what are we trying to do here? Is the purpose so that certain consumers can say that they have no emissions, or is the purpose to set up an incentive system so that when those consumers actually. Do you say they have no emissions that they've gone through all of the challenges of grid decarbonization?

So they've bought renewables. So they've invested in storage. So, fine, you can consume solar power at nighttime if you put it in a battery during the daytime. They're thinking about, demand flexibility. Are they consuming a bit less when there's less wind and sun? They're hard challenges, right?

We need to do a lot more of those type of things, and a proper accounting framework will make sure that in getting to zero that you have to think about and take all of those boxes. Whereas today, you can just be 100 percent solar powered and obviously that's just not going to lead to the grid decarbonization in the real world that we want to see.

Chris Adams: Maybe if you're in space it might work, but mostly no. Okay.

Killian Daly: Mostly, no. Yeah, 

Chris Adams: Okay, so we spoke a little bit about why there are some problems with the existing process, and like you, we've spoke a little bit, hinted at some kind of ways you could plausibly fix this. So do you, could you mind just talking me through some of the key things that EnergyTag is pushing for in that case?

Because it doesn't sound like you're trying to do something totally wacky, like, say you're never allowed, sorry, you're, it's not like you're asking for something like a significant change, like you're not allowed to split the greenness from power and or stuff like that. It sounds like you're still working inside the current ways that people are used to buying power and do all that stuff at the moment, right?

Maybe you could tell me about how it's supposed to work on the newer schemes that you're working with.

Killian Daly: Yeah. So basically what we're advocating for is that, if you're gonna claim to use green energy based on how you contract for power, then, well, you have to temporally match, right? So you can only claim to use green energy produced in the same hour as your consumption. Not in the same year, Okay. number 1. Number 2 is we need to think about the deliverability constraints, right,

and this geographical matching issue. And what we're saying is that, for example, in Europe, Europe is not a perfectly interconnected grid. And so you shouldn't be able to claim you're consuming green energy from anywhere else in Europe, you should be doing it, in the same bidding zone or, at least at a

Chris Adams: There needs to be some physical deliverable, physical connection to make it possible. Okay.

Killian Daly: Or fine, you can go across border, but you have to show that actually the power actually did come across border and that you're not violating like fun. You're not importing, 10 times more certificates than you are real power between 2 countries, right? So we need to have those, limits put in place.

And another thing that we think is important is that there needs to be some sort of controls on individual consumers just buying a load of certificates, for example, from very old assets. And I'm totally relying on those to be 100 percent green. For example, if I'm in Germany, right, and I just sign a deal with a hydro power plant, that has existed for 100 years and I'm time matched and I'm also within Germany, spatially matched, and I'm claiming to be 100 percent renewable

Chris Adams: it's not speedytransition if it's a hundred years old, that feels like that's stretching the definition of being an agent of that. Okay.

Killian Daly: that's another thing to kind of, you know, having this 3 pillar framework.

Sometimes we call about, and that is very important. I think for an existing consumer, it is legitimate to claim a certain amount of that existing power, but that must have a limit, right? You can't just be resource shuffling and "well I'm the one who's taking all the green energy" and everyone else is left with the, fossil that needs to be controlled also.

Chris Adams: All right. I think I follow that. So basically, so timely has to be more or less the same time, right? Deliverable, like you need to be able to demonstrate that the power could actually be delivered to that place. So deliverable there. And this other one was like, additional, like we need to transition, so you can't look at something which is 100 years old or 50 years old and say "I'm using that, I'm fine." There is this notion of bringing new supply stream to kind of presumably displace or move us away from our current fossil based default, which is not great from a climate point of view, right?

Killian Daly: Exactly. And I think one way, there's a really, a good friend of mine, who's in the Rocky Mountain Institute, Nathan Iyer, smart guy. We've worked a lot on US federal policy topics, and he actually has a really, good analogy about this stuff. BYOB, right?

So, yeah, of these 3 pillars. So, like, when you're going to a party, you need to bring your beer to the party on time. You can't bring it yesterday, you need to bring it when the party is happening. You need to bring it to the party, not to another party. And it needs to also be your own beer.

You can't just be taking someone else's. And it's it's kind of like a bit simplified, but it's a good analogy, I think for what we're trying to get out here. It's if we get everyone to start like thinking that way and acting on those kind of fundamental principles, obviously, we're going to end up being much more effective in deeply decarbonizing our power systems.

Chris Adams: So, decarbonization of the grid communicated through the power of carbonated beverages, basically. Wow!

Killian Daly: What could be better?

Chris Adams: I think it's, well, it's topical, at least it's still talking about CO2, just on slightly different scales, actually. I quite like that, actually. I might borrow that one myself, actually. Okay. So, there's one thing that you mentioned then. So this notion of, we spoke a little bit before about there's this idea of greenness that could be split, you're still keeping that, so you're not, saying, there's no ban on saying you're not allowed to sell power, that is unbundled from that, there is, that is still a kind of key idea of flexibility, could you maybe, I mean, cause from someone who isn't familiar with it, they might say, "why do we even have this, idea of being able to have separate these in the first place.

Doesn't this make things much more complicated?" I mean, I might be going down into the weeds, but is there a reason for that? is it just because that's how it's such a big change there that, or it's really hard to make that, to get people to shift to a new way of doing things or, what was that, what's the thinking around that part there?

Killian Daly: Well, basically, right, anytime you want to claim or have a contract, whether that be an unbundled or a bundle PPA contract, 

Chris Adams: Power Purchase Agreement, right?

Killian Daly: Yeah, a power, like a long term power purchase agreement, for example, right? so anytime you have a contract for a specific type of electricity, you need an accounting mechanism or a tracking mechanism that kind of sits on top of the grid and allocates generation to consumption, because

obviously, the way that the grid actually works, is that electrons are just oscillating around the place. there's not really a methodology to physically trace this individual electron started here and went there, right? And so, much like power markets do, and they have mechanisms for contractually allocating power between different buyers and sellers, as long as it's matched in time and space, that's a fundamental premise of our power markets work, we're basically borrowing that concept, but attaching the greenness attribute,

Chris Adams: Ah,

Killian Daly: and saying "provided that this system, of detaching greenness from the power is respecting temporal and geographical matching requirements, deliverability requirements, sufficiently, then that should be the basis of legitimate green claims and that essentially creates a market mechanism for financing renewables.

If you don't do that, then you cannot have a green power market basically, right? You,= don't have a way of differentiating buyers who are contracted for green power and those who are not doing anything. So, yeah, for example, a few years ago in Air Liquide, we only did this, we didn't look at what contracts we were sourcing.

We just did this location based accounting where you take an average of all the generation in the grid. Which is another way of looking at electricity emissions and a very valid way of doing it. But obviously one disadvantage that has is that it basically leaves all consumers passive.

They have no incentive to do anything in terms of driving electricity decarbonization. So that's why we need these, these mechanisms of essentially having tracking 

Chris Adams: systems. 

Oh, okay, I see. So, if you, if there's no recognition, if I'm working at a large company, why would I, why would I choose to buy something green if I can't be recognized for me doing something, doing that green step? And, so the downside of the location based approach is that yes, it gives you one single answer, but it takes away this idea that organizations which have honestly massive amounts of resources can influence or speed up a transition.

That's what it seems to be a kind of it's trying to respect that reality or at least acknowledge that this is what we expect of organizations if they're that powerful.

Killian Daly: And one person, I know you've had Olivier Corradi from Electricity Maps on before they've done, some very good blog series on this topic. They're obviously have insanely deep knowledge of grid emissions is really no one better that I've come across.

And they did a very kind of simplified explanation of this stuff. And you have the location based method, which is like maximizing physical accuracy and then you have the market based method, which is trying to maximize incentives and financing. And what this 24/7 accounting framework that we're advocating is basically trying to make those things meet in the middle, right? Today we have a market based system that is too much focused on, I would say, flexibility, making it easy for people to say they're green. and so has led to very valid criticism. And what we're trying to do now is bring that market based mechanism back closer to the physical realities of the grid,

Chris Adams: Oh, I see.

Killian Daly: But keeping the, incentive system, because if you don't have that, then, well, I don't really see the point in even doing the exercise.

Chris Adams: Okay. So there's two things that I wanted to kind of just see if I could maybe dive into a little bit on that then. So it sounds like this whole notion of not having this stuff tied to each other is to reflect the fact that people have all these complicated ways to purchase power in the first place.

So in my world as a cloud, as like someone working as a cloud engineer, right, I might buy computing by the hour, but I might also buy it, in advance for three years, for example, for a lower price, and that, that provides a bit of stability for whoever's running my server, but this kind of, this is an example of me having multiple different ways of being able to buy something, and essentially, some of that unbundling there is actually trying to capture the fact that there is, there are all these complicated ways to arrange to pay for something, and this is one way that we can use to value some of the Flexibility and stuff you said before.

So for example, you spoke about you can't run something on solar power, right? But if you had a battery, you can capture that and then use a battery bit like a time machine to kind of run at night almost right so but therefore you're trying to but that's more expensive than just making some claims.

So you need to have some way to recognize the fact that it takes a battery and a bunch of extra smarts to run something at night from that. That's what you're trying to go for with that, right?

Killian Daly: Yeah, exactly. And again, basing things on how power markets contractual, they have ways of already have contracted with allocating power between generators and consumers. I think the biggest issue with unbundling, so, selling the energy attributes and the power to different people. Actually, I think what the fundamental problem is the lack of time matching and deliverability requirements. That's where unbundling has gone wrong. Because it's, it said, "we're going to take the green attribute from this energy in Norway, and we're going to allow it to be used at any time of year, anywhere in Europe."

That's insane. That's where it starts to get completely insane. I don't have any particular problem with you producing it in one hydro plant, and selling the power into a power pool. and then that being consumed in Norway in the same hour. That's literally how power markets work on a short term power market.

Everyone bids into a common pool. And why not just put the attributes into the same pool and well, they, all have the same properties anyway. So it doesn't make a difference. It's the only way you're ever going to have liquidity, right? so I don't see any fundamental issue with, that.

The fundamental issue is with the annual matching and the

Chris Adams: the physics beyond breaking point, essentially.

Killian Daly: And that's, I think that's why I'm bundling, it's got such a bad name, right? And I think that's actually been fair, but I do think that it's not that bundling around bundling or necessarily the issue is, kind of the

Chris Adams: like those three pillars you mentioned. Okay, gotcha. Thank you for indulging me as I went down that thing, because I didn't know the answer to that, and I've always been wondering. Okay, so, we spoke about this thing called EnergyTag. We've spoke a little bit about how it's supposed to work and how it's basically an improvement on some of the approaches before.

And, maybe we could talk a little bit about who's using it? Is anyone, adopting it? maybe we could go from there, because this sounds like a cool idea, but there are many, cool ideas. That no one is paying attention to. And I suspect that would be quite a demoralizing conversation if that was the case.

So, yeah, I mean, who's using this and where, are there any kind of big name adopters you might point people to or anything like that?

Killian Daly: Yeah, so, yeah, two of the leading ones that kind of come to mind immediately, obviously, especially for software folks like yourselves or Google and Microsoft, they have 24/7 clean energy targets by 2030. Basically, they're committing to buying clean power for every hour, their data centers are consuming electricity, everywhere in, in which they're operating.

So they're two of the most, I would say, advanced, ambitious, corporate climate commitments in terms of scope 2 electricity procurement, at least. And they're obviously two major buyers. And they've been signing some really interesting deals as well. So there's, gigawatts now already of these 24/7 or close to 24/7 PPAs signed, 80, 90 percent firmed, portfolios of renewables, and that's game changing, right?

that's something we've seen emerge in the last few years where traditionally, the way of buying renewables has been "I'm going to buy a solar contract, and I'm going to blend that into whatever I'm buying elsewhere." And that's fine, right? But it's only giving you maybe 20, 20 percent of your electricity on an annual basis.

Now, we're seeing new contract structures that are blending together. Solar, wind, batteries, and getting maybe 80, 90 percent like of a flattened,

Chris Adams: so that's what I mean by firmed then, so firmed is this idea that it's basically it's when you say, so if it's not firmed, it's like I'm gonna buy the same amount totally without thinking about when it's matched, but if it's firmed then I am trying to think, I'm taking the steps necessary so that I can make a much more credible claim that the power I'm using is coming from generation or from stored amounts of power or something like that.

Ah,

Killian Daly: And that's, as I said, there's gigawatts of deals done already to date. Are there people doing this hourly matching stuff? Yes, absolutely. Check out our website. There's 30 projects there, with millions of megawatt hours of hourly matching being done.

So, this is not 40 organizations or something doing it 5 continents. So, This is not rocket science, right? This is literally taking meter data. That's very common, hourly production and gen data. You could do it on an Excel file with three columns if you wanted, and matching those things together and seeing where we're at. So it's absolutely demonstrated and leaders are doing it. Is everyone doing this? Is this now the status quo way of doing it? No, absolutely not. And that's what we work every day to try change, right? so we're still, I would say, relatively in the early days of this transition, but, as far as I'm concerned, it's kind of inevitable for credibility reasons, transparency reasons also for pretty fundamental economic reasons. Companies going out there and committing to buy loads of energy that is unmatched to their consumption profile.

They're leaving themselves open to a lot of risks. So, what if you say, okay, I'm just going to buy a load of solar. That has no connection to how I actually consume electricity. You're leaving yourself open to a lot of volatility that we're seeing electricity markets today. A lot of super high prices in the evening.

For example, when you're, when your solar contract is not delivering you anything, then what do you do? Right? you have all this gas volatility and exposure. So it's not just about decarbonization. It's also about things like electricity price hedging. So there's kind of various, I think, fundamentals that mean that.

We are going to move in this direction.

Chris Adams: okay, so So if I understand that final point that you've basically made is if I want to do this kind of matched thing for example, or if I want to, if I want to be buying some power, one of the advantages of doing like a longer term deal is that there's a degree of stability. So let's say, I don't know, a one country decides to invade another country and then totally make gas prices go through the roof.

I'm somewhat insulated from all that stuff so that it's not gonna massively destroy, it's not gonna destroy the, make impossible to kind of pay my own bills, for example. And like we've seen those of examples of that over the last few years, for example. So there's a bit of insulation from that kind of stuff.

Yeah.

Killian Daly: Exactly. So now we do get into kind of contracting mechanisms here. It's a little bit similar to what basically, if you're committing to a fixed price, for example, for a number of years, if you sign like one of these PPAs and you commit, let's say, to a 10 year fixed price for power. And if you're committing to like a affirmed profile, let's say 90 percent matched,

that has a very significant hedging value. So it means that basically you fixed like a lot of your power price. So no matter what happens, if, there's a massive spike in gas prices and power prices go through the roof. You're protected against that. We actually worked on a really interesting study on this a couple of years back or 18 months ago that said.

With Pexapark, who are like PPA analysts, and they basically showed that like a 10 megawatt consumer in Germany could save over 10 million euro, in the best of cases, and at least millions of euro in a given year by signing these 24/7, or close to 24/7 power purchase agreements with clean electricity assets, because one thing that clean energy has as an advantage in an ever more uncertain world is that the costs are basically known up front. You know how much money you need to build a wind turbine to build a battery up front.

It's all capex heavy. And that means that renewables can basically Give you a fixed price up front where honestly, gas cannot, because, most of their costs are operational. It's about buying the gas when you need it to.

Chris Adams: And there's a constant flow is not 

Okay, I guess with the sun, I mean, there's maybe a scenario where, I mean, it's not like there's a Mr burns style blackout of the sun kind of thing, right? if you're relying on something where no one has control over, no one can, kind of blockade the wind or blockade the sun.

That's where some of the stability is coming from, right?

Killian Daly: Yeah, exactly. Right. so you have those things, and you know that those fuel sources basically don't cost anything. Right? so you're all your costs are in construction, materials, all things you basically know, largely upfront, and that does enable you to provide long term contracts, typically way beyond the terms that fossil fuel generators can offer.

And so it can protect you for, the consumers willing to take that long term price risk. It can really offer really significant hedging benefits. not above alternatives.

Chris Adams: Buy that on like the spot market as it were or buying something just like on the regular market. Okay. All right. So, so you mentioned a few large companies doing that stuff and outside of technology, I know that 

I think it's the federal government. They've, it sounds like you said one or two things, which are quite interesting.

There is this idea that 100 percent is obviously really, good. Right. And that's what you want to head towards. But given there are some places where aren't, they're not going, they're not shooting for 100 percent straight away, for example, they might be going for 50 percent or 60 percent or something like that.

This is something that is kind of okay to do, or that's okay to start at. Cause I think I heard about the government, the US government had a plan for something about this by 2030 or something.

Killian Daly: Yeah. So basically, what we, we started the conversation talking about accounting. So I think the first thing you need to do is get, the accounting right. So that when you say 50, it means 50 or when you say 100, it means 100 because if you're just saying 100 and it means 50, then well, you're screwed, right?

You have a bad system. So, I think, actually being at 70 percent renewable, but saying that out loud

Chris Adams: 70%. Yeah.

Killian Daly: and addressing the, the basic fact that you're only there that's much better than kind of saying I'm 100 percent renewable on some annualized basis and kind of like misleading people about where you're at with, decarbonization.

Chris Adams: So it's better to be a real 70 than a fake 100, basically, yeah? 

Killian Daly: Yeah.

And, so, you have, electricity, like suppliers, for example, who are, there's like Good Energy in the UK, Octopus Energy in the UK, most of the electricity suppliers now in the UK, in fact, are, offering these like hourly tariffs.

And, I don't think any of

Chris Adams: it was only one or two that did that. Whoa. That's

Killian Daly: Now, I think this year it'll become more, a kind of a norm, where they will offer this alongside their a hundred percent renewable tariff. And none of those hourly tariffs are gonna start off being a hundred percent renewable, but it's bringing that extra bit of transparency, which I think is great.

And, the likes of good energy, they're already offering to thousands of customers, right? This is not just the Googles and the Microsofts who have their long term targets on this. This is already being offered to thousands of customers around the world because electricity suppliers are basically taking.

They're doing all the work. They're just giving the consumer the number on some dashboard saying, this is how much matching you have. if you look at the Octopus Energy example, it's quite interesting. They have a tariff called Electric Match for some of their B2B customers and they're actually basically reducing your price of power. when you're more matched, so that's quite cool, yeah, they're charging you less the more that your demand is matched to their generation. Right? And I think that's quite a cool gamification of this. They're saying basically try to consume when there's more wind and sun in the UK, you'll be more matched and we'll cut, we'll cut your rates because obviously it's sort of, it costs them less to deliver that in the first place.

So that's. That's the type of cool mechanism.

Chris Adams: So, I swear, every single time I speak to energy people, and they say, "oh yeah, the price is totally changing." Then I think of one level up, when we're like paying for cloud, and it's the same price all the time. Someone's making a bunch of money off us doing all the kind of carbon aware computing stuff, because if the price is going, low, I would expect to see those numbers go low.

This feels like something we might want to have a conversation about inside the tech industry then, if they are, if there's savings being made here, because it feels like it would be nice if those were passed on, I suppose. So, all right, let's speak, go on,

Killian Daly: I think just very importantly, of the, the more I think one fundamental truth that we're going to see,

it's already the case in some parts of the world, but this is going to be an essential truth of the transition. The more renewables you have, the more volatility you're going to have in power prices. And the more flexible you can be in your consumption. It is going to be very rewarding economically, if you can consume, at the times of day when there's loads of wind and sun, power prices are going to be very low and you're going to get rewarded for that. If you can't, if you can only be base load, then that is going to cost you.

Chris Adams: Ah, okay, alright. Okay. Alright, that's it, that's a useful thing to take into account. And so, we spoke before about, scope 2 and stuff like that, and you spoke about this idea that you're defining this standard. Now, EnergyTag is a standard in its own right, but, as I understand it, it's not like you're stepping outside of this.

You are still engaging with the protocols and all the stuff like that right now, yeah?

Killian Daly: Basically, so yeah, 

EnergyTag is a nonprofit. we do a couple of different things. we're obviously focused on this area of electricity accounting, electricity markets and better green energy claims and all that. And so yeah one of the things that we do is we have a voluntary standard for hourly energy tracking because one of the kind of blocking points we have today, is that the way we do this tracking with these energy certificates, it tends to be on a monthly or even an annual basis globally.

And sometimes we don't have the information on the certificates to do this hourly matching. So we're trying to un debottleneck that particular technical issue. Think about how do we track through storage, like doing some novel things there. So we have a standard for that, but that's only one of the building blocks, I would say of this much larger question of, like, how do companies do electricity accounting or how do they do carbon accounting more generally? Our standard is there to work on that specific topic, but actually a lot if not most of what we do today is like working on policy advocacy around the world, working on global standards and basically advocating for those to change because ultimately it's the meta-levers, regulations,

standards. Once they change, then we're just there to help technically put that all together with some voluntary standards as long as they're needed.

But it's not our aim to be the world's next greenhouse gas protocol. That's really not in our wheelhouse. What we want to do is make sure that global standards and regulations are as good as possible.

Chris Adams: Oh, I see. Okay, so that, so if we go for a concrete example of this. So, in Europe, if you want to do a hydrogen project, which is, in some ways, kind of a bit like an AI project in that it's like a building that uses loads and loads and loads of power in one place, right?

Really dense. If you're going to make, green hydrogen, for example, you're taking water, adding loads of electricity to split that, and that's incredibly energy intensive. So you've probably want that, if you want the green hydrogen to be green, probably only use green energy. And one of the things you told me about before was, yes, we won that fight so that any, and if people want to get any of the subsidies from the government to kind of do this green energy thing, they need to have those three pillars style approach, right?

That's what, that's an example of your strategy, yeah?

Killian Daly: Yeah, so this is actually the reason I what really brought me into EnergyTag, it was a Greenhouse Gas Protocol thing, but basically are the key to one of the world's largest hydrogen producers. Right? And so I got put onto this topic a few years ago, which I found incredibly important and fascinating and, maybe not well enough understood.

It's like, when we're going to produce hydrogen using electricity, we need to really make sure that the electricity is squeaky clean, because of the efficiency issues and losses that you just inherently have with electrolysis. And so, just to give a quick example, Jesse Jenkins lab in Princeton University, a guy called Wilson Ricks, who is a rock star of power system modeling, they model this right?

And they show that in the US, if you basically use today's carbon accounting rules, this annual matching stuff, and you built out a hydrogen sector based on those rules, you will have hydrogen that is twice, maybe even three times as bad as today's fossil fuel hydrogen production. and you'd be calling it clean and subsidizing that production. Totally insane, just literally wasting money.

And so it's actually really, important. Billions of dollars of subsidy are going to go into hydrogen in Europe and in the United States. And so we worked a lot with NGOs, advanced companies and other partners to advocate for these strong requirements on green electricity sourcing for hydrogen, both in the US and also in Europe, and we won on both fronts, which has

Chris Adams: Oh, the US way as well!

Killian Daly: Yeah. Yeah. Yeah. Yeah. And it hasn't been, so both of those are legislation in 

Chris Adams: place.

They're in! Yay science!

Killian Daly: Yeah, that's the legal way now to qualify for the tax credit in the US. In Europe, there's a phase in period on the hourly part to 2030. So, in 5 years or whatever.

But anyway, projects built now, they have to be designed to comply with that. And so, 

Chris Adams: if you know,

it's going to be in the law of five, you're just going to make sure you 

Killian Daly: going to start doing it now, right? more or less. yeah, so that's, yeah, obviously, this is kind of like hundreds of millions of tons of CO2 per year on the line between good and bad rules and that, that's kind of a concrete example of, why these things matter. Right? Like accounting sounds boring sometimes. I definitely thought it was boring before I realized like, "Oh my God, I'm working for a huge power consumer and this is changing everything." So yeah, it's definitely super, super important that we get this stuff right.

Right.

Chris Adams: Okay, so we spoke about, it sounds like you've done the work with Air Liquide and you've kind of essentially laid the groundwork to move from a fossil based hydrogen thing to hopefully a greener way of making hydrogen, which ends up being used in all these places. And now it seems like you've got the, okay, you said Google and Microsoft, same power usage as Air Liquide in a single year.

Maybe they might've changed, but back then, there's, so it looks like we're seeing some promising signs. For that over here. So maybe, I mean, if we, want to see that, what do we need to see at a policy level? Do you need to have, government saying, "if you want to have green energy for data centers, you need to be at least as good as the hydrogen, industry."

Is it something like that you need to do? Because what you've described for the hydrogen thing sounds awesome, but I'm not aware of that in the, kind of IT sector yet. That's something that I haven't seen people doing yet. 

Killian Daly: That is also coming, right? So hydrogen has just been the first battleground or the first palce, I think. 

Interestingly, actually, on the 14th of January, just before the inauguration of Donald Trump, as US president, so the Biden administration issued an executive order, which hasn't yet been rescinded.

Basically on data centers on federal lands and in that they do require these 3 pillars. So they do have a 3 pillar requirement on electricity sourcing, which is very interesting. Right? I think that's quite a good template. And I think, we definitely need to think about, okay, if you're going to start building loads of data centers in Ireland, for example, Ireland, 20 percent 25 percent of electricity consumption in Ireland is from data centers. That's way more than anywhere else in the world in relative terms. Yeah, there's a big conversation at the moment in Ireland about "okay, well, how do we make sure this is clean?" How do we think about

procurement requirements for building a new data center? That's a piece of legislation. That's being written at the moment. And how do we also require these data centers to do reporting of their emissions once they're operational? So, the Irish government, is also putting together a reporting framework for data centers and the energy agency.

So the Sustainable Energy Authority of Ireland, SEAI they published a report a couple of weeks ago saying, yeah, they, you know what, they need to do this hourly reporting based on contracts bought in Ireland. So I think we're seeing already promising signs of legislation coming down the road in other sectors outside of hydrogen.

And I think data centers is, probably an obvious one.

Chris Adams: So people are starting to win. Wow, I didn't realize that. I knew somewhat about that there was an executive order that there was a bit of buzz about. But I didn't realize that, set the precedent. So, yeah, we should do what that massive industry over there is doing because that's now the new baseline that, that's where the bar should be.

We should do that as well, basically.

Killian Daly: Exactly, because that those hydrogen rules, it's actually what it actually is. Well, actually, the whole debate was about is what is clean electricity procurement? What does that mean? What does it mean to use clean electricity? And that has been defined now in hydrogen rules and that can be copy and pasted to any large new load.

Well, if you want it to be clean, we already know the answer. It's in legislation,

Chris Adams: It's how to tell when energy is green,

Killian Daly: MIT, IEA, the who's who of energy experts have all modeled this and they've all found that this is the way to do it. So, there's a template there, right? And it's, if you're going to go against that, it, yeah, well, obviously, then you're, obviously sacrificing the integrity of your accounting schemes.

Chris Adams: Wow! That was, we spoke about how to tell when energy is green, and you've, We seem to be ending on a high, I didn't realise we'd actually got to that. That's really, awesome. You've really made my day for that, Killian. Thank you so much for coming on and diving into the minutiae of carbon accounting for electricity, but also ending it with a slightly less depressing piece of news, which I'll take in this current political climate,

Killian Daly: just to interject before I say goodbye, there's one, one really, it's good to end on a positive note, I suppose, in this mad world we live in. 

There was a project announced recently. I think people should go check it out in the Middle East in UAE, where basically for the first time, they're going to deliver basically, around the clock solar power. So 1 gigawatt of solar, all night long because they're basically, building a massive battery and a huge solar farm, and basically all year round is going to deliver, green electricity at under 70 us dollars per megawatt hour, which is extremely competitive.

So, I think solar and storage, what they're going to do together is going to be, is going to change the world. Right? I really think that is going to happen faster than people think. They're going to start to kill gas. So, yeah, I think green energy economics, despite what politicians will want to do with their culture wars,

I think will at the end of the day, hopefully, answer some of the questions we're trying to solve here. So, yeah, thanks so much for having me on. It's been a real pleasure.

Chris Adams: Brilliant, thank you so much for that mate, and may the fossil age end. That's really, that's so, so cool to actually see that, I totally forgot about the Masdar thing, which is the city. Yeah, and we'll share a link to that so people can read about that, because if you care about, I don't know, continued existence on this planet, then yeah, it's probably one to, good one to read about.

Killian, this has been loads of fun, thanks a lot mate, and next time I'm in Brussels I'll let you know, and maybe we can catch up for, have a shoof or something like that. Take care

Killian Daly: Yeah. A hundred percent. Thanks. Bye.  

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode. 



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]]> Backstage: Impact Framework Backstage: Impact Framework Thu, 13 Feb 2025 08:00:00 GMT 27:39 81nkvz20 false https://podcasts.castplus.fm/e/lnq2wj38-backstage-impact-framework 68dc7d02d5c8a1f9b326ef06 68dc7cef46a2532cdd8d1c23 96 This episode of Backstage focuses on the Impact Framework (IF), a pioneering tool designed to Model, Measure, siMulate, and Monitor the environmental impacts of software. By simplifying the process of calculating and sharing the carbon footprint of software, IF empowers developers to integrate sustainability into their workflows effortlessly. Recently achieving Graduated Project status within the Green Software Foundation, this framework has set a benchmark for sustainable practices in tech. Today, we’re joined by Navveen Balani, Srinivasan Rakhunathan, the project leads and Joseph Cook, the Head of R&D at GSF and Product Owner for Impact Framework, to discuss the journey of the project, its innovative features, and how it’s enabling developers and organizations to make meaningful contributions toward a greener future.

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TRANSCRIPT BELOW:
Chris Skipper: Hello, and welcome to Environment Variables, where we bring you the latest news from the world of sustainable software development. I'm the producer of this podcast, Chris Skipper, and today we're excited to bring you another episode of Backstage, where we peel back the curtain at the GSF and explore the stories, challenges and triumphs of the people shaping the future of green software. We're no longer gatekeeping what it takes to set new standards and norms for sustainability in tech.

This episode focuses on the Impact Framework, also known as IF, a pioneering tool designed to model, measure, simulate, and monitor the environmental impacts of software. By simplifying the process of calculating and sharing the carbon footprint of software, IF empowers developers to integrate sustainability into their workflows effortlessly.

Recently achieving graduated project status within the Green Software Foundation, this framework has set a benchmark for sustainable practices in tech. Today, we have audio snippets from Naveen Balani, Srinivasan Rakhunathan, the project leads. And Joseph Cook, the head of R&D at GSF and product owner for Impact Framework, to discuss the journey of the project, its innovative features, and how it's enabling developers and organizers to make meaningful contributions toward a greener future.

And before we dive in, here's a reminder that everything we talk about will be linked in the show notes below this episode. So without further ado, let's dive into the first question about the Impact Framework for Naveen Balani.

Naveen, the Impact Framework has been described as a tool to model, measure, simulate and monitor the environmental impacts of software.

Could you provide a brief overview how this works and the inspiration behind creating such a framework?

Navveen Balani: Thank you, Chris. And thanks to all the listeners for tuning in. Let's first understand the problem we're solving with the Impact Framework. Software runs the world, but its environmental impact is often invisible. Every CPU cycle, every page load, every API call, these all contribute to energy consumption, carbon emissions, and water usage.

Yet, without the right tools, measuring and managing this impact remains a challenge. This is where the Impact Framework comes in. It's an open source tool designed to transform raw system metrics like CPU usage or page views into tangible environmental insights, helping organizations take action. Built on a plugin based architecture, it allows users to integrate, customize, and extend measurement capabilities, ensuring scalability and adaptability.

More importantly, the Impact Framework helps realize the software carbon intensity specification, making sustainability reporting transparent, auditable, and verifiable. Every calculation, assumption, and methodology is documented in a manifest file, ensuring that impact assessments are replicable and open for collaboration.

At its core, the Impact Framework is built on a simple yet powerful idea. If we can observe it, we can measure its impact. And once we can measure it, we can drive real change, reducing emissions, optimizing resource use and building truly sustainable software.

Chris Skipper: What were some of the most significant technical or organizational challenges you faced during the development of the Impact Framework and how did you and the team overcome them?

Navveen Balani: The Impact Framework wasn't just built, it evolved. It was shaped by real world challenges. Lessons learned and the need for a scalable, transparent way to measure software's environmental footprint. The foundation of the Impact Framework was laid through previous projects and ideas. Starting with SCI Open Data, which tackled the lack of reliable emissions data, and SCI Guide, which helped organizations navigate different datasets and methodologies.

Another critical component was the SCI Open Ontology, which defines relationships between architecture components, establishing clear boundaries for calculating measurements. Alongside these foundational efforts, real world use cases from member organizations applying software carbon intensity measurement played a crucial role.

These practical implementations tested SCI in diverse environments, refining methodologies, and ensuring that SCI calculations were not just theoretical, but applicable and scalable across industries, but data alone wasn't enough. We needed to scale measurement across thousands of observations.

Sustainability assessments had to be continuous, automated, and seamlessly integrated into software development. This led to key innovations like aggregation, which enables organizations to condense vast amounts of data into meaningful, structured insights, rolling up emissions data across software components to provide a holistic system wide view.

Technology, however, was just one piece of the puzzle. Adoption was equally critical. To accelerate real world impact, we opened up the Impact Framework to our annual Carbon Hackathon event. Where teams worldwide build projects that pushed its capabilities. This was a turning point, validating its flexibility and refining it through community driven development.

At its core, the Impact Framework is built on transparency. Unlike black box solutions, every input, assumption, and calculation is fully recorded in a manifest file. Making assessments auditable and verifiable. This commitment to openness has been crucial in building trust and driving adoption.

Chris Skipper: Looking ahead, what are the next steps for the Impact Framework? Are there specific new features or partnerships on the roadmap that you're particularly excited about?

Navveen Balani: That's a great question, Chris. Looking ahead, the Impact Framework is entering an exciting new phase with a major focus on expanding measurement capabilities for AI. Right now, we're working on the SCI for AI specification. which extends software carbon intensity to both classical AI and generative AI workloads.

Measuring AI's environmental impact comes with a new level of complexity. AI isn't just another software workload. The environmental footprint varies significantly depending on whether you're training a model from scratch, fine tuning a large language model, or simply using an AI API like ChatGPT or Gemini.

Each scenario has different compute demands. Memory requirements and energy consumption patterns, making standardized measurement both challenging and essential. Through the Impact Framework, we aim to tackle this by developing new plugins and contributions that enable precise measurement of AI related energy use, hardware efficiency, and emissions across training, fine tuning, and inference workloads.

These capabilities will collectively evolve, through community participation with researchers, developers, and organizations, contributing to refining methodologies, expanding data sets, and ensuring that AI measurement remains transparent, auditable, and standardized. This collaborative approach will allow organizations to quantify, compare, and optimize their AI workloads.

Making sustainability a key consideration in AI deployment. Beyond AI, we are also exploring new partnerships to further enhance the Impact Framework's adaptability. Collaboration with cloud providers, software vendors, and sustainability researchers will be crucial in ensuring that the framework evolves alongside industry needs.

Our goal is to make environmental impact measurement not just an option, but a fundamental part of software and AI development at scale.

Chris Skipper: Moving on, we have some questions for Srini. Srini, IF emphasizes composability and the ability to create and use plugins. Could you explain how this innovative approach has enabled more accurate and flexible environmental impact calculations for different types of software environments?

Srini Rakhunathan: Absolutely. The Impact Framework's emphasis on composability and the use of plugins is actually a game changer for different environmental impact calculations. If you notice that the framework is highly modular, making and allowing users to create and integrate various plugins. What it means is you can tailor the framework to fit the specific needs of your software and it doesn't matter what type of software you have, whether it's cloud based, on prem or hybrid.

What is also advantageous is that the plugin ecosystem has a wide range of tasks. For example, it has something around data collection, it can do impact calculation, it can do reporting. It can do also very, very specific tasks like math functions and aggregation functions. What this means, you can mix and match plugins to create a mashed up pipeline that reflects your environment, whether you are running your software on web, cloud, mobile, doesn't really matter. As long as you know what your software boundaries are, you will be able to combine these plugins and create your own, um, pipeline, if you will. And that pipeline will help you, uh, create your calculation pipeline that can either run one time or run as a batch or, you know, run based on certain triggers.

What it also means, and if you notice, there is also manifest files, and we will be talking more about it later in this conversation, is that the manifest files ensures that you have a repeatable way of calculation. I mean, you mash up these different plugins and you create a pipeline and you embed it in a manifest file and it's repeatable.

So what I think is this framework's capability of composability and plug in can help you make very, very accurate impact calculations.

Chris Skipper: How have collaborations with organizations like Accenture and Microsoft, as well as the open source community, contributed to the success of the Impact Framework? Are there any standout moments or partnerships you'd like to highlight?

Srini Rakhunathan: Thanks, Chris. That's a great question. So the cornerstone of the success of Impact Framework has been collaborations. And this has been ongoing from the time this project was conceptualized. Bear in mind that when we, like Naveen, who's there also with us, and I, along with the Joseph and Asim started thinking about the project.

The initial vision of the project was very different. So we started off with something called SCI Guide, where we wanted to collate datasets across the open source community to help calculate emissions from software. And we built the SCI Guide and that transitioned into something called CarbonQL, which is a primitive version of what we see today in the Impact Framework, which is more like how do we make sure that it is easier for users or developers to calculate emissions from software and the learnings that Naveen, Joseph, I and Asim went through to come up with the initial version of Impact Framework and the amount of work that the team has put together to get it to graduation state is amazing and it speaks volumes about the collaborations that has gone ahead into the building of the tool.

One particular highlight I want to call out is every year, GSF organizes what they, what is called the CarbonHack. And in 2024, the CarbonHack focused on getting the open source community to come and build tools.

On top of Impact Framework, either extension of the tool or building content or newer areas where the Impact Framework can be used. And you would be amazed at the amount of contributions that came in and newer use cases that looked at calculating emissions, not just from carbon, but from water and other forms of renewable resources was also identified.

And that's great. That, I believe, was a standout moment for the tool.

Chris Skipper: The IF documentation highlights the use of a manifest file and a CLI tool to calculate environmental impacts. Could you walk us through how these tools work and how they lower the barriers for developers to adopt sustainable practices?

Srini Rakhunathan: Definitely, we can talk about both the CLI tool and the manifest file. These are actually cornerstone capabilities built within the Impact Framework, and they help us to calculate the environmental impacts. What happens is, the manifest file contains a list of of the software's infrastructure boundary encoded as YAML files.

It's in the standard YAML format, and it contains every bit of component that is part of the software, whether it's front end, middle tier, back end, database, API, everything encoded as what's the hardware used, what's the utilization, what's the telemetry involved. So much so that it can be used to give us an input to the Impact Framework CLI tool that calculates emissions.

The use of the file enables transparency and rerunability. That means it can allow anyone to re execute the manifest file and everyone will come up with the same calculations. The second piece that we spoke about, which is a CLI tool, it's a command line tool, which means it can be used to run on any environment.

It processes the manifest file and computes the environmental impacts. So the way it works is developers can pass the path to the manifest file to the CLI tool, and it'll take care of the calculations. The tool has capabilities to do phased execution and that allows efficient and flexible use of the framework.

Chris Skipper: And finally, what lessons have you learned from working on this project that might benefit other teams looking to build tools or frameworks for sustainability in tech?

Srini Rakhunathan: Thanks for asking this question. At an overall level, I would like to respond to this question by focusing on lessons learned from two aspects. The first is the execution model and the second will be the technical design. In the execution model space, this project is a good example of how open source collaboration works.

The team used GitHub extensively, and most of the meetings were asynchronous. And the engineers and the product managers and everyone who worked on the project worked through GitHub. And collaborated extensively using the open source tools available, which is a great model for scale. The second aspect we should look at from an execution model, and which is a success story here, is how the team used customer feedback as inputs to make the product better.

There were constant, if not many sessions with many customers with whom the team worked to engage with them and understand what the requirements are for building a tool that can help them calculate emissions and use that feedback into the process, into the backlog to make the tool better. The second aspect of lessons learned will be on technical design.

And here I would want to call out that. The whole concept of building a plugin ecosystem and make them composable such that it can, you have a, you know, you have a set of plugins that you deliver to the community, like a base framework, and then you allow extensibility. So that's a great model, which can help tools that can use sustainability as a calculation engine.

And then the second piece is, which is also equally important. As you do this. You also make sure that you have extensive and good documentation that can help anyone who's coming on board understand the framework and be able to get on board and run with building a new plugin as soon as possible. The IF code, the GitHub site, if you go there, You will have a link to the docs page.

And if you read through the docs, it's very, very self explanatory and will allow anyone who can come in and who's interested in building a plugin, do that at the fastest possible time. So these are, in my mind, lessons learned both from an execution model and the technical design aspect.

Chris Skipper: Moving on, we now have some questions for Joseph. Joseph, the Impact Framework recently achieved the status of a graduated project under the GSF. What does this milestone mean for the project, and what were some of the key factors that led to its graduation?

Joseph Cook: The Impact Framework graduation was a huge milestone because it represents the moment when the project is considered sufficiently mature that it no longer needs to be incubated and instead it can largely be handed over to the community. We consider the software to be feature rich and stable enough that people can integrate it into their systems, and in order to graduate, the project had to meet a quite stringent set of requirements, including demonstrating that Impact Framework had real world users, and that we had addressed community requests and bug reports, and that we had suitably comprehensive test coverage, and that the documentation and the onboarding materials were all fit for purpose.

Now that milestone has passed, development activity is going to be much more ad hoc and driven by the community, rather than following a development roadmap that's defined by Green Software Foundation. Our efforts at the GSF will now be in driving adoption instead.

Chris Skipper: How does the Impact Framework engage with the broader tech community to encourage adoption? Can you tell us what steps the GSF is taking to include the community as part of the IF development?

Joseph Cook: Impact Framework is used by all kinds of organizations, but it also has a thriving open source community. And most of the discussion with the community happens on GitHub, either through issues or on the discussion board. But we also have a Google group where we share updates and collect feedback. Open source development on Impact Framework is really fundamental.

It's really baked into the very core of the project. Instead of trying to ship Impact Framework with all the built in features to connect to thousands of different services and systems that people want to measure, we instead focused on making it really easy to build plugins, and then encouraged an open source community to develop, where people create their own plugins for all the features that they care about, and share them with each other on our Explorer website, which is like a free marketplace for Impact Framework plugins. This model actually makes the Impact Framework much more robust and much more stable because we have a much greater diversity of voices influencing what Impact Framework can do and what it can connect to. It decentralizes the development of the project without compromising the core software, and it also means that our small development team doesn't shoulder the burden of maintaining a huge code base with lots of different brittle connectors to third party APIs and services.

And going forward, we want to keep this community thriving and see thousands more Impact Framework plugins listed on the Explorer.

Chris Skipper: How do you see the Impact Framework setting new benchmarks for environmental responsibility in tech? Are there specific metrics or practices that you believe will influence industry standards?

Joseph Cook: Impact Framework is a lightweight piece of software for processing what we call manifest files. These are YAML files that follow a simple format that captures the architecture of the system that you're studying. All the observations that you've made about that system and all of the operations that are applied to your data.

I like to refer to these files as executable audits because they mean that you don't just report emissions numbers anymore, you actually show you're working too. And this enables the community to fork and modify your manifests and challenge you. And through iteration, you can come to crowdsourced consensus over your environmental reports. We would love to see this radical transparency become the gold standard for environmental impact reporting for software. Not only that, but manifests can be the basis for experimentation or forecasting, and help decision makers to assess the environmental benefits of implementing some change. Imagine you're challenged about why you chose some specific action.

Your manifests are your evidence. And we think this combination of transparency and reproducibility, composability, and openness is a unique selling point for Impact Framework, and it could transform the way projects and organizations report their emissions and introspect their own operations.

Chris Skipper: For listeners who are interested in getting involved with the Impact Framework, what are the ways they can contribute or support the project? Are there specific skills or areas where the community can make the most impact?

Joseph Cook: If you would like to get involved in Impact Framework, there are many ways to do so. If you're a developer, you can head to the GitHub, where we have plenty of open issues, including some specific good first issues to help people get started. If you want to build plug ins, then you can download our template and use that to bootstrap your way in, and then submit your plug in to the Explorer using a simple typeform on our website.

We always appreciate updates to the documentation too, and if you're interested in integrating Impact Framework into your systems, we'd You can always reach out to research at greensoftware. foundation to discuss it with us directly. We're always happy to help. If you just want to test the water or you have general questions about Impact Framework, you can start discussions on our GitHub discussion board or communicate via our Google group, IF-community@greensoftware.foundation.

Chris Skipper: Awesome. So I'd like to thank Naveen, Srini, and Joseph for their contributions to this episode. Before we finish off this episode, I have a few events that need announcing.

Starting us off, we have an event that will be happening today, the date of the publication of this episode, February the 13th, 2025 at 5 p.m. CET in Utrecht, Netherlands. Any Netherlands based listeners, you're invited to a Green Software Community Meetup today from 5pm until 8pm at Werkspoorkathedraal. Join us for a free in person event to kickstart a more sustainable year in tech. You'll hear insightful talks about reducing your software's energy footprint, scaling down for greener computing and building a grassroots digital sustainable movement. This is a great opportunity to connect with like-minded professionals, share ideas, and be part of a growing Dutch community that's dedicated to building a greener tech future. Food and drinks are provided free of charge.

Next up is an event in Brighton in the UK, happening on February the 19th from 6:00 PM to 8:00 PM at Runway East, which features Senior Digital and Sustainability Manager for OVO, Mark Buss, speaking about the challenges with advocating for digital sustainability within his company. The talk will also be live streamed, so we will have a link in the show notes below for that.

Next up for any Spanish listeners, we have the first ever meetup of the Green Software Community in Spain that will be happening online at 6pm On February the 20th, Dia Zero, Comunidad, Meetup, Green Software Foundation, España will be a chance for you to discuss how to collaborate with other people passionate about climate change and green software. And we'll have a link to that in the show notes below too.

Next up down under in Australia on February the 20th at 6pm AEDT in Melbourne, we have Digging Deeper into Digital Sustainability. How to design and build tech solutions. This will be happening at ChargeFox. Katherine Buzza will be talking about the impact that software is having on the world's carbon emissions, and how to align your career in tech with the decarbonized future we can all play a role in creating.

Next up, another UK event on February the 27th at 6pm GMT in London. Practical Advice for Responsible AI will be held in person at the Adaptivist offices. Talks about Green AI with Charles Humble and AI Governance with Jovita Tam. Click the link below to find out more.

And finally, on our events list, we have GSF Oslo will be having its February meetup on the 27th of February at 5pm in person at the Accenture offices from 5 until 8pm. Come along to find out how leveraging data and technology can drive sustainability initiatives and enhance security measures and dive into green AI. Talks from Abhishek Dewangan and Johnny Mauland. Details in the podcast notes below.

So that's the end of this episode about the Impact Framework project at the GSF. I hope you enjoyed the podcast. To listen to more podcasts about the Green Software Foundation, please visit podcast.greensoftware.foundation, and we'll see you on the next episode. Bye for now!



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]]> This episode of Backstage focuses on the Impact Framework (IF), a pioneering tool designed to Model, Measure, siMulate, and Monitor the environmental impacts of software. By simplifying the process of calculating and sharing the carbon footprint of software, IF empowers developers to integrate sustainability into their workflows effortlessly. Recently achieving Graduated Project status within the Green Software Foundation, this framework has set a benchmark for sustainable practices in tech. Today, we’re joined by Navveen Balani, Srinivasan Rakhunathan, the project leads and Joseph Cook, the Head of R&D at GSF and Product Owner for Impact Framework, to discuss the journey of the project, its innovative features, and how it’s enabling developers and organizations to make meaningful contributions toward a greener future.

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TRANSCRIPT BELOW:
Chris Skipper: Hello, and welcome to Environment Variables, where we bring you the latest news from the world of sustainable software development. I'm the producer of this podcast, Chris Skipper, and today we're excited to bring you another episode of Backstage, where we peel back the curtain at the GSF and explore the stories, challenges and triumphs of the people shaping the future of green software. We're no longer gatekeeping what it takes to set new standards and norms for sustainability in tech.

This episode focuses on the Impact Framework, also known as IF, a pioneering tool designed to model, measure, simulate, and monitor the environmental impacts of software. By simplifying the process of calculating and sharing the carbon footprint of software, IF empowers developers to integrate sustainability into their workflows effortlessly.

Recently achieving graduated project status within the Green Software Foundation, this framework has set a benchmark for sustainable practices in tech. Today, we have audio snippets from Naveen Balani, Srinivasan Rakhunathan, the project leads. And Joseph Cook, the head of R&D at GSF and product owner for Impact Framework, to discuss the journey of the project, its innovative features, and how it's enabling developers and organizers to make meaningful contributions toward a greener future.

And before we dive in, here's a reminder that everything we talk about will be linked in the show notes below this episode. So without further ado, let's dive into the first question about the Impact Framework for Naveen Balani.

Naveen, the Impact Framework has been described as a tool to model, measure, simulate and monitor the environmental impacts of software.

Could you provide a brief overview how this works and the inspiration behind creating such a framework?

Navveen Balani: Thank you, Chris. And thanks to all the listeners for tuning in. Let's first understand the problem we're solving with the Impact Framework. Software runs the world, but its environmental impact is often invisible. Every CPU cycle, every page load, every API call, these all contribute to energy consumption, carbon emissions, and water usage.

Yet, without the right tools, measuring and managing this impact remains a challenge. This is where the Impact Framework comes in. It's an open source tool designed to transform raw system metrics like CPU usage or page views into tangible environmental insights, helping organizations take action. Built on a plugin based architecture, it allows users to integrate, customize, and extend measurement capabilities, ensuring scalability and adaptability.

More importantly, the Impact Framework helps realize the software carbon intensity specification, making sustainability reporting transparent, auditable, and verifiable. Every calculation, assumption, and methodology is documented in a manifest file, ensuring that impact assessments are replicable and open for collaboration.

At its core, the Impact Framework is built on a simple yet powerful idea. If we can observe it, we can measure its impact. And once we can measure it, we can drive real change, reducing emissions, optimizing resource use and building truly sustainable software.

Chris Skipper: What were some of the most significant technical or organizational challenges you faced during the development of the Impact Framework and how did you and the team overcome them?

Navveen Balani: The Impact Framework wasn't just built, it evolved. It was shaped by real world challenges. Lessons learned and the need for a scalable, transparent way to measure software's environmental footprint. The foundation of the Impact Framework was laid through previous projects and ideas. Starting with SCI Open Data, which tackled the lack of reliable emissions data, and SCI Guide, which helped organizations navigate different datasets and methodologies.

Another critical component was the SCI Open Ontology, which defines relationships between architecture components, establishing clear boundaries for calculating measurements. Alongside these foundational efforts, real world use cases from member organizations applying software carbon intensity measurement played a crucial role.

These practical implementations tested SCI in diverse environments, refining methodologies, and ensuring that SCI calculations were not just theoretical, but applicable and scalable across industries, but data alone wasn't enough. We needed to scale measurement across thousands of observations.

Sustainability assessments had to be continuous, automated, and seamlessly integrated into software development. This led to key innovations like aggregation, which enables organizations to condense vast amounts of data into meaningful, structured insights, rolling up emissions data across software components to provide a holistic system wide view.

Technology, however, was just one piece of the puzzle. Adoption was equally critical. To accelerate real world impact, we opened up the Impact Framework to our annual Carbon Hackathon event. Where teams worldwide build projects that pushed its capabilities. This was a turning point, validating its flexibility and refining it through community driven development.

At its core, the Impact Framework is built on transparency. Unlike black box solutions, every input, assumption, and calculation is fully recorded in a manifest file. Making assessments auditable and verifiable. This commitment to openness has been crucial in building trust and driving adoption.

Chris Skipper: Looking ahead, what are the next steps for the Impact Framework? Are there specific new features or partnerships on the roadmap that you're particularly excited about?

Navveen Balani: That's a great question, Chris. Looking ahead, the Impact Framework is entering an exciting new phase with a major focus on expanding measurement capabilities for AI. Right now, we're working on the SCI for AI specification. which extends software carbon intensity to both classical AI and generative AI workloads.

Measuring AI's environmental impact comes with a new level of complexity. AI isn't just another software workload. The environmental footprint varies significantly depending on whether you're training a model from scratch, fine tuning a large language model, or simply using an AI API like ChatGPT or Gemini.

Each scenario has different compute demands. Memory requirements and energy consumption patterns, making standardized measurement both challenging and essential. Through the Impact Framework, we aim to tackle this by developing new plugins and contributions that enable precise measurement of AI related energy use, hardware efficiency, and emissions across training, fine tuning, and inference workloads.

These capabilities will collectively evolve, through community participation with researchers, developers, and organizations, contributing to refining methodologies, expanding data sets, and ensuring that AI measurement remains transparent, auditable, and standardized. This collaborative approach will allow organizations to quantify, compare, and optimize their AI workloads.

Making sustainability a key consideration in AI deployment. Beyond AI, we are also exploring new partnerships to further enhance the Impact Framework's adaptability. Collaboration with cloud providers, software vendors, and sustainability researchers will be crucial in ensuring that the framework evolves alongside industry needs.

Our goal is to make environmental impact measurement not just an option, but a fundamental part of software and AI development at scale.

Chris Skipper: Moving on, we have some questions for Srini. Srini, IF emphasizes composability and the ability to create and use plugins. Could you explain how this innovative approach has enabled more accurate and flexible environmental impact calculations for different types of software environments?

Srini Rakhunathan: Absolutely. The Impact Framework's emphasis on composability and the use of plugins is actually a game changer for different environmental impact calculations. If you notice that the framework is highly modular, making and allowing users to create and integrate various plugins. What it means is you can tailor the framework to fit the specific needs of your software and it doesn't matter what type of software you have, whether it's cloud based, on prem or hybrid.

What is also advantageous is that the plugin ecosystem has a wide range of tasks. For example, it has something around data collection, it can do impact calculation, it can do reporting. It can do also very, very specific tasks like math functions and aggregation functions. What this means, you can mix and match plugins to create a mashed up pipeline that reflects your environment, whether you are running your software on web, cloud, mobile, doesn't really matter. As long as you know what your software boundaries are, you will be able to combine these plugins and create your own, um, pipeline, if you will. And that pipeline will help you, uh, create your calculation pipeline that can either run one time or run as a batch or, you know, run based on certain triggers.

What it also means, and if you notice, there is also manifest files, and we will be talking more about it later in this conversation, is that the manifest files ensures that you have a repeatable way of calculation. I mean, you mash up these different plugins and you create a pipeline and you embed it in a manifest file and it's repeatable.

So what I think is this framework's capability of composability and plug in can help you make very, very accurate impact calculations.

Chris Skipper: How have collaborations with organizations like Accenture and Microsoft, as well as the open source community, contributed to the success of the Impact Framework? Are there any standout moments or partnerships you'd like to highlight?

Srini Rakhunathan: Thanks, Chris. That's a great question. So the cornerstone of the success of Impact Framework has been collaborations. And this has been ongoing from the time this project was conceptualized. Bear in mind that when we, like Naveen, who's there also with us, and I, along with the Joseph and Asim started thinking about the project.

The initial vision of the project was very different. So we started off with something called SCI Guide, where we wanted to collate datasets across the open source community to help calculate emissions from software. And we built the SCI Guide and that transitioned into something called CarbonQL, which is a primitive version of what we see today in the Impact Framework, which is more like how do we make sure that it is easier for users or developers to calculate emissions from software and the learnings that Naveen, Joseph, I and Asim went through to come up with the initial version of Impact Framework and the amount of work that the team has put together to get it to graduation state is amazing and it speaks volumes about the collaborations that has gone ahead into the building of the tool.

One particular highlight I want to call out is every year, GSF organizes what they, what is called the CarbonHack. And in 2024, the CarbonHack focused on getting the open source community to come and build tools.

On top of Impact Framework, either extension of the tool or building content or newer areas where the Impact Framework can be used. And you would be amazed at the amount of contributions that came in and newer use cases that looked at calculating emissions, not just from carbon, but from water and other forms of renewable resources was also identified.

And that's great. That, I believe, was a standout moment for the tool.

Chris Skipper: The IF documentation highlights the use of a manifest file and a CLI tool to calculate environmental impacts. Could you walk us through how these tools work and how they lower the barriers for developers to adopt sustainable practices?

Srini Rakhunathan: Definitely, we can talk about both the CLI tool and the manifest file. These are actually cornerstone capabilities built within the Impact Framework, and they help us to calculate the environmental impacts. What happens is, the manifest file contains a list of of the software's infrastructure boundary encoded as YAML files.

It's in the standard YAML format, and it contains every bit of component that is part of the software, whether it's front end, middle tier, back end, database, API, everything encoded as what's the hardware used, what's the utilization, what's the telemetry involved. So much so that it can be used to give us an input to the Impact Framework CLI tool that calculates emissions.

The use of the file enables transparency and rerunability. That means it can allow anyone to re execute the manifest file and everyone will come up with the same calculations. The second piece that we spoke about, which is a CLI tool, it's a command line tool, which means it can be used to run on any environment.

It processes the manifest file and computes the environmental impacts. So the way it works is developers can pass the path to the manifest file to the CLI tool, and it'll take care of the calculations. The tool has capabilities to do phased execution and that allows efficient and flexible use of the framework.

Chris Skipper: And finally, what lessons have you learned from working on this project that might benefit other teams looking to build tools or frameworks for sustainability in tech?

Srini Rakhunathan: Thanks for asking this question. At an overall level, I would like to respond to this question by focusing on lessons learned from two aspects. The first is the execution model and the second will be the technical design. In the execution model space, this project is a good example of how open source collaboration works.

The team used GitHub extensively, and most of the meetings were asynchronous. And the engineers and the product managers and everyone who worked on the project worked through GitHub. And collaborated extensively using the open source tools available, which is a great model for scale. The second aspect we should look at from an execution model, and which is a success story here, is how the team used customer feedback as inputs to make the product better.

There were constant, if not many sessions with many customers with whom the team worked to engage with them and understand what the requirements are for building a tool that can help them calculate emissions and use that feedback into the process, into the backlog to make the tool better. The second aspect of lessons learned will be on technical design.

And here I would want to call out that. The whole concept of building a plugin ecosystem and make them composable such that it can, you have a, you know, you have a set of plugins that you deliver to the community, like a base framework, and then you allow extensibility. So that's a great model, which can help tools that can use sustainability as a calculation engine.

And then the second piece is, which is also equally important. As you do this. You also make sure that you have extensive and good documentation that can help anyone who's coming on board understand the framework and be able to get on board and run with building a new plugin as soon as possible. The IF code, the GitHub site, if you go there, You will have a link to the docs page.

And if you read through the docs, it's very, very self explanatory and will allow anyone who can come in and who's interested in building a plugin, do that at the fastest possible time. So these are, in my mind, lessons learned both from an execution model and the technical design aspect.

Chris Skipper: Moving on, we now have some questions for Joseph. Joseph, the Impact Framework recently achieved the status of a graduated project under the GSF. What does this milestone mean for the project, and what were some of the key factors that led to its graduation?

Joseph Cook: The Impact Framework graduation was a huge milestone because it represents the moment when the project is considered sufficiently mature that it no longer needs to be incubated and instead it can largely be handed over to the community. We consider the software to be feature rich and stable enough that people can integrate it into their systems, and in order to graduate, the project had to meet a quite stringent set of requirements, including demonstrating that Impact Framework had real world users, and that we had addressed community requests and bug reports, and that we had suitably comprehensive test coverage, and that the documentation and the onboarding materials were all fit for purpose.

Now that milestone has passed, development activity is going to be much more ad hoc and driven by the community, rather than following a development roadmap that's defined by Green Software Foundation. Our efforts at the GSF will now be in driving adoption instead.

Chris Skipper: How does the Impact Framework engage with the broader tech community to encourage adoption? Can you tell us what steps the GSF is taking to include the community as part of the IF development?

Joseph Cook: Impact Framework is used by all kinds of organizations, but it also has a thriving open source community. And most of the discussion with the community happens on GitHub, either through issues or on the discussion board. But we also have a Google group where we share updates and collect feedback. Open source development on Impact Framework is really fundamental.

It's really baked into the very core of the project. Instead of trying to ship Impact Framework with all the built in features to connect to thousands of different services and systems that people want to measure, we instead focused on making it really easy to build plugins, and then encouraged an open source community to develop, where people create their own plugins for all the features that they care about, and share them with each other on our Explorer website, which is like a free marketplace for Impact Framework plugins. This model actually makes the Impact Framework much more robust and much more stable because we have a much greater diversity of voices influencing what Impact Framework can do and what it can connect to. It decentralizes the development of the project without compromising the core software, and it also means that our small development team doesn't shoulder the burden of maintaining a huge code base with lots of different brittle connectors to third party APIs and services.

And going forward, we want to keep this community thriving and see thousands more Impact Framework plugins listed on the Explorer.

Chris Skipper: How do you see the Impact Framework setting new benchmarks for environmental responsibility in tech? Are there specific metrics or practices that you believe will influence industry standards?

Joseph Cook: Impact Framework is a lightweight piece of software for processing what we call manifest files. These are YAML files that follow a simple format that captures the architecture of the system that you're studying. All the observations that you've made about that system and all of the operations that are applied to your data.

I like to refer to these files as executable audits because they mean that you don't just report emissions numbers anymore, you actually show you're working too. And this enables the community to fork and modify your manifests and challenge you. And through iteration, you can come to crowdsourced consensus over your environmental reports. We would love to see this radical transparency become the gold standard for environmental impact reporting for software. Not only that, but manifests can be the basis for experimentation or forecasting, and help decision makers to assess the environmental benefits of implementing some change. Imagine you're challenged about why you chose some specific action.

Your manifests are your evidence. And we think this combination of transparency and reproducibility, composability, and openness is a unique selling point for Impact Framework, and it could transform the way projects and organizations report their emissions and introspect their own operations.

Chris Skipper: For listeners who are interested in getting involved with the Impact Framework, what are the ways they can contribute or support the project? Are there specific skills or areas where the community can make the most impact?

Joseph Cook: If you would like to get involved in Impact Framework, there are many ways to do so. If you're a developer, you can head to the GitHub, where we have plenty of open issues, including some specific good first issues to help people get started. If you want to build plug ins, then you can download our template and use that to bootstrap your way in, and then submit your plug in to the Explorer using a simple typeform on our website.

We always appreciate updates to the documentation too, and if you're interested in integrating Impact Framework into your systems, we'd You can always reach out to research at greensoftware. foundation to discuss it with us directly. We're always happy to help. If you just want to test the water or you have general questions about Impact Framework, you can start discussions on our GitHub discussion board or communicate via our Google group, IF-community@greensoftware.foundation.

Chris Skipper: Awesome. So I'd like to thank Naveen, Srini, and Joseph for their contributions to this episode. Before we finish off this episode, I have a few events that need announcing.

Starting us off, we have an event that will be happening today, the date of the publication of this episode, February the 13th, 2025 at 5 p.m. CET in Utrecht, Netherlands. Any Netherlands based listeners, you're invited to a Green Software Community Meetup today from 5pm until 8pm at Werkspoorkathedraal. Join us for a free in person event to kickstart a more sustainable year in tech. You'll hear insightful talks about reducing your software's energy footprint, scaling down for greener computing and building a grassroots digital sustainable movement. This is a great opportunity to connect with like-minded professionals, share ideas, and be part of a growing Dutch community that's dedicated to building a greener tech future. Food and drinks are provided free of charge.

Next up is an event in Brighton in the UK, happening on February the 19th from 6:00 PM to 8:00 PM at Runway East, which features Senior Digital and Sustainability Manager for OVO, Mark Buss, speaking about the challenges with advocating for digital sustainability within his company. The talk will also be live streamed, so we will have a link in the show notes below for that.

Next up for any Spanish listeners, we have the first ever meetup of the Green Software Community in Spain that will be happening online at 6pm On February the 20th, Dia Zero, Comunidad, Meetup, Green Software Foundation, España will be a chance for you to discuss how to collaborate with other people passionate about climate change and green software. And we'll have a link to that in the show notes below too.

Next up down under in Australia on February the 20th at 6pm AEDT in Melbourne, we have Digging Deeper into Digital Sustainability. How to design and build tech solutions. This will be happening at ChargeFox. Katherine Buzza will be talking about the impact that software is having on the world's carbon emissions, and how to align your career in tech with the decarbonized future we can all play a role in creating.

Next up, another UK event on February the 27th at 6pm GMT in London. Practical Advice for Responsible AI will be held in person at the Adaptivist offices. Talks about Green AI with Charles Humble and AI Governance with Jovita Tam. Click the link below to find out more.

And finally, on our events list, we have GSF Oslo will be having its February meetup on the 27th of February at 5pm in person at the Accenture offices from 5 until 8pm. Come along to find out how leveraging data and technology can drive sustainability initiatives and enhance security measures and dive into green AI. Talks from Abhishek Dewangan and Johnny Mauland. Details in the podcast notes below.

So that's the end of this episode about the Impact Framework project at the GSF. I hope you enjoyed the podcast. To listen to more podcasts about the Green Software Foundation, please visit podcast.greensoftware.foundation, and we'll see you on the next episode. Bye for now!



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]]> Backstage: Carbon Aware SDK Backstage: Carbon Aware SDK Thu, 23 Jan 2025 08:00:00 GMT 12:31 81x72690 false https://podcasts.castplus.fm/e/vnw4pr38-backstage-carbon-aware-sdk 68dc7d03d5c8a1f9b326ef7e 68dc7cef46a2532cdd8d1c23 In this episode, we go behind the scenes of the Carbon Aware SDK, a groundbreaking tool enabling developers to reduce software emissions by running workloads where and when energy is greenest. Featuring insights from Vaughan Knight, chair and project lead of the SDK, the episode dives into its origins, real-world applications, challenges, and milestones, including early contributions from UBS and Microsoft and its recent 1.7 release with NPM and Java libraries. Learn about how the SDK supports Software Carbon Intensity (SCI) metrics, practical examples of carbon-aware workload scheduling, and the roadmap for expanding developer resources and geolocation-based solutions. 95 In this episode, we go behind the scenes of the Carbon Aware SDK, a groundbreaking tool enabling developers to reduce software emissions by running workloads where and when energy is greenest. Featuring insights from Vaughan Knight, chair and project lead of the SDK, the episode dives into its origins, real-world applications, challenges, and milestones, including early contributions from UBS and Microsoft and its recent 1.7 release with NPM and Java libraries. Learn about how the SDK supports Software Carbon Intensity (SCI) metrics, practical examples of carbon-aware workload scheduling, and the roadmap for expanding developer resources and geolocation-based solutions.

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]]> In this episode, we go behind the scenes of the Carbon Aware SDK, a groundbreaking tool enabling developers to reduce software emissions by running workloads where and when energy is greenest. Featuring insights from Vaughan Knight, chair and project lead of the SDK, the episode dives into its origins, real-world applications, challenges, and milestones, including early contributions from UBS and Microsoft and its recent 1.7 release with NPM and Java libraries. Learn about how the SDK supports Software Carbon Intensity (SCI) metrics, practical examples of carbon-aware workload scheduling, and the roadmap for expanding developer resources and geolocation-based solutions.

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Find out more about the GSF:

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]]> Deep Green Technologies Deep Green Technologies Thu, 16 Jan 2025 07:00:00 GMT 45:15 703pr471 false https://podcasts.castplus.fm/e/xn142v68-deep-green-technologies 68dc7d12597bc7d53fb837da 68dc7cef46a2532cdd8d1c23 In this episode of Environment Variables, host Chris Adams sits down with Mark Bjornsgaard of Deep Green to explore a transformative approach to data center design and sustainability. Mark shares insights into how Deep Green reimagines traditional data centers by co-locating them in urban areas to provide heat reuse for facilities like swimming pools, district heating systems, and industrial processes. They discuss the challenges of planning and policy, the rise of high-density computing driven by AI, and the potential for data centers to become integral components of community infrastructure. Tune in to learn about the intersection of digital innovation and environmental responsibility, and how new business models can turn waste into opportunity. 95 In this episode of Environment Variables, host Chris Adams sits down with Mark Bjornsgaard of Deep Green to explore a transformative approach to data center design and sustainability. Mark shares insights into how Deep Green reimagines traditional data centers by co-locating them in urban areas to provide heat reuse for facilities like swimming pools, district heating systems, and industrial processes. They discuss the challenges of planning and policy, the rise of high-density computing driven by AI, and the potential for data centers to become integral components of community infrastructure. Tune in to learn about the intersection of digital innovation and environmental responsibility, and how new business models can turn waste into opportunity.

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TRANSCRIPT BELOW:

Mark Bjornsgaard: The government does need to legislate. There is just not enough structure and there's not enough impetus for people to do the right thing. But the also, and particularly in the UK, what the government needs to do is planning is a huge, huge hurdle. I never really understood that until we'd be working with Deep Green for, you know, building data centers.

It is breathtaking how Kafka-esque the planning system in the UK is. It's just, 

It's beyond insane.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Okay, Mark, a few years back, when people were asked what a data center was, if they knew what one was at all, they might talk about some kind of thing, room, cupboard full of a few machines, maybe in a rack inside a unused room inside a building, for example. But these days, in the 2020s, people are more likely to talk about a warehouse full of hyperscale kind of data servers in a building, which is maybe the size of a football field or larger, for example, the kind of things that are run by massive firms like Google, Microsoft and Amazon, for example.

Now, as I understand it, you work with data centers, too, but they can take a rather different shape and interact rather differently with the built environment. So for those who've never heard of Deep Green, or how the stuff you're doing is different, give a kind of brief introduction to like how your approach to like building data centers is and how that has an impact on how it works with the surrounding area, for example, communities.

Mark Bjornsgaard: Yeah. So, as you say, most data centers are built in the middle of nowhere, and the vast majority are built without heat reuse. So the vast majority simply eject the heat that comes out of the computers. Data centers, we know, two to four percent of the world's electricity supply, and computers themselves are incredibly efficient electric heaters.

So 97 percent of the electrons that go into a computer come out as heat. So you've got us as a species, us in a climate emergency, taking two to four percent of the world's electricity supply, converting it into heat, and then ejecting it into the atmosphere, which 10 years ago, that might have sounded kind of plausible or even sort of necessary.

But in a world, as I said, in a climate emergency, that doesn't look so clever. So the difference between Deep Green and every other data center, most other data centers is we are building the data center where the heat can be reused. So very hard to transport heat, but relatively easy to transport electrons to take the data center to where the heat's required.

So that's what we do. We build smaller data centers, co-locate them where heat's required. Now that might be a laundry, it might be a distillery, it might be food production, it might be antibiotic production, it might be a swimming pool, but more often than not, it's what's called a district heating system.

So these large centralized heat networks that through super insulated pipes supply heat to large areas of different cities. That sort of principle, that district heat systems and heat networks, we're not very good at them in the UK specifically, but we are, the government is certainly planning for us to get a lot best them in the years to come.

So, that's where we're anchored. We, you don't build them in the middle of nowhere, you build them where they're required. There's a further, there's a further caveat and a sort of, a kind of context to this, I suppose, if you'd like. Up until the point where AI started to become part of our everyday lives, those normal data centers aren't on very much.

They're only on 20, 30 percent of the time, and they don't actually generate very good waste heat. So you can certainly forgive the great, the good of the data center industry for not necessarily trying too hard to reuse heat in the old world. But in the world that's coming where we've got these incredibly dense racks of NVIDIA and other chips, where, you know, she utilising a massive, huge amount more energy than previously the datasets had.

That, it's at this point where those are on 70, 80 percent of the time, and they're generating an enormous amount of heat, and the heat's relatively high grade. It's not high grade heat as class within, but it's good low grade heat. So at this point, then the ability to reuse heat becomes a real thing. And that's why we exist.

Chris Adams: Ah, I see. Okay, so there's a couple of things I'd like to unpack if I may. So the first thing you said was, okay, so there used to be data centers if they were going to be built in a kind of hyperscale thing. You're looking for kind of cheap land and then that's why they're often kind of miles away and probably maybe near things like say a grid connection or fiber connection or something like that, all right?

So that was like one of the previous approaches, but the downside of that is that, well, you've, you might have all this heat, but no one's able to use it, so you just vent it into the sky, so it's basically wasted in that way. So the other, another way you could do this is you can actually build these, where they kind of interact more, where they're kind of more complementary to the kind of urban fabric, as it were, and then you can use that.

But the thing that we've seen, one of the reasons that's been stopping that before is that essentially the data centers might have generated some heat, but it wasn't enough heat. So, you said low grade, and when you talk about low grade heat, that's like maybe 40 degrees, 50 degrees? Like, maybe you could expand on that, what that might mean, because I think for people who've never heard of the world of heat reuse, they don't know what high grade heat or low grade heat might be or what some of these uses might be, for example.

Mark Bjornsgaard: Yes. Yeah. No. It's so as you say, low grade heat in industrial settings can be as high as a couple of hundred degrees. So when you say a data center is going to be producing heat at 45, 50, 55 degrees, then that doesn't sound very warm at all. That said, 30 percent of all of the economy, 30 percent of all of the industry can use that very low grade heat.

So for example, a swimming pool very reliably loses a degree of temperature every hour. And it only needs to be 30 degrees. So if you've got, if you're trying to push heat from a, from one side of heat exchanger into another, if you've got kind of pool temperature water at 25 degrees, one side of that's the heat exchanger, and you've got, you know, our heat at 55, the other side, then heat flows the right way.

When it comes to district heating systems and heat networks, the old ones, actually, again, they weren't very, it was quite difficult to plug data centers into them because those old heat networks were quite high heat. They needed heat at 80, 90 degrees. So if you were a data center and you said, I'll give you heat at 35 degrees, it really wasn't that useful. Now, fifth generation district heating systems, the ones that we're building in the UK and the ones that are beginning to be built elsewhere in the world, they can use very much lower temperature heat because the buildings themselves are better insulated. So the whole, the kind of what we think of as ecology, industrial ecology, the kind of ecology starts to, to make sense because lots more offtakers can use this relatively low grade heat,

Chris Adams: Ah, I see. And you also said one other thing about, this is kind of one of the kind of flip sides of massively more dense compute. Here's one thing we've spoken about before. People talk about, okay, there is like worry about data centers, basically, or like AI data centers being massively more dense.

Like the examples, I think I saw you share a link on LinkedIn, which kind of blew my mind. Like, some of these new racks from Dell can have like half a megawatt of

Mark Bjornsgaard: half a megawatt per rack.

Chris Adams: and like, I couldn't really kind of picture what that was. I know it's about 30, it's around 30 times minimum, or around 30, more than 30 times what you might have for an enterprise data center rack.

So like, that's quite a lot of energy there. But like, can you maybe just like, what does half a megawatt even look like for most people, because it's really hard to

Mark Bjornsgaard: it's really, yeah, it is, it's really, it's sort of so vague, it's very hard to get your head around, isn't it? So, I always like to think of it in terms of your boiler on your wall at home. So that's going to be about 10 to 20 kilowatts, right? Your boiler at home. So that one Dell rack is, produces 50 times the amount of heat on the basis that on the basis that 97 percent of the electrons that go into it come out as heat.

That 500 kilowatt rack is producing anywhere between 30, 40, 50 times more heat than the boiler on the wall of your house. And so, an unfathomable, you know, amount of kind of heat. Then if you look at it in the context of a normal data center, if you go into a conventional data center now, you might have rack densities of between 7 and 12 kilowatts a rack.

So when you're talking about densities of again, kind of, you know, 20, 30 times. the density of compute in a single space. Now for us, we love that because we have the opposite problem of every other data center. We're space constrained, not power constrained. So if we can go to a swimming pool and we can heat a very large swimming pool with only two racks of gear, like a megawatt of, that for us is amazing because we spend much less money on building a data center, fencing, security, containers, all the other gubbins, fire suppressant systems, all the other gubbins that you'd have around a data center, when you compress them and you squidge them down, you make them much easier to deploy in the fabric of our communities and society. And then you get this really crazy kind of stats where I was in a data center in Sacramento, a couple of weeks ago, and you got this massive data hall,

it's meant to be one and a half megawatts. It is one and a half megawatts of power, but the whole hall is empty. There are just three or four racks just at the end of the hall because those racks are 130 kilowatts a rack. And so they've built a data center. The physical shell of the data center is built for those rack densities, but they don't need all of that space.

So actually what's going on at the moment in the data center industry is we believe is this sort of giant misallocation of capital where people are building data centers in the old way, when they actually should be building them for the world that's emerging, which is this really high dense, these rack densities that look nothing like conventional data centers.

Chris Adams: So you, okay, that's interesting, and I'd like to come back to some of the things you said there about what the implications of massively more dense compute might actually be. But you also said a few things interesting about this idea of saying, you know, community involvement and things like that.

Because one thing that I've never heard anyone else talk about in the data center industry or even the kind of like tech IT industry talk about was this idea of a, borrowing the idea of a social license to operate. This is an idea that people talk about in say fossil fuels and oil majors and stuff like that.

And you said, well, this is one way that we can actually essentially keep that social license to operate by actually offering a much, much more kind of equitable deal with the communities we're kind of trying to integrate with rather than having this kind of like standoffish approach. Maybe you could like talk a little bit more about that, because I don't really hear people saying that much about data centers.

They usually say, "well, you should be grateful because without us, you wouldn't have your cat pics without and and and..." It does feel like it's kind of missing a huge power of why people might push back against data centers or why they even talk about why they, you know, whatever the deal is when someone comes in and says, "Hey, can we build a bunch of digital infrastructure in your part of the world," for example.

Mark Bjornsgaard: Yeah, I mean, as you say, we talk a lot about a social license to operate because, and we believe that in the future, you will get more and more pushback from communities around having data centers in their backyard, 

because you've got these huge sheds which are hogging and clogging transmission grids.

So these transmission grids to be built by public money and then their commercial enterprise, yeah, dumps down there and says, "well, I want 100 megawatts" and then suddenly you realize that half the streets in the area can't put in heat pumps because there's no more grid capacity in the substages or they can't have electric cars. So, we think that social license to operate will be increasingly important in the future. No doubt. But the also the other, I guess the other on the other sort of flip side of this is that datacenters don't really employ anyone, right? I think the datacenter industry is a bit naughty when it says, "oh, you know, we're going to build a datacenter, we're going to employ 4,000 people."

It's like, that's actually not true. You might employ 4,000 people while it's being built, but the reality is once a datacenter is up and running, the number of people who have to be employed in the actual vicinity are very low. But if you build a data center and then you say "I'm going to reuse the heat with a aquaculture park or a distillery or a laundry," suddenly then you then produce genuine net new jobs in a local area.

So not only is the kind of the environmental bit of the social license talk very important, we think increasingly data centers are going to be looked on as having to be good citizens in terms of, you know, employment and doing the right thing with the community and we've already seen a lot of this, right?

We've had moratoriums on data centers in the Netherlands and in Ireland and Singapore. We think we're in this sort of grace period in the transition. In the next 3 to 5 years electrons, then the amount, the number of electrons are going to become very constrained. We're not actually yet in the bottleneck, but in the next three to five years, we're going to start going to that period of time where they just genuinely are not enough electrons to go around.

And we are going to have to make genuine choices about what we do with scarce electrons. And at that point, we believe, that if you're a data center and you're not doing the right thing, then, you know, you're the very least your operations going to be severely curtailed. Stroke, you're going to be in the midst of a full scale culture war, which you just don't want to go anywhere near.

Right?

Chris Adams: Okay, so you said a couple of things which I think might be worth exploring or kind of diving into there because a one of the key things I think I'm getting from you is that, yes, you might be able to kind of force some changes through quickly or you might say like, okay, well, I think one of the key things is that we need this transition itself to be sustainable and if you are able to kind of maybe push through some changes now you'll end up with so much pushback that you won't be able to sustain that state of changing as we end up like essentially moving away from fossil fuels a society based on electrification in many cases.

Mark Bjornsgaard: That's exactly. Yeah, exactly. So, yeah, I think what we see is that we see that. We are energy and software folk and we're venture capitalists by trade. We see, we don't see the data center industry as a, we don't take it as sort of face value. What we see is 70 percent of the UK's total energy budget being the heating of spaces.

So what, 

we're looking at from the other end of the telescope, we're saying, well, how could we, how can we best, what's the fastest, quickest way of heating all our shops and offices and factories? And the reality is, the quickest, fastest way of doing that is using computers as electric heaters.

The fact that they happen to be there as data centers is almost, you know, that's kind of just a happy circumstance for us. We're solving what we see as a, as the meta problem, if you like. And just seeing what tools and capabilities we have to be able to solve that problem.

Chris Adams: Okay all right so this is actually one thing that you... 

Because I think this is the thing that some of us forget about when we just think about IT like okay there's other transition, other changes that need to take place and before we, before you came on to this, I remember I saw you did a talk about these kind of for the wicked problems related to climate.

And I wonder if you might get a kind of maybe kind of expand on some of that because I think it's quite a useful context to help people who are thinking about their role as a technologist. But, okay, like, why would you even care about heat reuse, and why would you care about anything other than just the efficiency of your code directly, rather than this kind of wider, more systemic view, for example?

Mark Bjornsgaard: Yeah. Of course, we are. We all see our worlds in kind of what's in front of us, and that's completely understandable. As you say, we frame heat reuse and the electrification of heat, as you say, in context of what we think of as four wicked problems. So and these wicked problems make out make up roughly about 50 percent of the entire transition.

So if we solve these four problems, then we will be somewhere around 50% of the challenge of the transition take place and those problems are the heating of, of spaces, so all of our homes and offices, the industrial use of heat, so all industrial processes need to be de decarbonized and kind of electrified, and then we think of, controlled environment agriculture

and what's going on with how we grow stuff, the sustainability movement is rapidly kind of moot, sort of casting its eye across agriculture is realizing that actually how we feed 8 billion people on this planet is actually kind of some like 70 to 80 percent of all of our food is intensively farmed and based on fossil fuels.

And then the fourth wicked problem is carbon sequestration. So how do you, actually sequester carbon out of the atmosphere? That is also a problem around heating. If you take those four wicked problems, they can all be somewhat or completely solved with data center heat, with low grade heat on it. And so we're sitting there saying, well, look, if those datacenters are going to be built anyway, if we already need to spend between 10 and 20 percent of our entire

electricity budget for our country on data centers, then all logic says you build those data centers where you can use the electron twice. The electron can do its funky thing in the data center. We can have all that utility. And then so long as you've done in the right way, like we're doing it, you can just pass on 97 percent of that electron in the form of heat for it to then be used in those four wicked problems. So to us, that is, there's sort of a beautiful, immutable logic there, particularly in a world where you haven't got enough electrons. If you had bountiful, you know, fusion, fission, whichever the good nuclear bit is, if you had a bountiful electricity supply, then you might not be that bothered.

But the reality is in the next 10, 20 years, we're going to be so constrained by the amount of electricity that we have, we're going to have to get really good at being as efficient as we can.

Chris Adams: And I suppose it's actually, I mean, in the I mean, I'm calling you from in Germany, where most of our, almost all of our heating is still coming from basically combustion, burning like gas and stuff like that, for example, which is expensive. And even when you look at the UK gas again is one of the, what was the, I think it's the largest source of heating in the UK by quite a long way.

And these are two things which are volatile and where you're exposed to all kinds of changes in prices and things like that. And this is one thing that we probably do need to move away from. So that seems to be one thing like you're kind of, this is one of the approaches that you're looking at doing here, I suppose.

This is one thing I should ask you about then, because we spoke a little bit about this being a thing that we, that is valued and this is like a shift in how the role that digital infrastructure plays in kind of like the wider societal role. We've also spoken about in the UK, there is this goal to get entirely off, essentially have like some as close to as possible as a fossil free grid by 2030, which basically mean getting rid of a bunch of this heating from burning fossil fuels, right? Now that's a really ambitious goal. And like, as an as someone who grew up in London or grew up in the UK, I'm like, "wow, this is really cool."

This is like, I'm really impressed by that kind of ambition. And it's also one thing we've seen where a number of larger providers have basically said, "well this 2030 goal, it was a nice idea, but the moon has moved," to quote president having Brad Smith at Microsoft saying, "Oh, yeah, we were not pushing for 2030 anymore."

And I kind of feel like if there is this goal of 2030 in the UK, for example, and we have very similar goals in other parts of the world. Like what needs to happen at policy level to actually make this possible for the actual data center or the kind of digital infrastructure there because right now, I'm not aware of the kind of support or how policy kind of values this kind of different way of thinking about the role that digital infrastructure plays.

But we have seen with new government, basically in the UK, they do seem to be very keen on having a massive rollout of infrastructure. So. what's the deal here? Is it gonna be, how do we make, how do we square this circle basically?

Mark Bjornsgaard: It's not, 

the declaration of data centers as critical infrastructure isn't quite as good news as it looks. So the, so that is that predicated on regulatory capture and if you declare data centers as critical infrastructure, you can then basically run ride roughshod over any local objections.

So the fact that the labor government announced that isn't necessarily a good thing. It's probably the opposite. In Europe, we've got the EED, we've got the European Energy Directive, I think it is, and by an 

Energy Efficiency Directive, which is, which effectively says that certainly in Germany by 2028, you won't be able to build a new data center without reusing 20 percent of the heat. So there is a, there is already a, some sort of regulatory framework out there that's saying "you've got to do the right thing.

You've got to have, you've got to use green electrons. You've got to reuse the heat." So that's good. The reality is, as we all know, governments probably have to use carrot and stick. So you probably have to do a little bit more stick and a little bit more carrot. Those people who are being good citizens and reusing heat should get some brown points and should get some economic benefit from that.

And those who aren't, increasingly should be penalised. I mean, now you'd expect us to say that because obviously we're on what we think of as the right side of history. So I think the short answer is the government does need to legislate. There is just not enough structure and there's not enough impetus for people to do the right thing. But the also, particularly in the UK, what the government needs to do is planning is a huge, huge hurdle.

I never really understood that until we'd be working with DeDeep Greenor, you know, building data centers. It is breathtaking how Kafka-esque the planning system in the UK is. It's just, 

it's beyond insane. it's crazy. So you've got regulations like, because you're leased of a council on a district heating system means that you only got that lease because you said you'd use green energy.

If you put a data center within the environment of your district heating system, because we've got generators that kick in if, you know, for redundancy and resiliency, that then means that you're in contravention of your lease. So instead of somebody just going, "yeah, that's a shit idea, let's not do that. Put across through that. That's an unfathomably complicated year long process."

We've had to put one pool we're trying to qualify, we've had to resubmit planning seven times. So this is just, I mean it's beyond rank stupidity, it's just a madness in this country, in the UK at least, around, we hate success in this country. We just hate success. This will be the third business that we develop in the UK and then scale in the US because in this country it is, yeah, we just can't get out of our own way.

It's really sad. And, you know, everyone says, "oh, we'll try and change." It's like, it's very simple. It's like, you either want people to do this or you don't. Do you know what I mean? Like no amount of meetings or nice coffees or platitudes or strongly worded emails. Do you know what I mean? Like, it's very fucking simple.

Can I build a data center or not? If I can't, then I can't. You know what I mean? Like it is, yeah. So this country is, it's very difficult to do here. And I suspect in a lot of Europe it is. So we need government to get out of its own way and clear a path for us.

Chris Adams: So you said a couple of things that I think maybe we could just go into a bit more detail before we move on from there. Because you said one of the things was, things like the, there is one like regulation, the energy efficiency directive, which is It's one of the ideally one of the drivers of transparency for organization for people operating digital infrastructure, like they'll, you know, as a result, you know, for you to comply with this, you need to be able to listen information like the carbon intensity of the power, how much your, you know, how clean the power is, for example, how much of it is coming from, say, fossil fuels, how much water you're using and things like this.

And presumably, like, these are some of the metrics that you might be able to kind of look good on, as it were, or this kind of way of building infrastructure might look a bit better, for example, like, if you're reusing some of the heat, I suppose, then does that have an implication on maybe how much water might be used, for example, and things like that?

Mark Bjornsgaard: Yes. And you've got to be very careful that it's not whack a mole that you don't, you know, you don't drop your PUE, but then you raise your, so you use evaporative cooling, you might drop your PUE or your, the energy use, you know, the Power Utilization Effectiveness of your data center, but then you massively increase the amount of water you use.

So there is a balance. There is a balance to be struck across all of these metrics. That's why there isn't one perfect kind of measure, if you like. Certainly in our case, we don't use any water, so the way that we cool, the way that the direct chip cooling and, the types of cooling we use, we don't use any water and, you know, there really isn't, as far as I understand, and I'm not an expert in terms of a techie expert in this area, but, really using water is a question of just how much margin you're prepared to sacrifice, you know, it is perfectly possible to cool the data center without using any water.

It's just you make a small amount more money on each data center if you use water and people again, the great and good of the data center industry are always be good environmental citizens. They could choose to use no water and just spend a little bit and make a little bit less money. Okay.

Chris Adams: You, ah, so you said something quite interesting there about how So you're using essentially liquid cooling as one way we can, as I understand it, liquid cooling in cars is way more efficient than air cooling in cars, which is why we've moved over. Presumably it's the same kind of idea here. So that's, that would result in a more efficient system that you'd be looking at using here.

Okay. And that, okay. That helps me understand how that might actually fit into heating a swimming pool or something like that. So if you've got an efficient way to move the heat from one place. to another place and like the whole point about you know people use water for heat storage and stuff like that it makes total sense I can see why you'd have like a nice chunky kind of like sink I suppose and if you if these are the things that you're doing then I suppose there's a chance to be more transparent, I suppose, with the kind of figures you're using for this.

So this might be, okay, that's, okay, that's interesting. All right, so if I could, I'd like to ask you a little bit about this AI question, because the approach you're describing here, of having lots and lots of distributed, having series of smaller data centers, like, built into the kind of fabric around us, that seems quite a bit different to the massive, centralized, gigascale data center, kind of paradigm that people talk about so I want to ask like if this is, I've always assumed that you need to have massive centralized data centers to do some of the kind of. AI workload stuff because you need to have these things network with each other. The way you're describing it sounds like that might not be the case.

You know, the things not being in the same building might not be the showstopper that people initially thought it was. Could you maybe talk a little bit about this? Because this suggests like a kind of post cloud way of thinking about computing, for example. And I want to ask, like, do you actually need a data, a mega cluster?

Or is there a, an alternative that you're suggesting here?

Mark Bjornsgaard: The truth is at the moment you need the mega clusters. So we, when we think of training large language models, those need to be done at the moment, those mega clusters need generally need to be all in one place. The trouble is, as data centers grow bigger and bigger, and as you build gigawatt data center campuses, and even larger, when we get, when we think of the trillion dollar cluster, the amount of compute we're going to need to, kind of enable artificial general intelligence, I think we're going to need something like 100 gigawatts of power, right?

100 gigawatt data center, which is, now, when you build, start to build data centers in these sizes, You Actually start to have a distributed problem anyway because you physically can't each sort of node running a version of the model has, it's so far away from the other node. You've got a distribution problem almost by default by size.

If that make if that makes any sense. So we've certainly got to be better at networking the architectures around large language models. And, there isn't very much academic research on this, there is a bit. We're doing a lot of work with NVIDIA and Nokia around this. The Chinese, we think, are doing a lot more work around this than other people, which is in itself interesting as we see a race to AGI emerging. So certainly the networking between data centers is going to become increasingly important. See, in the last six months, you've seen Microsoft spending billions laying massive fiber pipes between its AI data centers because it's trying to use these, you know, even 100 megawatt data center needs to be kind of physically clustered with another 100 megawatt data centers.

But that's also all in the world of training. Now, of course, when that, and that's where the models are learning, and that's great, and that's going to go on. The world that will emerge is obviously mostly going to be inference. So when you think of a world of AI in 10 years time, actually 90 percent is going to be inference, 10 percent is going to be training.

So we are, at DeepGreen, we're not necessarily trying to win the large language model, massive cluster game. What we're building is, the compute substrate for the future, where there will need to be thousands of megawatts of smaller data centers, smaller cluster sizes, much closer to where we all live and work.

So we're, this substrate, this compute substrate will be required in the future.

Chris Adams: Okay. All right. So, so basically, what I think you're saying, or what I'm kind of taking away from that, is that it was almost like a typology of different kinds of digital infrastructure that you might think about. So rather than just being one model, which is inherently better than the other, you probably would need to have different setups, depending on the different kinds of roles that you might actually be having.

And it's, you can kind of see people talking a little bit about this with the whole idea of like edge computing, but it sounds like for certain things you do need, you may, there may be a world where you do have big box Walmart-style out of town data centers doing certain things because, and you just, and you may have to accept that there's, you're not able to use some of the waste heat or you may need to like co locate things to use that and like have some kind of clusters and I guess China's, you can see some examples of people co-locating energy generation with industry and things like that.

But then there's this other kind of like other end of the scale, which is a more distributed thing. And that's something that you're looking, that you're looking at building, like, the kind of data centers that might actually integrate with, say, cities and things where they're closer to where it's actually being used.

But the, you're trying to go for a more kind of integrated approach by making as many of the outputs, the waste outputs, something that can be reused by other people for example because presumably there's a cost to like heating a swimming pool like it's non zero if you need to do that and if you've got the heat coming from what you're using then that's something economically benefit that's something that you might write into like currency benefits agreements and things like that. 

Mark Bjornsgaard: Yeah. If you think about some of the inference work use cases that are already emerging, whether that's, you know, you integrating, you interfacing or chatting, maybe your kids are talking to a chatbot and they're trying to learn about they've got some visualization, some rendering visualization, which takes a lot of GPU compute.

That will be, those GPUs will be, it is better that they are co-located, or they're located somewhere closer to where the user is, particularly in the US, where they'll see, or other countries, and not just the US, but, you know, across Europe and other large continents, large land masses, you want the compute to be physically closer to people.

So, you know, where they're living and working. So that that is very important. But of course that world is just emerging. So at the, but that said, there are already a, there's already a lot of refining training. There's already a lot of people who are taking the outputs of the very large language models and then applying their own data to them and then refining, training them.

And then there's a whole bunch of other use cases around medical science and fluid dynamics and all the other stuff that the robots are gonna do for us. That world is now, as we know, emerging fast. That's the world that we're really building for smaller compute clusters, much closer to where people live and work.

And then, as you say, then you start to change the economics about how society works. You know, in the UK, we're spending 1.5 billion pounds heating our swimming pools every year. Really, we shouldn't be spending anywhere near that. Because those, pools should be being heated by recaptured heat. If we allow ourselves to build the data center infrastructure in the right way, the interesting thing about the UK particularly and other countries is that there's lots of fiber in the ground.

So when we first started building a data center, we talked about them following the fiber. Now, data centers don't really need to do that. There's plenty of fiber around. You can pretty much build a data center wherever you like. Now you have to, now people are saying they're following the heat, sorry, the power, but the third generation, the third phase of data center development, we see is people following the heat.

So first of all, you went to where the fiber is, then you went to where the power is. that's the era we're in now, but very quickly you're then now going to build data centers where the heat's required.

Chris Adams: i see where there's presumably like someone who like an offtaker who would use that and then be in favor of something being set up in their neighborhood or in as part of their project, they're getting a bit set up. Okay, so you said one thing that was, I think, quite interesting from there about, okay, there's loads of fiber, there's more fiber than we thought, like all this kind of dark fiber from 20 years ago, the last boom and bust, there's people might reuse some of that.

And some of this has, this could feel a little bit kind of academic or maybe not, it might feel a little bit like, "okay, what's happening in the future?" But As I understand it, some of this stuff is like, what if I'm a, if I'm a developer, I think, "oh, this is kind of cool." I like the idea of actually being able to run infrastructure, run kind of the code or run my applications in somewhere like this, in this kind of environment, because I think it's maybe more interesting.

Or, and if I can have the same convenience and same, the same kind of experience as a developer deploying code, as then why, you know, I might try this out. Is it something that people can use? Like, is there like. I mean, if I'm used to, like, deploying things into, like, virtual computers, I mean, virtual private servers or Kubernetes, is there something like that?

How do I actually try out some of this or use some of this stuff, for example?

Mark Bjornsgaard: Yes, it's because we're we are just a dumb datacenter operator. We are making our capacity of our datacenters available. Then that's the physical space in our datacenters for people like Amazon and Microsoft and Google and loads of other people to come and put their kit in our datacenters. So the minute you put your kids in our datacenter, then it will be doing something useful with the heat.

So as you say that there are a few cloud providers who already partnering with our main partner who have been incredibly supportive to us for years is a platform called Civo. So yeah, again, a UK business paying UK tax. If you as a developer want to run, you want a cloud service that is every bit as good as AWS or Google or Amazon or Azure,

and you want it to be green, then just go to Civo. And then you will be, Civo are using our data centers. So you as a developer, you shouldn't have to make any compromises at all, right? You shouldn't have to worry about any of this stuff. This should all be abstracted away. And in time will be where you can just be assured that when you're running code, it's running in the most environmentally, you know, it's being run in the most sustainable way possible. Now, part of the problem with the large clouds is that their reporting, their ESG reporting, their sustainability reporting is pretty shunky, stroke, complete bullshit. So I think that's part of the problem that I think a lot of cloud services at the moment aren't really taking this very seriously.

And what is certainly very hard as a developer or as an end user of a cloud platform to know how green or not your cloud is. The reality is any cloud platform that's claimed to be green just by using green electrons is ignoring 90 percent of the problem, right? 90 percent of the carbon in a data center is in the kit itself.

The scope, what's called scope three, the carbon that has been used to manufacture the computers themselves. So however much you jump up and down and say, "I'm doing really well because I'm buying green electricity or I'm buying" that's pretty much. I mean, it's not

Chris Adams: 10 percent rather than the other,

Mark Bjornsgaard: exactly so really, as, we all get better at this and as reporting becomes better and as greenwashing gets, people start to come down on greenwashing, as developers, as a whole community, we will have much, much better visibility about how green our clouds really are, but the reality is a green cloud, it comes down to the carbon in the compute and what you're doing, what you're doing to mitigate and reduce and remove that carbon.

Chris Adams: Okay. Alright, so maybe this is one thing that, so, there's one thing, there's one project that we work on in the Green Software Foundation that may be relevant for this. There's one project called the Realtime Cloud Project, where there is an effort to basically work out the carbon intensity for on a kind of per hour for every single region that we have.

If this is something that, I mean, it would be wonderful to have groups like Civo or people like that share something like this. Because the whole effort is to have some standardized data sets, some standardized numbers that you can trust and you can optimize for. And if what you've described is basically saying that yeah, running stuff inside infrastructure here is essentially somewhat fungible compared to running in other infrastructure here.

But if the number, if you're able to kind of reflect that in a lower carbon intensity or lower embodied energy or lower water usage then or any of the any other metrics that are available then that feels like a useful thing to actually allow people to be able to do and it sounds like that is something people can do today rather than having to this being a conversation about 2026 or 2027, by the sounds of things.

Mark Bjornsgaard: Well, to be clear, we're still, we're bringing our capacity online now. So we'd be a year in sort of designing since raising the money from Octopus designing building and now getting shovels in the ground and actually getting our data set the first wave of data centers built. So we've not done, we deliberately not said anything about this because we didn't want to be kind of part of the problem.

We want to be very much part of the solution. Whatever we will be reporting next year will be, you know, we'll be holding our hands up saying this is. This is as good as it gets the moment and we're going to improve it. But I think it's incumbent on all of us to be very transparent about that. I think that's it.

No one's trying to be perfect. No one's going to get kind of shot down for not being perfect. I think it's much more about the attitude you bring to it as a business rather than being, you know, "this is the law and I'm telling you it's like this" when we all know that's not true. But I think it's much better to be more tentative about it and say, "look, we don't know everything, but, you know, we think our scope three is this, and we are removing it using these removals."

And if somebody says, "I don't like those removals, I think they're nonsense." And whilst you say, "well, okay, but we are paying, you know, $250 a tonne for that carbon, so they're not complete bullshit." You know what I mean? I think it's in the, in this next phase, it's all about hopefully not giving each other too hard a time, but actually getting a bit more transparency and a bit more kind of clarity on where we are, because only then can we then start chipping away at it, right?

Chris Adams: Yeah. And like in the UK, we have very, clear targets for the very least like 2030 to get there, for example.

Mark Bjornsgaard: Quite, which is incredibly short

Chris Adams: It's very, it's like, it's almost tomorrow, isn't it? Yeah.

Mark Bjornsgaard: I'm so old that the years pass like days these days, but yeah, five years doesn't feel very long at all, frankly. Yeah.

Chris Adams: I could definitely sympathize with that because we are a non profit focusing on a fossil free internet by 2030. So that is very, acute for us as well. All right, Mark, I've really enjoyed chatting with you. And I've learned a bunch from us, like wonder or wandering through the world of digital infrastructure and stuff, we're just coming to the end of the time.

So I want to ask, like, is, I mean, if you, 

are there any projects or things you want to kind of point people's attention to, or people, if people want to find out more about the work you're doing, where should people be looking, for example?

Mark Bjornsgaard: Yeah. If you're a developer, go to Civo. They're amazing people. It's an amazing platform, as I said. And the fastest, quickest way of supporting us is by using Civo. Buying Hewlett Packard Enterprise, Hewlett Packard GreenLake AI. So we're landing whenever you buy HPE kit in the UK and hopefully the US, you will have the option to land it in a Deep Green data center now.

So increasingly, developers and businesses can make green choices just by searching out our partners, you almost certainly never come to us directly. You're going to be consuming cloud services by a third party, but asking your cloud service providers to land that kit in our data center is the fastest, quickest way of helping us.

Yeah.

Chris Adams: Brilliant. Well, in that case, I'll speak to other friends to see if there's a way to filter any kind of like cloud providers for heat swimming pool as one of the kind of like features when I'm looking for my cloud computing in future. Mark, this has been fun. I really enjoyed it. Thank you so much for making the time, especially given like getting hit with COVID last week and everything like that.

So once again, thank you again for this and yeah, this is great. Take care of yourself and have a lovely week. All right, Mark.

Mark Bjornsgaard: Thanks very much for having me. Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.



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]]> In this episode of Environment Variables, host Chris Adams sits down with Mark Bjornsgaard of Deep Green to explore a transformative approach to data center design and sustainability. Mark shares insights into how Deep Green reimagines traditional data centers by co-locating them in urban areas to provide heat reuse for facilities like swimming pools, district heating systems, and industrial processes. They discuss the challenges of planning and policy, the rise of high-density computing driven by AI, and the potential for data centers to become integral components of community infrastructure. Tune in to learn about the intersection of digital innovation and environmental responsibility, and how new business models can turn waste into opportunity.

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TRANSCRIPT BELOW:

Mark Bjornsgaard: The government does need to legislate. There is just not enough structure and there's not enough impetus for people to do the right thing. But the also, and particularly in the UK, what the government needs to do is planning is a huge, huge hurdle. I never really understood that until we'd be working with Deep Green for, you know, building data centers.

It is breathtaking how Kafka-esque the planning system in the UK is. It's just, 

It's beyond insane.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Okay, Mark, a few years back, when people were asked what a data center was, if they knew what one was at all, they might talk about some kind of thing, room, cupboard full of a few machines, maybe in a rack inside a unused room inside a building, for example. But these days, in the 2020s, people are more likely to talk about a warehouse full of hyperscale kind of data servers in a building, which is maybe the size of a football field or larger, for example, the kind of things that are run by massive firms like Google, Microsoft and Amazon, for example.

Now, as I understand it, you work with data centers, too, but they can take a rather different shape and interact rather differently with the built environment. So for those who've never heard of Deep Green, or how the stuff you're doing is different, give a kind of brief introduction to like how your approach to like building data centers is and how that has an impact on how it works with the surrounding area, for example, communities.

Mark Bjornsgaard: Yeah. So, as you say, most data centers are built in the middle of nowhere, and the vast majority are built without heat reuse. So the vast majority simply eject the heat that comes out of the computers. Data centers, we know, two to four percent of the world's electricity supply, and computers themselves are incredibly efficient electric heaters.

So 97 percent of the electrons that go into a computer come out as heat. So you've got us as a species, us in a climate emergency, taking two to four percent of the world's electricity supply, converting it into heat, and then ejecting it into the atmosphere, which 10 years ago, that might have sounded kind of plausible or even sort of necessary.

But in a world, as I said, in a climate emergency, that doesn't look so clever. So the difference between Deep Green and every other data center, most other data centers is we are building the data center where the heat can be reused. So very hard to transport heat, but relatively easy to transport electrons to take the data center to where the heat's required.

So that's what we do. We build smaller data centers, co-locate them where heat's required. Now that might be a laundry, it might be a distillery, it might be food production, it might be antibiotic production, it might be a swimming pool, but more often than not, it's what's called a district heating system.

So these large centralized heat networks that through super insulated pipes supply heat to large areas of different cities. That sort of principle, that district heat systems and heat networks, we're not very good at them in the UK specifically, but we are, the government is certainly planning for us to get a lot best them in the years to come.

So, that's where we're anchored. We, you don't build them in the middle of nowhere, you build them where they're required. There's a further, there's a further caveat and a sort of, a kind of context to this, I suppose, if you'd like. Up until the point where AI started to become part of our everyday lives, those normal data centers aren't on very much.

They're only on 20, 30 percent of the time, and they don't actually generate very good waste heat. So you can certainly forgive the great, the good of the data center industry for not necessarily trying too hard to reuse heat in the old world. But in the world that's coming where we've got these incredibly dense racks of NVIDIA and other chips, where, you know, she utilising a massive, huge amount more energy than previously the datasets had.

That, it's at this point where those are on 70, 80 percent of the time, and they're generating an enormous amount of heat, and the heat's relatively high grade. It's not high grade heat as class within, but it's good low grade heat. So at this point, then the ability to reuse heat becomes a real thing. And that's why we exist.

Chris Adams: Ah, I see. Okay, so there's a couple of things I'd like to unpack if I may. So the first thing you said was, okay, so there used to be data centers if they were going to be built in a kind of hyperscale thing. You're looking for kind of cheap land and then that's why they're often kind of miles away and probably maybe near things like say a grid connection or fiber connection or something like that, all right?

So that was like one of the previous approaches, but the downside of that is that, well, you've, you might have all this heat, but no one's able to use it, so you just vent it into the sky, so it's basically wasted in that way. So the other, another way you could do this is you can actually build these, where they kind of interact more, where they're kind of more complementary to the kind of urban fabric, as it were, and then you can use that.

But the thing that we've seen, one of the reasons that's been stopping that before is that essentially the data centers might have generated some heat, but it wasn't enough heat. So, you said low grade, and when you talk about low grade heat, that's like maybe 40 degrees, 50 degrees? Like, maybe you could expand on that, what that might mean, because I think for people who've never heard of the world of heat reuse, they don't know what high grade heat or low grade heat might be or what some of these uses might be, for example.

Mark Bjornsgaard: Yes. Yeah. No. It's so as you say, low grade heat in industrial settings can be as high as a couple of hundred degrees. So when you say a data center is going to be producing heat at 45, 50, 55 degrees, then that doesn't sound very warm at all. That said, 30 percent of all of the economy, 30 percent of all of the industry can use that very low grade heat.

So for example, a swimming pool very reliably loses a degree of temperature every hour. And it only needs to be 30 degrees. So if you've got, if you're trying to push heat from a, from one side of heat exchanger into another, if you've got kind of pool temperature water at 25 degrees, one side of that's the heat exchanger, and you've got, you know, our heat at 55, the other side, then heat flows the right way.

When it comes to district heating systems and heat networks, the old ones, actually, again, they weren't very, it was quite difficult to plug data centers into them because those old heat networks were quite high heat. They needed heat at 80, 90 degrees. So if you were a data center and you said, I'll give you heat at 35 degrees, it really wasn't that useful. Now, fifth generation district heating systems, the ones that we're building in the UK and the ones that are beginning to be built elsewhere in the world, they can use very much lower temperature heat because the buildings themselves are better insulated. So the whole, the kind of what we think of as ecology, industrial ecology, the kind of ecology starts to, to make sense because lots more offtakers can use this relatively low grade heat,

Chris Adams: Ah, I see. And you also said one other thing about, this is kind of one of the kind of flip sides of massively more dense compute. Here's one thing we've spoken about before. People talk about, okay, there is like worry about data centers, basically, or like AI data centers being massively more dense.

Like the examples, I think I saw you share a link on LinkedIn, which kind of blew my mind. Like, some of these new racks from Dell can have like half a megawatt of

Mark Bjornsgaard: half a megawatt per rack.

Chris Adams: and like, I couldn't really kind of picture what that was. I know it's about 30, it's around 30 times minimum, or around 30, more than 30 times what you might have for an enterprise data center rack.

So like, that's quite a lot of energy there. But like, can you maybe just like, what does half a megawatt even look like for most people, because it's really hard to

Mark Bjornsgaard: it's really, yeah, it is, it's really, it's sort of so vague, it's very hard to get your head around, isn't it? So, I always like to think of it in terms of your boiler on your wall at home. So that's going to be about 10 to 20 kilowatts, right? Your boiler at home. So that one Dell rack is, produces 50 times the amount of heat on the basis that on the basis that 97 percent of the electrons that go into it come out as heat.

That 500 kilowatt rack is producing anywhere between 30, 40, 50 times more heat than the boiler on the wall of your house. And so, an unfathomable, you know, amount of kind of heat. Then if you look at it in the context of a normal data center, if you go into a conventional data center now, you might have rack densities of between 7 and 12 kilowatts a rack.

So when you're talking about densities of again, kind of, you know, 20, 30 times. the density of compute in a single space. Now for us, we love that because we have the opposite problem of every other data center. We're space constrained, not power constrained. So if we can go to a swimming pool and we can heat a very large swimming pool with only two racks of gear, like a megawatt of, that for us is amazing because we spend much less money on building a data center, fencing, security, containers, all the other gubbins, fire suppressant systems, all the other gubbins that you'd have around a data center, when you compress them and you squidge them down, you make them much easier to deploy in the fabric of our communities and society. And then you get this really crazy kind of stats where I was in a data center in Sacramento, a couple of weeks ago, and you got this massive data hall,

it's meant to be one and a half megawatts. It is one and a half megawatts of power, but the whole hall is empty. There are just three or four racks just at the end of the hall because those racks are 130 kilowatts a rack. And so they've built a data center. The physical shell of the data center is built for those rack densities, but they don't need all of that space.

So actually what's going on at the moment in the data center industry is we believe is this sort of giant misallocation of capital where people are building data centers in the old way, when they actually should be building them for the world that's emerging, which is this really high dense, these rack densities that look nothing like conventional data centers.

Chris Adams: So you, okay, that's interesting, and I'd like to come back to some of the things you said there about what the implications of massively more dense compute might actually be. But you also said a few things interesting about this idea of saying, you know, community involvement and things like that.

Because one thing that I've never heard anyone else talk about in the data center industry or even the kind of like tech IT industry talk about was this idea of a, borrowing the idea of a social license to operate. This is an idea that people talk about in say fossil fuels and oil majors and stuff like that.

And you said, well, this is one way that we can actually essentially keep that social license to operate by actually offering a much, much more kind of equitable deal with the communities we're kind of trying to integrate with rather than having this kind of like standoffish approach. Maybe you could like talk a little bit more about that, because I don't really hear people saying that much about data centers.

They usually say, "well, you should be grateful because without us, you wouldn't have your cat pics without and and and..." It does feel like it's kind of missing a huge power of why people might push back against data centers or why they even talk about why they, you know, whatever the deal is when someone comes in and says, "Hey, can we build a bunch of digital infrastructure in your part of the world," for example.

Mark Bjornsgaard: Yeah, I mean, as you say, we talk a lot about a social license to operate because, and we believe that in the future, you will get more and more pushback from communities around having data centers in their backyard, 

because you've got these huge sheds which are hogging and clogging transmission grids.

So these transmission grids to be built by public money and then their commercial enterprise, yeah, dumps down there and says, "well, I want 100 megawatts" and then suddenly you realize that half the streets in the area can't put in heat pumps because there's no more grid capacity in the substages or they can't have electric cars. So, we think that social license to operate will be increasingly important in the future. No doubt. But the also the other, I guess the other on the other sort of flip side of this is that datacenters don't really employ anyone, right? I think the datacenter industry is a bit naughty when it says, "oh, you know, we're going to build a datacenter, we're going to employ 4,000 people."

It's like, that's actually not true. You might employ 4,000 people while it's being built, but the reality is once a datacenter is up and running, the number of people who have to be employed in the actual vicinity are very low. But if you build a data center and then you say "I'm going to reuse the heat with a aquaculture park or a distillery or a laundry," suddenly then you then produce genuine net new jobs in a local area.

So not only is the kind of the environmental bit of the social license talk very important, we think increasingly data centers are going to be looked on as having to be good citizens in terms of, you know, employment and doing the right thing with the community and we've already seen a lot of this, right?

We've had moratoriums on data centers in the Netherlands and in Ireland and Singapore. We think we're in this sort of grace period in the transition. In the next 3 to 5 years electrons, then the amount, the number of electrons are going to become very constrained. We're not actually yet in the bottleneck, but in the next three to five years, we're going to start going to that period of time where they just genuinely are not enough electrons to go around.

And we are going to have to make genuine choices about what we do with scarce electrons. And at that point, we believe, that if you're a data center and you're not doing the right thing, then, you know, you're the very least your operations going to be severely curtailed. Stroke, you're going to be in the midst of a full scale culture war, which you just don't want to go anywhere near.

Right?

Chris Adams: Okay, so you said a couple of things which I think might be worth exploring or kind of diving into there because a one of the key things I think I'm getting from you is that, yes, you might be able to kind of force some changes through quickly or you might say like, okay, well, I think one of the key things is that we need this transition itself to be sustainable and if you are able to kind of maybe push through some changes now you'll end up with so much pushback that you won't be able to sustain that state of changing as we end up like essentially moving away from fossil fuels a society based on electrification in many cases.

Mark Bjornsgaard: That's exactly. Yeah, exactly. So, yeah, I think what we see is that we see that. We are energy and software folk and we're venture capitalists by trade. We see, we don't see the data center industry as a, we don't take it as sort of face value. What we see is 70 percent of the UK's total energy budget being the heating of spaces.

So what, 

we're looking at from the other end of the telescope, we're saying, well, how could we, how can we best, what's the fastest, quickest way of heating all our shops and offices and factories? And the reality is, the quickest, fastest way of doing that is using computers as electric heaters.

The fact that they happen to be there as data centers is almost, you know, that's kind of just a happy circumstance for us. We're solving what we see as a, as the meta problem, if you like. And just seeing what tools and capabilities we have to be able to solve that problem.

Chris Adams: Okay all right so this is actually one thing that you... 

Because I think this is the thing that some of us forget about when we just think about IT like okay there's other transition, other changes that need to take place and before we, before you came on to this, I remember I saw you did a talk about these kind of for the wicked problems related to climate.

And I wonder if you might get a kind of maybe kind of expand on some of that because I think it's quite a useful context to help people who are thinking about their role as a technologist. But, okay, like, why would you even care about heat reuse, and why would you care about anything other than just the efficiency of your code directly, rather than this kind of wider, more systemic view, for example?

Mark Bjornsgaard: Yeah. Of course, we are. We all see our worlds in kind of what's in front of us, and that's completely understandable. As you say, we frame heat reuse and the electrification of heat, as you say, in context of what we think of as four wicked problems. So and these wicked problems make out make up roughly about 50 percent of the entire transition.

So if we solve these four problems, then we will be somewhere around 50% of the challenge of the transition take place and those problems are the heating of, of spaces, so all of our homes and offices, the industrial use of heat, so all industrial processes need to be de decarbonized and kind of electrified, and then we think of, controlled environment agriculture

and what's going on with how we grow stuff, the sustainability movement is rapidly kind of moot, sort of casting its eye across agriculture is realizing that actually how we feed 8 billion people on this planet is actually kind of some like 70 to 80 percent of all of our food is intensively farmed and based on fossil fuels.

And then the fourth wicked problem is carbon sequestration. So how do you, actually sequester carbon out of the atmosphere? That is also a problem around heating. If you take those four wicked problems, they can all be somewhat or completely solved with data center heat, with low grade heat on it. And so we're sitting there saying, well, look, if those datacenters are going to be built anyway, if we already need to spend between 10 and 20 percent of our entire

electricity budget for our country on data centers, then all logic says you build those data centers where you can use the electron twice. The electron can do its funky thing in the data center. We can have all that utility. And then so long as you've done in the right way, like we're doing it, you can just pass on 97 percent of that electron in the form of heat for it to then be used in those four wicked problems. So to us, that is, there's sort of a beautiful, immutable logic there, particularly in a world where you haven't got enough electrons. If you had bountiful, you know, fusion, fission, whichever the good nuclear bit is, if you had a bountiful electricity supply, then you might not be that bothered.

But the reality is in the next 10, 20 years, we're going to be so constrained by the amount of electricity that we have, we're going to have to get really good at being as efficient as we can.

Chris Adams: And I suppose it's actually, I mean, in the I mean, I'm calling you from in Germany, where most of our, almost all of our heating is still coming from basically combustion, burning like gas and stuff like that, for example, which is expensive. And even when you look at the UK gas again is one of the, what was the, I think it's the largest source of heating in the UK by quite a long way.

And these are two things which are volatile and where you're exposed to all kinds of changes in prices and things like that. And this is one thing that we probably do need to move away from. So that seems to be one thing like you're kind of, this is one of the approaches that you're looking at doing here, I suppose.

This is one thing I should ask you about then, because we spoke a little bit about this being a thing that we, that is valued and this is like a shift in how the role that digital infrastructure plays in kind of like the wider societal role. We've also spoken about in the UK, there is this goal to get entirely off, essentially have like some as close to as possible as a fossil free grid by 2030, which basically mean getting rid of a bunch of this heating from burning fossil fuels, right? Now that's a really ambitious goal. And like, as an as someone who grew up in London or grew up in the UK, I'm like, "wow, this is really cool."

This is like, I'm really impressed by that kind of ambition. And it's also one thing we've seen where a number of larger providers have basically said, "well this 2030 goal, it was a nice idea, but the moon has moved," to quote president having Brad Smith at Microsoft saying, "Oh, yeah, we were not pushing for 2030 anymore."

And I kind of feel like if there is this goal of 2030 in the UK, for example, and we have very similar goals in other parts of the world. Like what needs to happen at policy level to actually make this possible for the actual data center or the kind of digital infrastructure there because right now, I'm not aware of the kind of support or how policy kind of values this kind of different way of thinking about the role that digital infrastructure plays.

But we have seen with new government, basically in the UK, they do seem to be very keen on having a massive rollout of infrastructure. So. what's the deal here? Is it gonna be, how do we make, how do we square this circle basically?

Mark Bjornsgaard: It's not, 

the declaration of data centers as critical infrastructure isn't quite as good news as it looks. So the, so that is that predicated on regulatory capture and if you declare data centers as critical infrastructure, you can then basically run ride roughshod over any local objections.

So the fact that the labor government announced that isn't necessarily a good thing. It's probably the opposite. In Europe, we've got the EED, we've got the European Energy Directive, I think it is, and by an 

Energy Efficiency Directive, which is, which effectively says that certainly in Germany by 2028, you won't be able to build a new data center without reusing 20 percent of the heat. So there is a, there is already a, some sort of regulatory framework out there that's saying "you've got to do the right thing.

You've got to have, you've got to use green electrons. You've got to reuse the heat." So that's good. The reality is, as we all know, governments probably have to use carrot and stick. So you probably have to do a little bit more stick and a little bit more carrot. Those people who are being good citizens and reusing heat should get some brown points and should get some economic benefit from that.

And those who aren't, increasingly should be penalised. I mean, now you'd expect us to say that because obviously we're on what we think of as the right side of history. So I think the short answer is the government does need to legislate. There is just not enough structure and there's not enough impetus for people to do the right thing. But the also, particularly in the UK, what the government needs to do is planning is a huge, huge hurdle.

I never really understood that until we'd be working with DeDeep Greenor, you know, building data centers. It is breathtaking how Kafka-esque the planning system in the UK is. It's just, 

it's beyond insane. it's crazy. So you've got regulations like, because you're leased of a council on a district heating system means that you only got that lease because you said you'd use green energy.

If you put a data center within the environment of your district heating system, because we've got generators that kick in if, you know, for redundancy and resiliency, that then means that you're in contravention of your lease. So instead of somebody just going, "yeah, that's a shit idea, let's not do that. Put across through that. That's an unfathomably complicated year long process."

We've had to put one pool we're trying to qualify, we've had to resubmit planning seven times. So this is just, I mean it's beyond rank stupidity, it's just a madness in this country, in the UK at least, around, we hate success in this country. We just hate success. This will be the third business that we develop in the UK and then scale in the US because in this country it is, yeah, we just can't get out of our own way.

It's really sad. And, you know, everyone says, "oh, we'll try and change." It's like, it's very simple. It's like, you either want people to do this or you don't. Do you know what I mean? Like no amount of meetings or nice coffees or platitudes or strongly worded emails. Do you know what I mean? Like, it's very fucking simple.

Can I build a data center or not? If I can't, then I can't. You know what I mean? Like it is, yeah. So this country is, it's very difficult to do here. And I suspect in a lot of Europe it is. So we need government to get out of its own way and clear a path for us.

Chris Adams: So you said a couple of things that I think maybe we could just go into a bit more detail before we move on from there. Because you said one of the things was, things like the, there is one like regulation, the energy efficiency directive, which is It's one of the ideally one of the drivers of transparency for organization for people operating digital infrastructure, like they'll, you know, as a result, you know, for you to comply with this, you need to be able to listen information like the carbon intensity of the power, how much your, you know, how clean the power is, for example, how much of it is coming from, say, fossil fuels, how much water you're using and things like this.

And presumably, like, these are some of the metrics that you might be able to kind of look good on, as it were, or this kind of way of building infrastructure might look a bit better, for example, like, if you're reusing some of the heat, I suppose, then does that have an implication on maybe how much water might be used, for example, and things like that?

Mark Bjornsgaard: Yes. And you've got to be very careful that it's not whack a mole that you don't, you know, you don't drop your PUE, but then you raise your, so you use evaporative cooling, you might drop your PUE or your, the energy use, you know, the Power Utilization Effectiveness of your data center, but then you massively increase the amount of water you use.

So there is a balance. There is a balance to be struck across all of these metrics. That's why there isn't one perfect kind of measure, if you like. Certainly in our case, we don't use any water, so the way that we cool, the way that the direct chip cooling and, the types of cooling we use, we don't use any water and, you know, there really isn't, as far as I understand, and I'm not an expert in terms of a techie expert in this area, but, really using water is a question of just how much margin you're prepared to sacrifice, you know, it is perfectly possible to cool the data center without using any water.

It's just you make a small amount more money on each data center if you use water and people again, the great and good of the data center industry are always be good environmental citizens. They could choose to use no water and just spend a little bit and make a little bit less money. Okay.

Chris Adams: You, ah, so you said something quite interesting there about how So you're using essentially liquid cooling as one way we can, as I understand it, liquid cooling in cars is way more efficient than air cooling in cars, which is why we've moved over. Presumably it's the same kind of idea here. So that's, that would result in a more efficient system that you'd be looking at using here.

Okay. And that, okay. That helps me understand how that might actually fit into heating a swimming pool or something like that. So if you've got an efficient way to move the heat from one place. to another place and like the whole point about you know people use water for heat storage and stuff like that it makes total sense I can see why you'd have like a nice chunky kind of like sink I suppose and if you if these are the things that you're doing then I suppose there's a chance to be more transparent, I suppose, with the kind of figures you're using for this.

So this might be, okay, that's, okay, that's interesting. All right, so if I could, I'd like to ask you a little bit about this AI question, because the approach you're describing here, of having lots and lots of distributed, having series of smaller data centers, like, built into the kind of fabric around us, that seems quite a bit different to the massive, centralized, gigascale data center, kind of paradigm that people talk about so I want to ask like if this is, I've always assumed that you need to have massive centralized data centers to do some of the kind of. AI workload stuff because you need to have these things network with each other. The way you're describing it sounds like that might not be the case.

You know, the things not being in the same building might not be the showstopper that people initially thought it was. Could you maybe talk a little bit about this? Because this suggests like a kind of post cloud way of thinking about computing, for example. And I want to ask, like, do you actually need a data, a mega cluster?

Or is there a, an alternative that you're suggesting here?

Mark Bjornsgaard: The truth is at the moment you need the mega clusters. So we, when we think of training large language models, those need to be done at the moment, those mega clusters need generally need to be all in one place. The trouble is, as data centers grow bigger and bigger, and as you build gigawatt data center campuses, and even larger, when we get, when we think of the trillion dollar cluster, the amount of compute we're going to need to, kind of enable artificial general intelligence, I think we're going to need something like 100 gigawatts of power, right?

100 gigawatt data center, which is, now, when you build, start to build data centers in these sizes, You Actually start to have a distributed problem anyway because you physically can't each sort of node running a version of the model has, it's so far away from the other node. You've got a distribution problem almost by default by size.

If that make if that makes any sense. So we've certainly got to be better at networking the architectures around large language models. And, there isn't very much academic research on this, there is a bit. We're doing a lot of work with NVIDIA and Nokia around this. The Chinese, we think, are doing a lot more work around this than other people, which is in itself interesting as we see a race to AGI emerging. So certainly the networking between data centers is going to become increasingly important. See, in the last six months, you've seen Microsoft spending billions laying massive fiber pipes between its AI data centers because it's trying to use these, you know, even 100 megawatt data center needs to be kind of physically clustered with another 100 megawatt data centers.

But that's also all in the world of training. Now, of course, when that, and that's where the models are learning, and that's great, and that's going to go on. The world that will emerge is obviously mostly going to be inference. So when you think of a world of AI in 10 years time, actually 90 percent is going to be inference, 10 percent is going to be training.

So we are, at DeepGreen, we're not necessarily trying to win the large language model, massive cluster game. What we're building is, the compute substrate for the future, where there will need to be thousands of megawatts of smaller data centers, smaller cluster sizes, much closer to where we all live and work.

So we're, this substrate, this compute substrate will be required in the future.

Chris Adams: Okay. All right. So, so basically, what I think you're saying, or what I'm kind of taking away from that, is that it was almost like a typology of different kinds of digital infrastructure that you might think about. So rather than just being one model, which is inherently better than the other, you probably would need to have different setups, depending on the different kinds of roles that you might actually be having.

And it's, you can kind of see people talking a little bit about this with the whole idea of like edge computing, but it sounds like for certain things you do need, you may, there may be a world where you do have big box Walmart-style out of town data centers doing certain things because, and you just, and you may have to accept that there's, you're not able to use some of the waste heat or you may need to like co locate things to use that and like have some kind of clusters and I guess China's, you can see some examples of people co-locating energy generation with industry and things like that.

But then there's this other kind of like other end of the scale, which is a more distributed thing. And that's something that you're looking, that you're looking at building, like, the kind of data centers that might actually integrate with, say, cities and things where they're closer to where it's actually being used.

But the, you're trying to go for a more kind of integrated approach by making as many of the outputs, the waste outputs, something that can be reused by other people for example because presumably there's a cost to like heating a swimming pool like it's non zero if you need to do that and if you've got the heat coming from what you're using then that's something economically benefit that's something that you might write into like currency benefits agreements and things like that. 

Mark Bjornsgaard: Yeah. If you think about some of the inference work use cases that are already emerging, whether that's, you know, you integrating, you interfacing or chatting, maybe your kids are talking to a chatbot and they're trying to learn about they've got some visualization, some rendering visualization, which takes a lot of GPU compute.

That will be, those GPUs will be, it is better that they are co-located, or they're located somewhere closer to where the user is, particularly in the US, where they'll see, or other countries, and not just the US, but, you know, across Europe and other large continents, large land masses, you want the compute to be physically closer to people.

So, you know, where they're living and working. So that that is very important. But of course that world is just emerging. So at the, but that said, there are already a, there's already a lot of refining training. There's already a lot of people who are taking the outputs of the very large language models and then applying their own data to them and then refining, training them.

And then there's a whole bunch of other use cases around medical science and fluid dynamics and all the other stuff that the robots are gonna do for us. That world is now, as we know, emerging fast. That's the world that we're really building for smaller compute clusters, much closer to where people live and work.

And then, as you say, then you start to change the economics about how society works. You know, in the UK, we're spending 1.5 billion pounds heating our swimming pools every year. Really, we shouldn't be spending anywhere near that. Because those, pools should be being heated by recaptured heat. If we allow ourselves to build the data center infrastructure in the right way, the interesting thing about the UK particularly and other countries is that there's lots of fiber in the ground.

So when we first started building a data center, we talked about them following the fiber. Now, data centers don't really need to do that. There's plenty of fiber around. You can pretty much build a data center wherever you like. Now you have to, now people are saying they're following the heat, sorry, the power, but the third generation, the third phase of data center development, we see is people following the heat.

So first of all, you went to where the fiber is, then you went to where the power is. that's the era we're in now, but very quickly you're then now going to build data centers where the heat's required.

Chris Adams: i see where there's presumably like someone who like an offtaker who would use that and then be in favor of something being set up in their neighborhood or in as part of their project, they're getting a bit set up. Okay, so you said one thing that was, I think, quite interesting from there about, okay, there's loads of fiber, there's more fiber than we thought, like all this kind of dark fiber from 20 years ago, the last boom and bust, there's people might reuse some of that.

And some of this has, this could feel a little bit kind of academic or maybe not, it might feel a little bit like, "okay, what's happening in the future?" But As I understand it, some of this stuff is like, what if I'm a, if I'm a developer, I think, "oh, this is kind of cool." I like the idea of actually being able to run infrastructure, run kind of the code or run my applications in somewhere like this, in this kind of environment, because I think it's maybe more interesting.

Or, and if I can have the same convenience and same, the same kind of experience as a developer deploying code, as then why, you know, I might try this out. Is it something that people can use? Like, is there like. I mean, if I'm used to, like, deploying things into, like, virtual computers, I mean, virtual private servers or Kubernetes, is there something like that?

How do I actually try out some of this or use some of this stuff, for example?

Mark Bjornsgaard: Yes, it's because we're we are just a dumb datacenter operator. We are making our capacity of our datacenters available. Then that's the physical space in our datacenters for people like Amazon and Microsoft and Google and loads of other people to come and put their kit in our datacenters. So the minute you put your kids in our datacenter, then it will be doing something useful with the heat.

So as you say that there are a few cloud providers who already partnering with our main partner who have been incredibly supportive to us for years is a platform called Civo. So yeah, again, a UK business paying UK tax. If you as a developer want to run, you want a cloud service that is every bit as good as AWS or Google or Amazon or Azure,

and you want it to be green, then just go to Civo. And then you will be, Civo are using our data centers. So you as a developer, you shouldn't have to make any compromises at all, right? You shouldn't have to worry about any of this stuff. This should all be abstracted away. And in time will be where you can just be assured that when you're running code, it's running in the most environmentally, you know, it's being run in the most sustainable way possible. Now, part of the problem with the large clouds is that their reporting, their ESG reporting, their sustainability reporting is pretty shunky, stroke, complete bullshit. So I think that's part of the problem that I think a lot of cloud services at the moment aren't really taking this very seriously.

And what is certainly very hard as a developer or as an end user of a cloud platform to know how green or not your cloud is. The reality is any cloud platform that's claimed to be green just by using green electrons is ignoring 90 percent of the problem, right? 90 percent of the carbon in a data center is in the kit itself.

The scope, what's called scope three, the carbon that has been used to manufacture the computers themselves. So however much you jump up and down and say, "I'm doing really well because I'm buying green electricity or I'm buying" that's pretty much. I mean, it's not

Chris Adams: 10 percent rather than the other,

Mark Bjornsgaard: exactly so really, as, we all get better at this and as reporting becomes better and as greenwashing gets, people start to come down on greenwashing, as developers, as a whole community, we will have much, much better visibility about how green our clouds really are, but the reality is a green cloud, it comes down to the carbon in the compute and what you're doing, what you're doing to mitigate and reduce and remove that carbon.

Chris Adams: Okay. Alright, so maybe this is one thing that, so, there's one thing, there's one project that we work on in the Green Software Foundation that may be relevant for this. There's one project called the Realtime Cloud Project, where there is an effort to basically work out the carbon intensity for on a kind of per hour for every single region that we have.

If this is something that, I mean, it would be wonderful to have groups like Civo or people like that share something like this. Because the whole effort is to have some standardized data sets, some standardized numbers that you can trust and you can optimize for. And if what you've described is basically saying that yeah, running stuff inside infrastructure here is essentially somewhat fungible compared to running in other infrastructure here.

But if the number, if you're able to kind of reflect that in a lower carbon intensity or lower embodied energy or lower water usage then or any of the any other metrics that are available then that feels like a useful thing to actually allow people to be able to do and it sounds like that is something people can do today rather than having to this being a conversation about 2026 or 2027, by the sounds of things.

Mark Bjornsgaard: Well, to be clear, we're still, we're bringing our capacity online now. So we'd be a year in sort of designing since raising the money from Octopus designing building and now getting shovels in the ground and actually getting our data set the first wave of data centers built. So we've not done, we deliberately not said anything about this because we didn't want to be kind of part of the problem.

We want to be very much part of the solution. Whatever we will be reporting next year will be, you know, we'll be holding our hands up saying this is. This is as good as it gets the moment and we're going to improve it. But I think it's incumbent on all of us to be very transparent about that. I think that's it.

No one's trying to be perfect. No one's going to get kind of shot down for not being perfect. I think it's much more about the attitude you bring to it as a business rather than being, you know, "this is the law and I'm telling you it's like this" when we all know that's not true. But I think it's much better to be more tentative about it and say, "look, we don't know everything, but, you know, we think our scope three is this, and we are removing it using these removals."

And if somebody says, "I don't like those removals, I think they're nonsense." And whilst you say, "well, okay, but we are paying, you know, $250 a tonne for that carbon, so they're not complete bullshit." You know what I mean? I think it's in the, in this next phase, it's all about hopefully not giving each other too hard a time, but actually getting a bit more transparency and a bit more kind of clarity on where we are, because only then can we then start chipping away at it, right?

Chris Adams: Yeah. And like in the UK, we have very, clear targets for the very least like 2030 to get there, for example.

Mark Bjornsgaard: Quite, which is incredibly short

Chris Adams: It's very, it's like, it's almost tomorrow, isn't it? Yeah.

Mark Bjornsgaard: I'm so old that the years pass like days these days, but yeah, five years doesn't feel very long at all, frankly. Yeah.

Chris Adams: I could definitely sympathize with that because we are a non profit focusing on a fossil free internet by 2030. So that is very, acute for us as well. All right, Mark, I've really enjoyed chatting with you. And I've learned a bunch from us, like wonder or wandering through the world of digital infrastructure and stuff, we're just coming to the end of the time.

So I want to ask, like, is, I mean, if you, 

are there any projects or things you want to kind of point people's attention to, or people, if people want to find out more about the work you're doing, where should people be looking, for example?

Mark Bjornsgaard: Yeah. If you're a developer, go to Civo. They're amazing people. It's an amazing platform, as I said. And the fastest, quickest way of supporting us is by using Civo. Buying Hewlett Packard Enterprise, Hewlett Packard GreenLake AI. So we're landing whenever you buy HPE kit in the UK and hopefully the US, you will have the option to land it in a Deep Green data center now.

So increasingly, developers and businesses can make green choices just by searching out our partners, you almost certainly never come to us directly. You're going to be consuming cloud services by a third party, but asking your cloud service providers to land that kit in our data center is the fastest, quickest way of helping us.

Yeah.

Chris Adams: Brilliant. Well, in that case, I'll speak to other friends to see if there's a way to filter any kind of like cloud providers for heat swimming pool as one of the kind of like features when I'm looking for my cloud computing in future. Mark, this has been fun. I really enjoyed it. Thank you so much for making the time, especially given like getting hit with COVID last week and everything like that.

So once again, thank you again for this and yeah, this is great. Take care of yourself and have a lovely week. All right, Mark.

Mark Bjornsgaard: Thanks very much for having me. Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.



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]]> Finding Signal Amongst the Noise in Carbon Aware Software Finding Signal Amongst the Noise in Carbon Aware Software Thu, 09 Jan 2025 07:00:00 GMT 35:15 81qq35z1 false https://podcasts.castplus.fm/e/v855j7m8-finding-signal-amongst-the-noise-in-carbon-aware-software 68dc7d096d92c33f9c88f35e 68dc7cef46a2532cdd8d1c23 In this episode of Environment Variables, host Chris Adams is joined by Tammy Sukprasert, a PhD student at the University of Massachusetts Amherst, to dive deep into her research on carbon-aware computing. Tammy explores the concept of shifting computing workloads across time and space to reduce carbon emissions, focusing on the benefits and limitations of this approach. She explains how moving workloads to cleaner regions or delaying them until cleaner energy sources are available can help cut emissions, but also discusses the challenges that come with real-world constraints like server capacity and latency. Together they discuss the findings from her recent papers, including the differences between average and marginal carbon intensity signals and how they impact decision-making. The conversation highlights the complexity of achieving carbon savings and the need for better metrics and strategies in the world of software development. 94 In this episode of Environment Variables, host Chris Adams is joined by Tammy Sukprasert, a PhD student at the University of Massachusetts Amherst, to dive deep into her research on carbon-aware computing. Tammy explores the concept of shifting computing workloads across time and space to reduce carbon emissions, focusing on the benefits and limitations of this approach. She explains how moving workloads to cleaner regions or delaying them until cleaner energy sources are available can help cut emissions, but also discusses the challenges that come with real-world constraints like server capacity and latency. Together they discuss the findings from her recent papers, including the differences between average and marginal carbon intensity signals and how they impact decision-making. The conversation highlights the complexity of achieving carbon savings and the need for better metrics and strategies in the world of software development.

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TRANSCRIPTION BELOW:

Tammy Sukprasert: With that one hour job with perfect knowledge of one year, we can reduce the carbon emission of the whole world by 37%.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables. Where we bring you the latest insights and updates from the world of sustainable software development. I'm your host, Chris Adams. One of the oft repeated quotes when people talk about sustainability in software is that if you can't measure it, then you can't manage it.

And when it comes to working out the carbon footprint of a software application, a significant portion of the footprint comes from what we refer to as the carbon intensity of the electricity in use, 

i.e., how green it is. And there are various steps you can take to make the same application using the same code, you can make it greener by running it where the grid is greener. So if you were to choose to run it in Iceland, that's one example. Or you can choose to run the grid, run the application at different times when the grid is greener, like when the sun is in the sky and your solar panels are wearing away. But how much greener can they get? And what else could we need to think about when trying to adopt a ways or ideas like this? Enter our guest for this episode today, Tammy Sukprasert, a PhD student at the Laboratory of Advanced Software Systems and Sustainable Computing Lab at the University of Massachusetts Amherst.

Tammy recently authored the paper on the limitations of carbon aware, temporal, and spatial workload shifting in the cloud, which examines how shifting computing workloads across time and space can help cut emissions. Tammy, we're going to spend a bit of time talking about why you chose to work in this field.

But to begin with, can I give you a bit of space to introduce yourself and what you do?

Tammy Sukprasert: Hi, Chris. Thanks for having me here. I'm Tammy Sukprasert. I'm a PhD student from the University of Massachusetts Amherst. I work on cloud and edge computing with a specific focus of decarbonizing computing. I'm currently calling you from Amherst, Massachusetts, and it's nice out here.

Chris Adams: Cool. That's nice. We've had a, it's snowing in Berlin, so I'm a little bit jealous, actually. Hi folks. If you are new to this podcast, my name is Chris Adams. I am the Director of Technology and Policy at the Green Web Foundation. And I'm also the, one of the chairs of the Green Software Foundation Policy Working Groups.

And also, the host of this podcast. Now, before we dive into the conversation with Tammy, if you're listening to this for the first time, here's a quick reminder. We will try to link to all the papers and all the links and all the projects on GitHub in this, and there will be extensive show notes as well as a transcript if there's anything you particularly missed.

And I think that's pretty much it. Tammy, are you sitting comfortable?

Tammy Sukprasert: Yep. Nice.

Chris Adams: In that case, I guess I'll begin. All right. We've linked to this in the show notes, but the paper title, On the Limitations of Carbon Aware Temporal and Spatial Workload Shifting in the Cloud, does kind of give a clue about what this research might actually be about.

But for those who are new to this idea, would you mind bringing listeners up to speed about what workloads are, what workload shifting is, when we talk about carbon aware computing?

Tammy Sukprasert: Sure. So to understand what workload shifting is, we need to have some idea of why we can shift the workload in the first place. So carbon intensity is based on the contributions of the different energy sources in the electric grid, right? So at different point in time, the demand changes. So there is different contribution of different sources.

That's why there's variation in carbon emissions. So there will be a high carbon period and low carbon period. And because of that, instead of running the workload during the high carbon period, you can actually schedule the workload to the lower carbon period or lower carbon region. So some of the workload, you can delay the start time.

The workload could be machine learning or some batch jobs. And instead of running right away when it was dispatched during the high carbon period, you can delay the start time and run it during the low current period. And at the same time, there are also, there is another type of workload that you can move or shift the workload around.

That could be a web request or an inference request. And instead of running your workload at your own region, you can look into other locations that have lower carbon intensity and migrate the in it.

Chris Adams: So if I, so let's say I'm using like maybe a chat bot or like, or I'm using something like maybe chat GPT and I am in, say, Germany, maybe it's dark, it's not very windy and it's not very sunny, for example, and most of the power is coming from coal being burned on the grid, for example, I might, rather than my request being served in Germany at the same time, it could plausibly be, say, forwarded to somewhere else in the world, as long as it's fast enough.

So, it might get forwarded to, say, Denmark, which is super windy instead. And that would mean that it would be slightly greener, for example. That's what you were referring to when you spoke about the inference. And then the other thing you mentioned before was like a machine learning job or like a video encoding thing.

That's something that I might not be seeing myself. But it's something that probably needs to happen within like a few days or something like that. So it's important, but it's not urgent. And because there's a bit of flexibility, I can choose when to do that to minimize the environmental impact of the extra amount of demand being put onto the grid.

Is that what you're, I think that's what you're saying there, right?

Tammy Sukprasert: Right. So it's just basically align your job schedule with low carbon period. Yeah. That's the key idea of the shifting.

Chris Adams: Gotcha. And then, so you spoke about there's one, which is if I'm doing something through time, that's like the temporal thing. Like I either bring it forward or wait till later. And then there's a spatial idea, which is me just moving it somewhere else. It might be happening at the same time, but it might be happening in Denmark, for example, or Iceland rather than in Germany.

Yeah?

Tammy Sukprasert: Yes, that's correct.

Chris Adams: Okay, cool. So, okay. We've got a good idea about what some of this might be. And a question I might ask is like, why is this interesting to you? Like what, how do you end up finding out or even kind of wanting to research this in the first place?

Tammy Sukprasert: Yeah. So there are many works that look into the benefits of reducing carbon reduction based on time shifting or spatial shifting, but it happened in a limited setting. i.e., a small number of regions or specific type of jobs, so people only look into spatial shifting or people only look into temporal shifting, or maybe they only look into a few number of regions but we were wondering, what if we look into both spatial and temporal and with the big picture of the whole world. So instead of looking to into a few regions, we look into 123 regions that we have in our data set and we want to see what is the broad impact of temporal and spatial shifting as a whole.

Chris Adams: I see. Okay. So thanks Tammy. So for this research paper, as I understand it, you decided to see how much, what kind of savings you really can achieve with things like Carbon Aware Computing. And a little bit about what kind of conditions might be necessary for these savings to be possible. So would you mind expanding on some of this?

We can start simple, fast, simple first, and then we can work our way up. So yeah, let's see, what were the first things we started with? And what were the first, what was like the ideal scenario for the savings? And we can go from there.

Tammy Sukprasert: All right. So with the current state of the world, right, the average carbon intensity is about 368 grams per kilowatt hour. And to achieve as much savings as possible in terms of carbon reduction, right, you will want to migrate your workload to Sweden, which is the region with the lowest carbon intensity in our data set. And migrating all the workload to Sweden, you can actually achieve 96 percent carbon reduction for the whole world.

Chris Adams: Okay, so what you're talking about there is you've basically gone from an average figure for carbon intensity of electricity to much, much cleaner electricity. And that's in this kind of ideal scenario, that's what you've essentially done. You've moved all of the computing jobs to the cleanest possible electricity there.

That's what we've done there. This is the ideal scenario. So where do we go from here then, for example, are there other constraints and things we know we need to take into account when doing this?

Tammy Sukprasert: Great. So of course, Sweden cannot take all the workloads in the world, right? So we were like, okay, instead of just moving everything to Sweden, what if we have capacity constraints? So we look into the scenario where every region in the world has an idle capacity of 50%. We're trying to be generous here because we want to understand the impact of the idle capacity on carbon reduction, right? So with every region having 50 percent idle capacity to absorb the job from other regions, instead of achieving, so now no one can actually migrate. So now not everyone can migrate to Sweden, right? Some other regions have to migrate to somewhere else. So, with that, the savings from 96 percent global reduction.

Drops to 51 percent.

Chris Adams: Okay.

Tammy Sukprasert: if not everyone can go to Sweden. Yeah,

Chris Adams: All right. That's still not bad. And when you're talking about capacity, you're referring to the fact that say, maybe there's a, like you've used the word region here, and for region, I think that's like a cloud region, like say AWS West or something like that. That's what you're referring to there.

And there's maybe a certain amount of reserve capacity they have to hold back. And that's what you're referring to there. So the idea that maybe different cloud places, different cloud data centers have a bunch of spare capacity and that's what they'd be using to move everything there, right? So, okay.

Okay. Well we never actually talked about latency constrains 

Tammy Sukprasert: as well, right. So let's say for example, a web request, you need some service level objective or SLO to respect, to be respected, right. And so we look into that as well. And with, so now we have capacity constraints. So the scenario gets more and more realistic, right?

So from 96% you added a capacity constraint, and now the saving drops from 96% to 51%. And we also look into a more realistic case where we think about web requests that have some latency constraint, where there's some service level objective that has to be respected. And so on top of the capacity constraints that we have, that we achieve 51%, we added a 50 milliseconds capacity constraint, and that further reduced the carbon savings to 31%. So in the real life scenario, we are really far from the 96% that we want to aim for, right.

Chris Adams: So if I understand that correctly, basically there is a speed, the speed of light is fast, but it's not infinite. And therefore there are certain parts of the world where you definitely need to get a response back in time. And that's why you've introduced this kind of 50 millisecond kind of budget. So it has to be, your ping, your request has to come back in that kind of time budget.

And that basically places a second constraint. And even with these two constraints, this is essentially talking about, okay, these are the carbon emissions that can be reduced. By moving things to the various regions that are available based on the capacity of all these other places, like Sweden and then the next cleanest one and the next cleanest one.

That's what you're referring to there. All right. Okay. I think I understand that part there. And that honestly, 31 percent still sounds pretty good, to be honest. But if we look at the figures for what, 2%, if we're looking at maybe, A hundred million tons of CO2 each year, and 30 percent of that is 300, is 30 million tons.

That's not bad. That's more than at Google, for example. So, okay. That's okay. So that is interesting, then. So this is one of the high level findings you found, assuming you could do this in this kind of decreasingly idealized scenarios. And eventually we get to a point where, okay, this is actually something that you might plausibly try adopting in, or you might be kind of advocating for in certain regions, for example.

Tammy Sukprasert: Right. Yeah. The point that we're trying to make is that as you added more constraints, the gap between the ideal case of 96%. Your achievable goal widens. So that's what we're trying to show in this paper.

Chris Adams: Okay, cool. And when you're talking about the regions here, these are largely the regions that are inside the electricity maps. Was it the electricity maps dataset or was it just the list of all of the regions for the biggest cloud hyperscalers? I wasn't quite sure when we were looking at this, cause there's a list of them, right?

Tammy Sukprasert: Right. So we used a dataset from ElectricityMaps. Shout out to ElectricityMaps. Thank you for the dataset. The dataset has 123 regions worldwide, right? But on the dataset, we group them up, we filtered the regions that overlap with the cloud region, and look at all exclusively the results for the cloud regions. 

Chris Adams: Ah, I see. So you created this way to make these comparisons basically by saying, maybe there's one data center, which we see in the cloud, like say Amazon AWS West, which is a lot of people refer to as like Oregon West 1. And because we know that a data set of carbon intensity from electricity map says, yes, this is Oregon.

You've been able to look at the numbers then in that way, right? That's where some of this is referring to.

Tammy Sukprasert: Yeah, so we did a mapping between the electricity map data with the location of the cloud region.

Chris Adams: Okay. All right. So, and that, and when we're looking at those numbers there, so you mentioned this figure of 96%. Was that looking at just location or was that looking at anything to do with time as well? Because I wasn't quite sure about that part there.

Tammy Sukprasert: So the 96 percent is just spatial shifting. So we have a separate result for temporal shifting where everyone in the world, every region in the world can schedule their workload based on one year ahead data. So everyone in the world can schedule their workload if they know about

Chris Adams: perfect forward knowledge. Yeah.

Tammy Sukprasert: yeah, perfect, knowledge for one year ahead.

And with that, we look at the extreme case, the most ideal case where the workload is a unit job, one hour job, to understand what is the best case scenario for temporal shifting, right? So with that one hour job with perfect knowledge of one year, we can reduce the carbon emission of the whole world by 37%.

Chris Adams: That's just temporal, not looking at location as well, right?

Tammy Sukprasert: Yes, so we have the results for temporal shifting that if we give every region a perfect knowledge of their carbon intensity a year ahead to plan their workload, what is going to be the best scheduling scenario for the future? Temporal shifting, right? So with everyone having the perfect knowledge for a year, you can reduce the carbon emission of the whole world by 37%.

Chris Adams: Ah, okay. So you're looking around maybe 30 percent when we were looking at purely locational, and then we're looking at just purely time. It's around, it's relatively similar, basically, but these are relying on. A kind of visibility that people don't really have a lot of the time, but, and, okay. So the next question I'm kind of asked is, it possible to look at time and space for this to get an idea of what the savings might be next from that then? 

Tammy Sukprasert: Yeah. So we also look into that in our paper. So if you look at spatial and temporal shifting combined, the result actually shows that spatial shifting dominates the carbon reduction. This is simply because when you move the workload to the lowest region possible in your data set, right, to achieve the savings, that region is already low in carbon intensity, so time shifting doesn't make much of a difference.

Chris Adams: Ah, I see. Okay. So it's, basically the clean regions tend to be clean most of the time anyway, rather than being kind of spiking up and down for example. So that's what it seems like you're suggesting there, right?

Tammy Sukprasert: Right. It still varies, but the variation between the high carbon period and low carbon period is relatively small.

Chris Adams: Okay, well, that kind of makes sense. Cause I mean, now that when you lay out like that, I don't really think about it until you framed it that way, but like Iceland is usually green because it's running on geothermal, which is like pretty standard. Like it's steady. And even when you look at like, say Sweden, for example, there's like a wind and everything like that, but there's lots of hydro and stuff like that.

So again, it's not nearly as spiky as, say, Germany, where we are the land of like wind is, we're land of coal and solar. We have lots of coal, which is high carbon intensity, and lots of solar, which is very, low intensity. And flicking back and forth between these things means that we might have big swings, but on average, it's not particularly low compared to Iceland or Sweden, for example.

Huh.

Tammy Sukprasert: Correct. Yeah.

Chris Adams: Oh, right. Wow. I, that's, 

in retrospect, it kind of seems obvious when you, but things are only obvious with when you look at it like that. And one thing you shared with me before we spoke about this was that some of this stuff is actually like, if people wanted to kind of explore some of these calculations, is this online somewhere? Is it like a GitHub repo or something where you can like poke around at some of these things?

Tammy Sukprasert: Yeah. So all the simulations in this paper, it's open source. So please check my lab website, my lab GitHub for the simulations.

Chris Adams: Okay, cool. All right. I think I've got the link here. So that's, this is from, so there's literally a repo called decarbonization potential. That's the one you're referring to here, right? On GitHub.

Tammy Sukprasert: Yes, that's correct.

Chris Adams: Brilliant. Okay. We'll definitely add that in the show notes because people who aren't like frantically exploring this themselves, it's where it's, right there.

Okay. So that was one of the first pieces of research. Essentially that there are some savings that can be made. It's around like the 30 percent mark in a kind of perfect world with location and sort of about the same with temporal. And if I understood it correctly, combining the two doesn't deliver massively more savings than that, right?

It's still never more than half this kind of intervention that you could possibly make, right?

Tammy Sukprasert: Right, yeah, combining the two doesn't give you double the benefits, because the benefits are dominated by spatial migration, but not much of the temporal, if you combine them together.

Chris Adams: Okay. Thank you. I'm really, glad you actually spoke about this because we can now have some of the numbers. To basically talk about the fact that, yeah, we still need to do other things. You can't just like leave your code and make no changes. That might get you some of the way. And if you're looking at Temporal, it'll get you 37 percent of the way in a perfect world.

But you still need to make some other changes if you want to kind of reduce the environmental footprint further. Brilliant. Okay. Thank you for that. So we talked about some of the savings you can get in your previous paper. The fact that there's maybe around the 30 percent figure. And if you can move everything through space, you get around maybe 30 ish percent savings.

If you look at, if you have perfect knowledge forward for the year, then it's maybe slightly higher than 30%, but it's in the same kind of ballpark. And if you were to look at moving all of your computing jobs through time and space, you can't just double this number. It's still going to be a meaning, it's going to be more than 30%, probably less than 50%.

So that's one of the figures that we have. We'll share a link to the GitHub repo for people who are curious about this and want to see if they know what jobs they ran last year, they could see what kind of savings they could have achieved. So that's one thing. And we've spoken so far about some constraints that we have, but there's a few more constraints that we need to take into account.

So for example, so far, we've been talking about how much, how many spare servers we have, like data center capacity inside this. But there are other constraints that we need to also think about, which are a little bit further down the stack, as it were. So there may be a certain limited amount of green energy, at which point when you have more demand than that, you might need to have some other forms of generation come on stream.

And like, this is something that I think you explored in one of the other papers. So maybe we could talk about that. So, okay, this other paper that you spoke about, maybe we can just like, let us know the name and then we'll see where we go from there.

Tammy Sukprasert: Right, so this paper, titled, On the Implications of Choosing Average vs marginal Carbon Intensity Signals on Carbon Aware Optimizations, basically, average vs marginal for carbon aware optimizations, right. So this paper came from the fact that, okay, People have been suggesting, let's shift the workload through time, let's shift the workload to different locations, but we never actually agree on which carbon intensity signal to use for carbon aware optimization, so as the title suggested, there are two types of carbon intensity signals that are mainly used, namely average carbon intensity signal and marginal carbon intensity signal.

So for average carbon intensity signal, just think of it as a snapshot of the grid at that point in time, right? And the way it's calculated is the weighted average of carbon emissions weighted by their production,

Chris Adams: Okay. So if I just check, I just want to start you there. So make sure I keep keeping up with you. So there's two ways you can measure carbon intensity, like how green electricity is. And this first one, this average one is basically saying, well, I've got maybe two coal fired power generators and one wind farm, so therefore I'll apply double the weighting of the coal versus one of the wind farm.

That's kind of what, that's a simplified version, but that's essentially how you work out an average figure, right? 

Tammy Sukprasert: Right, right, but marginal carbon intensity signal is different. The way it's calculated is the carbon intensity with respect to the change in demand. So let's say just now you said you have two wind farms and one coal, but the next unit of demand is going to be served by gas generator. So then the marginal carbon intensity signal is the current intensity signal of that of the gas generator.

Chris Adams: I see. Okay. So rather than looking at the average, it's almost like the kind of consequences of me doing a particular thing. That's what we're looking at there, right?

Tammy Sukprasert: That's correct.

Chris Adams: Okay. And this, so now we've got this. I hope if you're listening and you're struggling, this is really hard.

So, thank you for staying with us so far. So this was the general, this is what we were looking into. And, as I understand it, this incentivizes different actions, or if you were looking at this, you might choose to move things to a different region or choose to run a computing job or do something at a different time.

That's been my understanding of this. Is this is what you looked into then?

Tammy Sukprasert: Right, so the paper look into the fact that if you follow one signal as a scheduling signal, you might end up in more carbon emission based on the perspective of the other signal. Yeah, so it turns out like you cannot just follow one signal and hoping that you will do well based on the other signals perspective as well.

Chris Adams: Ah, okay. All right. So this adds another layer of complexity to this then. So if I understand it, I could be following one and that gives me some idea here, but there are certain places where they can be different. They can have different signals. So like some places might be the same, but there are certain parts of the world where I might have quite radically different signals between these two.

That's what I think I'm hearing from that.

Tammy Sukprasert: Right, because the two carbon intensity signals are calculated so differently, so in, within one region, the signals are generally not correlated. So when you schedule for one signal, let's say, for example, I use in the marginal carbon intensity signal as a scheduling signal, right? And I place a workload in this low carbon period based on marginal, but within the same time period, someone else is like, looking from the perspective of the average carbon intensity signal, they'll be like, "Hey, I wouldn't place my workload here because it's high carbon period right now."

So it has some conflicting decision making.

Chris Adams: And, presumably when you looked in the, when you're doing this research, were there particular parts of the world where you see wild spreads between these two places? Like there's some places that it's quite safe, right?

Tammy Sukprasert: So in the paper, we look into, Arizona and Virginia for this kind of conflicting scheduling. So Arizona has fluctuating average carbon intensity signal, but really flat marginal and vice versa for Virginia. So let's just take Arizona, for example. Like if. You want to schedule based on marginal carbon intensity signal, you wouldn't do anything because it's flat.

You can just place a workload wherever you want. But if you want to schedule the workload based on the average signal, you'll be like, I would place my workload at this particular time slot because it had the lowest carbon intensity signal during the day.

Chris Adams: Ah, I see. Okay. So this suggests that you're going to need to be really explicit about which kind of signal you're following. And, there are certain parts of the world where it, you're more exposed to the differences between this, for example. That's what I think I'm hearing there.

Wow. that sounds, yeah. Sustainability in software does not get easy. Okay. So that's one of the things we were looking at here. And, it sounds like that you've spent quite a lot of time looking into this, looking at this whole field then, and, presumably when people are taking their first steps to trying to work out the environmental impact of software, for example, would you suggest, is there like an order of things you might start with this?

Cause this feels like relatively advanced, high level, complicated, calculations here, and is it possible to kind of look at the environmental impact of software without this straight away? Like, can you add this a little bit later, perhaps? Maybe there's like some rules of thumb or some approaches you might suggest as a researcher who has looked into this and tried to understand the environmental footprint of some software and said, "well, okay, you might want to just look at the total amount of energy used or the total amount of resources used first, before you look at, say, this carbon aware stuff. And if you can look at carbon aware, then maybe look at location first" or something like that. Cause this feels like kind of exciting, but this also feels like it gets complicated very, very quickly.

Tammy Sukprasert: So when I started working, on carbon intensity signals, I find that the average carbon intensity signal is easier to understand simply because you just look at the overall picture of the grid and you take the average of the energy sources, right? But for marginal carbon intensity, it was interesting concept for me.

You look into the carbon emission based on the change in demand, but I was having a hard time understanding this because in a practical sense, I feel like it's going to be challenging of understanding which power plant is actually serving my compute workload. Like, it's not transparent enough.

Chris Adams: I see. So there's almost like a counterfactual you're, comparing it against like a, how do you know if someone, I think you, we spoke about this sort of like there's a power stack, right? Like, yes, I've switched off, I've stopped pulling power from the grid, for example, but, how do I know that no one else has pulled power from the grid at the same time?

Is that what you're kind of getting at there?

Tammy Sukprasert: Right. For marginal carbon intensity for me, the concept is actually good. Like, you're responsible for the carbon emission that you triggered, right. But, In, reality, like you don't know which power source is serving your demand and whether in the next time it's to serve by the same force. So for example, like I plug in my laptop only, maybe I could, my laptop maybe is fulfilled by coal, but someone, let's say, Chris, you unplug your lab, right? Maybe now you left the, now your, the demand decreases is my laptop still, my laptop power is still fulfilled by coal? Like I don't have that. So...

Chris Adams: Ah, I see. Okay. Alright. That makes, no, that makes a bit more sense. And I kind of, I think I understand why, I think I follow basically the reasoning between why you might start with one before starting with the other one. Because I think I agree with you on that. I found the average a bit easier for me to get my head around two as well.

And, marginal does sound really cool, but I don't think I'm very confident explaining it to other people. And I think that, I think my experiences seem to echo yours, actually. I'm glad you said that because I did wonder if it was just me and that does make it a bit easier for me too.

I feel a bit better about myself now, actually. Thanks for that, Tammy. Okay. So, this has basically been your day job for the last few months, diving into the world of carbon signals and things like that. Is this some of the continued research you're doing, or are you looking into other fields now beyond software carbon intensity and working out the differences of carbon, working out the, potentials of carbon aware computing here?

Tammy Sukprasert: So I'm still working on carbon aware computing stuff. Currently I'm working on a web service that harnesses renewable energy and I have to think about how we should handle the workload when there is no renewable energy available.

Chris Adams: Okay. All right. So one thing this does seem to suggest is that if we're just looking at carbon in here, that's not showing us the whole picture. And even when we just look at carbon. We end up with quite a, we can end up with like difficult or conflicting signals for this. So it may be that we need to, we might need to expand the way we think about as software engineers, we think about the next layer down and say, like, are there other things we take into account beyond just looking at marginal or looking at average?

Maybe there's something else we need to do or another way of thinking about the grid and how our interactions as software engineers kind of work with it and how that can have an impact there.

Tammy Sukprasert: Right. So I think we need to move beyond the static signal and instead maybe look into other characteristics to take into consideration when doing carbon aware optimization, maybe in future direction, maybe we would agree on some other signal that captures the long term impact of the grid, like average carbon intensity signal and the current, like the instantaneous change in carbon intensity, like marginal. So yeah, apart from optimizing for carbon efficiency as a community, I think everyone should keep in mind about like, we need a better metric to capture this carbon emission.

Chris Adams: Okay. Thank you for that. Tammy, this was a ride for me. Every single time I come to trying to understand the environmental footprint of software, I think I understand that there's a whole nother set for this. And you've really opened my eyes to this. Tammy, if people are interested in this field, are there any other projects or work that you've read about recently that you'd like to draw people's attention to?

Tammy Sukprasert: Yeah, I think you should look at Carbon Scaler. I think that's one of the things I

Chris Adams: Oh.

Tammy Sukprasert: recommend people to check it out.

Chris Adams: Okay, we'll have to share a link to that because that's totally new to me. I've never... I'm not aware of that one actually.

Tammy Sukprasert: So yeah, it's a system that reacts based on the available carbon intensity, and you scale the workload based on that. So you don't have to shift the workload.

Chris Adams: Okay. All right. And if people want to find out more about the work that you're doing, where should people be following? Is there maybe, is there a website or are you on LinkedIn? Like what's the best place for people to direct people's attention if they wanted to follow up and read actually some of the work that you've been publishing and talking about here today?

Tammy Sukprasert: Yeah, so, I'm on LinkedIn. You can search my name up, Tammy Sukprasert, or T Sukprasert for the link, yeah.

Chris Adams: Brilliant. All right. Well, Tammy, thank you so much for giving us some of your time and sharing what you've learned from here. It's been absolutely fascinating. And we now finally have some numbers about what we can achieve with carbon aware computing. At least we have some numbers now to work with. So thank you once, again for this, and I hope you have a lovely week.

Cheers, Tammy.

Tammy Sukprasert: Chris, cheers.

Chris Adams: Hey everyone. Thanks for listening. Just a reminder to follow Environment variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode!



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]]> In this episode of Environment Variables, host Chris Adams is joined by Tammy Sukprasert, a PhD student at the University of Massachusetts Amherst, to dive deep into her research on carbon-aware computing. Tammy explores the concept of shifting computing workloads across time and space to reduce carbon emissions, focusing on the benefits and limitations of this approach. She explains how moving workloads to cleaner regions or delaying them until cleaner energy sources are available can help cut emissions, but also discusses the challenges that come with real-world constraints like server capacity and latency. Together they discuss the findings from her recent papers, including the differences between average and marginal carbon intensity signals and how they impact decision-making. The conversation highlights the complexity of achieving carbon savings and the need for better metrics and strategies in the world of software development.

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TRANSCRIPTION BELOW:

Tammy Sukprasert: With that one hour job with perfect knowledge of one year, we can reduce the carbon emission of the whole world by 37%.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables. Where we bring you the latest insights and updates from the world of sustainable software development. I'm your host, Chris Adams. One of the oft repeated quotes when people talk about sustainability in software is that if you can't measure it, then you can't manage it.

And when it comes to working out the carbon footprint of a software application, a significant portion of the footprint comes from what we refer to as the carbon intensity of the electricity in use, 

i.e., how green it is. And there are various steps you can take to make the same application using the same code, you can make it greener by running it where the grid is greener. So if you were to choose to run it in Iceland, that's one example. Or you can choose to run the grid, run the application at different times when the grid is greener, like when the sun is in the sky and your solar panels are wearing away. But how much greener can they get? And what else could we need to think about when trying to adopt a ways or ideas like this? Enter our guest for this episode today, Tammy Sukprasert, a PhD student at the Laboratory of Advanced Software Systems and Sustainable Computing Lab at the University of Massachusetts Amherst.

Tammy recently authored the paper on the limitations of carbon aware, temporal, and spatial workload shifting in the cloud, which examines how shifting computing workloads across time and space can help cut emissions. Tammy, we're going to spend a bit of time talking about why you chose to work in this field.

But to begin with, can I give you a bit of space to introduce yourself and what you do?

Tammy Sukprasert: Hi, Chris. Thanks for having me here. I'm Tammy Sukprasert. I'm a PhD student from the University of Massachusetts Amherst. I work on cloud and edge computing with a specific focus of decarbonizing computing. I'm currently calling you from Amherst, Massachusetts, and it's nice out here.

Chris Adams: Cool. That's nice. We've had a, it's snowing in Berlin, so I'm a little bit jealous, actually. Hi folks. If you are new to this podcast, my name is Chris Adams. I am the Director of Technology and Policy at the Green Web Foundation. And I'm also the, one of the chairs of the Green Software Foundation Policy Working Groups.

And also, the host of this podcast. Now, before we dive into the conversation with Tammy, if you're listening to this for the first time, here's a quick reminder. We will try to link to all the papers and all the links and all the projects on GitHub in this, and there will be extensive show notes as well as a transcript if there's anything you particularly missed.

And I think that's pretty much it. Tammy, are you sitting comfortable?

Tammy Sukprasert: Yep. Nice.

Chris Adams: In that case, I guess I'll begin. All right. We've linked to this in the show notes, but the paper title, On the Limitations of Carbon Aware Temporal and Spatial Workload Shifting in the Cloud, does kind of give a clue about what this research might actually be about.

But for those who are new to this idea, would you mind bringing listeners up to speed about what workloads are, what workload shifting is, when we talk about carbon aware computing?

Tammy Sukprasert: Sure. So to understand what workload shifting is, we need to have some idea of why we can shift the workload in the first place. So carbon intensity is based on the contributions of the different energy sources in the electric grid, right? So at different point in time, the demand changes. So there is different contribution of different sources.

That's why there's variation in carbon emissions. So there will be a high carbon period and low carbon period. And because of that, instead of running the workload during the high carbon period, you can actually schedule the workload to the lower carbon period or lower carbon region. So some of the workload, you can delay the start time.

The workload could be machine learning or some batch jobs. And instead of running right away when it was dispatched during the high carbon period, you can delay the start time and run it during the low current period. And at the same time, there are also, there is another type of workload that you can move or shift the workload around.

That could be a web request or an inference request. And instead of running your workload at your own region, you can look into other locations that have lower carbon intensity and migrate the in it.

Chris Adams: So if I, so let's say I'm using like maybe a chat bot or like, or I'm using something like maybe chat GPT and I am in, say, Germany, maybe it's dark, it's not very windy and it's not very sunny, for example, and most of the power is coming from coal being burned on the grid, for example, I might, rather than my request being served in Germany at the same time, it could plausibly be, say, forwarded to somewhere else in the world, as long as it's fast enough.

So, it might get forwarded to, say, Denmark, which is super windy instead. And that would mean that it would be slightly greener, for example. That's what you were referring to when you spoke about the inference. And then the other thing you mentioned before was like a machine learning job or like a video encoding thing.

That's something that I might not be seeing myself. But it's something that probably needs to happen within like a few days or something like that. So it's important, but it's not urgent. And because there's a bit of flexibility, I can choose when to do that to minimize the environmental impact of the extra amount of demand being put onto the grid.

Is that what you're, I think that's what you're saying there, right?

Tammy Sukprasert: Right. So it's just basically align your job schedule with low carbon period. Yeah. That's the key idea of the shifting.

Chris Adams: Gotcha. And then, so you spoke about there's one, which is if I'm doing something through time, that's like the temporal thing. Like I either bring it forward or wait till later. And then there's a spatial idea, which is me just moving it somewhere else. It might be happening at the same time, but it might be happening in Denmark, for example, or Iceland rather than in Germany.

Yeah?

Tammy Sukprasert: Yes, that's correct.

Chris Adams: Okay, cool. So, okay. We've got a good idea about what some of this might be. And a question I might ask is like, why is this interesting to you? Like what, how do you end up finding out or even kind of wanting to research this in the first place?

Tammy Sukprasert: Yeah. So there are many works that look into the benefits of reducing carbon reduction based on time shifting or spatial shifting, but it happened in a limited setting. i.e., a small number of regions or specific type of jobs, so people only look into spatial shifting or people only look into temporal shifting, or maybe they only look into a few number of regions but we were wondering, what if we look into both spatial and temporal and with the big picture of the whole world. So instead of looking to into a few regions, we look into 123 regions that we have in our data set and we want to see what is the broad impact of temporal and spatial shifting as a whole.

Chris Adams: I see. Okay. So thanks Tammy. So for this research paper, as I understand it, you decided to see how much, what kind of savings you really can achieve with things like Carbon Aware Computing. And a little bit about what kind of conditions might be necessary for these savings to be possible. So would you mind expanding on some of this?

We can start simple, fast, simple first, and then we can work our way up. So yeah, let's see, what were the first things we started with? And what were the first, what was like the ideal scenario for the savings? And we can go from there.

Tammy Sukprasert: All right. So with the current state of the world, right, the average carbon intensity is about 368 grams per kilowatt hour. And to achieve as much savings as possible in terms of carbon reduction, right, you will want to migrate your workload to Sweden, which is the region with the lowest carbon intensity in our data set. And migrating all the workload to Sweden, you can actually achieve 96 percent carbon reduction for the whole world.

Chris Adams: Okay, so what you're talking about there is you've basically gone from an average figure for carbon intensity of electricity to much, much cleaner electricity. And that's in this kind of ideal scenario, that's what you've essentially done. You've moved all of the computing jobs to the cleanest possible electricity there.

That's what we've done there. This is the ideal scenario. So where do we go from here then, for example, are there other constraints and things we know we need to take into account when doing this?

Tammy Sukprasert: Great. So of course, Sweden cannot take all the workloads in the world, right? So we were like, okay, instead of just moving everything to Sweden, what if we have capacity constraints? So we look into the scenario where every region in the world has an idle capacity of 50%. We're trying to be generous here because we want to understand the impact of the idle capacity on carbon reduction, right? So with every region having 50 percent idle capacity to absorb the job from other regions, instead of achieving, so now no one can actually migrate. So now not everyone can migrate to Sweden, right? Some other regions have to migrate to somewhere else. So, with that, the savings from 96 percent global reduction.

Drops to 51 percent.

Chris Adams: Okay.

Tammy Sukprasert: if not everyone can go to Sweden. Yeah,

Chris Adams: All right. That's still not bad. And when you're talking about capacity, you're referring to the fact that say, maybe there's a, like you've used the word region here, and for region, I think that's like a cloud region, like say AWS West or something like that. That's what you're referring to there.

And there's maybe a certain amount of reserve capacity they have to hold back. And that's what you're referring to there. So the idea that maybe different cloud places, different cloud data centers have a bunch of spare capacity and that's what they'd be using to move everything there, right? So, okay.

Okay. Well we never actually talked about latency constrains 

Tammy Sukprasert: as well, right. So let's say for example, a web request, you need some service level objective or SLO to respect, to be respected, right. And so we look into that as well. And with, so now we have capacity constraints. So the scenario gets more and more realistic, right?

So from 96% you added a capacity constraint, and now the saving drops from 96% to 51%. And we also look into a more realistic case where we think about web requests that have some latency constraint, where there's some service level objective that has to be respected. And so on top of the capacity constraints that we have, that we achieve 51%, we added a 50 milliseconds capacity constraint, and that further reduced the carbon savings to 31%. So in the real life scenario, we are really far from the 96% that we want to aim for, right.

Chris Adams: So if I understand that correctly, basically there is a speed, the speed of light is fast, but it's not infinite. And therefore there are certain parts of the world where you definitely need to get a response back in time. And that's why you've introduced this kind of 50 millisecond kind of budget. So it has to be, your ping, your request has to come back in that kind of time budget.

And that basically places a second constraint. And even with these two constraints, this is essentially talking about, okay, these are the carbon emissions that can be reduced. By moving things to the various regions that are available based on the capacity of all these other places, like Sweden and then the next cleanest one and the next cleanest one.

That's what you're referring to there. All right. Okay. I think I understand that part there. And that honestly, 31 percent still sounds pretty good, to be honest. But if we look at the figures for what, 2%, if we're looking at maybe, A hundred million tons of CO2 each year, and 30 percent of that is 300, is 30 million tons.

That's not bad. That's more than at Google, for example. So, okay. That's okay. So that is interesting, then. So this is one of the high level findings you found, assuming you could do this in this kind of decreasingly idealized scenarios. And eventually we get to a point where, okay, this is actually something that you might plausibly try adopting in, or you might be kind of advocating for in certain regions, for example.

Tammy Sukprasert: Right. Yeah. The point that we're trying to make is that as you added more constraints, the gap between the ideal case of 96%. Your achievable goal widens. So that's what we're trying to show in this paper.

Chris Adams: Okay, cool. And when you're talking about the regions here, these are largely the regions that are inside the electricity maps. Was it the electricity maps dataset or was it just the list of all of the regions for the biggest cloud hyperscalers? I wasn't quite sure when we were looking at this, cause there's a list of them, right?

Tammy Sukprasert: Right. So we used a dataset from ElectricityMaps. Shout out to ElectricityMaps. Thank you for the dataset. The dataset has 123 regions worldwide, right? But on the dataset, we group them up, we filtered the regions that overlap with the cloud region, and look at all exclusively the results for the cloud regions. 

Chris Adams: Ah, I see. So you created this way to make these comparisons basically by saying, maybe there's one data center, which we see in the cloud, like say Amazon AWS West, which is a lot of people refer to as like Oregon West 1. And because we know that a data set of carbon intensity from electricity map says, yes, this is Oregon.

You've been able to look at the numbers then in that way, right? That's where some of this is referring to.

Tammy Sukprasert: Yeah, so we did a mapping between the electricity map data with the location of the cloud region.

Chris Adams: Okay. All right. So, and that, and when we're looking at those numbers there, so you mentioned this figure of 96%. Was that looking at just location or was that looking at anything to do with time as well? Because I wasn't quite sure about that part there.

Tammy Sukprasert: So the 96 percent is just spatial shifting. So we have a separate result for temporal shifting where everyone in the world, every region in the world can schedule their workload based on one year ahead data. So everyone in the world can schedule their workload if they know about

Chris Adams: perfect forward knowledge. Yeah.

Tammy Sukprasert: yeah, perfect, knowledge for one year ahead.

And with that, we look at the extreme case, the most ideal case where the workload is a unit job, one hour job, to understand what is the best case scenario for temporal shifting, right? So with that one hour job with perfect knowledge of one year, we can reduce the carbon emission of the whole world by 37%.

Chris Adams: That's just temporal, not looking at location as well, right?

Tammy Sukprasert: Yes, so we have the results for temporal shifting that if we give every region a perfect knowledge of their carbon intensity a year ahead to plan their workload, what is going to be the best scheduling scenario for the future? Temporal shifting, right? So with everyone having the perfect knowledge for a year, you can reduce the carbon emission of the whole world by 37%.

Chris Adams: Ah, okay. So you're looking around maybe 30 percent when we were looking at purely locational, and then we're looking at just purely time. It's around, it's relatively similar, basically, but these are relying on. A kind of visibility that people don't really have a lot of the time, but, and, okay. So the next question I'm kind of asked is, it possible to look at time and space for this to get an idea of what the savings might be next from that then? 

Tammy Sukprasert: Yeah. So we also look into that in our paper. So if you look at spatial and temporal shifting combined, the result actually shows that spatial shifting dominates the carbon reduction. This is simply because when you move the workload to the lowest region possible in your data set, right, to achieve the savings, that region is already low in carbon intensity, so time shifting doesn't make much of a difference.

Chris Adams: Ah, I see. Okay. So it's, basically the clean regions tend to be clean most of the time anyway, rather than being kind of spiking up and down for example. So that's what it seems like you're suggesting there, right?

Tammy Sukprasert: Right. It still varies, but the variation between the high carbon period and low carbon period is relatively small.

Chris Adams: Okay, well, that kind of makes sense. Cause I mean, now that when you lay out like that, I don't really think about it until you framed it that way, but like Iceland is usually green because it's running on geothermal, which is like pretty standard. Like it's steady. And even when you look at like, say Sweden, for example, there's like a wind and everything like that, but there's lots of hydro and stuff like that.

So again, it's not nearly as spiky as, say, Germany, where we are the land of like wind is, we're land of coal and solar. We have lots of coal, which is high carbon intensity, and lots of solar, which is very, low intensity. And flicking back and forth between these things means that we might have big swings, but on average, it's not particularly low compared to Iceland or Sweden, for example.

Huh.

Tammy Sukprasert: Correct. Yeah.

Chris Adams: Oh, right. Wow. I, that's, 

in retrospect, it kind of seems obvious when you, but things are only obvious with when you look at it like that. And one thing you shared with me before we spoke about this was that some of this stuff is actually like, if people wanted to kind of explore some of these calculations, is this online somewhere? Is it like a GitHub repo or something where you can like poke around at some of these things?

Tammy Sukprasert: Yeah. So all the simulations in this paper, it's open source. So please check my lab website, my lab GitHub for the simulations.

Chris Adams: Okay, cool. All right. I think I've got the link here. So that's, this is from, so there's literally a repo called decarbonization potential. That's the one you're referring to here, right? On GitHub.

Tammy Sukprasert: Yes, that's correct.

Chris Adams: Brilliant. Okay. We'll definitely add that in the show notes because people who aren't like frantically exploring this themselves, it's where it's, right there.

Okay. So that was one of the first pieces of research. Essentially that there are some savings that can be made. It's around like the 30 percent mark in a kind of perfect world with location and sort of about the same with temporal. And if I understood it correctly, combining the two doesn't deliver massively more savings than that, right?

It's still never more than half this kind of intervention that you could possibly make, right?

Tammy Sukprasert: Right, yeah, combining the two doesn't give you double the benefits, because the benefits are dominated by spatial migration, but not much of the temporal, if you combine them together.

Chris Adams: Okay. Thank you. I'm really, glad you actually spoke about this because we can now have some of the numbers. To basically talk about the fact that, yeah, we still need to do other things. You can't just like leave your code and make no changes. That might get you some of the way. And if you're looking at Temporal, it'll get you 37 percent of the way in a perfect world.

But you still need to make some other changes if you want to kind of reduce the environmental footprint further. Brilliant. Okay. Thank you for that. So we talked about some of the savings you can get in your previous paper. The fact that there's maybe around the 30 percent figure. And if you can move everything through space, you get around maybe 30 ish percent savings.

If you look at, if you have perfect knowledge forward for the year, then it's maybe slightly higher than 30%, but it's in the same kind of ballpark. And if you were to look at moving all of your computing jobs through time and space, you can't just double this number. It's still going to be a meaning, it's going to be more than 30%, probably less than 50%.

So that's one of the figures that we have. We'll share a link to the GitHub repo for people who are curious about this and want to see if they know what jobs they ran last year, they could see what kind of savings they could have achieved. So that's one thing. And we've spoken so far about some constraints that we have, but there's a few more constraints that we need to take into account.

So for example, so far, we've been talking about how much, how many spare servers we have, like data center capacity inside this. But there are other constraints that we need to also think about, which are a little bit further down the stack, as it were. So there may be a certain limited amount of green energy, at which point when you have more demand than that, you might need to have some other forms of generation come on stream.

And like, this is something that I think you explored in one of the other papers. So maybe we could talk about that. So, okay, this other paper that you spoke about, maybe we can just like, let us know the name and then we'll see where we go from there.

Tammy Sukprasert: Right, so this paper, titled, On the Implications of Choosing Average vs marginal Carbon Intensity Signals on Carbon Aware Optimizations, basically, average vs marginal for carbon aware optimizations, right. So this paper came from the fact that, okay, People have been suggesting, let's shift the workload through time, let's shift the workload to different locations, but we never actually agree on which carbon intensity signal to use for carbon aware optimization, so as the title suggested, there are two types of carbon intensity signals that are mainly used, namely average carbon intensity signal and marginal carbon intensity signal.

So for average carbon intensity signal, just think of it as a snapshot of the grid at that point in time, right? And the way it's calculated is the weighted average of carbon emissions weighted by their production,

Chris Adams: Okay. So if I just check, I just want to start you there. So make sure I keep keeping up with you. So there's two ways you can measure carbon intensity, like how green electricity is. And this first one, this average one is basically saying, well, I've got maybe two coal fired power generators and one wind farm, so therefore I'll apply double the weighting of the coal versus one of the wind farm.

That's kind of what, that's a simplified version, but that's essentially how you work out an average figure, right? 

Tammy Sukprasert: Right, right, but marginal carbon intensity signal is different. The way it's calculated is the carbon intensity with respect to the change in demand. So let's say just now you said you have two wind farms and one coal, but the next unit of demand is going to be served by gas generator. So then the marginal carbon intensity signal is the current intensity signal of that of the gas generator.

Chris Adams: I see. Okay. So rather than looking at the average, it's almost like the kind of consequences of me doing a particular thing. That's what we're looking at there, right?

Tammy Sukprasert: That's correct.

Chris Adams: Okay. And this, so now we've got this. I hope if you're listening and you're struggling, this is really hard.

So, thank you for staying with us so far. So this was the general, this is what we were looking into. And, as I understand it, this incentivizes different actions, or if you were looking at this, you might choose to move things to a different region or choose to run a computing job or do something at a different time.

That's been my understanding of this. Is this is what you looked into then?

Tammy Sukprasert: Right, so the paper look into the fact that if you follow one signal as a scheduling signal, you might end up in more carbon emission based on the perspective of the other signal. Yeah, so it turns out like you cannot just follow one signal and hoping that you will do well based on the other signals perspective as well.

Chris Adams: Ah, okay. All right. So this adds another layer of complexity to this then. So if I understand it, I could be following one and that gives me some idea here, but there are certain places where they can be different. They can have different signals. So like some places might be the same, but there are certain parts of the world where I might have quite radically different signals between these two.

That's what I think I'm hearing from that.

Tammy Sukprasert: Right, because the two carbon intensity signals are calculated so differently, so in, within one region, the signals are generally not correlated. So when you schedule for one signal, let's say, for example, I use in the marginal carbon intensity signal as a scheduling signal, right? And I place a workload in this low carbon period based on marginal, but within the same time period, someone else is like, looking from the perspective of the average carbon intensity signal, they'll be like, "Hey, I wouldn't place my workload here because it's high carbon period right now."

So it has some conflicting decision making.

Chris Adams: And, presumably when you looked in the, when you're doing this research, were there particular parts of the world where you see wild spreads between these two places? Like there's some places that it's quite safe, right?

Tammy Sukprasert: So in the paper, we look into, Arizona and Virginia for this kind of conflicting scheduling. So Arizona has fluctuating average carbon intensity signal, but really flat marginal and vice versa for Virginia. So let's just take Arizona, for example. Like if. You want to schedule based on marginal carbon intensity signal, you wouldn't do anything because it's flat.

You can just place a workload wherever you want. But if you want to schedule the workload based on the average signal, you'll be like, I would place my workload at this particular time slot because it had the lowest carbon intensity signal during the day.

Chris Adams: Ah, I see. Okay. So this suggests that you're going to need to be really explicit about which kind of signal you're following. And, there are certain parts of the world where it, you're more exposed to the differences between this, for example. That's what I think I'm hearing there.

Wow. that sounds, yeah. Sustainability in software does not get easy. Okay. So that's one of the things we were looking at here. And, it sounds like that you've spent quite a lot of time looking into this, looking at this whole field then, and, presumably when people are taking their first steps to trying to work out the environmental impact of software, for example, would you suggest, is there like an order of things you might start with this?

Cause this feels like relatively advanced, high level, complicated, calculations here, and is it possible to kind of look at the environmental impact of software without this straight away? Like, can you add this a little bit later, perhaps? Maybe there's like some rules of thumb or some approaches you might suggest as a researcher who has looked into this and tried to understand the environmental footprint of some software and said, "well, okay, you might want to just look at the total amount of energy used or the total amount of resources used first, before you look at, say, this carbon aware stuff. And if you can look at carbon aware, then maybe look at location first" or something like that. Cause this feels like kind of exciting, but this also feels like it gets complicated very, very quickly.

Tammy Sukprasert: So when I started working, on carbon intensity signals, I find that the average carbon intensity signal is easier to understand simply because you just look at the overall picture of the grid and you take the average of the energy sources, right? But for marginal carbon intensity, it was interesting concept for me.

You look into the carbon emission based on the change in demand, but I was having a hard time understanding this because in a practical sense, I feel like it's going to be challenging of understanding which power plant is actually serving my compute workload. Like, it's not transparent enough.

Chris Adams: I see. So there's almost like a counterfactual you're, comparing it against like a, how do you know if someone, I think you, we spoke about this sort of like there's a power stack, right? Like, yes, I've switched off, I've stopped pulling power from the grid, for example, but, how do I know that no one else has pulled power from the grid at the same time?

Is that what you're kind of getting at there?

Tammy Sukprasert: Right. For marginal carbon intensity for me, the concept is actually good. Like, you're responsible for the carbon emission that you triggered, right. But, In, reality, like you don't know which power source is serving your demand and whether in the next time it's to serve by the same force. So for example, like I plug in my laptop only, maybe I could, my laptop maybe is fulfilled by coal, but someone, let's say, Chris, you unplug your lab, right? Maybe now you left the, now your, the demand decreases is my laptop still, my laptop power is still fulfilled by coal? Like I don't have that. So...

Chris Adams: Ah, I see. Okay. Alright. That makes, no, that makes a bit more sense. And I kind of, I think I understand why, I think I follow basically the reasoning between why you might start with one before starting with the other one. Because I think I agree with you on that. I found the average a bit easier for me to get my head around two as well.

And, marginal does sound really cool, but I don't think I'm very confident explaining it to other people. And I think that, I think my experiences seem to echo yours, actually. I'm glad you said that because I did wonder if it was just me and that does make it a bit easier for me too.

I feel a bit better about myself now, actually. Thanks for that, Tammy. Okay. So, this has basically been your day job for the last few months, diving into the world of carbon signals and things like that. Is this some of the continued research you're doing, or are you looking into other fields now beyond software carbon intensity and working out the differences of carbon, working out the, potentials of carbon aware computing here?

Tammy Sukprasert: So I'm still working on carbon aware computing stuff. Currently I'm working on a web service that harnesses renewable energy and I have to think about how we should handle the workload when there is no renewable energy available.

Chris Adams: Okay. All right. So one thing this does seem to suggest is that if we're just looking at carbon in here, that's not showing us the whole picture. And even when we just look at carbon. We end up with quite a, we can end up with like difficult or conflicting signals for this. So it may be that we need to, we might need to expand the way we think about as software engineers, we think about the next layer down and say, like, are there other things we take into account beyond just looking at marginal or looking at average?

Maybe there's something else we need to do or another way of thinking about the grid and how our interactions as software engineers kind of work with it and how that can have an impact there.

Tammy Sukprasert: Right. So I think we need to move beyond the static signal and instead maybe look into other characteristics to take into consideration when doing carbon aware optimization, maybe in future direction, maybe we would agree on some other signal that captures the long term impact of the grid, like average carbon intensity signal and the current, like the instantaneous change in carbon intensity, like marginal. So yeah, apart from optimizing for carbon efficiency as a community, I think everyone should keep in mind about like, we need a better metric to capture this carbon emission.

Chris Adams: Okay. Thank you for that. Tammy, this was a ride for me. Every single time I come to trying to understand the environmental footprint of software, I think I understand that there's a whole nother set for this. And you've really opened my eyes to this. Tammy, if people are interested in this field, are there any other projects or work that you've read about recently that you'd like to draw people's attention to?

Tammy Sukprasert: Yeah, I think you should look at Carbon Scaler. I think that's one of the things I

Chris Adams: Oh.

Tammy Sukprasert: recommend people to check it out.

Chris Adams: Okay, we'll have to share a link to that because that's totally new to me. I've never... I'm not aware of that one actually.

Tammy Sukprasert: So yeah, it's a system that reacts based on the available carbon intensity, and you scale the workload based on that. So you don't have to shift the workload.

Chris Adams: Okay. All right. And if people want to find out more about the work that you're doing, where should people be following? Is there maybe, is there a website or are you on LinkedIn? Like what's the best place for people to direct people's attention if they wanted to follow up and read actually some of the work that you've been publishing and talking about here today?

Tammy Sukprasert: Yeah, so, I'm on LinkedIn. You can search my name up, Tammy Sukprasert, or T Sukprasert for the link, yeah.

Chris Adams: Brilliant. All right. Well, Tammy, thank you so much for giving us some of your time and sharing what you've learned from here. It's been absolutely fascinating. And we now finally have some numbers about what we can achieve with carbon aware computing. At least we have some numbers now to work with. So thank you once, again for this, and I hope you have a lovely week.

Cheers, Tammy.

Tammy Sukprasert: Chris, cheers.

Chris Adams: Hey everyone. Thanks for listening. Just a reminder to follow Environment variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show and of course, we'd love to have more listeners.

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]]> The Cloud and the Climate: Navigating AI-Powered Futures The Cloud and the Climate: Navigating AI-Powered Futures Thu, 19 Dec 2024 07:00:00 GMT 35:44 8056r7x1 false https://podcasts.castplus.fm/e/68rr7238-the-cloud-and-the-climate-navigating-ai-powered-futures 68dc7d356d92c33f9c89046f 68dc7cef46a2532cdd8d1c23 93 Environment Variables host Chris Adams is joined by Jo Lindsay Walton, a senior research fellow at the Sussex Digital Humanities Lab and co-author of the report The Cloud and the Climate: Navigating AI-Powered Futures. They delve into the intersection of climate and AI, exploring the environmental impact of AI technologies and the challenges of decarbonizing the ICT sector. Jo discusses key takeaways from the report, including the importance of understanding AI's direct and indirect impacts, the nuanced roles of big tech companies, and strategies for critically assessing claims of AI-driven sustainability. This insightful conversation highlights the need for interdisciplinary approaches and robust collaboration to navigate the complex relationship between technology and climate action.

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TRANSCRIPT BELOW:

Jo Lindsay Walton: There's this great metaphor that Arvind Narayanan and Sayash Kapoor have in their book, AI Snake Oil. They say, "imagine we just talked about vehicles. We didn't talk about bicycles or cars or buses or trains. And we tried to talk about the climate impact of vehicles." It would be very difficult to do.

And that's essentially what AI discourse does, right?

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Like seemingly everyone else in the industry, we've been talking about AI a fair amount recently, and earlier this year, in September, the Sussex Digital Humanities Lab published their report, The Cloud and the Climate, Navigating AI Powered Futures.

It's not a small report, weighing in at around 190 pages, and it has a number of key messages we'll be exploring in this episode. Also, one of the previous guests from back in September, 2023, Jo Walton was one of the authors of this report, and he was a nice enough to make some time to join us today on the pod.

So, Jo, thank you so much for coming onto the pod again. Can I give you a bit of time to introduce yourself and what you do in your day to day for people who missed the last episode back in September?

Jo Lindsay Walton: Hello. Yes. Thanks. Thanks so much for having me. So I'm a senior research fellow in arts, climate, and technology at the Sussex Digital Humanities Lab. My day to day is 90 percent playing with my cat, but I am also part of the Digital Humanities Climate Coalition and the newly launched Climate Acuity Initiative, which does facilitation and CPD training around climate and tech in hopefully fun ways involving storytelling and games and things like that.

And yeah, it's just, it's really nice to be back on this wonderful podcast. I feel like the host of SNL.

Chris Adams: Thanks. So, just before I check, when you say CPD, CPD is continuous professional development. People who want to build understanding of climate into their professional life, right? Is that what it is?

Jo Lindsay Walton: Yeah, that's it. Exactly. And it's, really I guess, part of my work is at the intersection of climate and technology, but I'm not personally super technical. Most of your listeners probably have a lot more technical knowledge than I do. What I am really interested in is communicating around these issues and education as well.

So I'm raising them for all the stakeholders for whom they might be important.

Chris Adams: Brilliant. Okay. And, on the subject of other three letter acronyms, I've just had my cat walking myself, so if it walks across the, when we're recording, please do forgive it. It's just, that's what he does sometimes. Okay. Folks, if you are listening and you've never heard my voice before, I am Chris Adams.

I am the executive director of the Green Web Foundation, which is one of the members of the Green Software Foundation. The Green Web Foundation is a nonprofit based in the Netherlands, focused on reaching an entirely fossil free internet. By 2030, I also work as one of the policy working group chairs inside the policy work in, inside the GSF, as well as being the host on this podcast as well.

All right, then. So just before we dive in, if we speak about a particular paper or a report or a link, we will add these in the show notes. And if you, if there's something you're missing, please do send us an email or get in contact us because we do our best to keep these available and like useful resources for people.

All right, then. Jo, are you sitting comfortably?

Jo Lindsay Walton: Very comfortably. Thank you.

Chris Adams: All right, then. I think I'll begin. All right, then. So before we dive into the report and some of the key takeaways, which we'll be going into more detail, can we talk a little bit about why you decided to put some time into this report in the first place and how this entire project came about, please?

Because I know that you're, you said yourself, you're a researcher and in the Unix School of Media Arts and Humanities at Sussex University. And this came from the Digital Humanities Climate Coalition. Now, most software developers, when they think about AI and reports, it might be something that's within the industry.

So I want to give you a bit of space to talk about why it's interesting or why it's relevant to have people who aren't inside technology, who aren't like practitioners per se, talking about some of this. Because I think there's a different, a couple of perspectives that you might have that is worth.

Making clear for people, for example, or some techniques that humanities people might actually have that, the developers or techies might not be so cognizant of. 

Jo Lindsay Walton: Oh Yeah. Absolutely.

So the report, as well as the kind of DHCC toolkit, which is an online resource, these are, they're very much community projects and they have an open source ethos and a part of that is an aspiration to interdisciplinarity. The report itself is a kind of stretch goal or spin off from a small innovate UK project that I was doing with GreenPixie, who are this fabulous cloud carbon data company.

Yeah. And we were basically exploring how to talk to a wider set of stakeholders about the cloud and about the climate. So not just IT people, but also, for example, chief sustainability officers, people who need to know about this stuff. That might not be quite so up to speed on the technical detail.

And over the course of that, it grew apparent to me that there was a gap really for an accessible resource that didn't oversimplify and that really tries to be a bit holistic. Can you really understand one bit of it without understanding the big picture? Can you, I don't know, understand how your little piece of software that you're trying to optimize is going to have an impact without thinking at least a little bit about carbon accounting and the greenhouse gas protocol and carbon offsetting? Can you really understand how green a data center is without understanding a little bit about how energy gets into the grid and then gets into a data center and the kind of energy procurement rules around that?

Chris Adams: Okay. I hadn't actually realized that you've been working with GreenPixie and just for people who are new to that term, GreenPixie is a UK-based SaaS provider of essentially carbon accounting tooling for cloud, just like, so if you're using Amazon's or Microsoft's or Google's cloud carbon calculator, they provide something very similar, but with a more kind of open methodology that allows them to be comparable to each other.

Really nice to know that there's, I didn't realize that you two would be working together on that. And that's cool, actually.

Jo Lindsay Walton: So, I mean, they, particularly that collaboration informed the green ops section of the report. But as you allude to, there is this attempt throughout the report to also bring in DHCC type perspectives, that kind of humanities flavor, really drilling into the details of the cultural factors.

So not just how we communicate things, but also how we imagine things, I guess. Big tech and tech communities don't just have direct impacts. They also shape the way that we imagine the future. So Google is not in the business of building kind of direct air capture, giant reverse hairdryers that are sucking carbon out of the sky. That's not something they do, but they do influence the way we think about technology and climate. And so they also influence the way that we think about things like greenhouse gas removal technologies.

Chris Adams: Although, earlier on this year, we saw that Microsoft patented, actually, some of the use of some particular things around carbon capture in data centers to use some of the waste heat to actually separate captured carbon, so it can be actually stored in other places. So, there's maybe more links than we actually had, yeah, exactly.

Jo Lindsay Walton: That's really interesting.

Chris Adams: Yeah, I'll share, we'll share a

Jo Lindsay Walton: link in the show notes for sure.

Chris Adams: Definitely. All right. Okay. So that gives me a good idea and then provides a bit of context to where this was and for people who are not used to the UK, Innovate UK is one of the government funding agencies that has provided some of the funding for some of this.

So that's where that has come from. All right. And so maybe we should talk a little bit about the report. So there's a number of takeaways. In fact, I counted more than five when I was running through the report. So there was a lot there, right? And there are some things which probably don't need too much attention because we're, because of the listenership.

So for example, we probably won't spend too much time dwelling on one of the takeaways being we're in a climate crisis or the other one bang, yeah, that digital has a physical basis. These are things that we can assume that people have internalized already, right? But there was actually some nuance to this because.

While people do talk about that, the kind of magnitude of the numbers might not be something that people are quite so comfortable about. And also, it's an area of contention in many cases, many places. And as someone who's been looking at a lot of the literature, I figured it might be interesting to have a bit of space to talk about one of the other takeaways, which you shared was basically the ICT sector is not a leading contributor to global warming, but it still must decarbonize rapidly.

Now, I think It'd be useful to unpack some of this because a lot of the time, a lot of the stories do talk about either data centers as like this new monster or new kind of like media baddie, for example. And it seems like there's, you've got a kind of more nuanced take on this and I wanted to give a bit of space for you to allow you to talk about some of that.

Jo Lindsay Walton: Yeah, I mean, coverage of the drivers of global warming is totally out of proportion to what those drivers actually are. We've seen data centers be in the mainstream media quite a lot recently. So I think maybe that's falling victim to that a little bit. Where do most emissions come from? Food production, for example, is absolutely huge.

And we hear a little bit about food miles. But food miles are not a massive part of it. A much bigger determinant of the impact is, "has the food come from a cow or from a nut?" Constructing and heating and lighting homes, road transport, fugitive emissions, fossil fuel companies basically being a little bit sloppy as they extract these fossil fuels and letting them escape.

There's a good, a lovely breakdown on Our World In Data, which is maybe we can put in the show notes as well, although a little bit dated now. ICT? What is the impact of ICT on global warming? Would like to offer a provocation and hope that maybe one of your listeners can, prove me wrong. I think nobody knows.

I think nobody knows ICT's impact on global warming. There's that 2021 Freitag et al. estimate that gets quoted quite a lot, but it's been a very, busy four or five years. I feel like I've lived through the AI singularity. And there's more complexity than that, right? When you factor in secondary and tertiary impacts, both good and bad, from the digital, then you're in the realm of deep uncertainty.

There is unlikely to be any expert consensus. Even so, despite that complexity, it's not controversial that tech needs to decarbonize along with everything else. It's all hands on deck. Everybody's on board with that. All the big companies have these ambitious pledges. What's concerning me a little bit is how that discourse is shifting.

So for example, Microsoft in 2020 sets out its pledge to achieve net zero. 

Chris Adams: Moonshot. The zero carbon moonshot you're referring

Jo Lindsay Walton: yeah, yes, And we talk about that term moonshot in, in the report, actually, cause it's a, it's an interesting metaphor. And the moon has, is now said to be 

Chris Adams: 5 years further away. Yeah. 

Jo Lindsay Walton: The moon has moved five times. So actually I think that's incorrect.

I think the moon. The moon has been vaporized. The moon, as in Neil Stevenson's science fiction novel, Seveneves, the moon no longer exists. The target has already been missed. And that happened this year. "Okay, how is that possible?" you're asking. Does Microsoft have a time machine? How can they fail their net zero pledge of 2030 in 2024?

Well, that's the way that net zero pledges work. They are about cumulative emissions. They're not about a snapshot of emissions at a particular date. They are about the pathway from the date of the pledge 2. 0 staying within a given emissions budget, right? So you could draw a descending line graph and it's about the area under that line, not about the point at which the line intersects the axis.

And to their credit, Microsoft absolutely was transparent about this back in 2020. They showed the linear descent to zero. And by my estimates, that budget was burst sometime this year. maybe now, maybe as we are recording this podcast. And poverty is no effect. The concerning bit is that this isn't being talked about more openly.

It's much more this discourse, as you say, of "okay, now we have AI." In 2020, we didn't know about that, but now we have AI and AI has these sustainability benefits. Okay, so if that's the argument, if that's the implied case for emissions increasing, let's be very clear about that. Are we saying that it is prudent to increase emissions from the tech sector for the next few years?

Are we saying that the tech sector has been doing the right thing emissions-wise for the past few years, because those emissions on a robust methodology are shown to be more than offset by the sustainability benefits that they can provide on an appropriate timescale?

Chris Adams: We'll be touching on that a little bit later, but, alright. Okay. Thank you that I appreciate you providing a bit of extra context on that. And just to check if I understand, you said one or two things about, okay there is, the way you could work out the environmental footprint of the ICT sector when people talk about the direct impact, you said there's like a primary, tertiary, sorry, secondary and tertiary, presumably you're talking about like there is a direct impact, but there's an impact from people, what you enable with that computing and stuff like that. Is that what you're referring to with that primary, secondary and tertiary stuff?

Jo Lindsay Walton: Yeah, absolutely. So you and I are on a, Zoom call now. If we weren't on the Zoom call, I probably would have ridden to you on a giant lump of blazing coal. Or some more carbon intensive mode of transport. And those are very, complex calculations to do. You have rebound effects where, things look like they're providing efficiencies, but those efficiencies are mitigated or more than offset by increased volume, it's complicated stuff.

Chris Adams: Okay, cool. All right. Thank you for providing that extra kind of elucidation or like clarifying that part there. Okay. There's another thing I wanted to give a bit of time for actually was this one. You said, and given that we just spoke about kind of cloud giants and one of the takeaways, which was none of the cloud giants is a monolith.

So this is a bit of a kind of more nuanced take on big tech bad, big tech good that we often see in the discourse, because it's very simple and attractive way to talk about that, but it sounds like you're trying to go for something a bit more sophisticated there, a bit more multidimensional there.

Maybe we could spend a bit of time trying to see what you were trying to get at there or what the report was trying to really get across to people.

Jo Lindsay Walton: Yeah. I think climate invites us to really reflect on our roles in our professional lives and other aspects of our lives. And sometimes to challenge and push back on the parameters that are set for us in those roles. And that may mean that your company is pushing a particular line or your bosses is pushing a particular line, but there is a kind of, there's a practical incentive and there's, frankly, there's a kind of ethical duty to be critical about that and to step outside of the boxes that you're asked to perform in.

And definitely these companies are huge companies. There's a great diversity of knowledge, a great diversity of kind of politics, really, within any particular industry. big tech company, nevermind between tech companies as well. So in the realm of the greenhouse gas protocol and how we do carbon accounting, there's a lot of disagreement within big tech between on the one hand, Amazon and Meta who want one kind of particular set of rules as the greenhouse gas protocol is revised and Google and perhaps Microsoft who would like to see it go another way.

I think we look at this a little bit in the report. We look at a nature article that is largely authored by, Microsoft researchers. And spend a little bit of time in a hopefully good natured roast of the estimate of the carbon impact of AI, which the methodology there just isn't really fit for purpose.

If you drill, really drill, drill, drill baby, drill down into the details, you find that it is based on one back of envelope kind of estimate by Vijay Rakesh, who is really a stock market analyst who said that he expected NVIDIA to deliver a hundred thousand AI servers in 2023.

It's not a sufficient basis for estimating the global impact of AI, but that's hopefully not the main point because the bigger part of this article, which I think speaks to your question about companies not being monoliths and trying to build alliances for progressive and robust climate policy that cut across your loyalty to a particular company.

The proposal of this article is that AI researchers should work more closely with climate, the climate modeling community, and that AI should be integrated into the IPCC's shared socioeconomic pathways and integrated assessment modeling. Which is, I have mixed feelings about that. Like the closer collaboration sounds really great. It does feel like in that particular article, there isn't yet a very deep understanding of how those climate models work. They're not really scenarios. They're more like building blocks for scenarios. And to some extent, they already do build in the possibility of technological change.

So you could go down a rabbit hole as to whether or not AI is already priced into these models or not. I think what it speaks to is a certain kind of nervousness here, like, okay, so we are big tech, we are AI, we're presenting this AI powered future, and we're increasing our emissions, and we're doing this on the basis that we think, we believe, that AI is going to unlock all these fantastic sustainability benefits.

But can somebody please check our working? We recognize that we may have conflict of interests. We need to do this in a more collaborative way. We need to have all kinds of expertise and we need to have more independent voices. I think that's what that article is ultimately calling for.

Chris Adams: Okay. So there was one thing you said that you were getting at there was the idea that cloud giants not being a monolith isn't just within the cloud giant. If you think about it horizontally, like Meta and Amazon having one point of view. And I think you're referring to the emissions first versus the 24/7 kind bond fight about how do you count energy as green?

Because the current process has a few significant issues with basically, there's people trying to work out a new approach and you have two camps. So that's one thing you were talking about. And then there's almost one within each company. Like there are different people who have different drivers inside that. If you just assume that someone's working for say Amazon, that ends up being a very lossy way of talking about, okay, what are they doing? And like, what might, the drivers be, for example?

Jo Lindsay Walton: Absolutely. And some of those disagreements might not be so visible for obvious reasons. People have to be tactful and work in constructive ways with their colleagues. I mean, to respond to that, I think that I, share a bit of alarm about timescales and, solutions being proposed that aren't immediately referred back.

If you've got any kind of plan to do with the climate, check it against IPCC timescales. We were supposed to, in the next six years, cut carbon emissions by more than half, four of which are going to be under a Trump administration. And I would definitely, I would celebrate that kind of all hands on deck approach where everybody's doing everything they can in their role and maybe rethinking their role in creating alliances.

At the same time, I also think we need a little bit of reflection on actually which hands are on deck. Are there problems that aren't owned by anybody, risks that are not being addressed by anybody. And I think that we need a little bit, in the AI space, there has been talk of pauses and kind of moratoriums, not always for the best reasons, but I do think these are really important tools in our toolkit rather than, "okay, we're going to just keep doing what we're doing and, hope to sustainabilize it as quickly as we can,"

actually, saying "maybe we need to pause this and maybe we can pick up where we left off, but we need to pause it while we're gathering more data or we're greening our energy supply or we're building capacity" or whatever it might be. I wrote an article about this in the Fantastic Branch magazine called Pause.

I just realized this morning, I should have called it after Andreas Malm's How to Blow Up a Pipeline, I should have called it How to Blow Up an AI Pipeline. But yeah, I something else for that.

Chris Adams: Yeah. All right. All right. Okay. Thank you for that. Let's move to the next one. Cause you spoke a little bit about AI and, in the report, you actually spend a bit of time talking about sustainability. Basically the sustainability of AI, but also AI for sustainability. Right. And these being two somewhat different things.

Now we talk about sustainability of AI on this podcast quite a lot. So we talk about how to use like more efficient algorithms or how to clean the energy and some of the steps you might take. And obviously the report talks about that, but there's actually something that you speak about in terms of the claims about AI for sustainability goals that you spend some time talking about and like you also raised, like "these are some of the red flags you might be looking for." Could you maybe, are there any like specific messages you might use or anything you draw to people's attention to when they're trying to navigate claims about AI for sustainability and like, "yes, there's a massive energy footprint, but the upside is this, for example, and these are the upsides that we're delivering."

Jo Lindsay Walton: Yeah, absolutely. And all that kind of sustainability of AI stuff is extremely exciting. And, as you say, we, touch on that in the report. AI for sustainability. There's this great metaphor that Arvind Narayanan and Sayash Kapoor have in their book, AI Snake Oil, they say, "imagine we just talked about vehicles.

We didn't talk about bicycles or cars or buses or trains. And we tried to talk about the climate impact of vehicles. It would be very difficult to do." And that's essentially what AI discourse does, right? We don't on a regular basis make these kinds of fine differentiations in public discourse, in journalism, in conversations with friends.

So right before the show, actually, we were talking about acronyms and I tried to come up with an, acronym of the things that you might want to ask when you find a claim that AI is delivering some kind of sustainability benefit. So the first thing to consider is maturity. That might be technology readiness level whatever it may be.

Often there is a claim is inflated. It says something is already happening when actually what we see is that there's been a study that says it might work. It could be rolled out commercially, scaled up in five, 10, 20 years, whatever it might be. So maturity is one. Then additionality. So AI is responsible for delivering this sustainability benefit.

Well, do your best to identify which bit the AI is responsible for. Often an AI sustainability project will involve data collection and analysis, and then some kind of efficiency gains from that. What could have been delivered with the, with more kind of traditional data analytic methods? And then generative or discriminative or some other type of AI.

What kind of AI are we talking about here? These are often conflated. Is it even machine learning at all? Is it something, some cool new thing like, I don't know if it's new, but active inference, for example. And how big is the model and so on? What kind of AI are we looking at? And then finally adaptation versus mitigation.

So these are the two broad categories of climate action that most climate scientists will recognize. And they're interrelated and they overlap in various ways, but mitigation is really about decarbonizing,

Chris Adams: Yeah. Green energy instead of fossil energy. And then the mitigation might be building the seawalls because the sea levels have risen. Stuff like that. 

Jo Lindsay Walton: other

Chris Adams: I say, yeah. adaptation is building the seawalls because the sea levels have risen. Mitigation would be switching out of fossil fuels and burning, using greener energy, for example, which

Jo Lindsay Walton: Absolutely.

Chris Adams: the

Jo Lindsay Walton: As can imagine with AI, if the AI has a problematic carbon footprint, but delivers substantial adaptation benefits, that again is a very hard calculation to do. You, can't simply. Subtract one, one from the other. The acronym unfortunately came out as MAGA, which has already been taken. So, I'll keep working on on it.

Chris Adams: Okay. All right. I don't know how far that's going to go. I'll be honest. But. All right then, so, so that's one of the things you're speaking about was this idea that these are two separate things and it's worth being aware that there's, there, there are different ways you can essentially critically engage with some of these claims.

And I think I'm get where you're going with some of that now. And I've realized that I'm basically an Englishman in Germany, speaking to someone, to an Englishman who's also in the UK. And this was a report that came from a UK research unit. And obviously there's a UK research focus on this, but it's also, we're also in a scenario where there is new government in the UK who have very aggressive goals of like decarbonizing the entire grid by 2030.

So we spoke about 2030 target before, and like, this is one where there is a goal to decarbonize the grid by 2030 and reduce nationally carbon emissions by more than 80 percent by 2035. So this is like, in many ways, this is like a similar kind of moonshot thing we have here, but there's also, it's the government is also very, Gung ho right now on the increased deployment of data centers around the UK as one of the kind of drivers for growth, for example. So I wanted to ask you, like, when you look at this, do you see these goals as complementary or compatible or are there any specific areas of attention for the UK that are like for policymakers should be thinking about if they want these goals to be possible, for example?

Because yeah, there's, it sounds like it's, there's probably a lot of nuance to it, and this is something that you've been having to navigate or have to think about.

Jo Lindsay Walton: Yeah. And I mean, I don't know. I really don't know. And I wonder what guests we will need to assemble on your show to solve this question. It's definitely a, it's an interdisciplinary type question, right? We need people who can think about the counterfactuals, the opportunity costs. If data centers are not expanding at this particular rate in the UK, what's happening in that alternative universe?

There's in the report, there's a quite upbeat section lead authored by my colleague, Benjamin Sovacool, which is all about the wonderful things data centers can do to be more efficient and environmentally friendly. And so from a UK perspective, you can see those things going together. Yes, we're going to, we're going to be a leader on net zero.

We're also going to be a leader on data centers. And we're going to do that by having the greenest, the best, the most efficient data centers. Microsoft is shifting from concrete and steel to a special new timber. The new exciting innovations happening all the time. As a thought experiment. If we were building global data center infrastructure from scratch, knowing everything that we know, how would we design it?

Maybe you can get some experts on your show and ask them this. I've heard it said that data centers are these kind of fabulous heat generators that just happen to be able to do computation as well. One of the reviewers of the report said that. And so we should really go in hard on small and medium data centers woven into the fabric of our urban environments.

Anne Currie, who, we did that previous really fun episode about data centers on the moon and various things. Anne has said that a key consideration is that you really don't want to be competing with other local energy needs. So this is a contrasting view. You don't want to be displacing demand into carbon-intensive, generation then claiming that you have these wonderful green credentials.

So then the question is really, where in the world would you locate a data center and the green energy to power that, data center where it otherwise wouldn't be used for, anything else? How will data center expansion in the UK affect data center expansion in the EU or in Trump's America? Who is doing all this?

This is the real question for me. Who is thinking about these things? I mean, I'm here and glimpsing how huge and complicated a question it is. Who is doing this difficult holistic joined up thinking, including thinking through those second and third order effects? Are policymakers in the UK thinking in those terms?

Is SECR reporting going to have any impact? The Environment Agency, they like the detail and the nuance, but their remit has tended to be a bit more narrow. Their budget has been absolutely slashed under the conservatives. Is the onus on civil society to, to work through consultations, local planning authorities on a kind of data center by data center basis?

Is it maybe up to Environment Variables? Maybe it's on you.

Chris Adams: Well, what I can share with you is that we've got someone who's leading one of the distributed data center companies to give their side of the story in a future one, precisely to talk about okay, just how you spoke about the idea of like you mentioned that quota of AI, and imagine if we only spoke about vehicles, I wonder if there's maybe a thing where we talk, there's a similar comparable way of thinking about data centers, right?

Like if we only think about data centers as one thing, rather than being like, there's a typology of this giant, gigantic out of town hyperscale data centers, like gigawatt scale. And there's one at the other end, which are not the same, for example. Maybe there's a need for a kind of different strategy to think about what kinds of data centers make sense in what circumstances.

So like, maybe that you want to have certain kinds of computation. Like you mentioned that word, like inside the urban fabric, and there's certain things where you don't want to have it because you might have a different use for this. This makes me think of actually China. So China does have something along these lines, where in China, there's a really aggressive target to A, get lots and lots of data center, lots and lots of computer computation out of relatively old data centers into much more advanced centralized hyperscale kind of facilities, which are being paired with the kind of energy bases where there are just significant amounts of clean generation being put together there.

So you've got co-locating hyperscale data centers with the kind of generation that you have. So that you have different approaches and maybe there's something that you might see like that in the UK. I, don't know, but, I found maybe it's someone we should speak to. And if you're listening to this podcast and who is thinking about that.

Please do suggest them because we'd like to cover that in a bit more detail. All right then, you've spoken about two of the things that I think we, I'd like to just, if I can, jump into. You mentioned SECR, I don't know, could you maybe expand on who that is or what that is for people who aren't familiar with that acronym?

Jo Lindsay Walton: Oh, Streamlined Energy and Carbon Reporting regulations.

Chris Adams: okay, all right, so that's basically UK government has that data centers above a certain size have to report, basically, right?

Jo Lindsay Walton: Companies, yeah.

Chris Adams: Oh, okay, got it, okay, thank you. All right then, okay, so we've touched on quite a few, we've gone into a number of different areas for this and we're coming up to time. So I guess to ask you, you've spent this time and you've put a labor of love into this report, for example, but that came out in September, in the last, in the kind of subsequent months.

And are there any, is there any kind of, what work is exciting you? What things do you want to, are you looking at, you think, "this is really exciting, I wish more people would, who are interested in sustainable software, I wish they would look at this," for example. What's on your radar these days?

Jo Lindsay Walton: Well, it's been a very kind of busy and strange couple of months. So just even as you say that, it just reminds me how quickly these things move. Basically, I feel like I'm a little bit behind and I need to listen to some podcasts and click on some LinkedIn links and, bring myself up to speed.

I continue to be delighted by the work of the Green Software Foundation. I'm a big fan of your podcast. The GARP Climate Risk podcast is one that I like. Top three podcasts, the other one would be the Bunta Vista podcast, but that's not actually about climate and environment. That's just people getting high and reading news stories.

I'm interested in further collaborative work at a smaller scale with individual kind of companies and organizations. We've been doing a little bit of work with kind of cultural heritage organizations, thinking about their carbon impact. The focus of that work is under the rubric of climate acuity.

Which we've recently launched. It's connected to the DHCC in that we have a workshop that we do called the Digital Sustainability Game. So I'm, excited about continuing to iterate that work with all the constant barrage of developments that happen week by week in this space.

Chris Adams: It's pretty exhausting. I could, I can definitely share that. I struggled to keep up myself and this is pretty much my job.

Jo Lindsay Walton: I think, yeah, I think we do need to take a break every now and then. Pause, moratorium.

Chris Adams: Okay. On that note, we're coming up to time actually. So Jo, thank you so much for coming onto this and providing extra context to the report. If people are curious, where should they be looking if they wanted to read this report themselves?

Jo Lindsay Walton: It will be in the show notes, or if you type in The Cloud and The Climate AI powered or Navigating AI-Powered Futures, I think it should pop up.

Chris Adams: As the first result in pretty all the search engines.

Jo Lindsay Walton: I hope so anyway, otherwise something's very wrong.

Chris Adams: Well, in that case, folks, that's what to look for then. All right then. Well, Jo, thank you so much for coming on to this. This has been really, fun. And let's do this again, maybe next year. Like continue this tradition of every 12 months, we have you come on and tell us what you've been up to.

Jo Lindsay Walton: I would absolutely love that. Thanks so much for having me.

Chris Adams: All right. Thanks, Jo. Have a lovely afternoon. All right. And take care of yourself. Bye! Hey everyone! Thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing.

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]]> Environment Variables host Chris Adams is joined by Jo Lindsay Walton, a senior research fellow at the Sussex Digital Humanities Lab and co-author of the report The Cloud and the Climate: Navigating AI-Powered Futures. They delve into the intersection of climate and AI, exploring the environmental impact of AI technologies and the challenges of decarbonizing the ICT sector. Jo discusses key takeaways from the report, including the importance of understanding AI's direct and indirect impacts, the nuanced roles of big tech companies, and strategies for critically assessing claims of AI-driven sustainability. This insightful conversation highlights the need for interdisciplinary approaches and robust collaboration to navigate the complex relationship between technology and climate action.

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TRANSCRIPT BELOW:

Jo Lindsay Walton: There's this great metaphor that Arvind Narayanan and Sayash Kapoor have in their book, AI Snake Oil. They say, "imagine we just talked about vehicles. We didn't talk about bicycles or cars or buses or trains. And we tried to talk about the climate impact of vehicles." It would be very difficult to do.

And that's essentially what AI discourse does, right?

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Like seemingly everyone else in the industry, we've been talking about AI a fair amount recently, and earlier this year, in September, the Sussex Digital Humanities Lab published their report, The Cloud and the Climate, Navigating AI Powered Futures.

It's not a small report, weighing in at around 190 pages, and it has a number of key messages we'll be exploring in this episode. Also, one of the previous guests from back in September, 2023, Jo Walton was one of the authors of this report, and he was a nice enough to make some time to join us today on the pod.

So, Jo, thank you so much for coming onto the pod again. Can I give you a bit of time to introduce yourself and what you do in your day to day for people who missed the last episode back in September?

Jo Lindsay Walton: Hello. Yes. Thanks. Thanks so much for having me. So I'm a senior research fellow in arts, climate, and technology at the Sussex Digital Humanities Lab. My day to day is 90 percent playing with my cat, but I am also part of the Digital Humanities Climate Coalition and the newly launched Climate Acuity Initiative, which does facilitation and CPD training around climate and tech in hopefully fun ways involving storytelling and games and things like that.

And yeah, it's just, it's really nice to be back on this wonderful podcast. I feel like the host of SNL.

Chris Adams: Thanks. So, just before I check, when you say CPD, CPD is continuous professional development. People who want to build understanding of climate into their professional life, right? Is that what it is?

Jo Lindsay Walton: Yeah, that's it. Exactly. And it's, really I guess, part of my work is at the intersection of climate and technology, but I'm not personally super technical. Most of your listeners probably have a lot more technical knowledge than I do. What I am really interested in is communicating around these issues and education as well.

So I'm raising them for all the stakeholders for whom they might be important.

Chris Adams: Brilliant. Okay. And, on the subject of other three letter acronyms, I've just had my cat walking myself, so if it walks across the, when we're recording, please do forgive it. It's just, that's what he does sometimes. Okay. Folks, if you are listening and you've never heard my voice before, I am Chris Adams.

I am the executive director of the Green Web Foundation, which is one of the members of the Green Software Foundation. The Green Web Foundation is a nonprofit based in the Netherlands, focused on reaching an entirely fossil free internet. By 2030, I also work as one of the policy working group chairs inside the policy work in, inside the GSF, as well as being the host on this podcast as well.

All right, then. So just before we dive in, if we speak about a particular paper or a report or a link, we will add these in the show notes. And if you, if there's something you're missing, please do send us an email or get in contact us because we do our best to keep these available and like useful resources for people.

All right, then. Jo, are you sitting comfortably?

Jo Lindsay Walton: Very comfortably. Thank you.

Chris Adams: All right, then. I think I'll begin. All right, then. So before we dive into the report and some of the key takeaways, which we'll be going into more detail, can we talk a little bit about why you decided to put some time into this report in the first place and how this entire project came about, please?

Because I know that you're, you said yourself, you're a researcher and in the Unix School of Media Arts and Humanities at Sussex University. And this came from the Digital Humanities Climate Coalition. Now, most software developers, when they think about AI and reports, it might be something that's within the industry.

So I want to give you a bit of space to talk about why it's interesting or why it's relevant to have people who aren't inside technology, who aren't like practitioners per se, talking about some of this. Because I think there's a different, a couple of perspectives that you might have that is worth.

Making clear for people, for example, or some techniques that humanities people might actually have that, the developers or techies might not be so cognizant of. 

Jo Lindsay Walton: Oh Yeah. Absolutely.

So the report, as well as the kind of DHCC toolkit, which is an online resource, these are, they're very much community projects and they have an open source ethos and a part of that is an aspiration to interdisciplinarity. The report itself is a kind of stretch goal or spin off from a small innovate UK project that I was doing with GreenPixie, who are this fabulous cloud carbon data company.

Yeah. And we were basically exploring how to talk to a wider set of stakeholders about the cloud and about the climate. So not just IT people, but also, for example, chief sustainability officers, people who need to know about this stuff. That might not be quite so up to speed on the technical detail.

And over the course of that, it grew apparent to me that there was a gap really for an accessible resource that didn't oversimplify and that really tries to be a bit holistic. Can you really understand one bit of it without understanding the big picture? Can you, I don't know, understand how your little piece of software that you're trying to optimize is going to have an impact without thinking at least a little bit about carbon accounting and the greenhouse gas protocol and carbon offsetting? Can you really understand how green a data center is without understanding a little bit about how energy gets into the grid and then gets into a data center and the kind of energy procurement rules around that?

Chris Adams: Okay. I hadn't actually realized that you've been working with GreenPixie and just for people who are new to that term, GreenPixie is a UK-based SaaS provider of essentially carbon accounting tooling for cloud, just like, so if you're using Amazon's or Microsoft's or Google's cloud carbon calculator, they provide something very similar, but with a more kind of open methodology that allows them to be comparable to each other.

Really nice to know that there's, I didn't realize that you two would be working together on that. And that's cool, actually.

Jo Lindsay Walton: So, I mean, they, particularly that collaboration informed the green ops section of the report. But as you allude to, there is this attempt throughout the report to also bring in DHCC type perspectives, that kind of humanities flavor, really drilling into the details of the cultural factors.

So not just how we communicate things, but also how we imagine things, I guess. Big tech and tech communities don't just have direct impacts. They also shape the way that we imagine the future. So Google is not in the business of building kind of direct air capture, giant reverse hairdryers that are sucking carbon out of the sky. That's not something they do, but they do influence the way we think about technology and climate. And so they also influence the way that we think about things like greenhouse gas removal technologies.

Chris Adams: Although, earlier on this year, we saw that Microsoft patented, actually, some of the use of some particular things around carbon capture in data centers to use some of the waste heat to actually separate captured carbon, so it can be actually stored in other places. So, there's maybe more links than we actually had, yeah, exactly.

Jo Lindsay Walton: That's really interesting.

Chris Adams: Yeah, I'll share, we'll share a

Jo Lindsay Walton: link in the show notes for sure.

Chris Adams: Definitely. All right. Okay. So that gives me a good idea and then provides a bit of context to where this was and for people who are not used to the UK, Innovate UK is one of the government funding agencies that has provided some of the funding for some of this.

So that's where that has come from. All right. And so maybe we should talk a little bit about the report. So there's a number of takeaways. In fact, I counted more than five when I was running through the report. So there was a lot there, right? And there are some things which probably don't need too much attention because we're, because of the listenership.

So for example, we probably won't spend too much time dwelling on one of the takeaways being we're in a climate crisis or the other one bang, yeah, that digital has a physical basis. These are things that we can assume that people have internalized already, right? But there was actually some nuance to this because.

While people do talk about that, the kind of magnitude of the numbers might not be something that people are quite so comfortable about. And also, it's an area of contention in many cases, many places. And as someone who's been looking at a lot of the literature, I figured it might be interesting to have a bit of space to talk about one of the other takeaways, which you shared was basically the ICT sector is not a leading contributor to global warming, but it still must decarbonize rapidly.

Now, I think It'd be useful to unpack some of this because a lot of the time, a lot of the stories do talk about either data centers as like this new monster or new kind of like media baddie, for example. And it seems like there's, you've got a kind of more nuanced take on this and I wanted to give a bit of space for you to allow you to talk about some of that.

Jo Lindsay Walton: Yeah, I mean, coverage of the drivers of global warming is totally out of proportion to what those drivers actually are. We've seen data centers be in the mainstream media quite a lot recently. So I think maybe that's falling victim to that a little bit. Where do most emissions come from? Food production, for example, is absolutely huge.

And we hear a little bit about food miles. But food miles are not a massive part of it. A much bigger determinant of the impact is, "has the food come from a cow or from a nut?" Constructing and heating and lighting homes, road transport, fugitive emissions, fossil fuel companies basically being a little bit sloppy as they extract these fossil fuels and letting them escape.

There's a good, a lovely breakdown on Our World In Data, which is maybe we can put in the show notes as well, although a little bit dated now. ICT? What is the impact of ICT on global warming? Would like to offer a provocation and hope that maybe one of your listeners can, prove me wrong. I think nobody knows.

I think nobody knows ICT's impact on global warming. There's that 2021 Freitag et al. estimate that gets quoted quite a lot, but it's been a very, busy four or five years. I feel like I've lived through the AI singularity. And there's more complexity than that, right? When you factor in secondary and tertiary impacts, both good and bad, from the digital, then you're in the realm of deep uncertainty.

There is unlikely to be any expert consensus. Even so, despite that complexity, it's not controversial that tech needs to decarbonize along with everything else. It's all hands on deck. Everybody's on board with that. All the big companies have these ambitious pledges. What's concerning me a little bit is how that discourse is shifting.

So for example, Microsoft in 2020 sets out its pledge to achieve net zero. 

Chris Adams: Moonshot. The zero carbon moonshot you're referring

Jo Lindsay Walton: yeah, yes, And we talk about that term moonshot in, in the report, actually, cause it's a, it's an interesting metaphor. And the moon has, is now said to be 

Chris Adams: 5 years further away. Yeah. 

Jo Lindsay Walton: The moon has moved five times. So actually I think that's incorrect.

I think the moon. The moon has been vaporized. The moon, as in Neil Stevenson's science fiction novel, Seveneves, the moon no longer exists. The target has already been missed. And that happened this year. "Okay, how is that possible?" you're asking. Does Microsoft have a time machine? How can they fail their net zero pledge of 2030 in 2024?

Well, that's the way that net zero pledges work. They are about cumulative emissions. They're not about a snapshot of emissions at a particular date. They are about the pathway from the date of the pledge 2. 0 staying within a given emissions budget, right? So you could draw a descending line graph and it's about the area under that line, not about the point at which the line intersects the axis.

And to their credit, Microsoft absolutely was transparent about this back in 2020. They showed the linear descent to zero. And by my estimates, that budget was burst sometime this year. maybe now, maybe as we are recording this podcast. And poverty is no effect. The concerning bit is that this isn't being talked about more openly.

It's much more this discourse, as you say, of "okay, now we have AI." In 2020, we didn't know about that, but now we have AI and AI has these sustainability benefits. Okay, so if that's the argument, if that's the implied case for emissions increasing, let's be very clear about that. Are we saying that it is prudent to increase emissions from the tech sector for the next few years?

Are we saying that the tech sector has been doing the right thing emissions-wise for the past few years, because those emissions on a robust methodology are shown to be more than offset by the sustainability benefits that they can provide on an appropriate timescale?

Chris Adams: We'll be touching on that a little bit later, but, alright. Okay. Thank you that I appreciate you providing a bit of extra context on that. And just to check if I understand, you said one or two things about, okay there is, the way you could work out the environmental footprint of the ICT sector when people talk about the direct impact, you said there's like a primary, tertiary, sorry, secondary and tertiary, presumably you're talking about like there is a direct impact, but there's an impact from people, what you enable with that computing and stuff like that. Is that what you're referring to with that primary, secondary and tertiary stuff?

Jo Lindsay Walton: Yeah, absolutely. So you and I are on a, Zoom call now. If we weren't on the Zoom call, I probably would have ridden to you on a giant lump of blazing coal. Or some more carbon intensive mode of transport. And those are very, complex calculations to do. You have rebound effects where, things look like they're providing efficiencies, but those efficiencies are mitigated or more than offset by increased volume, it's complicated stuff.

Chris Adams: Okay, cool. All right. Thank you for providing that extra kind of elucidation or like clarifying that part there. Okay. There's another thing I wanted to give a bit of time for actually was this one. You said, and given that we just spoke about kind of cloud giants and one of the takeaways, which was none of the cloud giants is a monolith.

So this is a bit of a kind of more nuanced take on big tech bad, big tech good that we often see in the discourse, because it's very simple and attractive way to talk about that, but it sounds like you're trying to go for something a bit more sophisticated there, a bit more multidimensional there.

Maybe we could spend a bit of time trying to see what you were trying to get at there or what the report was trying to really get across to people.

Jo Lindsay Walton: Yeah. I think climate invites us to really reflect on our roles in our professional lives and other aspects of our lives. And sometimes to challenge and push back on the parameters that are set for us in those roles. And that may mean that your company is pushing a particular line or your bosses is pushing a particular line, but there is a kind of, there's a practical incentive and there's, frankly, there's a kind of ethical duty to be critical about that and to step outside of the boxes that you're asked to perform in.

And definitely these companies are huge companies. There's a great diversity of knowledge, a great diversity of kind of politics, really, within any particular industry. big tech company, nevermind between tech companies as well. So in the realm of the greenhouse gas protocol and how we do carbon accounting, there's a lot of disagreement within big tech between on the one hand, Amazon and Meta who want one kind of particular set of rules as the greenhouse gas protocol is revised and Google and perhaps Microsoft who would like to see it go another way.

I think we look at this a little bit in the report. We look at a nature article that is largely authored by, Microsoft researchers. And spend a little bit of time in a hopefully good natured roast of the estimate of the carbon impact of AI, which the methodology there just isn't really fit for purpose.

If you drill, really drill, drill, drill baby, drill down into the details, you find that it is based on one back of envelope kind of estimate by Vijay Rakesh, who is really a stock market analyst who said that he expected NVIDIA to deliver a hundred thousand AI servers in 2023.

It's not a sufficient basis for estimating the global impact of AI, but that's hopefully not the main point because the bigger part of this article, which I think speaks to your question about companies not being monoliths and trying to build alliances for progressive and robust climate policy that cut across your loyalty to a particular company.

The proposal of this article is that AI researchers should work more closely with climate, the climate modeling community, and that AI should be integrated into the IPCC's shared socioeconomic pathways and integrated assessment modeling. Which is, I have mixed feelings about that. Like the closer collaboration sounds really great. It does feel like in that particular article, there isn't yet a very deep understanding of how those climate models work. They're not really scenarios. They're more like building blocks for scenarios. And to some extent, they already do build in the possibility of technological change.

So you could go down a rabbit hole as to whether or not AI is already priced into these models or not. I think what it speaks to is a certain kind of nervousness here, like, okay, so we are big tech, we are AI, we're presenting this AI powered future, and we're increasing our emissions, and we're doing this on the basis that we think, we believe, that AI is going to unlock all these fantastic sustainability benefits.

But can somebody please check our working? We recognize that we may have conflict of interests. We need to do this in a more collaborative way. We need to have all kinds of expertise and we need to have more independent voices. I think that's what that article is ultimately calling for.

Chris Adams: Okay. So there was one thing you said that you were getting at there was the idea that cloud giants not being a monolith isn't just within the cloud giant. If you think about it horizontally, like Meta and Amazon having one point of view. And I think you're referring to the emissions first versus the 24/7 kind bond fight about how do you count energy as green?

Because the current process has a few significant issues with basically, there's people trying to work out a new approach and you have two camps. So that's one thing you were talking about. And then there's almost one within each company. Like there are different people who have different drivers inside that. If you just assume that someone's working for say Amazon, that ends up being a very lossy way of talking about, okay, what are they doing? And like, what might, the drivers be, for example?

Jo Lindsay Walton: Absolutely. And some of those disagreements might not be so visible for obvious reasons. People have to be tactful and work in constructive ways with their colleagues. I mean, to respond to that, I think that I, share a bit of alarm about timescales and, solutions being proposed that aren't immediately referred back.

If you've got any kind of plan to do with the climate, check it against IPCC timescales. We were supposed to, in the next six years, cut carbon emissions by more than half, four of which are going to be under a Trump administration. And I would definitely, I would celebrate that kind of all hands on deck approach where everybody's doing everything they can in their role and maybe rethinking their role in creating alliances.

At the same time, I also think we need a little bit of reflection on actually which hands are on deck. Are there problems that aren't owned by anybody, risks that are not being addressed by anybody. And I think that we need a little bit, in the AI space, there has been talk of pauses and kind of moratoriums, not always for the best reasons, but I do think these are really important tools in our toolkit rather than, "okay, we're going to just keep doing what we're doing and, hope to sustainabilize it as quickly as we can,"

actually, saying "maybe we need to pause this and maybe we can pick up where we left off, but we need to pause it while we're gathering more data or we're greening our energy supply or we're building capacity" or whatever it might be. I wrote an article about this in the Fantastic Branch magazine called Pause.

I just realized this morning, I should have called it after Andreas Malm's How to Blow Up a Pipeline, I should have called it How to Blow Up an AI Pipeline. But yeah, I something else for that.

Chris Adams: Yeah. All right. All right. Okay. Thank you for that. Let's move to the next one. Cause you spoke a little bit about AI and, in the report, you actually spend a bit of time talking about sustainability. Basically the sustainability of AI, but also AI for sustainability. Right. And these being two somewhat different things.

Now we talk about sustainability of AI on this podcast quite a lot. So we talk about how to use like more efficient algorithms or how to clean the energy and some of the steps you might take. And obviously the report talks about that, but there's actually something that you speak about in terms of the claims about AI for sustainability goals that you spend some time talking about and like you also raised, like "these are some of the red flags you might be looking for." Could you maybe, are there any like specific messages you might use or anything you draw to people's attention to when they're trying to navigate claims about AI for sustainability and like, "yes, there's a massive energy footprint, but the upside is this, for example, and these are the upsides that we're delivering."

Jo Lindsay Walton: Yeah, absolutely. And all that kind of sustainability of AI stuff is extremely exciting. And, as you say, we, touch on that in the report. AI for sustainability. There's this great metaphor that Arvind Narayanan and Sayash Kapoor have in their book, AI Snake Oil, they say, "imagine we just talked about vehicles.

We didn't talk about bicycles or cars or buses or trains. And we tried to talk about the climate impact of vehicles. It would be very difficult to do." And that's essentially what AI discourse does, right? We don't on a regular basis make these kinds of fine differentiations in public discourse, in journalism, in conversations with friends.

So right before the show, actually, we were talking about acronyms and I tried to come up with an, acronym of the things that you might want to ask when you find a claim that AI is delivering some kind of sustainability benefit. So the first thing to consider is maturity. That might be technology readiness level whatever it may be.

Often there is a claim is inflated. It says something is already happening when actually what we see is that there's been a study that says it might work. It could be rolled out commercially, scaled up in five, 10, 20 years, whatever it might be. So maturity is one. Then additionality. So AI is responsible for delivering this sustainability benefit.

Well, do your best to identify which bit the AI is responsible for. Often an AI sustainability project will involve data collection and analysis, and then some kind of efficiency gains from that. What could have been delivered with the, with more kind of traditional data analytic methods? And then generative or discriminative or some other type of AI.

What kind of AI are we talking about here? These are often conflated. Is it even machine learning at all? Is it something, some cool new thing like, I don't know if it's new, but active inference, for example. And how big is the model and so on? What kind of AI are we looking at? And then finally adaptation versus mitigation.

So these are the two broad categories of climate action that most climate scientists will recognize. And they're interrelated and they overlap in various ways, but mitigation is really about decarbonizing,

Chris Adams: Yeah. Green energy instead of fossil energy. And then the mitigation might be building the seawalls because the sea levels have risen. Stuff like that. 

Jo Lindsay Walton: other

Chris Adams: I say, yeah. adaptation is building the seawalls because the sea levels have risen. Mitigation would be switching out of fossil fuels and burning, using greener energy, for example, which

Jo Lindsay Walton: Absolutely.

Chris Adams: the

Jo Lindsay Walton: As can imagine with AI, if the AI has a problematic carbon footprint, but delivers substantial adaptation benefits, that again is a very hard calculation to do. You, can't simply. Subtract one, one from the other. The acronym unfortunately came out as MAGA, which has already been taken. So, I'll keep working on on it.

Chris Adams: Okay. All right. I don't know how far that's going to go. I'll be honest. But. All right then, so, so that's one of the things you're speaking about was this idea that these are two separate things and it's worth being aware that there's, there, there are different ways you can essentially critically engage with some of these claims.

And I think I'm get where you're going with some of that now. And I've realized that I'm basically an Englishman in Germany, speaking to someone, to an Englishman who's also in the UK. And this was a report that came from a UK research unit. And obviously there's a UK research focus on this, but it's also, we're also in a scenario where there is new government in the UK who have very aggressive goals of like decarbonizing the entire grid by 2030.

So we spoke about 2030 target before, and like, this is one where there is a goal to decarbonize the grid by 2030 and reduce nationally carbon emissions by more than 80 percent by 2035. So this is like, in many ways, this is like a similar kind of moonshot thing we have here, but there's also, it's the government is also very, Gung ho right now on the increased deployment of data centers around the UK as one of the kind of drivers for growth, for example. So I wanted to ask you, like, when you look at this, do you see these goals as complementary or compatible or are there any specific areas of attention for the UK that are like for policymakers should be thinking about if they want these goals to be possible, for example?

Because yeah, there's, it sounds like it's, there's probably a lot of nuance to it, and this is something that you've been having to navigate or have to think about.

Jo Lindsay Walton: Yeah. And I mean, I don't know. I really don't know. And I wonder what guests we will need to assemble on your show to solve this question. It's definitely a, it's an interdisciplinary type question, right? We need people who can think about the counterfactuals, the opportunity costs. If data centers are not expanding at this particular rate in the UK, what's happening in that alternative universe?

There's in the report, there's a quite upbeat section lead authored by my colleague, Benjamin Sovacool, which is all about the wonderful things data centers can do to be more efficient and environmentally friendly. And so from a UK perspective, you can see those things going together. Yes, we're going to, we're going to be a leader on net zero.

We're also going to be a leader on data centers. And we're going to do that by having the greenest, the best, the most efficient data centers. Microsoft is shifting from concrete and steel to a special new timber. The new exciting innovations happening all the time. As a thought experiment. If we were building global data center infrastructure from scratch, knowing everything that we know, how would we design it?

Maybe you can get some experts on your show and ask them this. I've heard it said that data centers are these kind of fabulous heat generators that just happen to be able to do computation as well. One of the reviewers of the report said that. And so we should really go in hard on small and medium data centers woven into the fabric of our urban environments.

Anne Currie, who, we did that previous really fun episode about data centers on the moon and various things. Anne has said that a key consideration is that you really don't want to be competing with other local energy needs. So this is a contrasting view. You don't want to be displacing demand into carbon-intensive, generation then claiming that you have these wonderful green credentials.

So then the question is really, where in the world would you locate a data center and the green energy to power that, data center where it otherwise wouldn't be used for, anything else? How will data center expansion in the UK affect data center expansion in the EU or in Trump's America? Who is doing all this?

This is the real question for me. Who is thinking about these things? I mean, I'm here and glimpsing how huge and complicated a question it is. Who is doing this difficult holistic joined up thinking, including thinking through those second and third order effects? Are policymakers in the UK thinking in those terms?

Is SECR reporting going to have any impact? The Environment Agency, they like the detail and the nuance, but their remit has tended to be a bit more narrow. Their budget has been absolutely slashed under the conservatives. Is the onus on civil society to, to work through consultations, local planning authorities on a kind of data center by data center basis?

Is it maybe up to Environment Variables? Maybe it's on you.

Chris Adams: Well, what I can share with you is that we've got someone who's leading one of the distributed data center companies to give their side of the story in a future one, precisely to talk about okay, just how you spoke about the idea of like you mentioned that quota of AI, and imagine if we only spoke about vehicles, I wonder if there's maybe a thing where we talk, there's a similar comparable way of thinking about data centers, right?

Like if we only think about data centers as one thing, rather than being like, there's a typology of this giant, gigantic out of town hyperscale data centers, like gigawatt scale. And there's one at the other end, which are not the same, for example. Maybe there's a need for a kind of different strategy to think about what kinds of data centers make sense in what circumstances.

So like, maybe that you want to have certain kinds of computation. Like you mentioned that word, like inside the urban fabric, and there's certain things where you don't want to have it because you might have a different use for this. This makes me think of actually China. So China does have something along these lines, where in China, there's a really aggressive target to A, get lots and lots of data center, lots and lots of computer computation out of relatively old data centers into much more advanced centralized hyperscale kind of facilities, which are being paired with the kind of energy bases where there are just significant amounts of clean generation being put together there.

So you've got co-locating hyperscale data centers with the kind of generation that you have. So that you have different approaches and maybe there's something that you might see like that in the UK. I, don't know, but, I found maybe it's someone we should speak to. And if you're listening to this podcast and who is thinking about that.

Please do suggest them because we'd like to cover that in a bit more detail. All right then, you've spoken about two of the things that I think we, I'd like to just, if I can, jump into. You mentioned SECR, I don't know, could you maybe expand on who that is or what that is for people who aren't familiar with that acronym?

Jo Lindsay Walton: Oh, Streamlined Energy and Carbon Reporting regulations.

Chris Adams: okay, all right, so that's basically UK government has that data centers above a certain size have to report, basically, right?

Jo Lindsay Walton: Companies, yeah.

Chris Adams: Oh, okay, got it, okay, thank you. All right then, okay, so we've touched on quite a few, we've gone into a number of different areas for this and we're coming up to time. So I guess to ask you, you've spent this time and you've put a labor of love into this report, for example, but that came out in September, in the last, in the kind of subsequent months.

And are there any, is there any kind of, what work is exciting you? What things do you want to, are you looking at, you think, "this is really exciting, I wish more people would, who are interested in sustainable software, I wish they would look at this," for example. What's on your radar these days?

Jo Lindsay Walton: Well, it's been a very kind of busy and strange couple of months. So just even as you say that, it just reminds me how quickly these things move. Basically, I feel like I'm a little bit behind and I need to listen to some podcasts and click on some LinkedIn links and, bring myself up to speed.

I continue to be delighted by the work of the Green Software Foundation. I'm a big fan of your podcast. The GARP Climate Risk podcast is one that I like. Top three podcasts, the other one would be the Bunta Vista podcast, but that's not actually about climate and environment. That's just people getting high and reading news stories.

I'm interested in further collaborative work at a smaller scale with individual kind of companies and organizations. We've been doing a little bit of work with kind of cultural heritage organizations, thinking about their carbon impact. The focus of that work is under the rubric of climate acuity.

Which we've recently launched. It's connected to the DHCC in that we have a workshop that we do called the Digital Sustainability Game. So I'm, excited about continuing to iterate that work with all the constant barrage of developments that happen week by week in this space.

Chris Adams: It's pretty exhausting. I could, I can definitely share that. I struggled to keep up myself and this is pretty much my job.

Jo Lindsay Walton: I think, yeah, I think we do need to take a break every now and then. Pause, moratorium.

Chris Adams: Okay. On that note, we're coming up to time actually. So Jo, thank you so much for coming onto this and providing extra context to the report. If people are curious, where should they be looking if they wanted to read this report themselves?

Jo Lindsay Walton: It will be in the show notes, or if you type in The Cloud and The Climate AI powered or Navigating AI-Powered Futures, I think it should pop up.

Chris Adams: As the first result in pretty all the search engines.

Jo Lindsay Walton: I hope so anyway, otherwise something's very wrong.

Chris Adams: Well, in that case, folks, that's what to look for then. All right then. Well, Jo, thank you so much for coming on to this. This has been really, fun. And let's do this again, maybe next year. Like continue this tradition of every 12 months, we have you come on and tell us what you've been up to.

Jo Lindsay Walton: I would absolutely love that. Thanks so much for having me.

Chris Adams: All right. Thanks, Jo. Have a lovely afternoon. All right. And take care of yourself. Bye! Hey everyone! Thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing.

It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> Green Networking with Carlos Pignataro Green Networking with Carlos Pignataro Thu, 12 Dec 2024 07:00:00 GMT 39:51 j122rjl1 false https://podcasts.castplus.fm/e/2861qwln-green-networking-with-carlos-pignataro 68dc7cfed5c8a1f9b326ee09 68dc7cef46a2532cdd8d1c23 In this episode of Environment Variables, Anne Currie welcomes Carlos Pignataro, a leading expert in sustainable network architecture, to explore how networks can balance energy efficiency with performance and resilience. Carlos shares insights from his career at Cisco and beyond, including strategies for reducing emissions through dynamic software principles, energy-aware networking, and leveraging technologies like IoT and Content Delivery Networks (CDNs). They discuss practical applications, the alignment of green practices with business interests, and the role of multidisciplinary collaboration in driving innovation. Tune in for actionable advice and forward-thinking perspectives on making networks greener while enhancing their capabilities. 92 In this episode of Environment Variables, Anne Currie welcomes Carlos Pignataro, a leading expert in sustainable network architecture, to explore how networks can balance energy efficiency with performance and resilience. Carlos shares insights from his career at Cisco and beyond, including strategies for reducing emissions through dynamic software principles, energy-aware networking, and leveraging technologies like IoT and Content Delivery Networks (CDNs). They discuss practical applications, the alignment of green practices with business interests, and the role of multidisciplinary collaboration in driving innovation. Tune in for actionable advice and forward-thinking perspectives on making networks greener while enhancing their capabilities.

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TRANSCRIPT BELOW:

Carlos Pignataro: Many of the things that we do with a little bit of spin, hugely benefit the bottom line by cutting down costs, hugely benefiting the world by lowering emissions. I'm going to run this batch job whenever there's renewable. I'm going to turn off the lights and the APL, the access points on the ceiling automatically when there's no presence.

Basic things that make a difference.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne Currie: Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Anne Currie, and today we're joined by Carlos Pignataro, a leading voice in sustainable network architecture. Carlos has over 20 years of experience in technology, innovation, and strategic thinking, holding key roles at Cisco, including head of technology and data for Cisco's engineering sustainability.

Currently, he's a mentor in residence at Duke University New Ventures, adjunct facility member at NC State University. And he is the founder and principal at Blue Fern Consulting. In this episode, we'll dive into Carlos' work on architecting networks for environmental sustainability. He's contributed to cutting edge research on how networks, which are critical to our connected lives, can balance energy efficiency with performance and resilience.

How can the networking industry innovate without compromising the planet? What trade-offs do we have to consider? Things like uptime, speed, against, or as well as, environmental responsibility. Let's explore these questions and more with Carlos. So welcome Carlos. Can you tell us a little bit about yourself?

Carlos Pignataro: Thank you very much, Anne. I am so excited to be here, first of all, and I'm happy to tell you a little bit about myself, even though you covered a lot, that was a very kind intro. I appreciate it. My favorite number is 27. My favorite color is aquamarine, just for completeness. But on a more, on an equally serious note, Important for my introduction is to say that I have passions for technology and for sustainability.

And I look at tech as a means to an end and making things better is one of the ends. I've been involved, as you said, as you mentioned in building networks, computer networks, internet infrastructure, data centers. I have also been involved in other uses of technology, such as technoconservation or conservation technologies, protecting endangered species, such as rhinos in different countries in Africa or in India from poaching using IOT, Internet of Things technology, I've been involved in building data models and information models for recycling and overall circularity. So I try to build my overall experiential breadth through trying different aspects of technology and sustainability is one on which I'm super passionate about.

Anne Currie: Excellent. That's very good to hear. And that's why you're here today. So just a little bit about myself, because I'm not always the host. I'm a guest host for Environment Variables. My name is Anne Currie. I am, I've been in the tech industry for about 30 years. I am one of the co authors of O'Reilly's new book, Building Green Software, which covers a lot of, actually covers a lot of what Carlos and I will be talking about today, cause there is a networking chapter in it.

And I strongly recommend it to everybody who's listening to the podcast. I am also the CEO of learning and development company, Strategically Green. We do a lot of workshops to help people push knowledge and engagement with green subjects within their own businesses. So touch me up on LinkedIn if you're interested in that kind of thing.

So before we start in, we dive into today's content is a reminder that everything we talk about today will be linked to in the show notes below. So feel free to have a look, read through, read as we go, read afterwards, whatever, but the data is all there. So, let's kick off with an introductory question, Carlos.

How did you become a Green Software Foundation champion? What are your goals as a champion?

Carlos Pignataro: Thank you very much. And it's a very meaningful question. When I looked at green software champion, when I was exposed to the acronym, to the three words, actually, and the GSF acronym, I couldn't help but looking at each one of the words and understand that each one had a very, profound meaning for me. Number one, green is something that encapsulates what you and your book and all of us are trying to push our industry and our different industries towards. Software is corner of what we really do day in day out and our expertise where we can actually make a difference is where we live. And in champion, I really always wanted to be a champion.

And I try soccer. I try tennis. In Argentina, I have, which is where I come from, I have a very, below average soccer scores, no, jokes aside, I, champion is such an important word because I look at it as, the first followers, and I am very moved by the way in which we create a movement on what Derek Sivers describes into being the first follower. And having these champions are like reflectors. It seems this is a little bit of networking, BGP reflectors of the message. So we all different have insertions in different parts of the ecosystem, whether it's within corporate, within different software, different repositories, whether it's different standard bodies and being a champion means being a follower and reflector of that larger message and echo it within the all the different places that we live.

So it really aligns very deeply with my passions and frankly, something that I see you as I follow you as well, is like I see the three words very clearly and I don't know how you feel, Anne, but to me it's always a little bit of a work in progress. What we have achieved, I'll play that back to you a little bit, but as a question, but to me it's like we have achieved a lot of awareness and we have achieved a lot of education and we have achieved the realization that there's a lot more and a lot more to actually do as a champion. What is, I don't know,

Anne Currie: I think you're, very right there. We've done a lot. Things are a lot better than they were 10 years ago. Vastly better. And I feel constantly buoyed up by that, but there's just so much to do still. I mean, still, when I talk to most people outside of the immediate movement, folk don't realize that actually there is a lot of good stuff that the tech industry can do to cut carbon emissions.

I speak to folk in tech all the time who are really interested in climate change and doing their bit, and they often focus directly on individual change they can make, like becoming a vegan or something like that, which is fine. But that's as nothing compared to the far more scalable change they can make through their jobs.

Through being a software engineer, there's an enormous change that we can make. There's an enormous improvement that we can make there. And it's nice when people realize, because folk do want to fix things. They do want to make a difference. And a lot of folks don't realize.

Carlos Pignataro: A hundred percent. It's so interesting as you were saying that I was, and picturing myself the concept of bring your whole self to work and the fact that we are individuals with a set of values and we can project them in different personas that we have. And for me, it was actually a part of my professional growth and realization earlier on as an engineer that I could actually bring my values to work as well. And exactly like you said, that's when the difference really compounds. It's, not like I have a nights and weekends green version. I can apply that in my day job, not only nights and weekends. And also what I find super monumental and I applaud what you all do and what we do here is there's so much more resources, so many more resources available for anyone who says, "yeah, I want to learn, and apply it."

Anne Currie: Yes. Oh yeah. The number of resources are really, is really taking off, which is amazing. I like what you're saying about bringing your whole self to work. The good thing about building green software and being more carbon aware at work, if you are a tech company, is that, I mean, a lot of people feel, "Oh, well, it's a bit unprofessional because I'm bringing something to work and actually that's, I'm asking the business to do something that is not in their best interest to do."

When it comes to cutting carbon emissions, it is in the interest of the business to do that. It cuts costs. It makes the business more resilient. It's, it makes it more future proof to the way that the energy system is going. Eventually, folk are going to have to be ready to run on renewables because that is the new form of energy.

I mean, people can do this in a way that is against the interests of the business. If you decide that you're going to rewrite all these systems in Rust, that might well be against the interests of your business. But cutting your energy use, cutting your carbon emissions in half by just being more clever and smart and modern about the way you operate systems so they're more efficient and more secure and more cost effective.

That is in the best interest of your business. That's not going against it. And I think we do need to keep constantly hammering that message home. 

Carlos Pignataro: Absolutely. It's a message in my corporate tenure, within big tech. That's one of the key messages that resonated inside the company, resonated with customers, which is good for the world and good for the business and finding these things it's. And that's one that actually I don't think is a marketing tag.

I run numbers on, "shall I do this? Does it make sense or not?" And just like you say, many of the things that we do with a little bit of spin hugely benefit bottom line by cutting down costs, hugely benefiting the world by lowering emissions. I'm going to run this batch job whenever there's renewable. I'm going to turn off the lights and the APL, the access points on the ceiling automatically when there's no presence. Basic things that make a difference.

Anne Currie: Yeah. If you go back to the, I would say almost the inception of the modern thinking about being more efficient in data centers is the work that Google and Sun were doing at the beginning of this century around containers and orchestration, the use of that precursor to Kubernetes, their Borg orchestration system. That wasn't about cutting carbon emissions.

That was about cutting the cost of operating systems and improving the resilience of operating systems. Those two things are completely aligned with carbon reductions. It's the way of being more efficient, being more resilient, being more, usually adopting modern operational practices like auto scaling.

Do all the, deliver all the things. It's 

Carlos Pignataro: And I'll tell you one thing, if you don't mind, allow me extrapolate from what you're saying, the idea of actually having software, green software principles, really as a, in our workspace, visible and very aware, is so incredibly important because the virtualization concept that you're talking about, Kubernetes and so on, are things that we apply to fight in the networking world, are things that we apply to any type of SFC software function, virtualization, NFVs, et cetera. And trying to bring a software more dynamic approach of how we think about it, to think that are traditionally more, I have a big router and a big switch and a big antenna, softwarizing, if you will, the thinking and doing that with the principles that you can have within, things like that are at the reach of all of us, like some of the courses on green awareness, building those principles into any type of software practice is such a win/win.

Anne Currie: Yeah, absolutely. There's no reason for people to go, "Oh, I love the idea, but it's actually going to hurt my business." If it hurts your business, you're not doing it right. If you think it's going to hurt your business, don't do it that way. Do it a way that is materially aligned with your business. That will scale better, will deliver more value, and you'll actually make it happen.

So there is no reason why being green should hurt. If it does, you're probably doing it wrong. Stop.

Carlos Pignataro: And I tell you, one of the things that we often think about, and I thought about very much in my CTO roles or in when I do standardization is how can we actually make some of these things codified, reusable, repeatable. Someone can actually learn, someone can actually use, whether it's a standard or whether it's a certification or, and do that in the context of. Not only the architecting of software and networks, but also the operationalization of networks. One small example that I can share is work that we're doing with Alex Clem on sustainable network operations, which is an IEEE, SIG or special interest group. And the interesting thing there is that when we look at the overall life cycle of networking and software, we have the use phase within the life cycle that focuses on the specific operations of the network, operational aspects of networks. There's a lot to gain in terms of managing energy efficiency and carbon awareness and carbon efficiency. So how can, within our different insertion points, within the life cycle, some are earlier, closer to manufacturing networking equipment or designing chips, some are closer to doing architecting networks and actually designing networks and operations, and then plus plusing networks, right? Like updating and upgrading equipment in a more circular, sustainable way. Each one of those areas has a very strong sustainable benefit that we can actually bring. These are the reasons, and honestly, Anne, hearing you talk are the reasons why, going back to your earlier question, why I got really drawn into this green software champion concept.

Anne Currie: It's interesting. It's, I'm going to talk about something you don't, you wouldn't necessarily speak specifically at the moment, but one of the things that interests me, and we mentioned it a lot in Building Green Software. Is that networking is one of the few areas of the tech industry where there's already been a lot of thoughts, which is directly aligned with energy saving.

That's, that networking has a concept of, is it bits per watt or watts per bit?

Carlos Pignataro: Yeah. And the interesting thing there is that there's been a lot of research and there's still ongoing discussion into how much things like energy over a number of throughput, like Watts per bit or, it's over megabit, how useful something like that is because, which I think is actually useful in many areas.

And there's, it's still an ongoing discussion about it in some standard groups, because if you take, for example, a core in a router, piece of equipment of the guts of the internet. And you have it powered up without traffic that is consuming about 80 percent of the power.

Anne Currie: Which is astonishing, isn't it?

Carlos Pignataro: It's crazy. And then with power, 95, right? So the proportionality has a smaller slope.

Anne Currie: So actually let's move on to one of our, some of our discussion points from today. So we're going to be talking about two papers that you were involved with and, co-authored, contributed to the first one is an article entitled Challenges and Opportunities in Management for Green Networking, which explores the environmental impacts of networking technology.

Noting that while, that basically it says networking is great, there's loads of things come as of networking that can cut travel, that can, could significantly contribute to reducing carbon footprints. Networking is an amazing thing. But it also uses a lot of power. So if there's some way that we can get all the wins and reduce some of the losses and some of the waste, that would be fantastically good.

But at the same time, we don't want to lose any of the wins. So do you want to talk a little bit about the article? 

Carlos Pignataro: Absolutely. Thank you so much. I'm going to start, Anne, by honing into a couple of keywords that you mentioned, which are important in my mind. And the first one is the one about trade-offs. It's potentially tempting to say, "I'm going to move a part of the system to another part, which resides outside boundary conditions, and therefore things are more green." And it's one of the learnings that I've seen. I've worked in a number of technologies in my career and sustainability, environmental sustainability has been the more new one by far. We're looking at the system with broad boundary conditions. It's not like saying "I'm going to replace all the lights with LEDs" because we have material extraction to actually make the new light bulbs and we have to dispose of the old ones.

So if we look in the broader sense, we've really had trade-offs. Imagine to make it very practical that we have a link between two routers or two devices. If we add more links, we can have more redundancy. And if we add more routers to actually duplicate that and more links between them, we have even more redundancy. And that redundancy or resiliency improves asymptotically. It gets to a point in which I add more. And while my carbon emissions continue going up linearly, there's no benefit to redundancy and in fact, the system can become a little bit more brittle. So one of the concepts that we kind of talked about a little bit, we have a multi goal scenario.

We are optimizing for two different goals at the same time, one of which is resiliency and performance and traditional business metrics, and the other one is for sustainability at the same time. The main area where I feel that makes such a, strong difference is in Moving to automation, moving a lot of these processes to automated processes. It's one in which we can actually get to an optimal point in sustainability while lowering the extra links for redundancy based on the needed traffic at the time or the seasonality of the traffic or the seasonality of the requirement. So that's one of the key pieces of that work. And let me explore, if you don't mind, another area of this paper, which I think is very, relevant and important, which is, which really is how we define terms. It's a Socrates quote that the beginning of wisdom starts with the definition of terms. And one of the things that I found is that in such a multidisciplinary field in which you have people coming only from environmental sciences, people who are only tech, there's a little bit of an impedance mismatch sometimes in the dialogue.

So even simple things like saying, for example, sustainable something versus something for sustainability, right? Do we have sustainable AI, meaning AI systems that are sustainable in themselves? And we call that the footprint, or AI systems that are AI for sustainability, meaning the output of the system can actually help you with sustainable outcomes and we call that the handprint, right?

So the concept of footprint and handprint are not necessarily well understood within the networking and software spaces in my experience.

And I feel there are fundamentally, when I reduce the footprint, you want to improve or enhance or grow the handprint.

Anne Currie: Yeah. Yeah, I agree. It's not a well known phraseology. we use this in Building Green Software. It is a bit counterintuitive in that kind of like footprint, bad hand print good. But it's like, why is the foot so bad if you are in, if you are judging by your, reverse of the football analogy where hand is bad, foot is good, but in green software, we talk about footprint, foot being bad, and handprint, hand being good.

Carlos Pignataro: That's right. In football, you would get a red card and in green software, you're going to get a green card.

Anne Currie: Indeed. Indeed. But I don't terminology because I think it's a tad confusing, but it is well known that is, and that's what we're talking about here, which is fine.

Carlos Pignataro: Thank you. No, for sure. And I'm going to bring back also something that you said before, which is the networking industry have been thinking for quite some time in many sustainability aspects and at different levels, at the cheap level, as I was mentioning, at the power efficiency, there's metrics, so you cannot improve what you cannot measure trackers.

So we have power efficiency for data centers and so on. And that becomes much more and more important because the amount of electricity that gets consumed by networks and particularly data centers these days keeps growing and keeps growing. There's many things that we can learn from the way in which we design protocols.

Part of the work that I've done historically is protocol design within the Internet Engineering Task Force, the IETF. Reading RFCs and things like that. And there's very interesting work on protocols for the Internet of Things. And that is interesting, Anne, because IoT Internet of Things devices, just by their use case, need to minimize power consumption, need to be alive for years and years on battery power.

So protocol definitions evolved so that protocols are a lot less chatty. There's a lot less back and forth and wake up. Devices can actually go to different levels of sleep. The same way that we have sleep levels in the laptops that we're using you and I right now, and on the phones that on the handheld that we have. There's less of that in some of the networking areas, and that is an active area of research and development. How to bring dormant states, less chatty protocols into some of the networking arena.

Anne Currie: That is something that I, really liked in this paper. So stepping back for a minute, there's the IETF and the IRTF, which are both parts of the same organization. The IETF tends to focus on protocols, designing protocols, but actually, and the way it does that is it, talks about a famous, Definition of how, it approaches things, which is rough consensus working code, which is based on the fact that actually it's very hard to tell what's going to work in networking because it's so complicated until you actually get something out there and see whether it works or not.

And IRTF, which is a lot of what these papers are, is about kind of research and thinking and, but it's hard to marry those two things I would imagine in many ways, because the whole point is that you can't necessarily just think through which network protocol is going to work and which are not.

You almost have to suck it and see. But one of the things that you pointed out in your paper, which I liked, was there are already protocols out there that are working, that are achieving what we want to achieve here. There are already protocols for, Internet of Things, which are out there working. And it would be great if people looked at them and said, "What works there and what doesn't work there?"

"How can we apply that learning in other environments?" I think that's what you're saying.

Carlos Pignataro: A million percent. That is actually in a much more eloquent way with a couple of the very important points that we try to convey in the paper. Thank you very much, Anne, for explaining IRTF, IETF, ISTAR, it's, I wasn't sure if we want to, how boring that was, but it was actually very useful. So you put both of them in context.

Anne Currie: Well, what I quite like to do all the time when we're doing it, what we know works in terms of delivering good stuff in the human, in human history is to look at stuff that works in a similar but not identical field and say, let's steal some of those ideas. And something else I liked in your papers was when you talked about CDNs.

And the ideas in CDNs, Content Delivery Networks, if people aren't familiar with them, they are what I think one of the most interesting architectural approaches in networking and in technology above the network as well. And there were tons of ideas that feel that they could be borrowed. And you said the same thing in your, paper.

What was your, what's your thinking on CDNs?

Carlos Pignataro: Yeah, no, it's, exactly that. Thank you for it. Because the main point is exactly that. There's many areas that there's already been deployment, not only research and not only development and not only test and Q& A, but deployment, that can be applied to new things that we need to do today, right? So Spoton on that, IoT is one of them. And because naturally there's not necessarily a very smooth bridge in my experience between research and standardization and actual deployment and running code is a little bit bumpy. So having examples and use cases that work, that we can apply to the problems between code that we have today is critical.

When we look at the most dynamic and complex networks, I really look at CDNs. Because it's a network that is actually focused on delivering the content and in a CDN, it's incredibly critical to number one, replicate content near the receiver, right? So that you don't have to stream from transatlantic, but don't over replicate if there's not a lot of listeners and receivers. So the equations can really, help you to minimize the overall end to end system electricity, consumption, and maximize efficiency just because of. What to replicate, where to replicate it, at what times do we do this when we have a signal that the electricity feed that we have is coming from renewables. It's one of the systems that really gives you the flexibility to implement all of the things that we discuss in a paper. And if you allow me again to extrapolate a little bit more, I frankly think that talking about green and talking about sustainability, we can actually extrapolate further. And look more into what nature does and try to understand and replicate that in some of our systems.

The fact that our laptops hibernate, the first there were animals that's where the word come from, during the winter, they were saving mode, right? It was a bear in saving mode and we have a laptop in saving mode. And next we're going to have a data center cluster in saving mode potentially. And. Many, ways in which if we look at the amount of energy that our brain uses versus an LLM system uses, there's clearly a huge, ginormous opportunity for improvement.

Anne Currie: Yeah. Yeah, that's true. But yes, that actually it's interesting that you mentioned LLMs and AI there, although not, directly, but I'm very interested in networking and LLMs and how those are going to be merged together in a green way. I, as I said, I'm a huge fan of CDNs. So as you said, CDNs use buffering effectively close to users to do two things, to mean that every time somebody that, if you've got somebody in London and they want to look at a huge asset that was served from the US, maybe an episode of Game of Thrones. I still use Game of Thrones, even though it was a bit out of date. It's better to have that episode, one copy of that episode, move over the Atlantic and be cached somewhere local to the user in the UK.

And then they, and then all the, users there just take it over a shorter distance rather than have to take it all the way from the US, as you said. There's another benefit, which is that you can move that giant asset at times when the grid is, the internet is not busy, so it flattens, something that you've said in your papers you talked a lot about was the efficiency of flattening peaks in load, peaks in demand.

It's much better if you could spread demand, find some clever way of spreading demand out so that you don't have peaks that you have to provision for, because that means you need more equipment and more of everything. It's not as resilient and it's more expensive. So, CDNs are fantastic from that perspective, but AI feels like it could potentially benefit, AI inference feels like it could potentially benefit from CDNs as well.

If you could try and cache responses that are common questions so that you weren't having to run everything, sorry, I'm taking you off networking now. I don't know if you've had any thoughts about AI and networking and CDNs or anything like that. 

Carlos Pignataro: I work a lot on AI these days and I definitely, have thoughts. I think number one that, and thank you for, the bunny trail towards where the dialogue takes us and things are relevant, right? The way in which we actually train systems today can like immensely be improved and whether that's by some mechanism of incremental caching so that you don't have to relearn everything as you actually tweak the model and things like that, CDN like, absolutely.

And in another way in which I really think about it, and frankly, particularly with a couple of startups that I'm either working on or following or seeing is, do we want a, like the typical army Swiss knife B2C business to consumer that can actually solve everything and we need three cities worth of electricity to train? Or do we define more constrained SLMs instead of LLMs, small language models that are a lot more domain specific and a lot more domain shifted and more B2B potentially, business to business type. And regardless, I think that going back to sustainable X, versus X for sustainability, I always like to do like two by twos or X, Y.

And I think that AI as a broad technology from whatever, from machine learning and computer vision and, has not significantly into going AI for sustainability, we have Google maps today that can actually give me the most sustainable travel, fuel efficient route, and I go in to book my flights and I see carbons of each one of those.

And there's a lot of AI that is applied for sustainability. There has not been enough, or I should say, there's a enormous upside and opportunity for sustainable AI, right?

Right. So in the backwards lingo that we were using. A lot of handprint, not enough reduction of footprint.

And I think a lot of methods that we know from other domains, like CDNs, that we can apply to inference and learning of models. Absolutely. Please let's do.

Anne Currie: Yeah, I mean, in many ways, it feels like it is the lesson to take away from the internet, which is that it's really hard to make things work. You have to, like working code. Working code absolutely is the king. So if you can take someone else's working code and apply it in your situation, that's a great idea.

 

Carlos Pignataro: Exactly.

Exactly. A million percent. And one thing, Anne, that I'm going to mention now, because I fear that the way in which we're choosing conversational forking paths, I'm going to forget. So, so. Because it's important. It's a call to action. And one of the things that you mentioned, I mentioned, is how much more resources and material exists.

And really my call to action is to go to learn.greensoftware.foundation and start with the green software practitioner curricula. It is such an incredibly well packaged. Set of modules that go through a lot of the fundamentals and demystifies it provides lexicon, it demystifies, it talks about carbon.

And one of the important ways in which I wanna make this actionable is to really encourage any listener to, is super easy: learn.greensoftware.foundation.

Anne Currie: I totally second that. And I really, I apologize, Carlos. We've gone all over the place on our discussion today. So you're quite right to stop me and make sure that's a very important message got through. So, I mean, we are now coming towards the end of it. Is there anything else that you want to tell?

There's tons of interesting stuff in your two papers that are well worth reading. You don't have to feel that it's too, that it's too difficult. The papers are quite accessible. Is there anything that we haven't talked about that you would like to talk about?

Carlos Pignataro: Thank you, Anne first of all, thank you for actually reading the papers and, actually not only reading, but really reading because you actually distilled some of the fundamental principles that we, and I wanted to convey. More than anything, what's needed to drive green software is our full commitment and bringing, like you were talking about, like I was mentioning, our values and whole selves to the one which we call and the one which we do finger to keyboard.

This is a very multidisciplinary nuanced area. And after leading in Cisco technology and data for the engineering sustainability team, we really don't know what we don't know. And for me, learning and think humbly in every conversation is fundamental goal. One of the things that I love about the approach that the GSF is taking with, SCI is that it's data driven, right?

Let's get matrix, let the data as opposed to myths drive the conversation. And to continue to stay together because the ecosystem is multidisciplinary, and we all learn a little bit from each other and reusing and leveraging particularly code and principles that you talk so, so very well in your book and those principles, that code, bring it in our insertion points within the ecosystem, whether it's at the vendor or if we're cloning in a public repository and make some changes, or if we're thinking about the mixed networking protocol or networking operations.

Anne Currie: Yes, very true. So I think that's a very good place to be finishing up because we've pretty much come to the end of our episode. And I have one final question for you, which is where can listeners go to if they want to find out more about you? Obviously links to the papers will be in the show notes, so you can, you should, I strongly recommend you to read them, but where else can people find out about you?

Carlos Pignataro: Hey, thank you very much, Anne. LinkedIn is an easy place to go. And I'm always open to any connection and any messages. My website, you can check out also bluefern.consulting and has my email, has my contact, I really, I don't just say that IQ and you respond, I respond. So, super happy to continue the conversation and continue engaging.

Anne Currie: Excellent. That's very good to hear. I'm sure that lots of our listeners will reach out and talk to you, but certainly they should be reading your papers and they should be connecting to you on LinkedIn or looking at your LinkedIn, following your LinkedIn. And so thank you very much for being on this episode.

It's been a fascinating episode, a deep dive into networking and all the... networking is not so, it's been very interesting because a lot of the concepts are also applied to non-networking software. All the ideas and the overlap in CDNs, which really are the concept I think that's best suited to environmental sustainability and aligning with renewable power in the long run. And a final reminder to all our listeners that the resources for this episode are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. So see you all soon. Bye for now.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!



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]]> In this episode of Environment Variables, Anne Currie welcomes Carlos Pignataro, a leading expert in sustainable network architecture, to explore how networks can balance energy efficiency with performance and resilience. Carlos shares insights from his career at Cisco and beyond, including strategies for reducing emissions through dynamic software principles, energy-aware networking, and leveraging technologies like IoT and Content Delivery Networks (CDNs). They discuss practical applications, the alignment of green practices with business interests, and the role of multidisciplinary collaboration in driving innovation. Tune in for actionable advice and forward-thinking perspectives on making networks greener while enhancing their capabilities.

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TRANSCRIPT BELOW:

Carlos Pignataro: Many of the things that we do with a little bit of spin, hugely benefit the bottom line by cutting down costs, hugely benefiting the world by lowering emissions. I'm going to run this batch job whenever there's renewable. I'm going to turn off the lights and the APL, the access points on the ceiling automatically when there's no presence.

Basic things that make a difference.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne Currie: Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Anne Currie, and today we're joined by Carlos Pignataro, a leading voice in sustainable network architecture. Carlos has over 20 years of experience in technology, innovation, and strategic thinking, holding key roles at Cisco, including head of technology and data for Cisco's engineering sustainability.

Currently, he's a mentor in residence at Duke University New Ventures, adjunct facility member at NC State University. And he is the founder and principal at Blue Fern Consulting. In this episode, we'll dive into Carlos' work on architecting networks for environmental sustainability. He's contributed to cutting edge research on how networks, which are critical to our connected lives, can balance energy efficiency with performance and resilience.

How can the networking industry innovate without compromising the planet? What trade-offs do we have to consider? Things like uptime, speed, against, or as well as, environmental responsibility. Let's explore these questions and more with Carlos. So welcome Carlos. Can you tell us a little bit about yourself?

Carlos Pignataro: Thank you very much, Anne. I am so excited to be here, first of all, and I'm happy to tell you a little bit about myself, even though you covered a lot, that was a very kind intro. I appreciate it. My favorite number is 27. My favorite color is aquamarine, just for completeness. But on a more, on an equally serious note, Important for my introduction is to say that I have passions for technology and for sustainability.

And I look at tech as a means to an end and making things better is one of the ends. I've been involved, as you said, as you mentioned in building networks, computer networks, internet infrastructure, data centers. I have also been involved in other uses of technology, such as technoconservation or conservation technologies, protecting endangered species, such as rhinos in different countries in Africa or in India from poaching using IOT, Internet of Things technology, I've been involved in building data models and information models for recycling and overall circularity. So I try to build my overall experiential breadth through trying different aspects of technology and sustainability is one on which I'm super passionate about.

Anne Currie: Excellent. That's very good to hear. And that's why you're here today. So just a little bit about myself, because I'm not always the host. I'm a guest host for Environment Variables. My name is Anne Currie. I am, I've been in the tech industry for about 30 years. I am one of the co authors of O'Reilly's new book, Building Green Software, which covers a lot of, actually covers a lot of what Carlos and I will be talking about today, cause there is a networking chapter in it.

And I strongly recommend it to everybody who's listening to the podcast. I am also the CEO of learning and development company, Strategically Green. We do a lot of workshops to help people push knowledge and engagement with green subjects within their own businesses. So touch me up on LinkedIn if you're interested in that kind of thing.

So before we start in, we dive into today's content is a reminder that everything we talk about today will be linked to in the show notes below. So feel free to have a look, read through, read as we go, read afterwards, whatever, but the data is all there. So, let's kick off with an introductory question, Carlos.

How did you become a Green Software Foundation champion? What are your goals as a champion?

Carlos Pignataro: Thank you very much. And it's a very meaningful question. When I looked at green software champion, when I was exposed to the acronym, to the three words, actually, and the GSF acronym, I couldn't help but looking at each one of the words and understand that each one had a very, profound meaning for me. Number one, green is something that encapsulates what you and your book and all of us are trying to push our industry and our different industries towards. Software is corner of what we really do day in day out and our expertise where we can actually make a difference is where we live. And in champion, I really always wanted to be a champion.

And I try soccer. I try tennis. In Argentina, I have, which is where I come from, I have a very, below average soccer scores, no, jokes aside, I, champion is such an important word because I look at it as, the first followers, and I am very moved by the way in which we create a movement on what Derek Sivers describes into being the first follower. And having these champions are like reflectors. It seems this is a little bit of networking, BGP reflectors of the message. So we all different have insertions in different parts of the ecosystem, whether it's within corporate, within different software, different repositories, whether it's different standard bodies and being a champion means being a follower and reflector of that larger message and echo it within the all the different places that we live.

So it really aligns very deeply with my passions and frankly, something that I see you as I follow you as well, is like I see the three words very clearly and I don't know how you feel, Anne, but to me it's always a little bit of a work in progress. What we have achieved, I'll play that back to you a little bit, but as a question, but to me it's like we have achieved a lot of awareness and we have achieved a lot of education and we have achieved the realization that there's a lot more and a lot more to actually do as a champion. What is, I don't know,

Anne Currie: I think you're, very right there. We've done a lot. Things are a lot better than they were 10 years ago. Vastly better. And I feel constantly buoyed up by that, but there's just so much to do still. I mean, still, when I talk to most people outside of the immediate movement, folk don't realize that actually there is a lot of good stuff that the tech industry can do to cut carbon emissions.

I speak to folk in tech all the time who are really interested in climate change and doing their bit, and they often focus directly on individual change they can make, like becoming a vegan or something like that, which is fine. But that's as nothing compared to the far more scalable change they can make through their jobs.

Through being a software engineer, there's an enormous change that we can make. There's an enormous improvement that we can make there. And it's nice when people realize, because folk do want to fix things. They do want to make a difference. And a lot of folks don't realize.

Carlos Pignataro: A hundred percent. It's so interesting as you were saying that I was, and picturing myself the concept of bring your whole self to work and the fact that we are individuals with a set of values and we can project them in different personas that we have. And for me, it was actually a part of my professional growth and realization earlier on as an engineer that I could actually bring my values to work as well. And exactly like you said, that's when the difference really compounds. It's, not like I have a nights and weekends green version. I can apply that in my day job, not only nights and weekends. And also what I find super monumental and I applaud what you all do and what we do here is there's so much more resources, so many more resources available for anyone who says, "yeah, I want to learn, and apply it."

Anne Currie: Yes. Oh yeah. The number of resources are really, is really taking off, which is amazing. I like what you're saying about bringing your whole self to work. The good thing about building green software and being more carbon aware at work, if you are a tech company, is that, I mean, a lot of people feel, "Oh, well, it's a bit unprofessional because I'm bringing something to work and actually that's, I'm asking the business to do something that is not in their best interest to do."

When it comes to cutting carbon emissions, it is in the interest of the business to do that. It cuts costs. It makes the business more resilient. It's, it makes it more future proof to the way that the energy system is going. Eventually, folk are going to have to be ready to run on renewables because that is the new form of energy.

I mean, people can do this in a way that is against the interests of the business. If you decide that you're going to rewrite all these systems in Rust, that might well be against the interests of your business. But cutting your energy use, cutting your carbon emissions in half by just being more clever and smart and modern about the way you operate systems so they're more efficient and more secure and more cost effective.

That is in the best interest of your business. That's not going against it. And I think we do need to keep constantly hammering that message home. 

Carlos Pignataro: Absolutely. It's a message in my corporate tenure, within big tech. That's one of the key messages that resonated inside the company, resonated with customers, which is good for the world and good for the business and finding these things it's. And that's one that actually I don't think is a marketing tag.

I run numbers on, "shall I do this? Does it make sense or not?" And just like you say, many of the things that we do with a little bit of spin hugely benefit bottom line by cutting down costs, hugely benefiting the world by lowering emissions. I'm going to run this batch job whenever there's renewable. I'm going to turn off the lights and the APL, the access points on the ceiling automatically when there's no presence. Basic things that make a difference.

Anne Currie: Yeah. If you go back to the, I would say almost the inception of the modern thinking about being more efficient in data centers is the work that Google and Sun were doing at the beginning of this century around containers and orchestration, the use of that precursor to Kubernetes, their Borg orchestration system. That wasn't about cutting carbon emissions.

That was about cutting the cost of operating systems and improving the resilience of operating systems. Those two things are completely aligned with carbon reductions. It's the way of being more efficient, being more resilient, being more, usually adopting modern operational practices like auto scaling.

Do all the, deliver all the things. It's 

Carlos Pignataro: And I'll tell you one thing, if you don't mind, allow me extrapolate from what you're saying, the idea of actually having software, green software principles, really as a, in our workspace, visible and very aware, is so incredibly important because the virtualization concept that you're talking about, Kubernetes and so on, are things that we apply to fight in the networking world, are things that we apply to any type of SFC software function, virtualization, NFVs, et cetera. And trying to bring a software more dynamic approach of how we think about it, to think that are traditionally more, I have a big router and a big switch and a big antenna, softwarizing, if you will, the thinking and doing that with the principles that you can have within, things like that are at the reach of all of us, like some of the courses on green awareness, building those principles into any type of software practice is such a win/win.

Anne Currie: Yeah, absolutely. There's no reason for people to go, "Oh, I love the idea, but it's actually going to hurt my business." If it hurts your business, you're not doing it right. If you think it's going to hurt your business, don't do it that way. Do it a way that is materially aligned with your business. That will scale better, will deliver more value, and you'll actually make it happen.

So there is no reason why being green should hurt. If it does, you're probably doing it wrong. Stop.

Carlos Pignataro: And I tell you, one of the things that we often think about, and I thought about very much in my CTO roles or in when I do standardization is how can we actually make some of these things codified, reusable, repeatable. Someone can actually learn, someone can actually use, whether it's a standard or whether it's a certification or, and do that in the context of. Not only the architecting of software and networks, but also the operationalization of networks. One small example that I can share is work that we're doing with Alex Clem on sustainable network operations, which is an IEEE, SIG or special interest group. And the interesting thing there is that when we look at the overall life cycle of networking and software, we have the use phase within the life cycle that focuses on the specific operations of the network, operational aspects of networks. There's a lot to gain in terms of managing energy efficiency and carbon awareness and carbon efficiency. So how can, within our different insertion points, within the life cycle, some are earlier, closer to manufacturing networking equipment or designing chips, some are closer to doing architecting networks and actually designing networks and operations, and then plus plusing networks, right? Like updating and upgrading equipment in a more circular, sustainable way. Each one of those areas has a very strong sustainable benefit that we can actually bring. These are the reasons, and honestly, Anne, hearing you talk are the reasons why, going back to your earlier question, why I got really drawn into this green software champion concept.

Anne Currie: It's interesting. It's, I'm going to talk about something you don't, you wouldn't necessarily speak specifically at the moment, but one of the things that interests me, and we mentioned it a lot in Building Green Software. Is that networking is one of the few areas of the tech industry where there's already been a lot of thoughts, which is directly aligned with energy saving.

That's, that networking has a concept of, is it bits per watt or watts per bit?

Carlos Pignataro: Yeah. And the interesting thing there is that there's been a lot of research and there's still ongoing discussion into how much things like energy over a number of throughput, like Watts per bit or, it's over megabit, how useful something like that is because, which I think is actually useful in many areas.

And there's, it's still an ongoing discussion about it in some standard groups, because if you take, for example, a core in a router, piece of equipment of the guts of the internet. And you have it powered up without traffic that is consuming about 80 percent of the power.

Anne Currie: Which is astonishing, isn't it?

Carlos Pignataro: It's crazy. And then with power, 95, right? So the proportionality has a smaller slope.

Anne Currie: So actually let's move on to one of our, some of our discussion points from today. So we're going to be talking about two papers that you were involved with and, co-authored, contributed to the first one is an article entitled Challenges and Opportunities in Management for Green Networking, which explores the environmental impacts of networking technology.

Noting that while, that basically it says networking is great, there's loads of things come as of networking that can cut travel, that can, could significantly contribute to reducing carbon footprints. Networking is an amazing thing. But it also uses a lot of power. So if there's some way that we can get all the wins and reduce some of the losses and some of the waste, that would be fantastically good.

But at the same time, we don't want to lose any of the wins. So do you want to talk a little bit about the article? 

Carlos Pignataro: Absolutely. Thank you so much. I'm going to start, Anne, by honing into a couple of keywords that you mentioned, which are important in my mind. And the first one is the one about trade-offs. It's potentially tempting to say, "I'm going to move a part of the system to another part, which resides outside boundary conditions, and therefore things are more green." And it's one of the learnings that I've seen. I've worked in a number of technologies in my career and sustainability, environmental sustainability has been the more new one by far. We're looking at the system with broad boundary conditions. It's not like saying "I'm going to replace all the lights with LEDs" because we have material extraction to actually make the new light bulbs and we have to dispose of the old ones.

So if we look in the broader sense, we've really had trade-offs. Imagine to make it very practical that we have a link between two routers or two devices. If we add more links, we can have more redundancy. And if we add more routers to actually duplicate that and more links between them, we have even more redundancy. And that redundancy or resiliency improves asymptotically. It gets to a point in which I add more. And while my carbon emissions continue going up linearly, there's no benefit to redundancy and in fact, the system can become a little bit more brittle. So one of the concepts that we kind of talked about a little bit, we have a multi goal scenario.

We are optimizing for two different goals at the same time, one of which is resiliency and performance and traditional business metrics, and the other one is for sustainability at the same time. The main area where I feel that makes such a, strong difference is in Moving to automation, moving a lot of these processes to automated processes. It's one in which we can actually get to an optimal point in sustainability while lowering the extra links for redundancy based on the needed traffic at the time or the seasonality of the traffic or the seasonality of the requirement. So that's one of the key pieces of that work. And let me explore, if you don't mind, another area of this paper, which I think is very, relevant and important, which is, which really is how we define terms. It's a Socrates quote that the beginning of wisdom starts with the definition of terms. And one of the things that I found is that in such a multidisciplinary field in which you have people coming only from environmental sciences, people who are only tech, there's a little bit of an impedance mismatch sometimes in the dialogue.

So even simple things like saying, for example, sustainable something versus something for sustainability, right? Do we have sustainable AI, meaning AI systems that are sustainable in themselves? And we call that the footprint, or AI systems that are AI for sustainability, meaning the output of the system can actually help you with sustainable outcomes and we call that the handprint, right?

So the concept of footprint and handprint are not necessarily well understood within the networking and software spaces in my experience.

And I feel there are fundamentally, when I reduce the footprint, you want to improve or enhance or grow the handprint.

Anne Currie: Yeah. Yeah, I agree. It's not a well known phraseology. we use this in Building Green Software. It is a bit counterintuitive in that kind of like footprint, bad hand print good. But it's like, why is the foot so bad if you are in, if you are judging by your, reverse of the football analogy where hand is bad, foot is good, but in green software, we talk about footprint, foot being bad, and handprint, hand being good.

Carlos Pignataro: That's right. In football, you would get a red card and in green software, you're going to get a green card.

Anne Currie: Indeed. Indeed. But I don't terminology because I think it's a tad confusing, but it is well known that is, and that's what we're talking about here, which is fine.

Carlos Pignataro: Thank you. No, for sure. And I'm going to bring back also something that you said before, which is the networking industry have been thinking for quite some time in many sustainability aspects and at different levels, at the cheap level, as I was mentioning, at the power efficiency, there's metrics, so you cannot improve what you cannot measure trackers.

So we have power efficiency for data centers and so on. And that becomes much more and more important because the amount of electricity that gets consumed by networks and particularly data centers these days keeps growing and keeps growing. There's many things that we can learn from the way in which we design protocols.

Part of the work that I've done historically is protocol design within the Internet Engineering Task Force, the IETF. Reading RFCs and things like that. And there's very interesting work on protocols for the Internet of Things. And that is interesting, Anne, because IoT Internet of Things devices, just by their use case, need to minimize power consumption, need to be alive for years and years on battery power.

So protocol definitions evolved so that protocols are a lot less chatty. There's a lot less back and forth and wake up. Devices can actually go to different levels of sleep. The same way that we have sleep levels in the laptops that we're using you and I right now, and on the phones that on the handheld that we have. There's less of that in some of the networking areas, and that is an active area of research and development. How to bring dormant states, less chatty protocols into some of the networking arena.

Anne Currie: That is something that I, really liked in this paper. So stepping back for a minute, there's the IETF and the IRTF, which are both parts of the same organization. The IETF tends to focus on protocols, designing protocols, but actually, and the way it does that is it, talks about a famous, Definition of how, it approaches things, which is rough consensus working code, which is based on the fact that actually it's very hard to tell what's going to work in networking because it's so complicated until you actually get something out there and see whether it works or not.

And IRTF, which is a lot of what these papers are, is about kind of research and thinking and, but it's hard to marry those two things I would imagine in many ways, because the whole point is that you can't necessarily just think through which network protocol is going to work and which are not.

You almost have to suck it and see. But one of the things that you pointed out in your paper, which I liked, was there are already protocols out there that are working, that are achieving what we want to achieve here. There are already protocols for, Internet of Things, which are out there working. And it would be great if people looked at them and said, "What works there and what doesn't work there?"

"How can we apply that learning in other environments?" I think that's what you're saying.

Carlos Pignataro: A million percent. That is actually in a much more eloquent way with a couple of the very important points that we try to convey in the paper. Thank you very much, Anne, for explaining IRTF, IETF, ISTAR, it's, I wasn't sure if we want to, how boring that was, but it was actually very useful. So you put both of them in context.

Anne Currie: Well, what I quite like to do all the time when we're doing it, what we know works in terms of delivering good stuff in the human, in human history is to look at stuff that works in a similar but not identical field and say, let's steal some of those ideas. And something else I liked in your papers was when you talked about CDNs.

And the ideas in CDNs, Content Delivery Networks, if people aren't familiar with them, they are what I think one of the most interesting architectural approaches in networking and in technology above the network as well. And there were tons of ideas that feel that they could be borrowed. And you said the same thing in your, paper.

What was your, what's your thinking on CDNs?

Carlos Pignataro: Yeah, no, it's, exactly that. Thank you for it. Because the main point is exactly that. There's many areas that there's already been deployment, not only research and not only development and not only test and Q& A, but deployment, that can be applied to new things that we need to do today, right? So Spoton on that, IoT is one of them. And because naturally there's not necessarily a very smooth bridge in my experience between research and standardization and actual deployment and running code is a little bit bumpy. So having examples and use cases that work, that we can apply to the problems between code that we have today is critical.

When we look at the most dynamic and complex networks, I really look at CDNs. Because it's a network that is actually focused on delivering the content and in a CDN, it's incredibly critical to number one, replicate content near the receiver, right? So that you don't have to stream from transatlantic, but don't over replicate if there's not a lot of listeners and receivers. So the equations can really, help you to minimize the overall end to end system electricity, consumption, and maximize efficiency just because of. What to replicate, where to replicate it, at what times do we do this when we have a signal that the electricity feed that we have is coming from renewables. It's one of the systems that really gives you the flexibility to implement all of the things that we discuss in a paper. And if you allow me again to extrapolate a little bit more, I frankly think that talking about green and talking about sustainability, we can actually extrapolate further. And look more into what nature does and try to understand and replicate that in some of our systems.

The fact that our laptops hibernate, the first there were animals that's where the word come from, during the winter, they were saving mode, right? It was a bear in saving mode and we have a laptop in saving mode. And next we're going to have a data center cluster in saving mode potentially. And. Many, ways in which if we look at the amount of energy that our brain uses versus an LLM system uses, there's clearly a huge, ginormous opportunity for improvement.

Anne Currie: Yeah. Yeah, that's true. But yes, that actually it's interesting that you mentioned LLMs and AI there, although not, directly, but I'm very interested in networking and LLMs and how those are going to be merged together in a green way. I, as I said, I'm a huge fan of CDNs. So as you said, CDNs use buffering effectively close to users to do two things, to mean that every time somebody that, if you've got somebody in London and they want to look at a huge asset that was served from the US, maybe an episode of Game of Thrones. I still use Game of Thrones, even though it was a bit out of date. It's better to have that episode, one copy of that episode, move over the Atlantic and be cached somewhere local to the user in the UK.

And then they, and then all the, users there just take it over a shorter distance rather than have to take it all the way from the US, as you said. There's another benefit, which is that you can move that giant asset at times when the grid is, the internet is not busy, so it flattens, something that you've said in your papers you talked a lot about was the efficiency of flattening peaks in load, peaks in demand.

It's much better if you could spread demand, find some clever way of spreading demand out so that you don't have peaks that you have to provision for, because that means you need more equipment and more of everything. It's not as resilient and it's more expensive. So, CDNs are fantastic from that perspective, but AI feels like it could potentially benefit, AI inference feels like it could potentially benefit from CDNs as well.

If you could try and cache responses that are common questions so that you weren't having to run everything, sorry, I'm taking you off networking now. I don't know if you've had any thoughts about AI and networking and CDNs or anything like that. 

Carlos Pignataro: I work a lot on AI these days and I definitely, have thoughts. I think number one that, and thank you for, the bunny trail towards where the dialogue takes us and things are relevant, right? The way in which we actually train systems today can like immensely be improved and whether that's by some mechanism of incremental caching so that you don't have to relearn everything as you actually tweak the model and things like that, CDN like, absolutely.

And in another way in which I really think about it, and frankly, particularly with a couple of startups that I'm either working on or following or seeing is, do we want a, like the typical army Swiss knife B2C business to consumer that can actually solve everything and we need three cities worth of electricity to train? Or do we define more constrained SLMs instead of LLMs, small language models that are a lot more domain specific and a lot more domain shifted and more B2B potentially, business to business type. And regardless, I think that going back to sustainable X, versus X for sustainability, I always like to do like two by twos or X, Y.

And I think that AI as a broad technology from whatever, from machine learning and computer vision and, has not significantly into going AI for sustainability, we have Google maps today that can actually give me the most sustainable travel, fuel efficient route, and I go in to book my flights and I see carbons of each one of those.

And there's a lot of AI that is applied for sustainability. There has not been enough, or I should say, there's a enormous upside and opportunity for sustainable AI, right?

Right. So in the backwards lingo that we were using. A lot of handprint, not enough reduction of footprint.

And I think a lot of methods that we know from other domains, like CDNs, that we can apply to inference and learning of models. Absolutely. Please let's do.

Anne Currie: Yeah, I mean, in many ways, it feels like it is the lesson to take away from the internet, which is that it's really hard to make things work. You have to, like working code. Working code absolutely is the king. So if you can take someone else's working code and apply it in your situation, that's a great idea.

 

Carlos Pignataro: Exactly.

Exactly. A million percent. And one thing, Anne, that I'm going to mention now, because I fear that the way in which we're choosing conversational forking paths, I'm going to forget. So, so. Because it's important. It's a call to action. And one of the things that you mentioned, I mentioned, is how much more resources and material exists.

And really my call to action is to go to learn.greensoftware.foundation and start with the green software practitioner curricula. It is such an incredibly well packaged. Set of modules that go through a lot of the fundamentals and demystifies it provides lexicon, it demystifies, it talks about carbon.

And one of the important ways in which I wanna make this actionable is to really encourage any listener to, is super easy: learn.greensoftware.foundation.

Anne Currie: I totally second that. And I really, I apologize, Carlos. We've gone all over the place on our discussion today. So you're quite right to stop me and make sure that's a very important message got through. So, I mean, we are now coming towards the end of it. Is there anything else that you want to tell?

There's tons of interesting stuff in your two papers that are well worth reading. You don't have to feel that it's too, that it's too difficult. The papers are quite accessible. Is there anything that we haven't talked about that you would like to talk about?

Carlos Pignataro: Thank you, Anne first of all, thank you for actually reading the papers and, actually not only reading, but really reading because you actually distilled some of the fundamental principles that we, and I wanted to convey. More than anything, what's needed to drive green software is our full commitment and bringing, like you were talking about, like I was mentioning, our values and whole selves to the one which we call and the one which we do finger to keyboard.

This is a very multidisciplinary nuanced area. And after leading in Cisco technology and data for the engineering sustainability team, we really don't know what we don't know. And for me, learning and think humbly in every conversation is fundamental goal. One of the things that I love about the approach that the GSF is taking with, SCI is that it's data driven, right?

Let's get matrix, let the data as opposed to myths drive the conversation. And to continue to stay together because the ecosystem is multidisciplinary, and we all learn a little bit from each other and reusing and leveraging particularly code and principles that you talk so, so very well in your book and those principles, that code, bring it in our insertion points within the ecosystem, whether it's at the vendor or if we're cloning in a public repository and make some changes, or if we're thinking about the mixed networking protocol or networking operations.

Anne Currie: Yes, very true. So I think that's a very good place to be finishing up because we've pretty much come to the end of our episode. And I have one final question for you, which is where can listeners go to if they want to find out more about you? Obviously links to the papers will be in the show notes, so you can, you should, I strongly recommend you to read them, but where else can people find out about you?

Carlos Pignataro: Hey, thank you very much, Anne. LinkedIn is an easy place to go. And I'm always open to any connection and any messages. My website, you can check out also bluefern.consulting and has my email, has my contact, I really, I don't just say that IQ and you respond, I respond. So, super happy to continue the conversation and continue engaging.

Anne Currie: Excellent. That's very good to hear. I'm sure that lots of our listeners will reach out and talk to you, but certainly they should be reading your papers and they should be connecting to you on LinkedIn or looking at your LinkedIn, following your LinkedIn. And so thank you very much for being on this episode.

It's been a fascinating episode, a deep dive into networking and all the... networking is not so, it's been very interesting because a lot of the concepts are also applied to non-networking software. All the ideas and the overlap in CDNs, which really are the concept I think that's best suited to environmental sustainability and aligning with renewable power in the long run. And a final reminder to all our listeners that the resources for this episode are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. So see you all soon. Bye for now.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!



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]]> The Quantum Entanglement of Software Sustainability with Wilco Burggraaf The Quantum Entanglement of Software Sustainability with Wilco Burggraaf Thu, 05 Dec 2024 07:00:00 GMT 52:23 70y7pvz0 false https://podcasts.castplus.fm/e/x8vqlw6n-the-quantum-entanglement-of-software-sustainability-with-wilco-burggraaf 68dc7d0ed5c8a1f9b326f427 68dc7cef46a2532cdd8d1c23 In this episode of Environment Variables, host Anne Currie speaks to Wilco Burggraaf, a lead green practitioner and architect at HighTech Innovators, for an engaging discussion on integrating sustainability into software development. Wilco shares his journey into green software, the inspiration behind his innovative workshops, and his efforts to build a vibrant green tech community in the Netherlands. The conversation explores his articles on the Software Carbon Intensity standard, the complexities of balancing micro and macro sustainability goals, and the synergy between FinOps and green software. Tune in for actionable insights and strategies to make greener choices in tech while aligning sustainability with business goals. 91 In this episode of Environment Variables, host Anne Currie speaks to Wilco Burggraaf, a lead green practitioner and architect at HighTech Innovators, for an engaging discussion on integrating sustainability into software development. Wilco shares his journey into green software, the inspiration behind his innovative workshops, and his efforts to build a vibrant green tech community in the Netherlands. The conversation explores his articles on the Software Carbon Intensity standard, the complexities of balancing micro and macro sustainability goals, and the synergy between FinOps and green software. Tune in for actionable insights and strategies to make greener choices in tech while aligning sustainability with business goals.

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Transcript below:

Wilco: At some point I came to the conclusion, like, okay, we can measure a lot of things, we can have all these metrics but at some point the numbers are not going to change outcomes. Decisions do.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field, who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne: Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host today, Anne Currie. And joining us is Wilco Burggraaf, lead green practitioner and green architect at HighTech Innovators. Wilco brings a wealth of experience in software development, been over 20 years in the industry, and is an active Green Software Foundation champion, and we'll be talking a lot about that today.

So he cares a great deal about integrating sustainable practices directly into the code and architecture of software, helping to make greener choices not only possible, but essential in tech. And in this episode, well, this episode really is the Wilco show. We will be talking about three articles that he's written on LinkedIn

and what they mean and what people should learn from them, what he's learned on his journey in becoming a Green Software Foundation champion, a green software practitioner. So yes, he has a lot of interesting thoughts on integrating software sustainability at the lowest, the deepest level, the lowest micro scale, the code level scale, and the macro scale.

Bizarrely today, we're going to be talking about those in the reverse order, focusing on his articles on the micro scale first, and then moving over to the macro scale, which I, and I'm a big fan of macro scale. So that'll be interesting when we get there. Actually, I'm a fan of all the things, but I'm a big fan of starting at the macro scale.

So yeah. So Wilco's going to be talking about his articles. And he'll also be talking about his experience using the SCI, the Software Carbon Intensity standard and the Impact Framework, because I'm very keen on his thoughts about whether they're useful or not, why they're useful and what they add to the software development process. So, welcome, Wilco. Can you tell us a little bit about yourself?

Wilco: Yeah. Hi, Anne. Yeah. Thanks for having me. Big fan, by the way. I love the book you worked on, Building Green Software. So I'm Wilco. 41, married with no kids and I live in the Netherlands. We have an Airedale Terrier named Iron. And although the country I live in is small with only 18 million inhabitants, I grew up in the South near the coast on a factory plant tied to the coal industry in the eighties. And my dad was a night guard. So we lived in the factory plant and yeah, when you come out of the bed and you smell the stench of chemical processes in the air and when the wash was hanging out to dry, but beautiful weather, but, the coal dust came on the clothes.

Yeah, that was, but yeah, if I look now back on it, that was kind of weird, but that was normal. That was, home for us. So I deeply love nature. I spend a lot of my time on hikes of two or three hours in the forests and the heat lands, and that's only 10 minutes from my home. So, yeah, I love to live here in the South and what we call the nature.

Anne: That's great. That's lovely. And a really interesting backstory that your first, 

coal was your nemesis was your laundry's nemesis from a very early age.

Wilco: Yeah. It's always a story that my mom tells people because yeah, a lot of people who didn't experience that can not have a understanding of how it must been.

Anne: That is really interesting. That is a very interesting backstory. So my backstory is not quite so interesting. So my name is Anne Currie. I am, as I mentioned, one of the co-authors of the new O'Reilly book, Building Green Software. And I said that in the last podcast, and I'll say it again, if you care about this kind of stuff, if you're listening to this podcast, Building Green Software from O'Reilly is a really good book to read to get cracking. And it doesn't, it's not particularly techie, it is useful for everybody. So if you're a product manager, if you're a marketing person, you can read that and understand it. And it's a good, place for you to kick off because I think a lot of the changes that we're going to need to make to build green software actually start with product managers, not necessarily with techies, but that's an interesting other point. I'm also the CEO of the learning and development company, Strategically green.

And we do workshops as Wilco also does workshops. We'll be talking a little bit about that later, but we do workshops to kind of get your company started on getting people understanding what it is to be great and kicking off some interest and excitement, as well as helping you build some internal expertise in that.

So if you want to do any of those things, hit me up on LinkedIn. Before we dive in, I want to make a quick reminder that everything we talk about in this podcast today will be linked to in the show notes below the episode. So you can go and you can read it and you can follow along as you listen to the podcast.

So back to you, Wilco. I think the place for us to start is what started this off for you? What kicked it off? What led you to transition into green IT and how has your journey evolved over time?

Wilco: Well, only 10 months ago, it's not even that long ago, I dived into green IT and sustainable coding, starting with no background in green. In IT, of course, with 20 years of experience. And. Now I'm progressing to discuss things with university professors. So it went kind of quickly. And also since March this year, I'm a co-founder and co-host alongside Pini Reznik, I think a familiar person for you, of Green Software Meetups in the Netherlands.

Anne: And you've had a lot of success with meetups in the Netherlands, which is really good. So, what, role do you see your current work playing in the larger mission of sustainability?

Wilco: Well, maybe a fun detail. I work in secondment, for some countries that is not a familiar thing, but it's meaning I'm contracted by various companies. And this year I'm working with the National Databank for Flora and Fauna as a solution architect and together with a fantastic team, we're making hundreds of million biodiversity observations publicly accessible to everyone in the Netherlands.

And that is kind of something really cool. And we're on track to reach our first major release in the new year.

Anne: That is very cool. That's very good. And that quite interests me, links back to something you said on LinkedIn when I was talking about the last environment variables, where I was talking to Stefana Sopco, who also lives in the Netherlands. And you pointed out another Dutchie. Which, you're quite right.

We have a, there's a lot of interest in this in the Netherlands. Do you have a feeling for why that is?

Wilco: I hope I helped a bit with that the last half year. But no, of course, no, that's a, just a joke. But when I started like at the beginning of this year, I was looking on Google, searching for information and information was hard to come by. And at some point I was thinking, yeah, of course, books and podcasts, the GreenIO and Environmental Variables.

That is a place where I find a lot of that information that I needed. But at some point I was like, okay, so maybe I need to talk to people to gather more information. And when I was searching on LinkedIn for people who knew more about green IT and green coding and green software, I found out that there were all these kind of bubbles, yeah, in the Netherlands we call them bubbles, like you have 20, 30 people working on a certain topic.

And I was like, also at the same time, we were thinking, okay, how can we build a community for the meetups? And I was like, yeah, the only thing that I can do is connect to these people and make aware that the other bubbles exist and to keep on doing that. And when I was finding out, and I found the other group and another group and eventually there are, I think, right now, yeah, I think a small 2000 people in the Netherlands busy with this topic.

But a lot of those people are not aware of each other. So you have to think about people working on CSRD and monitoring, people on FinOps, but are really that are interested in sustainability, people who are like, "yeah, we, need to measure not only emissions, but also nitrogen and other things and PFAS," is it how we call it in Netherlands?

So yeah, I don't know if it's because of a trend or because of a lot of people now with CSRD are looking, "okay, how do we need to do this?" But yeah, there's a lot of activity in the Netherlands.

Anne: That's, that is really interesting and there's a lesson there for anybody who wants to grow a community is that you went out and found all the small communities and hook them together. That's an incredibly valuable thing to be doing.

Wilco: And it's also cool that there are also, there's an organization, the National Coalition of Digital Sustainability, and it's a little bit different, the acronym in the Netherlands, but they are already busy with this topic for more than 10 years. And then when I was doing my thing on LinkedIn, and then I found out that there was an other meetup group from a bank and a consultancy company, and then we're already busy with doing meetups in the year before.

And, but they weren't aware of even sometimes other organizations and also like a Green Software Foundation, but there's also of course the Cloud Native Computing Foundation, where you have a sustainability group. And I'm not even talking about things like Climate Action Tech and, that kind of organizations of groups.

Anne: And of course actually trying to link together these groups is incredibly, so we have actually met in person, we met at a Green IO conference in London in September, which was great. And that was very good. That was a very good way of getting a whole load of people in Western Europe basically to all connect together and have a drink and see one another face to face.

Very effective. So..

Wilco: Yeah, it's very inspiring to see other practitioners and also other perspectives from UX to Green Ops to yeah, all the different, because that is something that is so clear. And this is also maybe eventually, if we go to macro, why it's hard to implement is because sustainability, it hits so many fronts within a company or an organization, there are so many roles.

Where if you start thinking about, "okay, what am I, actually doing?" So the impact, what we're doing from boardroom to eventually DevOps teams or members of DevOps teams. And it's cool to see that all those people come then together in such a conference. Yeah.

Anne: Yes, it is true. And I've said this many times before that it's, everybody's being bonded together by having the same goal, which is reducing carbon in the atmosphere. An intrinsic goal that's, you know, it's doing good. It's improving the world. And it does mean that you can share common ground with people you wouldn't necessarily previously have shared much common ground with.

Wilco: Yes.

Anne: So, I mean, you, said you've only been interested in green software for about 10 months and you've certainly done an awful lot during that time. How did you get started? How did you get started organizing meetups particularly?

Wilco: Well, in January, my former boss introduced me to the Green Software Foundation website, and I immediately noticed two things. So CarbonHack24 was on the website, the Hackathon and the company I work for, they really love Hackathons. So I formed a group of volunteers together. And beside that, I will come back to that later.

There was also a lag. I saw the website of meetups in the Netherlands. So I reached out to Asim for advice and he connected me with a group of Green Software Foundation employees and the Green Software members in the Netherlands. And including with Pini Reznik and together, we started planning. And by April, we had our first meetup.

And my team even won the CarbonHack24 Best Contribution, which is crazy if I think about it, which was such an incredible motivator. And each step I took from organizing meetups to winning the hackathon felt like a chance to make a meaningful impact.

Anne: Which is fantastically good. That is good. So, but you didn't stop there, did you? You became a green software champion, which is a new Green Software Foundation kind of a project to build up people who know more and can go out and shout about green software. How did that happen?

Wilco: So by May, after hosting two meetups and writing over 10 articles on green software, I felt certain that this was my calling, right? I felt so much passion and fire. So, I mean, I think through all the content I create and all the conversations, that was kind of clear. And I discovered the Green Software Champion program on the Green Software Web Foundation website.

And I knew it was the right path to amplify my impact because I believe that if you have a recognition of a certain organizations that especially like multinationals and big organizations are like, "Hey. This is something that we maybe need to take more seriously." Not because of me, because of the, "Hey, this is, there's something going on here."

And yeah, forward four months and we've organized five meetups in just over seven months. And with the sixth one on the way on the 22nd of November, with the Green Waves Hackathon at the TU Delft, that's a university in Delft. And I now written over 150 articles on LinkedIn, collaborated with professors to bring green theory into practice.

And we're still, I'm still doing that and have given six talks so far. So, with more plans and each step has deepened my commitment to building a sustainable tech community.

Anne: Which is absolutely fascinating. It is amazing how much you've done in 10 months. So, but what next for Wilco and the green IT community?

Wilco: Yeah, what is next? That is a good question. So what I really try to do is to follow this certain path. So when I started gathering the information, I found kind of out, okay, there is already a decennia of research done and a lot of information, but to some degree, we have a hard time transferring this information to other developers and we are kind of stuck.

So for me personally, I was really invested. "Okay. How can I make this first stepping stone on making this a thing that other people can understand?" And that's why I started to invest what I now call EQUAL, 

Energy, Quality, Utilization, and Load. So the idea that you have an application that has a certain algorithm or a certain logic that everyone understands.

So kind of a loop. And if you have this loop, so a few important things in, in, in green coding and green software is, okay, how can we, based on utilization, can we estimate and not exact utilization, but can we estimate the energy and then eventually related to emissions? So if I started understanding, hey, wait, utilization to some degree is like the amount of threads that the CPU is running.

Also, of course, based on the cores. So 50%. You would maybe expect, like, if you have like 12 cores that with 50%, six cores are running, but it is not necessary at the truth because frequencies, of course, can be higher and lower and there are some things going on, but if you start, okay, so the amount of threads, so let's say the 12 cores.

So you can have then most easiest for a clock, 24 threads. So if you have a loop that you can start playing around with two threads, four threads, eight threads, 12 threads. So that's a first parameter you can give to this loop that I placed in the API. Then the next one is the amount of iterations. So do I want to do a small test?

And the funny thing is one line, one normal line of code, because I can make a line of code that just can gas pedal the CPU 200 percent for an hour for one line of code. If you take an average line of code, it's most of the time so insignificant for a CPU that like, if you have a loop that is running within this very small time, then 10 million iterations is quickly over that's very fast.

So my EQUAL starts with 10 million and it goes eventually to in the billions of iterations. And then the third parameter of equal is the use case. So you can place in the iteration just an I++ or just any use case you kind of want. And then what I kind of start doing when the loop is running, I start asynchronically, I start measuring the utilization of the cores in a very high time resolution. Like 10ms, 20ms, 30ms, so very small. And then after the whole loop is done, what I then do is I can place, those samples, I can eventually connect them back again to the traces of the code. And then you can see a certain few things happening here. So what you can see happening is if you will reduce the amount of operations happening on the CPU, yeah, of course you probably,

your, utilization will be lower and your energy use. But there's also another thing is because CPU, how CPU works, that sometimes you will see unexpected behavior. So although you start to play around with these use cases and you think, "Hey, this should be more efficient" and you start rerunning it and then you're seeing things happening like, "Hey, wait a minute, if I run this on 18 threads, this use case works more efficient energy-wise on 12 threads.

How does that make sense?" Well, that is something I tried to figure out, but this is what I place in a demonstration style, because if you demo this and you show us this loop and everybody understands the loop and you show this in the user interface, and then with Prometheus, with eventually graph set that you show to everyone,

then it's makes more clear for, "Oh, okay. Wait a minute. There is beside time efficiency, compute, power, and there's also this third dimension, energy consumption. And it has sometimes another effect than we sometimes expect." And I started, okay, so if I can eventually use this in talks, if I can start using this in eventually a use case for blogs.

And eventually this is also where the workshop that I'm going to give from January is built on. So this is for me is like the future. And then my estimation model that I just created on Prox. Which is kind of built in, of course, in your Linux kernel with just a dumps is now not the most perfect model, but this is the reason why I work where I have contact with, especially University of Groningen to make this model eventually better with socket management of measurements and real kind of measurements.

And yeah,

Anne: So that so the all sounds very, so basically you're working on a tool that helps people measure, at least proxy measure, their carbon emissions through energy use,

then tune it and improve it. And I'm guessing that there's kind of several advantages to that tool. If you work on that tool to deliver the same functionality using less energy, the product, your application will run faster.

As you say, CPU cycles are another proxy for energy use. So is that commonly what happens? It improves the performance of the application?

Wilco: Well, if you say performance in time, well, this is a funny question. So if your focus is on performance in time, sometimes if you say I make my code quicker, it sometimes start using more energy. So, and then the question is, I have this value, "is it okay for a user to wait on it or does it need to have this very fast?"

And there's also a difference between the performance if, and this isn't on the, on the, in the cloud, on a server, almost impossible. If you on A CPU only use a few cores, it has often a very higher CPU frequency, so it probably will be with this exact same code will, be quicker than if the complete CPU has 80% of the cores or a hundred percent of the cores active because the higher, especially with a hundred percent because of heat, the frequency goes down and it kind of becomes slower.

And this is what I say, you can, of course, if you lower the amount of code or operations to the CPU, it will eventually be more efficient. But there is also this thing going on that the CPU has sometimes 20, 30, 40 percent influence based on the state of the CPU it's in. And yeah, your code can have some influence on it, but it's more in a different way.

So how many threads am I spinning up? Or how many things are going on this server that I'm running my code on? And yeah.

Anne: So yeah, I see now why you, and when we go over onto your next, onto your second article, talking about trying to balance these micro line level changes with a more macro perspective. That's yeah, it gets quite complicated and you don't always know what's going to work until you try it. So obviously, you know, the whole point of running this tool will be to make a more energy efficient application within your kind of high level goals of your SLAs. But I'm imagining it's also quite fun, that it's quite a good thing for a hackathon, it's quite a fun thing developers to play with.

Wilco: Yeah. Yeah. That. And also you can just replace, that's what I every time keep saying. The loop is just for demonstration purposes that people understand it. But I use this whole logic in an API and you can just put your own code in there. That's the whole thing that we're, with the workshop, going to do.

So people will build their own API. And then with the same process of asynchronically measure when you run this code, what's happening, because you will see funny things going on when you're waiting or things are connecting to a database or connecting to another API. And based on how things are programmed.

So are you waiting with a loop that is pushing your CPU high? Or are you using smarter mechanics so that there is a drop, but is a drop sometimes something you want because if you want to be very efficient with your resource, you kind of want to maximize it around 80%. Well, I don't want to be come too fast to conclusions yet, because I think we still need to figure out what the patterns are and what are good patterns and bad patterns, but yeah.

Anne: Yes, because as you say, and again, this leads back to the kind of macro micro picture. It's definitely.. In certain circumstances is definitely the right thing to do to if you're waiting on an API call or something to kind of say, "right, I yield all the threads and everything running on the machine to somebody else to use the machine while I'm waiting" so that the machine is still highly utilized whilst I'm waiting for my API call, but that relies on you having a design or an architecture, which might be within your application, but it might be within your operational decisions. You know, are you multi tenant? Is there somebody else or some other company or some other application that is going to be able to pick up and use the machine while you're yielded?

But if you're just waiting around. then that's less good. So then the machine is just going to waste during the time that you're waiting. So you're, right in saying that there's so much context to this.

Wilco: Yeah. Yeah. And, okay, so, okay, I'm really getting excited about this topic. So, because I instantly thinking like, yeah, of course in the cloud, in the server, you maybe they have not full control, but one thing we know the grid is getting fuller, the electricity grid. And one of the things is that some university have research done is how can we optimize the devices that we have better?

And one of the things that we in the Netherlands have, we have a lot of people with solar panels. We're not using optimally the, electricity that is generated by the solar panel. So if you think about the following, so what are the devices that you can easiest, how do you say, charge based on the solar panels, then it's mobile devices or tablets, or maybe a laptop with maybe a good battery lifetime.

So now we're from originally, we always been like, "okay, we need to move logic to the server because it's more secure and you can not manipulate it." But if you start thinking about, "Hey, we need to optimize the devices better." What if we start using a WebAssembly in a better way? So things in your browser or on IOT device, or in this case, then on mobile or tablet, and use that green energy, especially if someone is as smart that like not charging it at night,

but more than the day when the solar panels are active, that is always like an important catch. And of course the solar panels have some embodied carbon, but yeah, but still, so there are so many cool things you can go on in this.

Anne: It is really interesting that renewables, unlike, you know, you aren't going to run your own coal fired power station. You say, nobody apart from Wilco has ever lived on a coal fired power station, but particularly solar, it's a very distributed technology. There are lots of people, I've got solar panels, and when they're running and the sun's shining, I've got more power than I know what to do with.

When I had, when I got it installed, I said to the, chap who was installing it, "what should I do with this? You know, and he said, "Oh, well, you will have, there are times when you are going to have more power than you know what to do with." So make sure that you, it might seem historically, it's always been very inefficient to heat, your water with an immersion heater rather than using, a gas burner or something like that. So it's a very inefficient way to do that, to heat the water. But if you've got free energy and it's just otherwise going to go to waste, heat your water with an immersion heater. He said, "get a swimming pool." Not that I did get a swimming pool, but get a swimming pool and heat that up because some of that has changed the way we need to think about, we still talk about green software very much from the perspective of efficiency and improving efficiency and reducing waste.

But I would say even more importantly than that, it's about doing more when the sun's shining. You know, don't forget efficiency when the sun's shining. You might want to write applications that are very efficient, that operate in totally different ways at night and while the sun's shining.

Yes. 

Wilco: Yeah. I would even dare to challenge the following. So. If the sun is very shining in the day, what I see in the Netherlands a lot is if you look to the electrical grid, especially also on windy days, that's, or somehow, and I don't see the relation yet, is that the industry seems to be working harder in the general.

So you still see the gas turbines in the Netherlands emit a lot of emissions. So it's very sunny. And if you go to electricitymaps.com, to the Netherlands. And you look there, you will see then the solar panels, generating a lot of energy. And sometimes of course, also the wind turbines, but that's also the gas turbines.

And that is mainly because there's a higher demand or there's instability on the net. And so you could even start. And that's why I think that carbon-aware is a very complex topic, because are you gonna do a weather forecast and then run, but then find out that maybe the grid was emitting more than you expected?

And lately, the last days, we had a lot of emissions in the Netherlands. Or are you more going to try to indeed optimize the devices we already have that maybe run on green electricity? There's no perfect answer in this, but we need more data, we need more access, but I understand from security standpoint, even with electricity grids, I mean, they want us to give us the information, but they're scared for terrorist attacks or for things that the information that we want to do for good that can be used for bad.

But yeah..

Anne: I mean, and quite often, grids just don't have the information yet. I mean, there is, for carbon awareness, we were a long way from having really good data on that. So I always tend to say, don't start, well, you could pick proxies, perfectly reasonable to pick a proxy because actually the difficult thing is designing systems that can respond.

That's going to take years to do. So you can, in many ways, pick a proxy now, even if it isn't great. Design a system that is responsive to that proxy. And then as that proxy gets better, your system will get better. So you might be going, " actually that proxy's terrible" now, but the difficulty is, well, getting the data is often somebody else's problem.

It is put pressure on, you know, suppliers and energy grids and everything to provide good data to us. But in the meantime, the big job for us, the thing that's going to take us a long time is redesigning our systems to be able to respond to that data. So that's things like thinking about what your graceful downgrade options are for when the grids are very dirty. You might have to move big, having big latency-insensitive tasks that you can move to when the sun's shiny. The Texan grid is doing a lot of good work on that. And I talk, again, I'm sorry, I talk endlessly about large, flexible loads. So the Texan grid is putting out a call to industry for large, flexible loads, which you can run, which are latency intensive, don't matter when they run.

But they can run when the grid is full of solar because Texas is quite rightly putting a whole load of solar panels because it's very hot in Texas, it's very sunny in Texas and there's a lot of desert. So they want something to run on that, solar power. It's very sad at the moment that the people who are really responding to it are the Bitcoin miners, but AI is another potential customer who have large flexible loads.

So very CPU intensive loads.

Wilco: Yeah. Oh, but this is perfect because, okay, you kind of influenced me when we had talked in London. So to think more about, okay, if you can better react to renewables in a more flexible way, because I started thinking about it, especially if you put it in the following perspective. So most front-ends have a lifetime of two, three years.

There is of course always shorter and longer. Back-end systems often have like a longer lifetime span. So if you build something today in 2024, and it runs five to six years, that means that it still runs in 2030 when we have our big first milestone that we should have reached. So if you're not building your software today that it can adapt, that it can be flexible, you have to refactor things in the future, or you're getting in a stuck position because most of the time, especially with big systems, the more you build, the more dependencies you get, the harder it becomes to eventually change things when that foundation is set.

So, yeah, I really like that idea that, although we maybe not have all the right answers now, and maybe the situations are not always perfect, but it doesn't mean that you shouldn't start thinking and implement in this way.

Anne: Absolutely, I think it's going to take ages to do this, it's a completely different way of thinking about it. I mean, there are tools out there that already exist, that can help you get into this way of thinking. So I'm a huge fan of spot instances on AWS and, or Azure or, preemptible instances on GCP, because they're, a kind of mini version of Texas's large flexible loads.

You say it's a small flexible load. You say. What you're saying is "I've got this load, it's flexible, run it. I don't really mind where it runs. I don't have any particular SLA associated with it." And you can use it for, the clouds use it to improve operational efficiency, which is good for green as well. But in the future, I can see those loads absolutely match to what we're going to need in order to shift work forward in time or later in time or forward in time.

Wilco: Yeah, I really believe to be, to some degree as much in control as possible because it's easy to let some other company or SaaS solution fix things for you, but especially from a board perspective, it's a good idea. Like, okay, we're working together with hyperscalers and we're doing things serverless.

And especially if you're a big organization. And then mainly like if we do serverless, they are kind of responsible to fix if the utilization on the background is well organized. And I find this always very interesting because yeah, to some degree that's true. So for consumers, normally, if you go to Azure, you're probably, if you're using serverless, I think they can really optimize it very well.

But if you have a very big multinational where kind of, they already reserve a certain space for you in the data center and you're running serverless. I'm very curious because we don't have the information now. So do they reach indeed that more utilization because you work serverless or do you have virtual machines where often in a while some function comes by and it runs and it's done?

So that's why, and you mentioned spot, that's why I like, and not because I have stocks in them, because it's not possible, I think, but I, that's why I love Kubernetes and Cloud Native thinking so much. That's also why I really like to check out also every time what's happening in the Cloud Native Computing Foundation environment, because course that is where Kubernetes is very active, because I strongly believe that if you are in control as far as possible, you have, not only you can better measure what's happening to some degree, although you're doing some estimates, I think it's also from a security perspective, it's a good idea. And I think also from just the willing, yeah, the willing to be responsible because nine of the time you're also responsible of the value that's running in your cluster.

And I think just outsourcing the company's core values or the product values outsourcing to somewhere else, it's possible, but you're giving them also some control away. Yeah. And this is something that I think a lot about, but not having all the, I think I will never have all the answers, but, yeah..

Anne: I think you're very right to be skeptical about serverless running on prem rather than in the cloud. Cause it does feel to me like, I touched a little bit earlier about multi tenancy. When you're not doing something, what's somebody else doing on the same machine? A lot of these tools like serverless work really well because you're in a multi tenant environment.

So the classic example is with, if you're in the cloud and you're, you might be sharing physical resources with a company that has very different demand profile to you. So if all of your, if you are, say a retailer, I used to be head of IT for an online retailer, then all of your resources are assigned to your demand.

So if there's a peak, then you have provision for your peak. And that means that a lot of the time, so say Christmas might be your peak. You have a provision for Christmas. And then most of the year, the machines are underutilized because you had to provision for Christmas. If you move into a multi tenant environment, one of the root things that the hyperscalers attempt to do is to pair up, or not just pair up, but group users on machines in such a way that they have different demand profiles. So everybody has a correlated demand at Christmas. Maybe if you're a retailer, you might be sharing a machine with a training company. And the retailer is very busy at Christmas and the training company is very quiet at Christmas.

So you kind of, you, rather than needing to provision for the peak, you are getting better utilization of those machines over time. And serverless is a little bit of an example of that. It's, the win is with multi tenancy and multi tenancy is easier in the cloud than it is on prem.

Now, having said that there are some multinationals that are so big and have such complicated systems internally that they are effectively their own multi tenant. I mean, Google is a hyperscaler, but forgetting GCP for the moment and looking at Google's internal tools and applications, they are their own multi tenants, they have enough variability in what individual tools are doing that they can act to keep their machines fully utilized all the time. They're kind of designed for that, but most companies are not at that level and quite haven't quite designed for that yet. But I agree serverless internally on prem for a small enterprise probably doesn't buy you that much.

Wilco: And there's also an other perspective on this. And I heard this in a conversation with a bank and that was also very inspiring for me. So. The fun thing is everything we just talked about can also work in harmony. So if you always have a baseline utilization, you could do that on prem where you know, "okay, with these applications, we always have activity.

So we have a certain utilization." And what they say is what then a big, they have done in control. They know exactly the energy usage and because of adventure, the energy uses also the negative impact and emissions and avoid carbon. But when they have peaks, they overload to the cloud. So they're like, "okay, so if we have, then we go there." And you could kind of also do sometimes maybe do the same if you want to save costs to maybe the devices to some degree that using your application.

So there are ways, and this is complex, but I think that is a way. And also there is a topic that we had in the past and nobody's really talking about anymore because it's complex. It's distributed computing, of course. And that's also another pattern that could, have a play in this. So, yeah..

Anne: Yeah. I mean, distributed computing is very potentially well aligned with this whole thing of demand shifting and shaping and saying, you know, "actually I've got a, thing here. We need to treat it as essentially asynchronous." And of course, asynchronousness is, or asynchronicity, is a really key part of distributed systems, designing distributed systems well.

Completely synchronous distributed systems are often a little bit of a distributed monolith. You don't, you often don't get the same breadth view of

Wilco: a funny thing. I heard, yeah, if you're YouTube, you can indeed see, like serverless monolith, microservice monolith, there are the.. So sometimes you think, "okay, this is a good start, if we design it that way," but if you create so much dependencies and then you're still, on an abstract level, creating a monolith.

Yeah.. 

Anne: Yeah, except a more difficult one. Because a monolith, the value of monolith is, quite simple and, well, it's, yeah.. Monoliths and microservices and distributor systems all have their place. It's always a matter of choose the right tool for the job. That is efficiency 101 is choose the right tool for the job.

Wilco: Oh yeah. A hundred percent. Yeah.

Anne: So we have chatted so long, we've got hardly any time to actually go over your articles. So do you? Let's see, we were going to talk about three articles. One was about your using the, SCI and the impact framework.

Wilco: The thing that I would like to say on that one is that sometimes maybe people think that I'm only interested in energy and that is absolutely not the case. The only thing that I came to the conclusion, not that you have to do it that way, but to some degree, because we're still also figuring out how to best measure emissions based on the grid.

And the other thing is embodied carbon. So we can do a lot of stuff with lifecycle access data of assessments and hopefully also the correct information we get from our scope three suppliers, but to some degree, if you know, and this is what I always keep saying, and maybe it's, if you know, at what moment, at what location your software was running on what kind of resource type on what kind of hardware, and you log that down, the most important thing is energy you cannot historically get back. It's you compute it and it's gone. So, and we have still with hardware disproportionality, but we have still things to figure out, okay, how do we really measure it? And I really strongly believe if I can help to get it down somehow, we to some degree can historically get back. Okay, we, knew that in that moment in time, even with electricity maps, with historical data, we can get the emissions from that moment, that location.

And also with the information we gathered with past procurement information or supplier information, we can get the embodied carbon right and that kind of stuff. So that is the main reason why I really focus on that E of energy and the SCI, the Software Carbon Intensity, you know.

Anne: The thing I like about the E is it's something that software developers can have effect, you know, it's not like we're, we don't care about the other things. We're just trying to focus on where we can have, make a material impact. And there is somewhere where we can really make an enormous impact. Our ability to change things in other areas is more limited and that's why we started the Green Software Foundation, was to find ways that software engineers can improve things and people related to software development.

So product managers, testers, that kind of thing. It doesn't mean that we don't care. It means that we're looking at where we can have the an impact.

Wilco: Oh and the quantum entanglement. That came from the idea, I'm a big nerd and I, like to know things, a lot of things, and it's also counts for physics. And I'm watching a lot of YouTube videos, also science videos, and a lot of videos every time came back on the concept of, okay, we have Einstein's, real, real, 

Anne: Relativity. Yeah. Yeah. 

Wilco: Relatable.

I'm still going wrong here, like, like about the, eventually can calculate things going on in a black hole. And we have quantum mechanics with quarks and all cool stuff. And when they try to bring this theories together, they have all these kinds of issues that doesn't really match up. And that's what I've found really interesting because I started before my journey, I had some information from the holistic view perspective.

So from boardroom perspective, from more top, if you think from top to bottom. And they are more interested in compliant data. So they, their data that they're using has to be valid, has to be compliant and also streamlined. So eventually they can report and also use it to create internally policies on it and that kind of stuff.

But if you talk like the software engineers, we're really like, "yeah, we're missing data on calculating the correct energy consumption. If we run software on this CPU or this GPU and we're still figuring things out," but very on the lower level, but I think on a boardroom level, they are probably not that interested in how correct the CPU or GPU was, as long as it is correct enough to make it right for the reporting.

So, and then the other thing where it really aligns to is at some point I came to the conclusion, like, okay, we can measure a lot of things. We can have all these metrics. But at some point the numbers are not going to change outcomes, decisions do. That was kind of a big moment for me. So there has to be at some point the sustainable decision making process going on from bottom to top or the other way around where those worlds connect to each other.

And that's what I really, with this article for the first time, it was also a few months back. That I was thinking, "okay, how can I connect this worlds together?" So what would the steps be? And the main, I think outcome for me from this whole thought experiment was, is that based on certain levels in the organization, you have different requirements for the data you want, different tools you probably want to use, and also different reasons you want to use this. And yeah, that is still a thing that I'm working on. One thing where I think that everything in comes together is a simple concept that if you set up an organization, a big IT resource list, or even resource less in general. And you have this resource list where you have like your mobile phones, your laptops, but also your cloud resource.

We have the infrastructure as code. We could generate those resources. And you have all these resources in this list. And if it's a multiple of the same resources, you just do a count after it. And then you would say to each resource, you would say you're using so much kilowatt hour a year. This is the footprint estimated benchmark or real time.

But also this is the security need because the security people are in the same thing as us. They really want more information. And that would be a great starting point because from that perspective, you can eventually bring that to information that is more on the higher level of the organization and you also can connect it to the really nitty gritty things on the bottom.

But yeah.

Anne: I was very interested in your, quantum versus relativity. I also, I have, my degrees in theoretical physics, but I will say, it kind of, when I read it, I thought, ah, now this, I find this quite an interesting analogy because I was, there's two. I was the fan, I was a particularly fan girl of neither.

I was always a fan girl of classical physics. I like you remember that enterprises operate at the level of classical physics. I think it's actually a really good analogy that enterprises are thinking about, "is my data compliance, you know, are my bills ridiculous, are my system staying up?"

And I think that actually, and only once that's, you know, in the Maslow's hierarchy of needs or the Maturity Matrix that the green software maturity matrix from the Green Software Foundation that Pini and I run together, the first thing you need to do is get your operations right. And that is classical physics.

You know, it's kind of like, are you paying too much? Are you over provisioned? Do you have a whole load of machines that you're not properly monitoring and using anymore? Maybe they are security holes. So you've got security problems there, you've got financial problems there, and you've got waste there as well.

You've got loads of carbon as a result of, 

Wilco: yeah. That's the one I forgot on my list. Waste that also needs to be Yeah on there. But yeah..

Anne: I love relativity, I love quantum physics. But for most enterprises, I would say start with classical physics, really just focus on getting your basic ops good, do those thrift a thons. Your last article, which you're not going to have time to go over, but I would strongly recommend people read, is about the alignment of FinOps and green software.

And I would say that the alignment of FinOps and green software is your classical physics. Oh, that's awesome. It works for every single enterprise. Nothing fancy is required. It's totally aligned with the business, with the desires and the goals of your business. No one is going to complain that you saved the money.

Wilco: But I think that is,

Anne: once.

Wilco: yeah, I think that's very important because sometimes, I mean, a lot of people, they say, "why are our companies not starting?" Or "I have this idea, why is no one picking it up?" And I think we also have to be honest to ourself. If you invest money in something, you want it to be, have a certain maturity level and also, especially if we buy something, we want to know that it's going to work and that's, yeah, it's going to have the value that you expect it to be.

And so together, I really believe together that we can, and the whole, or the whole line of field can bring it to a certain maturity level. So with the maturity matrix, that's a different story. Yeah. But because I think that a lot of people always focusing on, "yeah, but if you do this, you have lower of cost," I think the most important thing for a company to be more interested in how can we solve the things we need to do for sustainability 

is to make it more frictionless implementation and have it less risk, because I think that if you can do it in a way and it doesn't have to be perfect, but that it's easy to implement and to use, I think companies will start doing more.

I strongly believe in that.

Anne: I do. I think there's a huge, people really care about being green. People do really care about the environment. They don't know that there are changes they can make through their work as software engineers, that will make a huge difference. And if they do think that there are changes, they have a tendency to think those changes are misaligned with their company goals, which quite often they are because people think, "Oh, I'll just rewrite everything in Rust."

And that would generally be misaligned with the company's goals. But going through and making sure that you're not over-provisioned in your data centers and you turn off stuff that's not in use, that you're being cost-minded, that's totally aligned with your business goals. And it's also aligned with being green.

On that, I think we really need to raise awareness of that.

Wilco: Yeah. Yeah. And that's what I really can bring back to the fact, like if you.. And like, Hey, the mention of time efficiency, something has to be fast, but you want to do it in the most low energy consumption, but then the most important thing to, so what is the value, what you're doing is going to bring. And it's something that we struggling in for a while because we really tried with agile, define like the customer value, business value.

But I think those three together, if we figure that one out in sustainability in a better way, we really can make some jumps.

Anne: And with that, I think we'll need to end because we have been talking for ages and it's been absolutely fascinating and I've really enjoyed it. So thank you very much indeed, Wilco, for this. So where can people find you and get involved in your meta communities?

Wilco: Well, mainly I'm active on LinkedIn on my name, Wilco Burggraaf. I try to post every two days, a new content. And yeah, you can also, if you look up meetup.com, you can find the meetup group under the green software meetup Netherlands. And another thing is of course, from January, I'm starting with my workshops.

So if you're located in the Netherlands and you're interested in that, yeah, reach out to me.

Anne: Excellent. Thank you very much for coming on this episode. It's been really fun. And if anybody wants to contact me or chat to me about my, I also do workshops, which are not the same as Wilco's. So we are, I would say we run a complimentary workshops, then you can also take me through LinkedIn. And this is a final reminder that all the, well, we didn't really talk through the resources for this episode, but they are good background resources for our discussion.

They're quite easy and pleasant to read. So have a look at the links to Wilco's posts, follow Wilco on LinkedIn and read his articles. They're very, very good. So thank you very much. And I will see you all in the next episode. Goodbye for now.

Wilco: Bye.

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To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.

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]]> In this episode of Environment Variables, host Anne Currie speaks to Wilco Burggraaf, a lead green practitioner and architect at HighTech Innovators, for an engaging discussion on integrating sustainability into software development. Wilco shares his journey into green software, the inspiration behind his innovative workshops, and his efforts to build a vibrant green tech community in the Netherlands. The conversation explores his articles on the Software Carbon Intensity standard, the complexities of balancing micro and macro sustainability goals, and the synergy between FinOps and green software. Tune in for actionable insights and strategies to make greener choices in tech while aligning sustainability with business goals.

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Transcript below:

Wilco: At some point I came to the conclusion, like, okay, we can measure a lot of things, we can have all these metrics but at some point the numbers are not going to change outcomes. Decisions do.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field, who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. 

Anne: Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host today, Anne Currie. And joining us is Wilco Burggraaf, lead green practitioner and green architect at HighTech Innovators. Wilco brings a wealth of experience in software development, been over 20 years in the industry, and is an active Green Software Foundation champion, and we'll be talking a lot about that today.

So he cares a great deal about integrating sustainable practices directly into the code and architecture of software, helping to make greener choices not only possible, but essential in tech. And in this episode, well, this episode really is the Wilco show. We will be talking about three articles that he's written on LinkedIn

and what they mean and what people should learn from them, what he's learned on his journey in becoming a Green Software Foundation champion, a green software practitioner. So yes, he has a lot of interesting thoughts on integrating software sustainability at the lowest, the deepest level, the lowest micro scale, the code level scale, and the macro scale.

Bizarrely today, we're going to be talking about those in the reverse order, focusing on his articles on the micro scale first, and then moving over to the macro scale, which I, and I'm a big fan of macro scale. So that'll be interesting when we get there. Actually, I'm a fan of all the things, but I'm a big fan of starting at the macro scale.

So yeah. So Wilco's going to be talking about his articles. And he'll also be talking about his experience using the SCI, the Software Carbon Intensity standard and the Impact Framework, because I'm very keen on his thoughts about whether they're useful or not, why they're useful and what they add to the software development process. So, welcome, Wilco. Can you tell us a little bit about yourself?

Wilco: Yeah. Hi, Anne. Yeah. Thanks for having me. Big fan, by the way. I love the book you worked on, Building Green Software. So I'm Wilco. 41, married with no kids and I live in the Netherlands. We have an Airedale Terrier named Iron. And although the country I live in is small with only 18 million inhabitants, I grew up in the South near the coast on a factory plant tied to the coal industry in the eighties. And my dad was a night guard. So we lived in the factory plant and yeah, when you come out of the bed and you smell the stench of chemical processes in the air and when the wash was hanging out to dry, but beautiful weather, but, the coal dust came on the clothes.

Yeah, that was, but yeah, if I look now back on it, that was kind of weird, but that was normal. That was, home for us. So I deeply love nature. I spend a lot of my time on hikes of two or three hours in the forests and the heat lands, and that's only 10 minutes from my home. So, yeah, I love to live here in the South and what we call the nature.

Anne: That's great. That's lovely. And a really interesting backstory that your first, 

coal was your nemesis was your laundry's nemesis from a very early age.

Wilco: Yeah. It's always a story that my mom tells people because yeah, a lot of people who didn't experience that can not have a understanding of how it must been.

Anne: That is really interesting. That is a very interesting backstory. So my backstory is not quite so interesting. So my name is Anne Currie. I am, as I mentioned, one of the co-authors of the new O'Reilly book, Building Green Software. And I said that in the last podcast, and I'll say it again, if you care about this kind of stuff, if you're listening to this podcast, Building Green Software from O'Reilly is a really good book to read to get cracking. And it doesn't, it's not particularly techie, it is useful for everybody. So if you're a product manager, if you're a marketing person, you can read that and understand it. And it's a good, place for you to kick off because I think a lot of the changes that we're going to need to make to build green software actually start with product managers, not necessarily with techies, but that's an interesting other point. I'm also the CEO of the learning and development company, Strategically green.

And we do workshops as Wilco also does workshops. We'll be talking a little bit about that later, but we do workshops to kind of get your company started on getting people understanding what it is to be great and kicking off some interest and excitement, as well as helping you build some internal expertise in that.

So if you want to do any of those things, hit me up on LinkedIn. Before we dive in, I want to make a quick reminder that everything we talk about in this podcast today will be linked to in the show notes below the episode. So you can go and you can read it and you can follow along as you listen to the podcast.

So back to you, Wilco. I think the place for us to start is what started this off for you? What kicked it off? What led you to transition into green IT and how has your journey evolved over time?

Wilco: Well, only 10 months ago, it's not even that long ago, I dived into green IT and sustainable coding, starting with no background in green. In IT, of course, with 20 years of experience. And. Now I'm progressing to discuss things with university professors. So it went kind of quickly. And also since March this year, I'm a co-founder and co-host alongside Pini Reznik, I think a familiar person for you, of Green Software Meetups in the Netherlands.

Anne: And you've had a lot of success with meetups in the Netherlands, which is really good. So, what, role do you see your current work playing in the larger mission of sustainability?

Wilco: Well, maybe a fun detail. I work in secondment, for some countries that is not a familiar thing, but it's meaning I'm contracted by various companies. And this year I'm working with the National Databank for Flora and Fauna as a solution architect and together with a fantastic team, we're making hundreds of million biodiversity observations publicly accessible to everyone in the Netherlands.

And that is kind of something really cool. And we're on track to reach our first major release in the new year.

Anne: That is very cool. That's very good. And that quite interests me, links back to something you said on LinkedIn when I was talking about the last environment variables, where I was talking to Stefana Sopco, who also lives in the Netherlands. And you pointed out another Dutchie. Which, you're quite right.

We have a, there's a lot of interest in this in the Netherlands. Do you have a feeling for why that is?

Wilco: I hope I helped a bit with that the last half year. But no, of course, no, that's a, just a joke. But when I started like at the beginning of this year, I was looking on Google, searching for information and information was hard to come by. And at some point I was thinking, yeah, of course, books and podcasts, the GreenIO and Environmental Variables.

That is a place where I find a lot of that information that I needed. But at some point I was like, okay, so maybe I need to talk to people to gather more information. And when I was searching on LinkedIn for people who knew more about green IT and green coding and green software, I found out that there were all these kind of bubbles, yeah, in the Netherlands we call them bubbles, like you have 20, 30 people working on a certain topic.

And I was like, also at the same time, we were thinking, okay, how can we build a community for the meetups? And I was like, yeah, the only thing that I can do is connect to these people and make aware that the other bubbles exist and to keep on doing that. And when I was finding out, and I found the other group and another group and eventually there are, I think, right now, yeah, I think a small 2000 people in the Netherlands busy with this topic.

But a lot of those people are not aware of each other. So you have to think about people working on CSRD and monitoring, people on FinOps, but are really that are interested in sustainability, people who are like, "yeah, we, need to measure not only emissions, but also nitrogen and other things and PFAS," is it how we call it in Netherlands?

So yeah, I don't know if it's because of a trend or because of a lot of people now with CSRD are looking, "okay, how do we need to do this?" But yeah, there's a lot of activity in the Netherlands.

Anne: That's, that is really interesting and there's a lesson there for anybody who wants to grow a community is that you went out and found all the small communities and hook them together. That's an incredibly valuable thing to be doing.

Wilco: And it's also cool that there are also, there's an organization, the National Coalition of Digital Sustainability, and it's a little bit different, the acronym in the Netherlands, but they are already busy with this topic for more than 10 years. And then when I was doing my thing on LinkedIn, and then I found out that there was an other meetup group from a bank and a consultancy company, and then we're already busy with doing meetups in the year before.

And, but they weren't aware of even sometimes other organizations and also like a Green Software Foundation, but there's also of course the Cloud Native Computing Foundation, where you have a sustainability group. And I'm not even talking about things like Climate Action Tech and, that kind of organizations of groups.

Anne: And of course actually trying to link together these groups is incredibly, so we have actually met in person, we met at a Green IO conference in London in September, which was great. And that was very good. That was a very good way of getting a whole load of people in Western Europe basically to all connect together and have a drink and see one another face to face.

Very effective. So..

Wilco: Yeah, it's very inspiring to see other practitioners and also other perspectives from UX to Green Ops to yeah, all the different, because that is something that is so clear. And this is also maybe eventually, if we go to macro, why it's hard to implement is because sustainability, it hits so many fronts within a company or an organization, there are so many roles.

Where if you start thinking about, "okay, what am I, actually doing?" So the impact, what we're doing from boardroom to eventually DevOps teams or members of DevOps teams. And it's cool to see that all those people come then together in such a conference. Yeah.

Anne: Yes, it is true. And I've said this many times before that it's, everybody's being bonded together by having the same goal, which is reducing carbon in the atmosphere. An intrinsic goal that's, you know, it's doing good. It's improving the world. And it does mean that you can share common ground with people you wouldn't necessarily previously have shared much common ground with.

Wilco: Yes.

Anne: So, I mean, you, said you've only been interested in green software for about 10 months and you've certainly done an awful lot during that time. How did you get started? How did you get started organizing meetups particularly?

Wilco: Well, in January, my former boss introduced me to the Green Software Foundation website, and I immediately noticed two things. So CarbonHack24 was on the website, the Hackathon and the company I work for, they really love Hackathons. So I formed a group of volunteers together. And beside that, I will come back to that later.

There was also a lag. I saw the website of meetups in the Netherlands. So I reached out to Asim for advice and he connected me with a group of Green Software Foundation employees and the Green Software members in the Netherlands. And including with Pini Reznik and together, we started planning. And by April, we had our first meetup.

And my team even won the CarbonHack24 Best Contribution, which is crazy if I think about it, which was such an incredible motivator. And each step I took from organizing meetups to winning the hackathon felt like a chance to make a meaningful impact.

Anne: Which is fantastically good. That is good. So, but you didn't stop there, did you? You became a green software champion, which is a new Green Software Foundation kind of a project to build up people who know more and can go out and shout about green software. How did that happen?

Wilco: So by May, after hosting two meetups and writing over 10 articles on green software, I felt certain that this was my calling, right? I felt so much passion and fire. So, I mean, I think through all the content I create and all the conversations, that was kind of clear. And I discovered the Green Software Champion program on the Green Software Web Foundation website.

And I knew it was the right path to amplify my impact because I believe that if you have a recognition of a certain organizations that especially like multinationals and big organizations are like, "Hey. This is something that we maybe need to take more seriously." Not because of me, because of the, "Hey, this is, there's something going on here."

And yeah, forward four months and we've organized five meetups in just over seven months. And with the sixth one on the way on the 22nd of November, with the Green Waves Hackathon at the TU Delft, that's a university in Delft. And I now written over 150 articles on LinkedIn, collaborated with professors to bring green theory into practice.

And we're still, I'm still doing that and have given six talks so far. So, with more plans and each step has deepened my commitment to building a sustainable tech community.

Anne: Which is absolutely fascinating. It is amazing how much you've done in 10 months. So, but what next for Wilco and the green IT community?

Wilco: Yeah, what is next? That is a good question. So what I really try to do is to follow this certain path. So when I started gathering the information, I found kind of out, okay, there is already a decennia of research done and a lot of information, but to some degree, we have a hard time transferring this information to other developers and we are kind of stuck.

So for me personally, I was really invested. "Okay. How can I make this first stepping stone on making this a thing that other people can understand?" And that's why I started to invest what I now call EQUAL, 

Energy, Quality, Utilization, and Load. So the idea that you have an application that has a certain algorithm or a certain logic that everyone understands.

So kind of a loop. And if you have this loop, so a few important things in, in, in green coding and green software is, okay, how can we, based on utilization, can we estimate and not exact utilization, but can we estimate the energy and then eventually related to emissions? So if I started understanding, hey, wait, utilization to some degree is like the amount of threads that the CPU is running.

Also, of course, based on the cores. So 50%. You would maybe expect, like, if you have like 12 cores that with 50%, six cores are running, but it is not necessary at the truth because frequencies, of course, can be higher and lower and there are some things going on, but if you start, okay, so the amount of threads, so let's say the 12 cores.

So you can have then most easiest for a clock, 24 threads. So if you have a loop that you can start playing around with two threads, four threads, eight threads, 12 threads. So that's a first parameter you can give to this loop that I placed in the API. Then the next one is the amount of iterations. So do I want to do a small test?

And the funny thing is one line, one normal line of code, because I can make a line of code that just can gas pedal the CPU 200 percent for an hour for one line of code. If you take an average line of code, it's most of the time so insignificant for a CPU that like, if you have a loop that is running within this very small time, then 10 million iterations is quickly over that's very fast.

So my EQUAL starts with 10 million and it goes eventually to in the billions of iterations. And then the third parameter of equal is the use case. So you can place in the iteration just an I++ or just any use case you kind of want. And then what I kind of start doing when the loop is running, I start asynchronically, I start measuring the utilization of the cores in a very high time resolution. Like 10ms, 20ms, 30ms, so very small. And then after the whole loop is done, what I then do is I can place, those samples, I can eventually connect them back again to the traces of the code. And then you can see a certain few things happening here. So what you can see happening is if you will reduce the amount of operations happening on the CPU, yeah, of course you probably,

your, utilization will be lower and your energy use. But there's also another thing is because CPU, how CPU works, that sometimes you will see unexpected behavior. So although you start to play around with these use cases and you think, "Hey, this should be more efficient" and you start rerunning it and then you're seeing things happening like, "Hey, wait a minute, if I run this on 18 threads, this use case works more efficient energy-wise on 12 threads.

How does that make sense?" Well, that is something I tried to figure out, but this is what I place in a demonstration style, because if you demo this and you show us this loop and everybody understands the loop and you show this in the user interface, and then with Prometheus, with eventually graph set that you show to everyone,

then it's makes more clear for, "Oh, okay. Wait a minute. There is beside time efficiency, compute, power, and there's also this third dimension, energy consumption. And it has sometimes another effect than we sometimes expect." And I started, okay, so if I can eventually use this in talks, if I can start using this in eventually a use case for blogs.

And eventually this is also where the workshop that I'm going to give from January is built on. So this is for me is like the future. And then my estimation model that I just created on Prox. Which is kind of built in, of course, in your Linux kernel with just a dumps is now not the most perfect model, but this is the reason why I work where I have contact with, especially University of Groningen to make this model eventually better with socket management of measurements and real kind of measurements.

And yeah,

Anne: So that so the all sounds very, so basically you're working on a tool that helps people measure, at least proxy measure, their carbon emissions through energy use,

then tune it and improve it. And I'm guessing that there's kind of several advantages to that tool. If you work on that tool to deliver the same functionality using less energy, the product, your application will run faster.

As you say, CPU cycles are another proxy for energy use. So is that commonly what happens? It improves the performance of the application?

Wilco: Well, if you say performance in time, well, this is a funny question. So if your focus is on performance in time, sometimes if you say I make my code quicker, it sometimes start using more energy. So, and then the question is, I have this value, "is it okay for a user to wait on it or does it need to have this very fast?"

And there's also a difference between the performance if, and this isn't on the, on the, in the cloud, on a server, almost impossible. If you on A CPU only use a few cores, it has often a very higher CPU frequency, so it probably will be with this exact same code will, be quicker than if the complete CPU has 80% of the cores or a hundred percent of the cores active because the higher, especially with a hundred percent because of heat, the frequency goes down and it kind of becomes slower.

And this is what I say, you can, of course, if you lower the amount of code or operations to the CPU, it will eventually be more efficient. But there is also this thing going on that the CPU has sometimes 20, 30, 40 percent influence based on the state of the CPU it's in. And yeah, your code can have some influence on it, but it's more in a different way.

So how many threads am I spinning up? Or how many things are going on this server that I'm running my code on? And yeah.

Anne: So yeah, I see now why you, and when we go over onto your next, onto your second article, talking about trying to balance these micro line level changes with a more macro perspective. That's yeah, it gets quite complicated and you don't always know what's going to work until you try it. So obviously, you know, the whole point of running this tool will be to make a more energy efficient application within your kind of high level goals of your SLAs. But I'm imagining it's also quite fun, that it's quite a good thing for a hackathon, it's quite a fun thing developers to play with.

Wilco: Yeah. Yeah. That. And also you can just replace, that's what I every time keep saying. The loop is just for demonstration purposes that people understand it. But I use this whole logic in an API and you can just put your own code in there. That's the whole thing that we're, with the workshop, going to do.

So people will build their own API. And then with the same process of asynchronically measure when you run this code, what's happening, because you will see funny things going on when you're waiting or things are connecting to a database or connecting to another API. And based on how things are programmed.

So are you waiting with a loop that is pushing your CPU high? Or are you using smarter mechanics so that there is a drop, but is a drop sometimes something you want because if you want to be very efficient with your resource, you kind of want to maximize it around 80%. Well, I don't want to be come too fast to conclusions yet, because I think we still need to figure out what the patterns are and what are good patterns and bad patterns, but yeah.

Anne: Yes, because as you say, and again, this leads back to the kind of macro micro picture. It's definitely.. In certain circumstances is definitely the right thing to do to if you're waiting on an API call or something to kind of say, "right, I yield all the threads and everything running on the machine to somebody else to use the machine while I'm waiting" so that the machine is still highly utilized whilst I'm waiting for my API call, but that relies on you having a design or an architecture, which might be within your application, but it might be within your operational decisions. You know, are you multi tenant? Is there somebody else or some other company or some other application that is going to be able to pick up and use the machine while you're yielded?

But if you're just waiting around. then that's less good. So then the machine is just going to waste during the time that you're waiting. So you're, right in saying that there's so much context to this.

Wilco: Yeah. Yeah. And, okay, so, okay, I'm really getting excited about this topic. So, because I instantly thinking like, yeah, of course in the cloud, in the server, you maybe they have not full control, but one thing we know the grid is getting fuller, the electricity grid. And one of the things is that some university have research done is how can we optimize the devices that we have better?

And one of the things that we in the Netherlands have, we have a lot of people with solar panels. We're not using optimally the, electricity that is generated by the solar panel. So if you think about the following, so what are the devices that you can easiest, how do you say, charge based on the solar panels, then it's mobile devices or tablets, or maybe a laptop with maybe a good battery lifetime.

So now we're from originally, we always been like, "okay, we need to move logic to the server because it's more secure and you can not manipulate it." But if you start thinking about, "Hey, we need to optimize the devices better." What if we start using a WebAssembly in a better way? So things in your browser or on IOT device, or in this case, then on mobile or tablet, and use that green energy, especially if someone is as smart that like not charging it at night,

but more than the day when the solar panels are active, that is always like an important catch. And of course the solar panels have some embodied carbon, but yeah, but still, so there are so many cool things you can go on in this.

Anne: It is really interesting that renewables, unlike, you know, you aren't going to run your own coal fired power station. You say, nobody apart from Wilco has ever lived on a coal fired power station, but particularly solar, it's a very distributed technology. There are lots of people, I've got solar panels, and when they're running and the sun's shining, I've got more power than I know what to do with.

When I had, when I got it installed, I said to the, chap who was installing it, "what should I do with this? You know, and he said, "Oh, well, you will have, there are times when you are going to have more power than you know what to do with." So make sure that you, it might seem historically, it's always been very inefficient to heat, your water with an immersion heater rather than using, a gas burner or something like that. So it's a very inefficient way to do that, to heat the water. But if you've got free energy and it's just otherwise going to go to waste, heat your water with an immersion heater. He said, "get a swimming pool." Not that I did get a swimming pool, but get a swimming pool and heat that up because some of that has changed the way we need to think about, we still talk about green software very much from the perspective of efficiency and improving efficiency and reducing waste.

But I would say even more importantly than that, it's about doing more when the sun's shining. You know, don't forget efficiency when the sun's shining. You might want to write applications that are very efficient, that operate in totally different ways at night and while the sun's shining.

Yes. 

Wilco: Yeah. I would even dare to challenge the following. So. If the sun is very shining in the day, what I see in the Netherlands a lot is if you look to the electrical grid, especially also on windy days, that's, or somehow, and I don't see the relation yet, is that the industry seems to be working harder in the general.

So you still see the gas turbines in the Netherlands emit a lot of emissions. So it's very sunny. And if you go to electricitymaps.com, to the Netherlands. And you look there, you will see then the solar panels, generating a lot of energy. And sometimes of course, also the wind turbines, but that's also the gas turbines.

And that is mainly because there's a higher demand or there's instability on the net. And so you could even start. And that's why I think that carbon-aware is a very complex topic, because are you gonna do a weather forecast and then run, but then find out that maybe the grid was emitting more than you expected?

And lately, the last days, we had a lot of emissions in the Netherlands. Or are you more going to try to indeed optimize the devices we already have that maybe run on green electricity? There's no perfect answer in this, but we need more data, we need more access, but I understand from security standpoint, even with electricity grids, I mean, they want us to give us the information, but they're scared for terrorist attacks or for things that the information that we want to do for good that can be used for bad.

But yeah..

Anne: I mean, and quite often, grids just don't have the information yet. I mean, there is, for carbon awareness, we were a long way from having really good data on that. So I always tend to say, don't start, well, you could pick proxies, perfectly reasonable to pick a proxy because actually the difficult thing is designing systems that can respond.

That's going to take years to do. So you can, in many ways, pick a proxy now, even if it isn't great. Design a system that is responsive to that proxy. And then as that proxy gets better, your system will get better. So you might be going, " actually that proxy's terrible" now, but the difficulty is, well, getting the data is often somebody else's problem.

It is put pressure on, you know, suppliers and energy grids and everything to provide good data to us. But in the meantime, the big job for us, the thing that's going to take us a long time is redesigning our systems to be able to respond to that data. So that's things like thinking about what your graceful downgrade options are for when the grids are very dirty. You might have to move big, having big latency-insensitive tasks that you can move to when the sun's shiny. The Texan grid is doing a lot of good work on that. And I talk, again, I'm sorry, I talk endlessly about large, flexible loads. So the Texan grid is putting out a call to industry for large, flexible loads, which you can run, which are latency intensive, don't matter when they run.

But they can run when the grid is full of solar because Texas is quite rightly putting a whole load of solar panels because it's very hot in Texas, it's very sunny in Texas and there's a lot of desert. So they want something to run on that, solar power. It's very sad at the moment that the people who are really responding to it are the Bitcoin miners, but AI is another potential customer who have large flexible loads.

So very CPU intensive loads.

Wilco: Yeah. Oh, but this is perfect because, okay, you kind of influenced me when we had talked in London. So to think more about, okay, if you can better react to renewables in a more flexible way, because I started thinking about it, especially if you put it in the following perspective. So most front-ends have a lifetime of two, three years.

There is of course always shorter and longer. Back-end systems often have like a longer lifetime span. So if you build something today in 2024, and it runs five to six years, that means that it still runs in 2030 when we have our big first milestone that we should have reached. So if you're not building your software today that it can adapt, that it can be flexible, you have to refactor things in the future, or you're getting in a stuck position because most of the time, especially with big systems, the more you build, the more dependencies you get, the harder it becomes to eventually change things when that foundation is set.

So, yeah, I really like that idea that, although we maybe not have all the right answers now, and maybe the situations are not always perfect, but it doesn't mean that you shouldn't start thinking and implement in this way.

Anne: Absolutely, I think it's going to take ages to do this, it's a completely different way of thinking about it. I mean, there are tools out there that already exist, that can help you get into this way of thinking. So I'm a huge fan of spot instances on AWS and, or Azure or, preemptible instances on GCP, because they're, a kind of mini version of Texas's large flexible loads.

You say it's a small flexible load. You say. What you're saying is "I've got this load, it's flexible, run it. I don't really mind where it runs. I don't have any particular SLA associated with it." And you can use it for, the clouds use it to improve operational efficiency, which is good for green as well. But in the future, I can see those loads absolutely match to what we're going to need in order to shift work forward in time or later in time or forward in time.

Wilco: Yeah, I really believe to be, to some degree as much in control as possible because it's easy to let some other company or SaaS solution fix things for you, but especially from a board perspective, it's a good idea. Like, okay, we're working together with hyperscalers and we're doing things serverless.

And especially if you're a big organization. And then mainly like if we do serverless, they are kind of responsible to fix if the utilization on the background is well organized. And I find this always very interesting because yeah, to some degree that's true. So for consumers, normally, if you go to Azure, you're probably, if you're using serverless, I think they can really optimize it very well.

But if you have a very big multinational where kind of, they already reserve a certain space for you in the data center and you're running serverless. I'm very curious because we don't have the information now. So do they reach indeed that more utilization because you work serverless or do you have virtual machines where often in a while some function comes by and it runs and it's done?

So that's why, and you mentioned spot, that's why I like, and not because I have stocks in them, because it's not possible, I think, but I, that's why I love Kubernetes and Cloud Native thinking so much. That's also why I really like to check out also every time what's happening in the Cloud Native Computing Foundation environment, because course that is where Kubernetes is very active, because I strongly believe that if you are in control as far as possible, you have, not only you can better measure what's happening to some degree, although you're doing some estimates, I think it's also from a security perspective, it's a good idea. And I think also from just the willing, yeah, the willing to be responsible because nine of the time you're also responsible of the value that's running in your cluster.

And I think just outsourcing the company's core values or the product values outsourcing to somewhere else, it's possible, but you're giving them also some control away. Yeah. And this is something that I think a lot about, but not having all the, I think I will never have all the answers, but, yeah..

Anne: I think you're very right to be skeptical about serverless running on prem rather than in the cloud. Cause it does feel to me like, I touched a little bit earlier about multi tenancy. When you're not doing something, what's somebody else doing on the same machine? A lot of these tools like serverless work really well because you're in a multi tenant environment.

So the classic example is with, if you're in the cloud and you're, you might be sharing physical resources with a company that has very different demand profile to you. So if all of your, if you are, say a retailer, I used to be head of IT for an online retailer, then all of your resources are assigned to your demand.

So if there's a peak, then you have provision for your peak. And that means that a lot of the time, so say Christmas might be your peak. You have a provision for Christmas. And then most of the year, the machines are underutilized because you had to provision for Christmas. If you move into a multi tenant environment, one of the root things that the hyperscalers attempt to do is to pair up, or not just pair up, but group users on machines in such a way that they have different demand profiles. So everybody has a correlated demand at Christmas. Maybe if you're a retailer, you might be sharing a machine with a training company. And the retailer is very busy at Christmas and the training company is very quiet at Christmas.

So you kind of, you, rather than needing to provision for the peak, you are getting better utilization of those machines over time. And serverless is a little bit of an example of that. It's, the win is with multi tenancy and multi tenancy is easier in the cloud than it is on prem.

Now, having said that there are some multinationals that are so big and have such complicated systems internally that they are effectively their own multi tenant. I mean, Google is a hyperscaler, but forgetting GCP for the moment and looking at Google's internal tools and applications, they are their own multi tenants, they have enough variability in what individual tools are doing that they can act to keep their machines fully utilized all the time. They're kind of designed for that, but most companies are not at that level and quite haven't quite designed for that yet. But I agree serverless internally on prem for a small enterprise probably doesn't buy you that much.

Wilco: And there's also an other perspective on this. And I heard this in a conversation with a bank and that was also very inspiring for me. So. The fun thing is everything we just talked about can also work in harmony. So if you always have a baseline utilization, you could do that on prem where you know, "okay, with these applications, we always have activity.

So we have a certain utilization." And what they say is what then a big, they have done in control. They know exactly the energy usage and because of adventure, the energy uses also the negative impact and emissions and avoid carbon. But when they have peaks, they overload to the cloud. So they're like, "okay, so if we have, then we go there." And you could kind of also do sometimes maybe do the same if you want to save costs to maybe the devices to some degree that using your application.

So there are ways, and this is complex, but I think that is a way. And also there is a topic that we had in the past and nobody's really talking about anymore because it's complex. It's distributed computing, of course. And that's also another pattern that could, have a play in this. So, yeah..

Anne: Yeah. I mean, distributed computing is very potentially well aligned with this whole thing of demand shifting and shaping and saying, you know, "actually I've got a, thing here. We need to treat it as essentially asynchronous." And of course, asynchronousness is, or asynchronicity, is a really key part of distributed systems, designing distributed systems well.

Completely synchronous distributed systems are often a little bit of a distributed monolith. You don't, you often don't get the same breadth view of

Wilco: a funny thing. I heard, yeah, if you're YouTube, you can indeed see, like serverless monolith, microservice monolith, there are the.. So sometimes you think, "okay, this is a good start, if we design it that way," but if you create so much dependencies and then you're still, on an abstract level, creating a monolith.

Yeah.. 

Anne: Yeah, except a more difficult one. Because a monolith, the value of monolith is, quite simple and, well, it's, yeah.. Monoliths and microservices and distributor systems all have their place. It's always a matter of choose the right tool for the job. That is efficiency 101 is choose the right tool for the job.

Wilco: Oh yeah. A hundred percent. Yeah.

Anne: So we have chatted so long, we've got hardly any time to actually go over your articles. So do you? Let's see, we were going to talk about three articles. One was about your using the, SCI and the impact framework.

Wilco: The thing that I would like to say on that one is that sometimes maybe people think that I'm only interested in energy and that is absolutely not the case. The only thing that I came to the conclusion, not that you have to do it that way, but to some degree, because we're still also figuring out how to best measure emissions based on the grid.

And the other thing is embodied carbon. So we can do a lot of stuff with lifecycle access data of assessments and hopefully also the correct information we get from our scope three suppliers, but to some degree, if you know, and this is what I always keep saying, and maybe it's, if you know, at what moment, at what location your software was running on what kind of resource type on what kind of hardware, and you log that down, the most important thing is energy you cannot historically get back. It's you compute it and it's gone. So, and we have still with hardware disproportionality, but we have still things to figure out, okay, how do we really measure it? And I really strongly believe if I can help to get it down somehow, we to some degree can historically get back. Okay, we, knew that in that moment in time, even with electricity maps, with historical data, we can get the emissions from that moment, that location.

And also with the information we gathered with past procurement information or supplier information, we can get the embodied carbon right and that kind of stuff. So that is the main reason why I really focus on that E of energy and the SCI, the Software Carbon Intensity, you know.

Anne: The thing I like about the E is it's something that software developers can have effect, you know, it's not like we're, we don't care about the other things. We're just trying to focus on where we can have, make a material impact. And there is somewhere where we can really make an enormous impact. Our ability to change things in other areas is more limited and that's why we started the Green Software Foundation, was to find ways that software engineers can improve things and people related to software development.

So product managers, testers, that kind of thing. It doesn't mean that we don't care. It means that we're looking at where we can have the an impact.

Wilco: Oh and the quantum entanglement. That came from the idea, I'm a big nerd and I, like to know things, a lot of things, and it's also counts for physics. And I'm watching a lot of YouTube videos, also science videos, and a lot of videos every time came back on the concept of, okay, we have Einstein's, real, real, 

Anne: Relativity. Yeah. Yeah. 

Wilco: Relatable.

I'm still going wrong here, like, like about the, eventually can calculate things going on in a black hole. And we have quantum mechanics with quarks and all cool stuff. And when they try to bring this theories together, they have all these kinds of issues that doesn't really match up. And that's what I've found really interesting because I started before my journey, I had some information from the holistic view perspective.

So from boardroom perspective, from more top, if you think from top to bottom. And they are more interested in compliant data. So they, their data that they're using has to be valid, has to be compliant and also streamlined. So eventually they can report and also use it to create internally policies on it and that kind of stuff.

But if you talk like the software engineers, we're really like, "yeah, we're missing data on calculating the correct energy consumption. If we run software on this CPU or this GPU and we're still figuring things out," but very on the lower level, but I think on a boardroom level, they are probably not that interested in how correct the CPU or GPU was, as long as it is correct enough to make it right for the reporting.

So, and then the other thing where it really aligns to is at some point I came to the conclusion, like, okay, we can measure a lot of things. We can have all these metrics. But at some point the numbers are not going to change outcomes, decisions do. That was kind of a big moment for me. So there has to be at some point the sustainable decision making process going on from bottom to top or the other way around where those worlds connect to each other.

And that's what I really, with this article for the first time, it was also a few months back. That I was thinking, "okay, how can I connect this worlds together?" So what would the steps be? And the main, I think outcome for me from this whole thought experiment was, is that based on certain levels in the organization, you have different requirements for the data you want, different tools you probably want to use, and also different reasons you want to use this. And yeah, that is still a thing that I'm working on. One thing where I think that everything in comes together is a simple concept that if you set up an organization, a big IT resource list, or even resource less in general. And you have this resource list where you have like your mobile phones, your laptops, but also your cloud resource.

We have the infrastructure as code. We could generate those resources. And you have all these resources in this list. And if it's a multiple of the same resources, you just do a count after it. And then you would say to each resource, you would say you're using so much kilowatt hour a year. This is the footprint estimated benchmark or real time.

But also this is the security need because the security people are in the same thing as us. They really want more information. And that would be a great starting point because from that perspective, you can eventually bring that to information that is more on the higher level of the organization and you also can connect it to the really nitty gritty things on the bottom.

But yeah.

Anne: I was very interested in your, quantum versus relativity. I also, I have, my degrees in theoretical physics, but I will say, it kind of, when I read it, I thought, ah, now this, I find this quite an interesting analogy because I was, there's two. I was the fan, I was a particularly fan girl of neither.

I was always a fan girl of classical physics. I like you remember that enterprises operate at the level of classical physics. I think it's actually a really good analogy that enterprises are thinking about, "is my data compliance, you know, are my bills ridiculous, are my system staying up?"

And I think that actually, and only once that's, you know, in the Maslow's hierarchy of needs or the Maturity Matrix that the green software maturity matrix from the Green Software Foundation that Pini and I run together, the first thing you need to do is get your operations right. And that is classical physics.

You know, it's kind of like, are you paying too much? Are you over provisioned? Do you have a whole load of machines that you're not properly monitoring and using anymore? Maybe they are security holes. So you've got security problems there, you've got financial problems there, and you've got waste there as well.

You've got loads of carbon as a result of, 

Wilco: yeah. That's the one I forgot on my list. Waste that also needs to be Yeah on there. But yeah..

Anne: I love relativity, I love quantum physics. But for most enterprises, I would say start with classical physics, really just focus on getting your basic ops good, do those thrift a thons. Your last article, which you're not going to have time to go over, but I would strongly recommend people read, is about the alignment of FinOps and green software.

And I would say that the alignment of FinOps and green software is your classical physics. Oh, that's awesome. It works for every single enterprise. Nothing fancy is required. It's totally aligned with the business, with the desires and the goals of your business. No one is going to complain that you saved the money.

Wilco: But I think that is,

Anne: once.

Wilco: yeah, I think that's very important because sometimes, I mean, a lot of people, they say, "why are our companies not starting?" Or "I have this idea, why is no one picking it up?" And I think we also have to be honest to ourself. If you invest money in something, you want it to be, have a certain maturity level and also, especially if we buy something, we want to know that it's going to work and that's, yeah, it's going to have the value that you expect it to be.

And so together, I really believe together that we can, and the whole, or the whole line of field can bring it to a certain maturity level. So with the maturity matrix, that's a different story. Yeah. But because I think that a lot of people always focusing on, "yeah, but if you do this, you have lower of cost," I think the most important thing for a company to be more interested in how can we solve the things we need to do for sustainability 

is to make it more frictionless implementation and have it less risk, because I think that if you can do it in a way and it doesn't have to be perfect, but that it's easy to implement and to use, I think companies will start doing more.

I strongly believe in that.

Anne: I do. I think there's a huge, people really care about being green. People do really care about the environment. They don't know that there are changes they can make through their work as software engineers, that will make a huge difference. And if they do think that there are changes, they have a tendency to think those changes are misaligned with their company goals, which quite often they are because people think, "Oh, I'll just rewrite everything in Rust."

And that would generally be misaligned with the company's goals. But going through and making sure that you're not over-provisioned in your data centers and you turn off stuff that's not in use, that you're being cost-minded, that's totally aligned with your business goals. And it's also aligned with being green.

On that, I think we really need to raise awareness of that.

Wilco: Yeah. Yeah. And that's what I really can bring back to the fact, like if you.. And like, Hey, the mention of time efficiency, something has to be fast, but you want to do it in the most low energy consumption, but then the most important thing to, so what is the value, what you're doing is going to bring. And it's something that we struggling in for a while because we really tried with agile, define like the customer value, business value.

But I think those three together, if we figure that one out in sustainability in a better way, we really can make some jumps.

Anne: And with that, I think we'll need to end because we have been talking for ages and it's been absolutely fascinating and I've really enjoyed it. So thank you very much indeed, Wilco, for this. So where can people find you and get involved in your meta communities?

Wilco: Well, mainly I'm active on LinkedIn on my name, Wilco Burggraaf. I try to post every two days, a new content. And yeah, you can also, if you look up meetup.com, you can find the meetup group under the green software meetup Netherlands. And another thing is of course, from January, I'm starting with my workshops.

So if you're located in the Netherlands and you're interested in that, yeah, reach out to me.

Anne: Excellent. Thank you very much for coming on this episode. It's been really fun. And if anybody wants to contact me or chat to me about my, I also do workshops, which are not the same as Wilco's. So we are, I would say we run a complimentary workshops, then you can also take me through LinkedIn. And this is a final reminder that all the, well, we didn't really talk through the resources for this episode, but they are good background resources for our discussion.

They're quite easy and pleasant to read. So have a look at the links to Wilco's posts, follow Wilco on LinkedIn and read his articles. They're very, very good. So thank you very much. And I will see you all in the next episode. Goodbye for now.

Wilco: Bye.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show. And of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.

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]]> The Week in Green Software: Powering AI on Atomic Energy The Week in Green Software: Powering AI on Atomic Energy Thu, 28 Nov 2024 07:00:00 GMT 54:54 x1ln6rq1 false https://podcasts.castplus.fm/e/p8lxly08-the-week-in-green-software-powering-ai-on-atomic-energy 68dc7d116d92c33f9c88f695 68dc7cef46a2532cdd8d1c23 90 TWiGS host Anne Currie is joined by Stefana Sopco, Marketing Manager at PortXchange and a passionate climate activist, for an insightful discussion on the intersection of AI, sustainability, and the maritime industry's decarbonization journey. Stefana shares how PortXchange leverages green technology to help ports achieve net-zero emissions through innovations like just-in-time arrival and emissions tracking. They also dive into the challenges posed by AI's growing energy demand and explore nuclear power as a potential ally in the climate fight. Throughout the conversation, Stefana emphasizes the importance of mindful technology use and the urgency of adopting sustainable solutions.

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Stefana: Is ChatGPT going to decarbonize the maritime sector? No freaking way!

 I haven't heard of anyone saying how they plan to use ChatGPT to decarbonize or to help ports account for their emissions.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne: Hello and welcome to The Week in Green Software, where we bring you the latest news and updates from the world of sustAInable software development. I'm your host today, Anne Currie. And today we're going to be joined by Stefana Sopco, Marketing Manager at PortXchange, a leader in driving ports towards a net zero future.

Stefana's work centers on using digital solutions to reduce emissions in the shipping industry. So a bit, a little bit different from our normal guests who are mostly directly programmers or very programmer related. So Stefana's vision extends beyond ports to broader decarbonization goals across the tech industry and the maritime industry.

She's also passionate about D&I, diversity and inclusion. And hopefully we'll talk a little bit about that today as well. So in this episode, we're going to talk about three articles that are all about AI, and the effects AI and the demand for AI is having on grids, particularly in Europe. We'll be talking a little bit about the power requirements of AI and the, grid capacity requirements.

And we'll be talking a little bit about whether or not it's always going to be a bad thing, or will actually could potentially be a very good thing for grids. And I'm quite a positive person. I think that there are massive advantages that come from the extension of the grid to support AI. So that'll be an interesting thing to talk about.

I'm also really interested in talking to Stefana about this because a lot of the issues around AI are effectively logistical issues. And logistics is something that we don't think enough about in the tech industry. But in the maritime industry, they think about it a very great deal, so I'll be very keen to hear Stefana's thoughts.

So, on that note, welcome Stefana. Can you start by telling us a little bit about yourself?

Stefana: Thank you so much, Anne, and thank you for the invitation. I'm really honored to be here and super nervous, to be honest, because it is a heavy subject, and I really hope I will do it honours. My name is Stefana Sopco. I'm the marketing manager at PortXchange. I've been working in the energy and maritime industry now for five years.

And for the past two years, I've also been a core member of Women in Tech, Netherlands, an NGO striving for diversity and inclusion, as you beautifully mentioned. Here I act as the marketing manager and spokesperson as well for our Dutch chapter. I'm an active member of WISTA NL and for those who don't know, WISTA stands for Women in Shipping and Trading Association.

And like I like to say about myself, I declare myself a climate activist next to my proud feminist tag, which I've learnt to embrace completely, especially in the past couple of years, and one fun thing about me, you will always find me mitigating for a cause. Either diversity in the industries that I work with, or animal rights, animals are my life. And of course, climate change. And next to my professional activity, I try my best to act as a role model for the younger generation.

I mentor and guide young professionals who either want to start their career in marketing and come into these industries that are very intimidating for some, or might want to switch careers. And I also dedicate a big bunch of my time to guiding expats, me being an expat as well in the Netherlands, and I try my best to help them navigate their journey here the way I wished someone would have guided me back when I moved here six years ago.

Anne: That's great. That all sounds very good. Interestingly, this isn't even our first all female Environment Variables podcast, but normally that's because I have one of my two co-authors from Building Green Software, the new O'Reilly book, Sara, or Sarah on with me. So I think this is the first one. I'm going to guess this might be the first one,

all women, that doesn't involve Sara or Sarah, so it'll be an interesting change. So say a little bit about me, because I'm not the normal host. I'm quite a common host, but I'm not the usual host of Environment Variables. My name is Anne Currie. I am, as I've said, one of the co-authors of the new O'Reilly book, Building Green Software.

And if anybody who's listening to this podcast, you really should be reading that book. It's suitable for everyone. You don't have to be a techie, you'll enjoy it. It'll be useful. I'm also the CEO of a learning and development company, Strategically Green. So my focus there, our focus is trying to get companies to build up enthusiasm and internal expertise on how to make these changes happen.

So we do workshops and training, things like that. So if you want to talk to me about any of that, then you can find me on LinkedIn. So what we're gonna be doing today is we're gonna be deep diving into three really interesting articles about AI and their impact on the grid. But before we do that, Stefana, do you want to talk to us a little bit about your work decarbonizing the shipping industry through Green Tech?

'Cause that's something that we have never heard about before, so I think it would be very interesting.

Stefana: Now, I feel even more stressed that I have to do the honors about talking about green tech in shipping. But before we dive into the goodies, I really want to take the chance to thank three of my colleagues, Eugene, Abhishek, and Sjoerd, because they helped me prepare for this session. I was a little bit nervous. I really wanted to put our technology in really a beautiful light and also talk mindfully about technology. And they've been extraordinary. They've been very, very supportive and I wanted to thank them. So maybe I should start by mentioning that PortXchange is a B Corp organization.

For those who don't know, B Corp by definition means impact before profit. And you see now a trend of more and more companies being B Corp certified, which is in direct relation with the Green Software Foundation's principles in a way or another when I looked into it. And in addition to striving towards sustAInability within the maritime sector, we also consider socially responsible and ethical ways for

everything throughout our business, so our entire value chAIn. Our mAIn mission at PortXchange, like you said, is to help port authorities, predominantly, worldwide to achieve net zero emissions. We help them by measuring their emissions throughout the entire port, and not only track them, but also analyze them and report them and take proactive steps to reduce them because that's the ultimate thing that we want in the end.

But I'm really proud to say that our green tech ecosystem is larger than that. We have developed many other products within the shipping ecosystem. So we address organisations beyond port authorities. We have tackled just-in-time arrival, I'm not sure if you are familiar with that, it's a solution that is now quite trendy, but a little bit more difficult to implement because of data sharing fears and thoughts from these organizations. But basically, just-in-time arrival helps limiting the fuel consumption of the vessels, because ships tend to rush through the sea to arrive to the port, like there's no tomorrow, but then, they arrive too early, and what does that mean?

It means that they will stay there, stranded, and the emissions that will go around the proximity of the port population will be higher because they are just sitting there wasting time and emitting more emissions. So our organization acts as a partner in the decarbonization journey of the maritime industry, players like port authorities and shipping companies.

But I really wanted to make a point here is that, as a technology provider ourselves, the computing power that we need also has a carbon footprint of its own. So what do we do to make sure that we are mindful and we remAIn mindful of that? Well, for example, we are extremely careful of using only energy providers that provide the majority of the power sourced through green channels.

We are very transparent with our emissions. We use all kinds of solutions like Goodwings, for example, to make sure that all our traveling is as green as possible. We account for our scope 3 emissions as well. And there's a saying that I really like to attach to PortXchange. How I envision it is business as a force for good.

Anne: That sounds fantastic. So there's loads of things that you've mentioned there, which we could talk about. The changes that you're making within the industry to make shipping more efficient, that's very analogous to something we often talk about in tech around operational efficiency, kind of not using, not being wasteful, not using more resources than you need to, in order to achieve the same goal.

So, and while ships are powered by fossil fuels, then everything that you are wasting is, it results in carbon emissions. So that's really good. But you're also balancing that with thinking about how your tech itself is going to be greener. So yeah, so very interesting things there, which I'm sure we'll focus in a little bit, a bit more on as we go along. But let me zoom in.

I'll get started now with talking about some of the articles that we're going to be talking about So we're going talk about three articles. And to kick us off, the first one is a McKinsey article entitled, The Role of Power in Unlocking the European AI Revolution. So it's an interesting paper and it's all about the fact that at the moment, and it's, there are some very interesting numbers in it.

They are estimated numbers, but they're useful and interesting in and of themselves. So McKinsey estimates that as a result of AI and the desire for AI, data centers in Europe are going to expand from using about 2 percent of Europe's total power consumption to around 5 percent by the end of the decade, which seems quite plausible to me.

And they point out that there are several challenges associated with that for the European power infrastructure. And the article is entirely about Europe, but I think that the issues also apply elsewhere. The first is that we're going to need a lot more green power. I mean, the good thing, the thing that it does say in the article is that the hyperscalers are saying that the power that they're going to want to use to power this new AI revolution will be green.

So that's good. And that was not the case 10 years ago. So I think it's really good that they're now all singing from our hymn sheets on that, they want the power to be green, but they need more green power. They need the ability to get it from where it's generated to the data centers. So they need better infrastructure, better grid infrastructure.

And we need ways to handle the fact that renewable power is not always guaranteed to be avAIlable in the same way that fossil fuels are. So they're gonna need to make changes to, well, we're all gonna need to make changes to handle that. So generally, I thought it was a pretty interesting article.

What was, what were your thoughts?

Stefana: Well, first of all, with any change, we will inevitably feel uncomfortable. But in order to achieve the great outcome that we are all hoping for, we must collectively feel uncomfortable. It should even hurt. And it's okay to hurt because we have to think beyond that. So how does that apply to my world, to the world of maritime and ports?

And do I believe that decarbonization within the port ecosystem is more or less following the same path? Definitely different worlds. Completely different worlds. We are here, within the port world, we are looking at a traditional sector that has been known and popular for having ways of following from back in time.

But we start our presentations at PortXchange whenever we are invited to an event with "ports as battlegrounds for decarbonization." Because if you look in a more simplistic way, ports are the puppeteers, but let's not put a negative connotation there. Let's put a good connotation, as in, it's where the good fight can start for real.

But, as in any other sector, you need proven and robust sustAInability practices. Everything should be backed by verifiable data. I think it's very important here, not verified data, verifiable data. And one thing that is very important and maybe one of the biggest differences between other sectors or other industries is that ports benefit directly from community relations, from public perception, which inevitably leads to public and private partnerships and policy support.

For example, if you look at Norwegian ports, which have declared themselves as the employee of the people. Their ports have managed to not only super digitalize their operations, but they are almost net zero. And it's all purely because of the change in mindset. And I actually talked about this earlier today with someone from Climate Pledge, how your mission and your intrinsically motives can make the difference in this fight for decarbonization. And one good thing to mention is that earlier this month, 

European Seaports Association launched their environmental report. In this report, it has been made a point that climate change is our top one priority for ports. So what does that mean? That means that we should also expect full transparency and full accountability because being at the forefront means you have probably the biggest impact in terms of carbon emissions, et cetera, et cetera.

So ports have the authority in my world. Ports have the authority and influence to move the need. And what I would like to mention is that in my world, I keep saying in my world, it's almost funny because while I'm saying it, I'm envisioning how we're moving from a world to another. We are quite dependent on regulations here, and we have plenty of them.

We have the IMO, I'm not sure if you know about IMO, that AIms to achieve net zero in emissions from international shipping by 2050. And, while we are doing that, we also have checkpoints, 2030 and 2040. Do I think that we will start seeing real progress by 2030? No, I don't think that, unfortunately. But then we also have, since 2023, other mandatory regulations, like in energy efficiency existing ship index.

And then we have the Carbon Intensity Indicator. We have the Monitoring, Reporting and Verification from EUMRV. We have the Emissions Trading System. And more recently, we have the CSRD, which is more predominant for Europe. And CSRD is actually one of our allies in green tech for green tech providers, because it somehow pushes these organizations to report, especially their emissions and their impact.

But now there is a question here that remAIns constantly. We have all of these things and the industry is being pushed from the left, from the right, et cetera. But we ask ourselves, how do we use technology to help us be compliant with all of this? And meet the requirements, of course. And we ask, is AI the answer?

Is, I don't know, well, large language models the answer? Is blockchAIn the answer? What is the answer? We don't know yet.

Anne: So your potential, I mean, obviously when you talk about increasing the efficiency of journeys, making sure people get to just-in-time, making sure that people aren't hanging around getting places early, getting places late, going too fast, optimally using their fuel and being as minimally wasteful as possible whilst delivering the service. So operational efficiency. The obvious thing that springs to mind is can AI help with that? Do you think it can, or do you?

Stefana: Yeah, absolutely. So, AI is not the enemy. Like, It can be. It can be the enemy, but not when used for this purpose. We use AI in our technology as well. We have a couple of case studies about that on our website, but we don't need AI for everything, Anne. That's another thing, because sometimes I ask myself, "do I even need AI in this situation?"

Last year I was nominated for an award by Women in AI, Netherlands. It was Responsible AI Leaders. And the nomination really made me think, what if instead of chasing unicorns, we grab the low hanging fruit and start implementing and bringing into operations the technology that is already avAIlable and viable.

Because we have so much, we have great technology already and we have the skills for it. So I would like to see, because I'm working in an industry that moves slower by default, because there are many stakeholders and many people and many other third parties involved, I would like to see what we have finally being implemented.

And meanwhile, we train and we upscale. But focus should be on scaling what's already there, instead of maybe continuously developing new tech that needs to go through the adoption curve anyway. One of our core specializations, sorry English is not my first language, like I mentioned is to provide optimization models.

And we also provide estimates based on data. Our technology might not be the fancy, super large language models, which everybody seems to be either an expert or an advocate or something nowadays, but statistical algorithms built for the goal that we want to help ports decarbonize by emissions monitoring or by just-in-time or more sAIling, et cetera.

It might not sound fancy. A couple of years ago, it sounded super fancy before GenAI popped, but it's doing the job and it's doing the job very well.

So sometimes I'm in doubt. Are we really that stubborn to think that we need AI for everything? Especially in sectors like the maritime sector, where there's still so much legacy that needs to be erased and so many things that need to be relearned,

forgotten and then learned. We are, the industry is still working with traditional professionals that are very good at their job, but in order for them to embrace AI, they need to embrace technology.

Anne: Yeah. Yeah. And in fact, we find the same in just pure tech as well, outside of the maritime industry. And that's. The lowest hanging fruit is usually very simple. It's turning things off that are no longer in use. It's right sizing. It's just minimizing waste in the same way that you're looking at minimizing basic waste.

The basic waste minimization is where you usually get your biggest reduction in carbon emissions. There's a French philosopher once said that, the perfect is the enemy of the good, that the pursuit of a perfect solution makes you step over a very good solution that you already have in your hand.

And that's true of, it sounds like it's true of your industry. It's certain true of our industry. Manual thriftathons, turning things off, right sizing, you don't have to do anything clever to really start cutting your carbon emissions quite significantly. Yeah, no, we're all the same, really.

Stefana: Yeah. And we also have to understand I had quite an aha moment last year. I went back to my home country, Romania, and was right after I was nominated for this award. And my mom is a teacher at a school in a small village. And people came to me, they asked me, okay, what's up? How's your life? All that. And it was a really big boom with AI and all that, and they asked me, so what is your opinion on AI?

What is AI? And I realized that the majority of the population needs a more simplistic understanding of artificial intelligence. It's not the fancy super professional profile that everyone is putting on LinkedIn or on their blogs and all that. And in order to see adoption, day to day adoption in our lives and in our employees' work lives, we need to make it simpler.

We need to... again, do we really need AI for everything? I keep getting, I keep coming back to this.

Anne: It's interesting you say that. So I read it, I think it was an article in The Economist, last week. It was talking about how AI, the companies are really struggling to work out what they're going to do with AI. Because interestingly, the people who are finding uses for AI are more likely to be individuals because making ChatGPT a live service that anybody could access for free really did kind of open it up to people could just go on, play with it, talk to it, use it to rewrite their essays at school, all the things, good or bad, that you can do with ChatGPT. There it's, interestingly, it's an interestingly accessible technology, isn't it? But businesses are really struggling to work out what they're going to do with it.

Stefana: Yeah, and is Chad GPT going to decarbonize the maritime sector? No freaking way. I haven't heard of anyone saying how they plan to use ChatGPT to decarbonize or to help ports account for their emissions. And another thing that I experienced actually in my close circle is that when they are, when ChatGPT is being taken from them, like for example, their companies blocked the ChatGPT server, et cetera.

They don't know how to do their job anymore, or they are panicked that they will not be as efficient as before. And it's just, for me, it's quite concerning because as a responsible AI advocate, it's scary to see that. Last year or two years ago, nobody have ever thought "I cannot do my job if I don't have this little chat to support me."

But now we see more and more. In marketing, I've been quite active in testing all kinds of AI solutions up to the point that I hit burnout because I was so afrAId that I will be left behind and I will not be employable anymore and companies will not see me as the next gen thing anymore, that I worked myself to burnout and I don't want to see that massively in all industries because there's, there are still many good things that we can do before we rush into something that might be a little bit over the top.

Anne: Yeah, it is quite sophisticated. It's still very early days. I'm very ambivalent about AI, but I think there are some amazing things about it. There's some bad things about it, but there are some amazing things about and I really want to see it. So what I'm going to do is talk about the next, because we're chatting a lot, we're running behind. So let's talk about the next thing, because I want to talk about the final one as well.

And the next one, I will put in some of my thoughts in there. So the next article was AI power demand might actually turn out to be good for climate. And it is an article in Energy Connects. And it's talking about what the last article talks about, which is the massively increasing electricity consumption driven by data centers, particularly those supporting AI, but it's, it, has a more positive take on it, which is that data centers are actually a pretty good potential consumer of renewables. We have a lot more ability to shift and shape our work. We're a lot more aware. There's a lot more demand for green, particularly from the hyperscalers, which, as I said before, is great news.

And we want to see it happen more to make sure that it's not just them saying it, but them actually doing it. And one of the things that's coming out of, what's happening at the moment is that AI is quite early in a, massive boost in electricity demand, which we know is coming because the, energy transition is going to drive a massive increase in electricity demand, so we are going to have to get grids ready for that.

And AI is, to a certAIn extent, giving us a little early exposure to that. There's a really interesting stat in the article about 

likening the demands to the biggest demand surge in electricity since World War II, which made me think, oh, I didn't realize there had been a massive electricity demand surge in World War II, but there was right at the beginning of World War II, there was a 60 percent increase in demand for, I went and looked this up on Wikipedia after reading this quote in there, 60 percent increase in electricity demand that came from the fact that actually there were a lot of new electrified factories that had to be built, but also an awful lot of Aluminium smelting. They needed a lot more aluminium to build ships, as you know yourself, and planes and aluminium smelting is very energy intensive, very electricity intensive.

So I thought this was, it was quite, it was a really quite interesting article that I would strongly recommend people have a read through. And it did make me think there were a few things in here. Yes, we do need an awful lot of additional, not just the ability to generate power, but we really need to upgrade grids all over Europe to get that power to where it's going to be used.

We are all going to need to learn to love the pylon is what I'm thinking. What were your thoughts on the article?

Stefana: So I think I have a little bit of understanding of where we are heading or where we should be heading but I'm definitely not an expert on this so more from the sideline. But also as a consumer I want to be very mindful. And PortXchange as well as a green tech provider needs to be mindful of that because we have to live by what we preach.

So from what we know right now, nuclear power does not have a large profile in emissions. So that's green, that's a green check.

Anne: Yeah, indeed.

Stefana: What we also know is that nuclear waste can be catastrophic. And it needs to be handled very carefully and also they are expensive as hell. Solar requires a lot of space.

Windmills produce lots of noise pollution. Also super expensive. And you cannot live nearby, so any communities or There's so many considerates there. Hydro requires a lot of space, and it's super expensive, dangerous as well if it breaks, and it has a direct impact on local climate and ecosystem.

And the one that should not be named, or the one that can't be named, it's fossil fuels.

The nastiest in terms of emissions profile, it's what we all want to leave behind. But unfortunately, as you already know, Anne, and all these articles mention it, with so much demand in AI, the question is, do we have enough time to test all these alternatives? Do we have enough bandwidth to keep looking into solutions and all that?

Or the demand is so high that we go drill, baby, drill, codes with codes, or even worse, we go back to coal, which is one of my recurrent nightmares, to be honest. And that's why I keep getting back to the question, do we really need AI for everything? Do you really need to have ChatGPT giving you that image with cats and dogs and whatever?

Because I don't know if you know, but the carbon footprint of Dall-E 2, similar with ChatGPTs, is at 2.2g CO2 emissions per query. Which is, sounds little,

but if you start doing the math, Jesus Christ... And then the CEO of MidJourney, David Holtz, said a while back that image generation requires about 1,000 trillion operations, which suggests a carbon footprint of about 1.9g CO2 emissions using NVIDIA, A100 GPUs, which consumes, only for that, right, for one image, 400 watts of power. For one image. And I, you probably did this, Anne, you went on ChatGPT, you said "create an image," blah blah, and then it gives you an image, and it sucks, it's horrible, it's... "Jesus Christ, what is this?"" And then you say "no, another," blah blah blah blah, and it gives you, and it sucks even more, because now it's something completely different, and you are stuck in that loophole for 15 minutes, and nine images later, you finally have something, but it's still not good enough, so you go on Google and you find an image that, but you just wasted God knows how much emissions, correct?

Anne: Well, yes. But we were at the beginning on AI. I mean, I, they're good and bad and sometimes, and, there's an awful lot of waste associated with AI, but I think we have to kind of say, "look, it's coming and we need to make the best of it." It's, people want it, it's useful. It's really at an early stage.

We will definitely get better at it, and we will get more efficient at it.

Stefana: What we need to get is a little bit more environmentally conscious. So I want it because it's shiny and cute and it helps me. But what do I do to compensate for the carbon footprint? Educate myself first of all, right? And then compensate with that.

Anne: So there was something in the article that wasn't what they meant it for me to take away from the article, but I took away article, from this article, which is that when they talked about the massive increase in electricity demand in World War II, and then I looked on Wikipedia and a lot of it was from aluminium smelting.

So aluminium smelting, really, we still do tons of that because we all want aluminum for everything. It's an incredibly useful metal part of our day to day lives. Nowadays, we don't do, we don't tend to do aluminum smelting off a national grid anymore, because it's a very expensive way of doing it. A lot of aluminum smelting, take it for Europe, takes place in Greenland, because Greenland has 24/7 carbon-free electricity from hydro, from water running off glaciers. It's another source of energy that we don't often think about, but should think about more these days. Water running, melt water running off glaciers is a potentially a really good source of cheap carbon-free electricity.

And so a lot of aluminum smelting moved to Greenland where it's done in a very low carbon way compared to doing it on a grid. And I talk about this with AI, I've talked about this with AI many times before in that, there's a lesson to be learned there. Sometimes you need to take the very electricity-heavy load to where there's a load of low carbon power to do it.

And yeah, I'd love to see. We've had this conversation on Environment Variables loads. I'd love some, a lot more AI going on in Greenland. 

Stefana: 100 percent sure that with this increase in demand, especially, we are putting more pressure on the ones that move the needle and can really do a change. And we also need to keep in mind that when we push for more green alternatives, there will be more pressure to invest in that. And with the conscious use of AI,

the same as there are activists that, fight against Shell or fight against the shipping industry because that happens as well. There are also now climate change activists that militate for green or conscious use of data, of technology, of AI, so on and so forth. So every trend creates different ecosystems and different communities. And communities and individuals have the power to push for more things like you mentioned.

The moment we become conscious of how we use them and the moment we become conscious of how they impact the global matters of things and not only us per se.

Anne: Yeah. You're quite right that we have more power than we realize. If we actually shout for things, if we shout for green AI, if we demand that hyperscalers are running off green power, if we demand that the growth, that things are running on smaller models, running on local devices, rather than over egging it and not thinking about these things.

Think green AI all the time. And so these articles have been quite useful because they suggest that the hyperscalers are pushing for green AI. And they're only doing that as well. They're doing that partly because they're not idiots. It's

Stefana: Exactly. That's what I wanted to say. 

Anne: That's, we know in the future that, renewable power is where all the growth is.

If you look on our, I always recommend people have a look on our world in data, excellent source of all graphs on anything you might want to have a graph on, but they have some excellent graphs on, global solar power generation, global wind generation, but mostly global solar power generation. It's going up exponentially.

And you kind of think, well, that is the future. Fossil fuels are not going up exponentially. Solar power is going up exponentially, so we need to be ready to use it. And then the hyperscalers know that, but at the same time, they feel the pressure from customers constantly saying, what is your green story?

Stefana: Absolutely.

I put my hopes into that.

Anne: Yeah, absolutely.

Because, 

need like you,

Stefana: and there was that ad right at the beginning of generative AI trend, where a construction company said, "Hey, ChatGPT, come and finish this building." It was a brilliant ad. Now, if you think a little bit at the environmental situation that is happening in Europe, and not only in Europe, but also the geopolitical situation that is happening, and one of our closest and one of the saddest examples that I will give right now is what happened in Spain,

Anne: Oh, yeah.

Stefana: And the floods that they've experienced.

And one thing that you will ask now is, how is AI fixing that? How is AI helping those people right now get their lives back together? Is it helping or is it contributing to their catastrophe? And that's a question that I ask myself constantly when I think of mindfulness in using technology and AI and so on and so forth.

If AI can make the world a better place. God, please do it. God, please save us. AI, artificial intelligence, robots, please save us from ourselves. But until they have that power, if they will have it, you never know, we need to consider our Shorter impact on that. The little step.

Anne: Well, actually, I think that is a, that's a really good question to be asking ourselves, it's like AI is just code. It's just technology. It's just another aspect of technology. But for all technology, what are we doing? Are we making the situation worse or are we making the situation better? If we're running systems that are over provisioned and we're just, we're being wasteful, we are making the situation worse.

We're not making situation better and, even if us, the tool that we're developing will help build it, absolutely fantastic. But then go back and make sure that it's not being wasteful. There's really no downside to going back later and just cutting waste, just trying to improve.

That doesn't mean we have to stop using technology. It doesn't mean we have to stop using AI. Use it in a mindful way. All good. In fact, we shouldn't stop using it. All these tools are fantastic and will be required for us almost certain to solve these problems. Quite possibly, AI will help with these problems in the future.

Predicting when storms are going to happen, predicting what the result of that is going to be, telling people where to go and what to do and how to survive it. Actually, AI can really potentially help with that, quite a lot of it, but yeah, but we just need to be mindful, as you say, mindful.

Stefana: And we need to be in balance. And like you said, AI has tremendous capabilities to improve operations too. We saw that AI has finally managed to understand how whales are speaking and what they are saying. We use AI in our methodology to track emissions and to identify the hotspots at ports. But remember what I said at the beginning of our conversation, business for good use.

Use it to make the world a better place, as cliche as it sounds, and if your organization desperately wants to use AI, work with technology specialists, but also with sustainability and environmental specialists in understanding the impact and the long term strategy that you want to follow in order to see feasible results from that.

And I think we are in a position right now where we need to be accountable for our footprint, for our carbon footprint. So whatever we do, track, monitor, analyze, keep an eye on it, because otherwise we're walking blindly without the data that is out there for us to see. Either it's AI, either it's electric cars, either it's whatever, it doesn't matter.

Anything can be used for a good cause.

Anne: That is an excellent point. And I realized we have, we've chatted way too long and we've completely used up our time to talk about the final article, which actually I thought was a bit of a, it was a bit of a weak article. So I'm not too worried. It was a very good article, but it was a, it was about Three Mile Island by somebody who had, it was written in their PhD about three mile island, which, and, it's about anti nuclear and the dangers of nuclear. And it felt a little bit like, well, because something bad happened in the seventies, doesn't necessarily mean... I would rather that we're focusing on what's France's experience of nuclear been for the past, since then?

What have they learned? Is it now a safe technology? How can we use it? We just, because there was a massive accident 40, 50 years ago doesn't mean that it's the wrong thing to do. We've learned a lot of lessons since then. If we'd given up flying in 1979, 'cause there were loads of plane crashes in the seventies, we would have given up flying and we would know, we would, we'd have missed out on the enormous improvements in safety that went on in flying and bizarrely that the improvements in safety that went on in the aviation industry had a huge positive impact on safety in all other industries, including the maritime industry and the nuclear industry. So sometimes you can't just stop. Sometimes you can't just stop. Hey, what did you, think of that article?

Stefana: You mentioned that if we stopped using planes to fly when we know how many accidents there have been, well, what would have happened if we stopped using vessels when Titanic sank? 

The titanic example like many aviation example, was a striking point for safety and for many other things, right? It was a breaking point, like we like to say nowadays.

I think Anne, unfortunately, I think the Hollywood, and it will sound weird that I say this, but the Hollywood and the media and all that have romanticized the catastrophe that have been happening at Chernobyl, at Fukushima, and all that. There have been so many collateral parties that have gained a lot from that.

And it was just very easy to constantly. It's very easy to rule by fear, right? And we have been brainwashed. Maybe it's harsh to say that, "oh my God, that's the enemy." But I will look at the facts here that I know from my research. Nuclear is stable. AI needs stability. If we really want output there, and if we really want to use AI for the greater use in any industry, it needs stability.

If I look at nuclear, I look at it more as an ally in the fight for climate change, because, like I said previously, it's emissions profile is close to zero. And of course, yeah, we are still scared because of what I've just said. We have romanticized in a very negative way the incidents that happened in the past.

But like you said, I really hope that we learned from those disasters. I really hope that there are people out there, super smart people, that know how to tackle this. In Romania, we have such a nuclear plant, and it's been staying there, doing nothing, and it has tremendous potential.

If I'm not mistaken, it might, we might have more than one, actually, but I don't want to say things that I'm not sure about here. Everyone was scared when they threatened to bomb the nuclear plant in Ukraine, right? For obvious reasons. But if we look at the facts, nuclear power is, it's emissions profile is very low, close to zero, so it's an ally for climate change, for environmentalists.

And an optimist would say "let's learn from the mistakes that we've done in the past with this alternative and make sure that we don't repeat those mistakes for the greater good." We can.

Yeah, we can't continue, we cannot by any chance continue with fossil fuels and I'm saying this from the bottom of my heart.

At PortXchange we can see the data, we have the data in front of our eyes and the emissions that we see on a daily basis are very concerning, extremely concerning. In the ports ecosystem, especially city ports, Anne, there have been studies that are quite, they are not, the media does not take them, the media does not shine a light on them, but the data shows and the research shows people living in city ports have a 3 percent higher increase, if not up to 7 percent higher increase in lung cancer.

Anne: Gosh.

Stefana: Yes! Fertility rates are heavily impacted in city ports. The closer you live to the port, the more you are affected. There are all kinds of health impacts that have been studied. I can send you a couple. We have quite some good studies in our database.

Anne: Send us that, and Chris will put links to that in the show notes. In fact, all the articles today are in the show notes, just as a reminder.

Stefana: So are there risks? Well, plenty, as with any other alternative. The same risks that we look at nuclear power, there is in solar panels, etc. Risks for everything, but we need to look, what are our ambitions for the future? And AI is very ambitious. And it will require a lot of power. But the Earth doesn't have the resources to power it anymore.

Not the way we've been doing it up until now. So I might sound like a nuclear advocate here, I think I might be, but from the analysis that I've read, it seems like the best solution for us. But of course it needs lots of financial investments because it's super expensive, lots of people to be trained and prepared and all that, and most of our nuclear plants are shut down, so I can only imagine the whole thing that will involve to start them again.

Do we have enough time? That's my worry. Do we have enough time? I don't know.

Anne: It's true. I love nuclear. I really wish we'd started 20 years ago. But, like France. We've done amazingly well on that.

Stefana: Yeah.

Anne: In the Maritime, on the high seas, loads of big stuff driven by nuclear engines. Obviously loads of the aircraft carriers, really heavy stuff driven by nuclear engines, ice breakers, lots of the Russian ice breakers have nuclear engines.

And we've got loads of experience in that. So it's interesting. Are we still about to have the nuclear future that we thought we were going to have back in the eighties? It's, very interesting, but I agree with you that Hollywood had really caused us a major problem there. It's too, it's terrible.

Anyway...

Stefana: sometimes, you sound lunatic when you say that, and I'm often being told, but let's just look at the facts. Why fear, why do you fear nuclear? When you ask a normal person. "Oh, because Chernobyl." Okay. Well, Chernobyl had very corrupt people working there, first of all. The whole system was corrupt, and the incompetency was at a high level, etc. So, yeah, I need to speak from 

I wear many hats when I talk to you, but right now I'm wearing my climate activist hat as well as a green tech advocate.

We must, not we should or we need, we must look into alternatives because we are running out of time.

Anne: And on that fantastic note, we will close our podcast for today. We must look at alternatives because we are running out of time. Absolutely the correct thing to be saying. So Stefana, before we go, where should listeners go to find out more about you?

Stefana: You can find me on LinkedIn under my name exactly as it is here, Stefana Sopco. I'm the only Stefana Sopco on LinkedIn. I pride myself with that. So it will be very easy to find me there.

Anne: Excellent. So that's very good. I'm also on LinkedIn, as I mentioned before. I don't know if I'm the only Anne Currie, but I'm sure you'll find me. So thank you very much for being on the episode, Stefana. It's been a really fun episode. I really liked that bit at the end where we talked about nuclear.

That was excellent. A final reminder that all the resources for this episode are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. Goodbye for now.

Stefana: Thank you, Anne. Bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> TWiGS host Anne Currie is joined by Stefana Sopco, Marketing Manager at PortXchange and a passionate climate activist, for an insightful discussion on the intersection of AI, sustainability, and the maritime industry's decarbonization journey. Stefana shares how PortXchange leverages green technology to help ports achieve net-zero emissions through innovations like just-in-time arrival and emissions tracking. They also dive into the challenges posed by AI's growing energy demand and explore nuclear power as a potential ally in the climate fight. Throughout the conversation, Stefana emphasizes the importance of mindful technology use and the urgency of adopting sustainable solutions.

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TRANSCRIOT BELOW:

Stefana: Is ChatGPT going to decarbonize the maritime sector? No freaking way!

 I haven't heard of anyone saying how they plan to use ChatGPT to decarbonize or to help ports account for their emissions.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne: Hello and welcome to The Week in Green Software, where we bring you the latest news and updates from the world of sustAInable software development. I'm your host today, Anne Currie. And today we're going to be joined by Stefana Sopco, Marketing Manager at PortXchange, a leader in driving ports towards a net zero future.

Stefana's work centers on using digital solutions to reduce emissions in the shipping industry. So a bit, a little bit different from our normal guests who are mostly directly programmers or very programmer related. So Stefana's vision extends beyond ports to broader decarbonization goals across the tech industry and the maritime industry.

She's also passionate about D&I, diversity and inclusion. And hopefully we'll talk a little bit about that today as well. So in this episode, we're going to talk about three articles that are all about AI, and the effects AI and the demand for AI is having on grids, particularly in Europe. We'll be talking a little bit about the power requirements of AI and the, grid capacity requirements.

And we'll be talking a little bit about whether or not it's always going to be a bad thing, or will actually could potentially be a very good thing for grids. And I'm quite a positive person. I think that there are massive advantages that come from the extension of the grid to support AI. So that'll be an interesting thing to talk about.

I'm also really interested in talking to Stefana about this because a lot of the issues around AI are effectively logistical issues. And logistics is something that we don't think enough about in the tech industry. But in the maritime industry, they think about it a very great deal, so I'll be very keen to hear Stefana's thoughts.

So, on that note, welcome Stefana. Can you start by telling us a little bit about yourself?

Stefana: Thank you so much, Anne, and thank you for the invitation. I'm really honored to be here and super nervous, to be honest, because it is a heavy subject, and I really hope I will do it honours. My name is Stefana Sopco. I'm the marketing manager at PortXchange. I've been working in the energy and maritime industry now for five years.

And for the past two years, I've also been a core member of Women in Tech, Netherlands, an NGO striving for diversity and inclusion, as you beautifully mentioned. Here I act as the marketing manager and spokesperson as well for our Dutch chapter. I'm an active member of WISTA NL and for those who don't know, WISTA stands for Women in Shipping and Trading Association.

And like I like to say about myself, I declare myself a climate activist next to my proud feminist tag, which I've learnt to embrace completely, especially in the past couple of years, and one fun thing about me, you will always find me mitigating for a cause. Either diversity in the industries that I work with, or animal rights, animals are my life. And of course, climate change. And next to my professional activity, I try my best to act as a role model for the younger generation.

I mentor and guide young professionals who either want to start their career in marketing and come into these industries that are very intimidating for some, or might want to switch careers. And I also dedicate a big bunch of my time to guiding expats, me being an expat as well in the Netherlands, and I try my best to help them navigate their journey here the way I wished someone would have guided me back when I moved here six years ago.

Anne: That's great. That all sounds very good. Interestingly, this isn't even our first all female Environment Variables podcast, but normally that's because I have one of my two co-authors from Building Green Software, the new O'Reilly book, Sara, or Sarah on with me. So I think this is the first one. I'm going to guess this might be the first one,

all women, that doesn't involve Sara or Sarah, so it'll be an interesting change. So say a little bit about me, because I'm not the normal host. I'm quite a common host, but I'm not the usual host of Environment Variables. My name is Anne Currie. I am, as I've said, one of the co-authors of the new O'Reilly book, Building Green Software.

And if anybody who's listening to this podcast, you really should be reading that book. It's suitable for everyone. You don't have to be a techie, you'll enjoy it. It'll be useful. I'm also the CEO of a learning and development company, Strategically Green. So my focus there, our focus is trying to get companies to build up enthusiasm and internal expertise on how to make these changes happen.

So we do workshops and training, things like that. So if you want to talk to me about any of that, then you can find me on LinkedIn. So what we're gonna be doing today is we're gonna be deep diving into three really interesting articles about AI and their impact on the grid. But before we do that, Stefana, do you want to talk to us a little bit about your work decarbonizing the shipping industry through Green Tech?

'Cause that's something that we have never heard about before, so I think it would be very interesting.

Stefana: Now, I feel even more stressed that I have to do the honors about talking about green tech in shipping. But before we dive into the goodies, I really want to take the chance to thank three of my colleagues, Eugene, Abhishek, and Sjoerd, because they helped me prepare for this session. I was a little bit nervous. I really wanted to put our technology in really a beautiful light and also talk mindfully about technology. And they've been extraordinary. They've been very, very supportive and I wanted to thank them. So maybe I should start by mentioning that PortXchange is a B Corp organization.

For those who don't know, B Corp by definition means impact before profit. And you see now a trend of more and more companies being B Corp certified, which is in direct relation with the Green Software Foundation's principles in a way or another when I looked into it. And in addition to striving towards sustAInability within the maritime sector, we also consider socially responsible and ethical ways for

everything throughout our business, so our entire value chAIn. Our mAIn mission at PortXchange, like you said, is to help port authorities, predominantly, worldwide to achieve net zero emissions. We help them by measuring their emissions throughout the entire port, and not only track them, but also analyze them and report them and take proactive steps to reduce them because that's the ultimate thing that we want in the end.

But I'm really proud to say that our green tech ecosystem is larger than that. We have developed many other products within the shipping ecosystem. So we address organisations beyond port authorities. We have tackled just-in-time arrival, I'm not sure if you are familiar with that, it's a solution that is now quite trendy, but a little bit more difficult to implement because of data sharing fears and thoughts from these organizations. But basically, just-in-time arrival helps limiting the fuel consumption of the vessels, because ships tend to rush through the sea to arrive to the port, like there's no tomorrow, but then, they arrive too early, and what does that mean?

It means that they will stay there, stranded, and the emissions that will go around the proximity of the port population will be higher because they are just sitting there wasting time and emitting more emissions. So our organization acts as a partner in the decarbonization journey of the maritime industry, players like port authorities and shipping companies.

But I really wanted to make a point here is that, as a technology provider ourselves, the computing power that we need also has a carbon footprint of its own. So what do we do to make sure that we are mindful and we remAIn mindful of that? Well, for example, we are extremely careful of using only energy providers that provide the majority of the power sourced through green channels.

We are very transparent with our emissions. We use all kinds of solutions like Goodwings, for example, to make sure that all our traveling is as green as possible. We account for our scope 3 emissions as well. And there's a saying that I really like to attach to PortXchange. How I envision it is business as a force for good.

Anne: That sounds fantastic. So there's loads of things that you've mentioned there, which we could talk about. The changes that you're making within the industry to make shipping more efficient, that's very analogous to something we often talk about in tech around operational efficiency, kind of not using, not being wasteful, not using more resources than you need to, in order to achieve the same goal.

So, and while ships are powered by fossil fuels, then everything that you are wasting is, it results in carbon emissions. So that's really good. But you're also balancing that with thinking about how your tech itself is going to be greener. So yeah, so very interesting things there, which I'm sure we'll focus in a little bit, a bit more on as we go along. But let me zoom in.

I'll get started now with talking about some of the articles that we're going to be talking about So we're going talk about three articles. And to kick us off, the first one is a McKinsey article entitled, The Role of Power in Unlocking the European AI Revolution. So it's an interesting paper and it's all about the fact that at the moment, and it's, there are some very interesting numbers in it.

They are estimated numbers, but they're useful and interesting in and of themselves. So McKinsey estimates that as a result of AI and the desire for AI, data centers in Europe are going to expand from using about 2 percent of Europe's total power consumption to around 5 percent by the end of the decade, which seems quite plausible to me.

And they point out that there are several challenges associated with that for the European power infrastructure. And the article is entirely about Europe, but I think that the issues also apply elsewhere. The first is that we're going to need a lot more green power. I mean, the good thing, the thing that it does say in the article is that the hyperscalers are saying that the power that they're going to want to use to power this new AI revolution will be green.

So that's good. And that was not the case 10 years ago. So I think it's really good that they're now all singing from our hymn sheets on that, they want the power to be green, but they need more green power. They need the ability to get it from where it's generated to the data centers. So they need better infrastructure, better grid infrastructure.

And we need ways to handle the fact that renewable power is not always guaranteed to be avAIlable in the same way that fossil fuels are. So they're gonna need to make changes to, well, we're all gonna need to make changes to handle that. So generally, I thought it was a pretty interesting article.

What was, what were your thoughts?

Stefana: Well, first of all, with any change, we will inevitably feel uncomfortable. But in order to achieve the great outcome that we are all hoping for, we must collectively feel uncomfortable. It should even hurt. And it's okay to hurt because we have to think beyond that. So how does that apply to my world, to the world of maritime and ports?

And do I believe that decarbonization within the port ecosystem is more or less following the same path? Definitely different worlds. Completely different worlds. We are here, within the port world, we are looking at a traditional sector that has been known and popular for having ways of following from back in time.

But we start our presentations at PortXchange whenever we are invited to an event with "ports as battlegrounds for decarbonization." Because if you look in a more simplistic way, ports are the puppeteers, but let's not put a negative connotation there. Let's put a good connotation, as in, it's where the good fight can start for real.

But, as in any other sector, you need proven and robust sustAInability practices. Everything should be backed by verifiable data. I think it's very important here, not verified data, verifiable data. And one thing that is very important and maybe one of the biggest differences between other sectors or other industries is that ports benefit directly from community relations, from public perception, which inevitably leads to public and private partnerships and policy support.

For example, if you look at Norwegian ports, which have declared themselves as the employee of the people. Their ports have managed to not only super digitalize their operations, but they are almost net zero. And it's all purely because of the change in mindset. And I actually talked about this earlier today with someone from Climate Pledge, how your mission and your intrinsically motives can make the difference in this fight for decarbonization. And one good thing to mention is that earlier this month, 

European Seaports Association launched their environmental report. In this report, it has been made a point that climate change is our top one priority for ports. So what does that mean? That means that we should also expect full transparency and full accountability because being at the forefront means you have probably the biggest impact in terms of carbon emissions, et cetera, et cetera.

So ports have the authority in my world. Ports have the authority and influence to move the need. And what I would like to mention is that in my world, I keep saying in my world, it's almost funny because while I'm saying it, I'm envisioning how we're moving from a world to another. We are quite dependent on regulations here, and we have plenty of them.

We have the IMO, I'm not sure if you know about IMO, that AIms to achieve net zero in emissions from international shipping by 2050. And, while we are doing that, we also have checkpoints, 2030 and 2040. Do I think that we will start seeing real progress by 2030? No, I don't think that, unfortunately. But then we also have, since 2023, other mandatory regulations, like in energy efficiency existing ship index.

And then we have the Carbon Intensity Indicator. We have the Monitoring, Reporting and Verification from EUMRV. We have the Emissions Trading System. And more recently, we have the CSRD, which is more predominant for Europe. And CSRD is actually one of our allies in green tech for green tech providers, because it somehow pushes these organizations to report, especially their emissions and their impact.

But now there is a question here that remAIns constantly. We have all of these things and the industry is being pushed from the left, from the right, et cetera. But we ask ourselves, how do we use technology to help us be compliant with all of this? And meet the requirements, of course. And we ask, is AI the answer?

Is, I don't know, well, large language models the answer? Is blockchAIn the answer? What is the answer? We don't know yet.

Anne: So your potential, I mean, obviously when you talk about increasing the efficiency of journeys, making sure people get to just-in-time, making sure that people aren't hanging around getting places early, getting places late, going too fast, optimally using their fuel and being as minimally wasteful as possible whilst delivering the service. So operational efficiency. The obvious thing that springs to mind is can AI help with that? Do you think it can, or do you?

Stefana: Yeah, absolutely. So, AI is not the enemy. Like, It can be. It can be the enemy, but not when used for this purpose. We use AI in our technology as well. We have a couple of case studies about that on our website, but we don't need AI for everything, Anne. That's another thing, because sometimes I ask myself, "do I even need AI in this situation?"

Last year I was nominated for an award by Women in AI, Netherlands. It was Responsible AI Leaders. And the nomination really made me think, what if instead of chasing unicorns, we grab the low hanging fruit and start implementing and bringing into operations the technology that is already avAIlable and viable.

Because we have so much, we have great technology already and we have the skills for it. So I would like to see, because I'm working in an industry that moves slower by default, because there are many stakeholders and many people and many other third parties involved, I would like to see what we have finally being implemented.

And meanwhile, we train and we upscale. But focus should be on scaling what's already there, instead of maybe continuously developing new tech that needs to go through the adoption curve anyway. One of our core specializations, sorry English is not my first language, like I mentioned is to provide optimization models.

And we also provide estimates based on data. Our technology might not be the fancy, super large language models, which everybody seems to be either an expert or an advocate or something nowadays, but statistical algorithms built for the goal that we want to help ports decarbonize by emissions monitoring or by just-in-time or more sAIling, et cetera.

It might not sound fancy. A couple of years ago, it sounded super fancy before GenAI popped, but it's doing the job and it's doing the job very well.

So sometimes I'm in doubt. Are we really that stubborn to think that we need AI for everything? Especially in sectors like the maritime sector, where there's still so much legacy that needs to be erased and so many things that need to be relearned,

forgotten and then learned. We are, the industry is still working with traditional professionals that are very good at their job, but in order for them to embrace AI, they need to embrace technology.

Anne: Yeah. Yeah. And in fact, we find the same in just pure tech as well, outside of the maritime industry. And that's. The lowest hanging fruit is usually very simple. It's turning things off that are no longer in use. It's right sizing. It's just minimizing waste in the same way that you're looking at minimizing basic waste.

The basic waste minimization is where you usually get your biggest reduction in carbon emissions. There's a French philosopher once said that, the perfect is the enemy of the good, that the pursuit of a perfect solution makes you step over a very good solution that you already have in your hand.

And that's true of, it sounds like it's true of your industry. It's certain true of our industry. Manual thriftathons, turning things off, right sizing, you don't have to do anything clever to really start cutting your carbon emissions quite significantly. Yeah, no, we're all the same, really.

Stefana: Yeah. And we also have to understand I had quite an aha moment last year. I went back to my home country, Romania, and was right after I was nominated for this award. And my mom is a teacher at a school in a small village. And people came to me, they asked me, okay, what's up? How's your life? All that. And it was a really big boom with AI and all that, and they asked me, so what is your opinion on AI?

What is AI? And I realized that the majority of the population needs a more simplistic understanding of artificial intelligence. It's not the fancy super professional profile that everyone is putting on LinkedIn or on their blogs and all that. And in order to see adoption, day to day adoption in our lives and in our employees' work lives, we need to make it simpler.

We need to... again, do we really need AI for everything? I keep getting, I keep coming back to this.

Anne: It's interesting you say that. So I read it, I think it was an article in The Economist, last week. It was talking about how AI, the companies are really struggling to work out what they're going to do with AI. Because interestingly, the people who are finding uses for AI are more likely to be individuals because making ChatGPT a live service that anybody could access for free really did kind of open it up to people could just go on, play with it, talk to it, use it to rewrite their essays at school, all the things, good or bad, that you can do with ChatGPT. There it's, interestingly, it's an interestingly accessible technology, isn't it? But businesses are really struggling to work out what they're going to do with it.

Stefana: Yeah, and is Chad GPT going to decarbonize the maritime sector? No freaking way. I haven't heard of anyone saying how they plan to use ChatGPT to decarbonize or to help ports account for their emissions. And another thing that I experienced actually in my close circle is that when they are, when ChatGPT is being taken from them, like for example, their companies blocked the ChatGPT server, et cetera.

They don't know how to do their job anymore, or they are panicked that they will not be as efficient as before. And it's just, for me, it's quite concerning because as a responsible AI advocate, it's scary to see that. Last year or two years ago, nobody have ever thought "I cannot do my job if I don't have this little chat to support me."

But now we see more and more. In marketing, I've been quite active in testing all kinds of AI solutions up to the point that I hit burnout because I was so afrAId that I will be left behind and I will not be employable anymore and companies will not see me as the next gen thing anymore, that I worked myself to burnout and I don't want to see that massively in all industries because there's, there are still many good things that we can do before we rush into something that might be a little bit over the top.

Anne: Yeah, it is quite sophisticated. It's still very early days. I'm very ambivalent about AI, but I think there are some amazing things about it. There's some bad things about it, but there are some amazing things about and I really want to see it. So what I'm going to do is talk about the next, because we're chatting a lot, we're running behind. So let's talk about the next thing, because I want to talk about the final one as well.

And the next one, I will put in some of my thoughts in there. So the next article was AI power demand might actually turn out to be good for climate. And it is an article in Energy Connects. And it's talking about what the last article talks about, which is the massively increasing electricity consumption driven by data centers, particularly those supporting AI, but it's, it, has a more positive take on it, which is that data centers are actually a pretty good potential consumer of renewables. We have a lot more ability to shift and shape our work. We're a lot more aware. There's a lot more demand for green, particularly from the hyperscalers, which, as I said before, is great news.

And we want to see it happen more to make sure that it's not just them saying it, but them actually doing it. And one of the things that's coming out of, what's happening at the moment is that AI is quite early in a, massive boost in electricity demand, which we know is coming because the, energy transition is going to drive a massive increase in electricity demand, so we are going to have to get grids ready for that.

And AI is, to a certAIn extent, giving us a little early exposure to that. There's a really interesting stat in the article about 

likening the demands to the biggest demand surge in electricity since World War II, which made me think, oh, I didn't realize there had been a massive electricity demand surge in World War II, but there was right at the beginning of World War II, there was a 60 percent increase in demand for, I went and looked this up on Wikipedia after reading this quote in there, 60 percent increase in electricity demand that came from the fact that actually there were a lot of new electrified factories that had to be built, but also an awful lot of Aluminium smelting. They needed a lot more aluminium to build ships, as you know yourself, and planes and aluminium smelting is very energy intensive, very electricity intensive.

So I thought this was, it was quite, it was a really quite interesting article that I would strongly recommend people have a read through. And it did make me think there were a few things in here. Yes, we do need an awful lot of additional, not just the ability to generate power, but we really need to upgrade grids all over Europe to get that power to where it's going to be used.

We are all going to need to learn to love the pylon is what I'm thinking. What were your thoughts on the article?

Stefana: So I think I have a little bit of understanding of where we are heading or where we should be heading but I'm definitely not an expert on this so more from the sideline. But also as a consumer I want to be very mindful. And PortXchange as well as a green tech provider needs to be mindful of that because we have to live by what we preach.

So from what we know right now, nuclear power does not have a large profile in emissions. So that's green, that's a green check.

Anne: Yeah, indeed.

Stefana: What we also know is that nuclear waste can be catastrophic. And it needs to be handled very carefully and also they are expensive as hell. Solar requires a lot of space.

Windmills produce lots of noise pollution. Also super expensive. And you cannot live nearby, so any communities or There's so many considerates there. Hydro requires a lot of space, and it's super expensive, dangerous as well if it breaks, and it has a direct impact on local climate and ecosystem.

And the one that should not be named, or the one that can't be named, it's fossil fuels.

The nastiest in terms of emissions profile, it's what we all want to leave behind. But unfortunately, as you already know, Anne, and all these articles mention it, with so much demand in AI, the question is, do we have enough time to test all these alternatives? Do we have enough bandwidth to keep looking into solutions and all that?

Or the demand is so high that we go drill, baby, drill, codes with codes, or even worse, we go back to coal, which is one of my recurrent nightmares, to be honest. And that's why I keep getting back to the question, do we really need AI for everything? Do you really need to have ChatGPT giving you that image with cats and dogs and whatever?

Because I don't know if you know, but the carbon footprint of Dall-E 2, similar with ChatGPTs, is at 2.2g CO2 emissions per query. Which is, sounds little,

but if you start doing the math, Jesus Christ... And then the CEO of MidJourney, David Holtz, said a while back that image generation requires about 1,000 trillion operations, which suggests a carbon footprint of about 1.9g CO2 emissions using NVIDIA, A100 GPUs, which consumes, only for that, right, for one image, 400 watts of power. For one image. And I, you probably did this, Anne, you went on ChatGPT, you said "create an image," blah blah, and then it gives you an image, and it sucks, it's horrible, it's... "Jesus Christ, what is this?"" And then you say "no, another," blah blah blah blah, and it gives you, and it sucks even more, because now it's something completely different, and you are stuck in that loophole for 15 minutes, and nine images later, you finally have something, but it's still not good enough, so you go on Google and you find an image that, but you just wasted God knows how much emissions, correct?

Anne: Well, yes. But we were at the beginning on AI. I mean, I, they're good and bad and sometimes, and, there's an awful lot of waste associated with AI, but I think we have to kind of say, "look, it's coming and we need to make the best of it." It's, people want it, it's useful. It's really at an early stage.

We will definitely get better at it, and we will get more efficient at it.

Stefana: What we need to get is a little bit more environmentally conscious. So I want it because it's shiny and cute and it helps me. But what do I do to compensate for the carbon footprint? Educate myself first of all, right? And then compensate with that.

Anne: So there was something in the article that wasn't what they meant it for me to take away from the article, but I took away article, from this article, which is that when they talked about the massive increase in electricity demand in World War II, and then I looked on Wikipedia and a lot of it was from aluminium smelting.

So aluminium smelting, really, we still do tons of that because we all want aluminum for everything. It's an incredibly useful metal part of our day to day lives. Nowadays, we don't do, we don't tend to do aluminum smelting off a national grid anymore, because it's a very expensive way of doing it. A lot of aluminum smelting, take it for Europe, takes place in Greenland, because Greenland has 24/7 carbon-free electricity from hydro, from water running off glaciers. It's another source of energy that we don't often think about, but should think about more these days. Water running, melt water running off glaciers is a potentially a really good source of cheap carbon-free electricity.

And so a lot of aluminum smelting moved to Greenland where it's done in a very low carbon way compared to doing it on a grid. And I talk about this with AI, I've talked about this with AI many times before in that, there's a lesson to be learned there. Sometimes you need to take the very electricity-heavy load to where there's a load of low carbon power to do it.

And yeah, I'd love to see. We've had this conversation on Environment Variables loads. I'd love some, a lot more AI going on in Greenland. 

Stefana: 100 percent sure that with this increase in demand, especially, we are putting more pressure on the ones that move the needle and can really do a change. And we also need to keep in mind that when we push for more green alternatives, there will be more pressure to invest in that. And with the conscious use of AI,

the same as there are activists that, fight against Shell or fight against the shipping industry because that happens as well. There are also now climate change activists that militate for green or conscious use of data, of technology, of AI, so on and so forth. So every trend creates different ecosystems and different communities. And communities and individuals have the power to push for more things like you mentioned.

The moment we become conscious of how we use them and the moment we become conscious of how they impact the global matters of things and not only us per se.

Anne: Yeah. You're quite right that we have more power than we realize. If we actually shout for things, if we shout for green AI, if we demand that hyperscalers are running off green power, if we demand that the growth, that things are running on smaller models, running on local devices, rather than over egging it and not thinking about these things.

Think green AI all the time. And so these articles have been quite useful because they suggest that the hyperscalers are pushing for green AI. And they're only doing that as well. They're doing that partly because they're not idiots. It's

Stefana: Exactly. That's what I wanted to say. 

Anne: That's, we know in the future that, renewable power is where all the growth is.

If you look on our, I always recommend people have a look on our world in data, excellent source of all graphs on anything you might want to have a graph on, but they have some excellent graphs on, global solar power generation, global wind generation, but mostly global solar power generation. It's going up exponentially.

And you kind of think, well, that is the future. Fossil fuels are not going up exponentially. Solar power is going up exponentially, so we need to be ready to use it. And then the hyperscalers know that, but at the same time, they feel the pressure from customers constantly saying, what is your green story?

Stefana: Absolutely.

I put my hopes into that.

Anne: Yeah, absolutely.

Because, 

need like you,

Stefana: and there was that ad right at the beginning of generative AI trend, where a construction company said, "Hey, ChatGPT, come and finish this building." It was a brilliant ad. Now, if you think a little bit at the environmental situation that is happening in Europe, and not only in Europe, but also the geopolitical situation that is happening, and one of our closest and one of the saddest examples that I will give right now is what happened in Spain,

Anne: Oh, yeah.

Stefana: And the floods that they've experienced.

And one thing that you will ask now is, how is AI fixing that? How is AI helping those people right now get their lives back together? Is it helping or is it contributing to their catastrophe? And that's a question that I ask myself constantly when I think of mindfulness in using technology and AI and so on and so forth.

If AI can make the world a better place. God, please do it. God, please save us. AI, artificial intelligence, robots, please save us from ourselves. But until they have that power, if they will have it, you never know, we need to consider our Shorter impact on that. The little step.

Anne: Well, actually, I think that is a, that's a really good question to be asking ourselves, it's like AI is just code. It's just technology. It's just another aspect of technology. But for all technology, what are we doing? Are we making the situation worse or are we making the situation better? If we're running systems that are over provisioned and we're just, we're being wasteful, we are making the situation worse.

We're not making situation better and, even if us, the tool that we're developing will help build it, absolutely fantastic. But then go back and make sure that it's not being wasteful. There's really no downside to going back later and just cutting waste, just trying to improve.

That doesn't mean we have to stop using technology. It doesn't mean we have to stop using AI. Use it in a mindful way. All good. In fact, we shouldn't stop using it. All these tools are fantastic and will be required for us almost certain to solve these problems. Quite possibly, AI will help with these problems in the future.

Predicting when storms are going to happen, predicting what the result of that is going to be, telling people where to go and what to do and how to survive it. Actually, AI can really potentially help with that, quite a lot of it, but yeah, but we just need to be mindful, as you say, mindful.

Stefana: And we need to be in balance. And like you said, AI has tremendous capabilities to improve operations too. We saw that AI has finally managed to understand how whales are speaking and what they are saying. We use AI in our methodology to track emissions and to identify the hotspots at ports. But remember what I said at the beginning of our conversation, business for good use.

Use it to make the world a better place, as cliche as it sounds, and if your organization desperately wants to use AI, work with technology specialists, but also with sustainability and environmental specialists in understanding the impact and the long term strategy that you want to follow in order to see feasible results from that.

And I think we are in a position right now where we need to be accountable for our footprint, for our carbon footprint. So whatever we do, track, monitor, analyze, keep an eye on it, because otherwise we're walking blindly without the data that is out there for us to see. Either it's AI, either it's electric cars, either it's whatever, it doesn't matter.

Anything can be used for a good cause.

Anne: That is an excellent point. And I realized we have, we've chatted way too long and we've completely used up our time to talk about the final article, which actually I thought was a bit of a, it was a bit of a weak article. So I'm not too worried. It was a very good article, but it was a, it was about Three Mile Island by somebody who had, it was written in their PhD about three mile island, which, and, it's about anti nuclear and the dangers of nuclear. And it felt a little bit like, well, because something bad happened in the seventies, doesn't necessarily mean... I would rather that we're focusing on what's France's experience of nuclear been for the past, since then?

What have they learned? Is it now a safe technology? How can we use it? We just, because there was a massive accident 40, 50 years ago doesn't mean that it's the wrong thing to do. We've learned a lot of lessons since then. If we'd given up flying in 1979, 'cause there were loads of plane crashes in the seventies, we would have given up flying and we would know, we would, we'd have missed out on the enormous improvements in safety that went on in flying and bizarrely that the improvements in safety that went on in the aviation industry had a huge positive impact on safety in all other industries, including the maritime industry and the nuclear industry. So sometimes you can't just stop. Sometimes you can't just stop. Hey, what did you, think of that article?

Stefana: You mentioned that if we stopped using planes to fly when we know how many accidents there have been, well, what would have happened if we stopped using vessels when Titanic sank? 

The titanic example like many aviation example, was a striking point for safety and for many other things, right? It was a breaking point, like we like to say nowadays.

I think Anne, unfortunately, I think the Hollywood, and it will sound weird that I say this, but the Hollywood and the media and all that have romanticized the catastrophe that have been happening at Chernobyl, at Fukushima, and all that. There have been so many collateral parties that have gained a lot from that.

And it was just very easy to constantly. It's very easy to rule by fear, right? And we have been brainwashed. Maybe it's harsh to say that, "oh my God, that's the enemy." But I will look at the facts here that I know from my research. Nuclear is stable. AI needs stability. If we really want output there, and if we really want to use AI for the greater use in any industry, it needs stability.

If I look at nuclear, I look at it more as an ally in the fight for climate change, because, like I said previously, it's emissions profile is close to zero. And of course, yeah, we are still scared because of what I've just said. We have romanticized in a very negative way the incidents that happened in the past.

But like you said, I really hope that we learned from those disasters. I really hope that there are people out there, super smart people, that know how to tackle this. In Romania, we have such a nuclear plant, and it's been staying there, doing nothing, and it has tremendous potential.

If I'm not mistaken, it might, we might have more than one, actually, but I don't want to say things that I'm not sure about here. Everyone was scared when they threatened to bomb the nuclear plant in Ukraine, right? For obvious reasons. But if we look at the facts, nuclear power is, it's emissions profile is very low, close to zero, so it's an ally for climate change, for environmentalists.

And an optimist would say "let's learn from the mistakes that we've done in the past with this alternative and make sure that we don't repeat those mistakes for the greater good." We can.

Yeah, we can't continue, we cannot by any chance continue with fossil fuels and I'm saying this from the bottom of my heart.

At PortXchange we can see the data, we have the data in front of our eyes and the emissions that we see on a daily basis are very concerning, extremely concerning. In the ports ecosystem, especially city ports, Anne, there have been studies that are quite, they are not, the media does not take them, the media does not shine a light on them, but the data shows and the research shows people living in city ports have a 3 percent higher increase, if not up to 7 percent higher increase in lung cancer.

Anne: Gosh.

Stefana: Yes! Fertility rates are heavily impacted in city ports. The closer you live to the port, the more you are affected. There are all kinds of health impacts that have been studied. I can send you a couple. We have quite some good studies in our database.

Anne: Send us that, and Chris will put links to that in the show notes. In fact, all the articles today are in the show notes, just as a reminder.

Stefana: So are there risks? Well, plenty, as with any other alternative. The same risks that we look at nuclear power, there is in solar panels, etc. Risks for everything, but we need to look, what are our ambitions for the future? And AI is very ambitious. And it will require a lot of power. But the Earth doesn't have the resources to power it anymore.

Not the way we've been doing it up until now. So I might sound like a nuclear advocate here, I think I might be, but from the analysis that I've read, it seems like the best solution for us. But of course it needs lots of financial investments because it's super expensive, lots of people to be trained and prepared and all that, and most of our nuclear plants are shut down, so I can only imagine the whole thing that will involve to start them again.

Do we have enough time? That's my worry. Do we have enough time? I don't know.

Anne: It's true. I love nuclear. I really wish we'd started 20 years ago. But, like France. We've done amazingly well on that.

Stefana: Yeah.

Anne: In the Maritime, on the high seas, loads of big stuff driven by nuclear engines. Obviously loads of the aircraft carriers, really heavy stuff driven by nuclear engines, ice breakers, lots of the Russian ice breakers have nuclear engines.

And we've got loads of experience in that. So it's interesting. Are we still about to have the nuclear future that we thought we were going to have back in the eighties? It's, very interesting, but I agree with you that Hollywood had really caused us a major problem there. It's too, it's terrible.

Anyway...

Stefana: sometimes, you sound lunatic when you say that, and I'm often being told, but let's just look at the facts. Why fear, why do you fear nuclear? When you ask a normal person. "Oh, because Chernobyl." Okay. Well, Chernobyl had very corrupt people working there, first of all. The whole system was corrupt, and the incompetency was at a high level, etc. So, yeah, I need to speak from 

I wear many hats when I talk to you, but right now I'm wearing my climate activist hat as well as a green tech advocate.

We must, not we should or we need, we must look into alternatives because we are running out of time.

Anne: And on that fantastic note, we will close our podcast for today. We must look at alternatives because we are running out of time. Absolutely the correct thing to be saying. So Stefana, before we go, where should listeners go to find out more about you?

Stefana: You can find me on LinkedIn under my name exactly as it is here, Stefana Sopco. I'm the only Stefana Sopco on LinkedIn. I pride myself with that. So it will be very easy to find me there.

Anne: Excellent. So that's very good. I'm also on LinkedIn, as I mentioned before. I don't know if I'm the only Anne Currie, but I'm sure you'll find me. So thank you very much for being on the episode, Stefana. It's been a really fun episode. I really liked that bit at the end where we talked about nuclear.

That was excellent. A final reminder that all the resources for this episode are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. Goodbye for now.

Stefana: Thank you, Anne. Bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> The Week in Green Software: Wooden Data Centers The Week in Green Software: Wooden Data Centers Thu, 21 Nov 2024 07:00:00 GMT 55:37 80z7nrm1 false https://podcasts.castplus.fm/e/5nzx7pmn-the-week-in-green-software-wooden-data-centers 68dc7d06d5c8a1f9b326f109 68dc7cef46a2532cdd8d1c23 89 This Week in Green Software, host Chris Adams and Asim Hussain, Executive Director of the GSF discuss the latest developments in sustainable software, exploring topics like Microsoft's innovative use of cross-laminated timber in data centers to reduce embodied carbon, the environmental challenges of generative AI hardware, and the groundbreaking Real Time Cloud dataset. They delve into the impact of new international energy efficiency directives, the interplay between geopolitics and sustainability, and surprising developments in China's approach to sustainable technology. Packed with insights, this episode offers an in-depth look at the intersection of technology and climate action.

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TRANSCRIPT BELOW:

Asim Hussain: We do not know how much electricity data centers are using. And the amounts, 

and I've spoken to researchers who are doing kind of research for the US department of energy, and they're like, "look, we've just had to... it's guesswork." 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to another episode of This Week in Green Software. And I probably should say welcome to Environment Variables, too, the parent name for this podcast, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Today, we're doing a quick check in with my friend and Executive Director of the Green Software Foundation, Asim Hussain.

Hi, Asim. 

Asim Hussain: Hi, how are you doing Chris? 

Chris Adams: And if you haven't heard this kind of format before, we are So, we are pretty much doing a roundup of interesting news or news that has caught our interest in the last few weeks in the world of sustainable software to basically share some of our takes and provide a bit of analysis or the things that we've seen, basically.

So that's pretty much it. We'll be sharing links to all the stories and all of the follow on links that we do from this. And I think that's it. That's pretty much it. Asim, before we start though, I know I've just said you're my friend and you work at the ED, but can I just give you a bit of space to introduce yourself about why you have something to do with Green Software?

Because I've just mentioned Executive Director, Green Software Foundation, but I suspect it might be a bit more useful to share a bit of background about where you came from in terms of professionally and where some of that credentials might be. And then I'll do mine and then we can dive into the story together.

Asim Hussain: Yeah. So Asim Hussain, based in the UK. Background and career throughout all the way to this point has been software engineering and about, six, seven years ago, really started to look at kind of sustainability space. That's when we met Chris, did a bunch of things, then launched the Green Software Foundation, helped co-found it three and a half years ago.

And I describe my job as, very luckily and very boringly being able to focus on answering one question and one question alone, which is "how can we have a future where software has zero harmful environmental impacts?" So I'm very excited to get on this call with Chris. 'Cause I get, I love getting wonky and there's not many people as refined in their thinking as Chris.

So we can really dive deep into some topics.

Chris Adams: That's very nice of you to say, and I assure you, I didn't put him up to that. Folks, if you're new to this podcast, my name is Chris Adams, I am the Director of Technology and Policy at the Green Web Foundation, which is not the same as the Green Software Foundation, but we're fellow travellers, we're a small Dutch non-profit focused on a fossil free, entirely fossil free internet, and we are members of the Green Software Foundation along with companies like Microsoft, GitHub, and Accenture, and so on.

I'm also the host of this podcast, and I actually met Asim in person for the first time ever at an unconference called OMG Climate back in 2019. So the connection is. 

Asim Hussain: I love that 

Chris Adams: I work at the Green Web Foundation. We publish open source software very similar to how the Green software Foundation publishes some open source software, but we also push out like reports and things like that. And I also work as one of the chairs of the policy working group inside the Green Software Foundation, because it makes sense to be working with other organizations with shared goals here.

If you are new to the format of this, it's literally a news roundup, so it should be, expect, hopefully some light entertainment and some interesting takes on this. We'll share links to all the stories that we discuss, and I think that's pretty much the general plan. Everything we share will be listed in these show notes.

So if you are listening using something like Spotify or YouTube, please remember to go to podcast.greensoftware.foundation to actually find the links that we start talking about. Alright then, I think that's pretty much it. Asim, are you sitting comfortably? 

Asim Hussain: Ah, yesss. 

Chris Adams: Okay, you look pretty happy, so I'm assuming we're going to go ahead with this.

Alright then, let's look at the first story. Does that sound okay? 

Asim Hussain: Yeah. Sounds good. 

Chris Adams: All right, the first story is, this is from the register, talking about Microsoft testing wooden data centers. So the headline for this is that Microsoft is starting to move into the world of what you might refer to as CLT, which is cross laminated timber.

This is a new building material that's used for construction in general. But because a significant chunk of Microsoft's emissions, their reported carbon emissions the last year or two actually came from the construction of data centers and the actual pouring of concrete.

This is, in my view, this is actually quite an interesting one because it's one potential lever for talking about the embodied carbon associated with using the kind of software that runs in these data centers. And there are some, honestly, quite impressive savings because the thing about wood compared to concrete is that wood can be a sink of CO2 rather than a source of CO2 emissions.

Asim, I'm pretty sure you've had a look at this and given that you used to work at Microsoft, you might have some kind of perspectives on this one, especially last time we spoke about data centers, we nerded out about the use of mushrooms in data center building materials as well, and you had some thoughts there.

So I'll hand over to you. When you read this, what kind of crossed your mind and was there anything you'd people's attention to on this one?

Asim Hussain: Yeah, I mean, I think one of the things I thought when I was reading it was, I don't think they did as good a job as they should have in communicating what the overall reason for using timber is. So basically this is all about mass timber and why is building things wood even a good thing when we're talking about actually growing trees is a good thing? Surely like chopping the tree down that you've just grown to sequester the carbon is a bad thing. And the rationale behind mass timber is that, that's basically the problem is that, when the durability of a tree I think is considered to be about a hundred years. I can't remember, don't quote me on that, I believe there is an official figure, which is when you grow a tree, like how long have you sequestered that carbon? And I think we've all agreed it's like a hundred years, whereas if you, and what happens, that tree then collapses, it goes to the ground, it decomposes, and it goes back into the atmosphere again.

So it's only sequestered it for a hundred years. Whereas the argument around mass timber is, well, let's take the tree before it's, So, let's take a carbon that's already dead and fallen to the ground and let's turn it into something else. It's a building, it's called mass timber. And through this process, we're effectively delaying the release of that carbon back into the atmosphere.

And I think some of the things I read in the past were like 300 years. So, it kind of goes that way.Now the theory there is it only really makes sense if you're building a building that is going to last for 300 years. I hope this data center is going to last for 300 years. That's a question in my mind. But Yeah, that is ultimately the argument on mass timber.

I think the argument here though is slightly different also because it's a relative difference between concrete versus timber as well, there's that aspect to it also, which as we know, concrete is a significant emitter of emissions, irrespective of the fact that, that tree just got felled. I mean, the relative difference is significant enough that the durability I don't think comes into play that much.

So it's an interesting direction. Just, I don't know if you saw this as well, Chris, but also OCP, Open Compute Project. Did you see they're doing, yeah, they're exploring, is it carbon negative concrete? I can't remember, it was, at the very least, it was less carbon intensive concrete, or it might be the kind of concrete which actually, as it cures, sucks carbon from the atmosphere.

Chris Adams: Yeah, few different ways you can make a cut or you can essentially use concrete or/and in particular, cement, which is a big source of emissions to reduce those ones there, because, for the story we're, linking to right now, when we look, talk about the wooden part, one of the things that's touted is, the use of timber instead of concrete can reduce the emissions associated something like two thirds lower emissions compared to steel, for example. That's one of the reasons why it actually is useful and if you're swapping, say, steel out for wood, there's also a saving around 30% around, is what is cited here. And that's largely because creating steel and creating concrete is incredibly energy intensive, but also just the actual process when you make concrete, for example, if you're using cement. If you're going to create cement when you're taking calcium carbonate, which is essentially limestone, there's a clue in the name, carbonate, you basically, you end up separating the carbon from the rest of it to actually make some of the materials, and that ends up being just emitted as part of the process, even if the cost of energy was entirely free, for example.

If you had entirely zero carbon heat, you'd still have that. So there's a few things that are problematic about using cement, but there is actually a lot of new work going into either creating low carbon cement or even using non different processes like using electrolysis, which isn't even relying on calcium carbonate, but using things like calcium silicate to basically precipitate out the necessary, essentially like compounds you would need it to actually create cement.

There's lots of things that are place right It's really interesting. And Microsoft is actually spending quite a lot of money in this field as well. We can share a few links to this because Microsoft is actually in many ways a complicated player in this role, because there's lots of things they're doing in terms of enabling emissions.

But when it comes to the actual construction of this, yes, they're building masses and masses of infrastructure, like in the order of tens of billions of dollars each quarter. But they're also one of the few organizations that's spending significant amounts on the different clean kinds of cement that we had inside this.

And that's the kind of thing I would respond to when you hand it over to me there.

Asim Hussain: Yeah. No, I think I'd also know that Bill Gates is also investing heavily, kind know he's not, officially leading the organization, 

"officially," 

he's not leading the organization anymore, but obviously there must be some influence there as well. So I didn't realize the carbon, so I actually assumed the carbon was from curing of the concrete, but you're saying it's from the construction, it's from actually generating the raw materials. 

Chris Adams: Yes, I'm very quickly leaving my domain of expertise, so I'll share a blog post that I wrote when I learned a bit about this, because there's a really interesting podcast by, okay, about as interesting as talking about cement actually gets, I suppose, that actually dives into this by a guy called David Roberts on the Volts podcast, where he does an interview about how this is made, how there are different approaches that you can use for doing this, how you can do it entirely fossil free.

so there are some options that you have here, but there's also, it brings up, raises all these other queries about, okay, if you are going to do this, how do you move cement around? Because in many cases, cement has a very, it sets in 90 minutes, right? So just like electricity is hard to store, things like cement can be very difficult to store.

So there is a whole question about, "okay, if someone wants to purchase this, how do they go about doing that?" And that is how some of this gets a bit more complicated. Complicated. So that's one of the options, and I think we should, if there's sufficient interest, it might be worth actually talking a little bit about reducing the embodied carbon and what levers are available, because this story is about wood, and we've gone down a kind of cementitious kind of sidebar, I suppose.

But there's a lot of interesting stuff happening with wood. For example, you mentioned the OCP, which that's the Open Compute Project. There're actually 3d models of wooden data center designs. There's one chap called Karl Rabe, who has been running the Wooden Data Center company for years, who's been doing this kind of work, and, they, I find it quite interesting and endearing at the same time, there's something pleasing about this, and you realize that this idea of cross laminated mass timber ends up being, it's getting increasing amounts of mindshare because it's one of the ways that you can build relatively large structures for a lower carbon footprint and the, for example, if you're in the UK and you ever go through London King's Cross, Google's shiny new headquarters uses all this kinds of CLT, this cross laminated timber well as one of their ways of reducing it. So it's very much seen as one of the trendy materials that you might actually use because it, aesthetically, is actually quite pleasing.

The thing, the elephant in the room when we talk about using wood for construction is "doesn't it burn down?" We had the Great Fire of London, which kind of redesigned London, as it were, right? And it turns out that the actual tooling that you have, the actual kind of material like CLT, it actually is surprisingly fire resistant.

It smoulders rather than burning in other ways. And this is actually one reason that there's actually something that you might initially worry about, but is actually not such a concern actually. So yeah, there's, I think we've come up with a bunch of links that we should add to this for people who are interested in this, because it's one of the levers.

And we know that this is increasingly, when you have organizations doing a massive build out of infrastructure, if they're going to build new buildings, Then This is one of the tools in the toolbox for reducing the embodied carbon, and Asim, what's the letter in the SCI that we have for this then?

Asim Hussain: I, intensity 

Chris Adams: yeah, so it'd be 

Asim Hussain: Oh no, it'd be the embodied. Yeah. Sorry. Yeah. 

Chris Adams: So that would be one, one lever you have for embodied intensity that is sometimes included.

Asim Hussain: And also I think we should probably like, correct me if I'm wrong, but just cement is, I believe, the last time I looked, 7 percent of global emissions. so yeah. So that's why this is such a.

So why are we talking about cement? It's 7 percent of global emissions, just cement. So it's a finding alternatives is not just an edge case. It's one of the main things that we need to do. 

Chris Adams: Absolutely. Yeah. So for context, that's about three to five times people's estimates of the digital sector. So this is one of the big ones that we need to be aware of. And it's essentially one of the most used materials in the world, basically, as well. We'll share some links to that because it's, when you start looking into it, it's surprisingly interesting if you care about reducing the emissions associated with digital, and you accept that a lot of these things take place in buildings, basically.

Alright, thanks for that, Asim! Shall we look at the next story? 

Asim Hussain: Yeah, let's do it. 

Chris Adams: The next one is, this is about the other part of embodied emissions actually. So this is a paper that was published, that initially published in Nature, but a preprint of it is available also on another website called NetworkDEE.Org. This was actually talking about the e-waste challenges associated with generative AI.

And essentially one of the things that comes up with AI. is that when you're using different kinds of hardware, while you can use existing hardware, like typical CPUs and things like that, a lot of the large providers and a lot of the kind of interest has gone into building new machines, or new kinds of specialized infrastructure, or specialized tooling, like NVIDIA's chips and stuff like that.

And this paper does a kind of material flow analysis of what this might look like if you project this forward over the next 5 to 10 years, for example. And this is actually, in my view, I think it's quite interesting because we often talk about the energy impact of AI and one of the reasons that it's quite difficult to actually even get a figure for the embodied impact of actually creating chips.

And when you move, if you're going to talk about, say, onshoring, moving the construction of chips to, say, the EU or in America, that has knock on implications on where the carbon emissions go and how visible they become, actually. Asim, I'll hand over to you for some 

Asim Hussain: I've, 

Chris Adams: here, actually.

Asim Hussain: I've not actually.

read this paper, so I can do what I normally do, which is to 

a

to wing, off the cuff. Yeah, it's interesting. A, you're right. The embodied carbon is still like the most stunningly hard thing to get. There's still only a couple of examples everybody kind of interpolates from to apply to everything else.

Sorry, just, so what this article is saying is that there is a faster, or should we say, what's the term in the cloud space? Not turnover, but the length of time.

Chris Adams: life of

Asim Hussain: Yeah. The life, is it saying that the useful life of AI chips is lower? 

Chris Adams: I think there's two things at work here. So one of them is just the fact that people are using this particular kind of hardware, which may be more energy intensive. That's one of the issues. And there are basically, I think there's two arguments being made here. One of them is that because the, 

because things are moving relatively quickly, a chip doesn't have the same kind of useful life cycle in the same data center, for example, so if you want to keep up, you're incentivized to buy the next chip because it's that much more efficient than the previous chips, right?

So, there's this idea that by doing that, because you have this kind of compressed hardware cycle in order to keep up with other providers, particularly in the kind of given space, then that is actually a driver of creation of much more in the way of electronics, particularly because there isn't much, this isn't a very circular sector right now.

So it's not like those chips are basically being broken down and then remanufactured into new feedstock for super efficient new chips. That's one of the things that is actually being, that's the argument that's being made here. And that has implications here. I think the figures that are used in the abstract is something in the region of 16 millions of cumulative waste by 2030, specifically for the tooling used for generative AI.

So that's like a, that's a non zero figure, that is something significant. I think when you look at this though, one thing it's worth actually, and one thing that I actually thought was, oh, this, if you've got this kind of endless treadmill. Does that mean that these chips are being thrown away?

And I'm actually, when I spoke to some people, I think it was, I actually asked Boris about this. Boris Gormazaychikov, who's the AI lead at Salesforce. Yeah, I asked him about this, and he said that is one of the factors. But one thing we're actually seeing is that given that there's, in the last couple of years, because it was just so hard to actually get hands on any of these chips, even things which were not the latest and greatest, they still were being used.

So it may be, it's not like these things are being thrown away entirely, right? It's not like they're going straight to landfill. But one of the problems we find is that there's almost, there's very little data on the circularity of these chips to see if they are being put to use, or if they are just going to essentially like landfill or not, basically.

This is one of the big problems that we do have and I would love to actually have some meaningful data on this because it's one of the big, it's one of the kind of generational shifts we're seeing in the sector right now. We've never seen so much money spent so quickly in this field with so little data being disclosed about what the knock on effects of this might actually be.

Asim Hussain: Two thoughts popped into my head as I was reading this, well, the abstract and what you said. And from other conversations I've had, especially in some of the places I've worked. The latest chips. Like, why do people want to use, why do Gen AI and AI startups and AI companies want to use the latest chips?

It's because dollar for dollar, it's cheaper for them from their perspective. They, it might cost more to rent those chips, but the AI job gets done sooner, so overall, it's just cheaper for them to use the latest generation of chips. And therefore it's more profitable for organizations.

But I wonder if there's this, almost this, and I don't know if this is because again, there's no data, but I wonder if there's this kind of constant battle then to get the latest chips. Is somebody left holding the bag with kind of slightly older chips, which aren't as profitable anymore, no one wants to use? And then that's just, know, it's not this perfectly free market where everything, all the information flows and the prices of the older chips, maybe it's just a mad battle for everybody to get the latest chips because that's the only thing anybody wants. And we're decommissioning, maybe we'll decommission these older chips sooner than the necessary just because of the incentives in the market.

That's one thought I had, and again, based off of nothing, so there's no data, so take that with the usual pinch of salt. But the other thought I had was just looking at some of the stuff that's happening in the space right now, like moving forward. I've forgotten the name of the project.

I will find it and I will get in the show notes, but there's, NVIDIA is now, they don't sell the boards, they don't sell like graphics cards, they sell entire vertically integrated units that you just like slot into a data center with everything baked in, the cooling baked in all these kinds of, because that's the only way you can get, The true levels of efficiency is if you, it's like the Apple ecosystem.

It's the only way to do it is everything is just owned by one company and it just does everything the way that product does it. so that I think will, A, make things more efficient, but B, potentially make things more complicated when it comes to e-waste, you know what I mean? if there's like a load of graphics cards.

Maybe I was thinking in my head, maybe in the future, everybody's going to have really great gaming experiences. Cause you'll have five year old, everybody's going to have a really amazing graphics card secondhand on their computer. But with these vertically integrated units, maybe that'll be a lot harder to do.

I don't know. I don't know what they look like inside. I don't know if it's a bunch of boards that you just unplug, or whether they're soldered together.

Chris Adams: so there are, you're right. So this is, maybe there's a couple of links that may be relevant for sharing this. So there's actually a really interesting piece by, I think the folks, the AI Now Institute actually spoke about the supply chains associated and where the concentration is for the creation of these chips.

Because when you have one or two extremely dominant players, then they're very much incentivized to not have too much stuff which is interoperable because that forces that, that means they end up being the people who get to set the prices, and we've seen NVIDIA, for example, briefly become the most valued, valuable company in the world, eclipsing both Amazon and Microsoft briefly over the summer before falling down to being in the top five position, for example.

So you do see that, and there is, that is one of the Issues that we do see is that you have this kind of vertical integration increasingly being pushed by this, but you also do see various other kind of pushes for this. For example, we know that like various hyperscalers have been looking to break this reliance on a single provider by having their own chips.

So Amazon has Published there, there's a, they have announced Trainium as an example. Google have been building their own chips, for example. Microsoft, I forget the name that they have for theirs as well, but set that you have and you do have AMD being in the kind of like low, far lower down compared to these other providers as well in terms of like sales and usage, I suppose.

'Cause they do have some options out there as well now, but yes, you're right. It's...

Asim Hussain: But the other argument, mean, you could argue it from that. I mean, I think that the thing to understand is like everything has 15 different arguments for it. So, you vertically alignment could be more efficient, but then you're right, it could be also vendor lock in.

It could be a bunch of these things all at same time. And the arguments for different like Amazon, everybody having their own chips is also pretty decent argument app I'm running on a MacBook, which has its own chip because once you vertically align, the efficiencies increase. And so the argument from Amazon, at least was like, "we build chips that are optimized for the exact workloads that we run.

And that's how we gain efficiencies." So there's multiple things in this, but you're right. That makes it much harder from an e-waste perspective, because then instead of having the same, even just, I don't even know if they're all x86, they must all be x86. actually, no, they're not now, not sure, but anyway, there's more, it's much more complicated when you come into e-waste when there's different protocols and different architectures. 

Chris Adams: About this whole idea of the cost of compute going down beneath a certain figure. There's a link that I've added to the show notes from the, actually, the publication, Latent Space, called the GPU Bubble, which does explicitly talk about how the cost of rented GPU compute has changed over the last few months, and what might happen as you have new clouds being available,

and what the introduction of new chips does to the cost of some of the other kinds of compute available, like you mentioned here, that's probably, in my view, I think that's one of the most interesting pieces of analysis about where the cost of this might actually go. And it may be the case that just how, in the 2000s, we end up with loads of dark fiber left around that we're still using now, as a result, when everyone had this massive build out for the initial dot com bubble.

You might see something comparable with AI, where we do have massively fast chips available for all this usage. So that could be the case, but yeah, it's a bit early now, we're not quite sure. I think there's a bunch we've got there. Well, we've added a few links, so if this, any of this is If this has been interesting to you, a listener, then there should be a bonanza of links available for you.

Thanks for that, Asim. Shall we move to the next one then? 

Asim Hussain: Yeah. Go on. 

Chris Adams: All right. So this one is, this is actually referring to the Green Software Foundation's, one of their projects called Real Time Cloud. This, I think sometime in October, the steering committee inside the Green Software Foundation pretty much merged in or approved the merging in of the yeah, ratified, the most recent work on Realtime Cloud.

Asim, you're in the steering committee, right? So maybe it might be useful for some of this to be explained a little bit to you, or like maybe you might talk about what this process looked like at your end, because I've been joining these meetings, but it's very much been the work of Adrian Cockcroft and Pindy Bhullar really pushing this to essentially create a datacenter-level list of all the carbon intensity figures for the three biggest clouds. So if you run a workload on Microsoft or Google or Amazon, you basically have, in my view, the most complete open data set available and where there has been information about the efficiency of a data center in the public domain, it's included.

Where there's information about water usage, it's also included. And this is, I'm really happy to actually finally see this kind of ratified because there is now something approaching a shared consensus view on what this data set, what this data should look like. And I think this is the thing I would.

Point people to, because I think this is actually used by some of the other software like Impact Framework and some tools like that actually. Maybe Asim, if I hand over to you to let you talk about what the process is when the steering committee inside the GSF ratify this and then maybe talk a little bit about the impact framework thing because there's some follow on things that I might talk about after that but I suspect that might be useful for people who are trying to figure out how they might use some of this information themselves when they're trying to quantify the environmental impact of the services they're operating.

Asim Hussain: Maybe I'll just start off with that. Actually. I'll just start off talking about how the Impact Framework team got involved. I think it was during the hackathon this year and we needed, what we wanted to do was a mechanism where we could help people like compute the carbon emissions of their cloud usage.

And so, the real time cloud was a data set at that point where it gave you regional information. So I forget all of it, but it was, PUE, power usage effectiveness. I think CFE was in there as well. It's. 

Chris Adams: Carbon free energy. Yeah.

Asim Hussain: Yeah, actually, it's for some reason, it's dropping from my head, which actual point of that data was needed for the carbon, for the hackathon.

But the impact framework team went, "wow, this is the data set that we need." And so they ended up building, I think all, the only thing we needed to add to actually was the geo location of the data center region so that then you could do good queries with Watttime and Electricity Maps and things like that.

So we ended up building a plugin for Real Time Cloud. We actually then turned into a baked in generic kind of CSV plugin, which was a good call. And so now you can use, now you can use this data set as one of the inputs to a computation, an end computation for your cloud usage and or, other end-to-end usage.

That's how the impact framework team got involved, which is, yeah, I think a really great example of kind of cross pollination of work inside the GSF. And just in terms of like how the process is here, I mean, this is driven through the standards working group. The RTC project is a standards working group project.

There, again, you've been part of it, Chris. It's a consensus-driven process. So the RTC group got together and said, we're happy with this dataset. We're happy with the specification associated with the columns. And that's the way we see it. This is a project which is not just data, but the specification for what the column should mean.

And this is our version of the data that we've published. Now, the goal, ultimately, is to get this into ISO, that if you wanted to disclose a dataset, similar dataset, and you were a different cloud provider, you could. As long as you exposed it with exactly the same columns, with exactly the same meanings. And that's the kind of power of standardization, is getting everybody to talk the same language. Right now I believe, Chris, that it's been ratified, but the process is that we're leaving it out there for six months for public feedback and broader review with an aim that in six months time, we'll take all the feedback, package it all together, and that's what we're going to publish into ISO, which will be another process. ISO is a process where you get reviewed by 175 country bodies, and then you've got to respond to their feedback and make adjustments, and if you have, and once you make those adjustments it gets accepted and you get into ISO.

Chris Adams: And when you say ISO, you're referring to the International Standards Organization, essentially the place where people standardize on stuff, basically, right?

Asim Hussain: One of the main ones, yeah. And one of the things we've noticed, as soon as, I mean, what does it mean? Why, is it important to get into ISO? It's important to get into ISO because what all that everybody's really looking for is, "oh, should I use this? God, is this the right thing to use?

Should I trust this?" And as soon you, the reason why people are really keen to use kind of ISO standards is they know it's going through an incredible consensus and review process, both internally at the GSF and then not only did the RTC team had to agree, then the whole standards work group had to agree, then we send it out to all of our members to see if they agree.

And if they don't, if they don't reject it, then it goes to the steering committee who have this ratification process. And in all honesty, I get very nervous at that process. Cause I'm like, yes, it is their right. And they do ratify things and they've never abused it. But it is kind of... I want people to have the conversation as part of the RTC team, rather than right at the end of that whole journey as a ratification process, but we see is more like ratification typically in the physical world is the actual process of everybody getting together and celebrating and signing a contract. It's actually more of a celebratory thing. It's ratified. So that's the way I think people should view kind of the ratification processes, the celebration from the steering committee that, "yeah, we signed this. We love it. We want it go ahead." So that's that.

Chris Adams: Okay, cool. Thank you for providing the extra context, because I work on one side where we're trying to figure out how to get the data into a dataset like this, but I wasn't so aware of okay, who gets to shoot this dataset down before it gets actually used in various places? And the thing I might share with you that What is probably of interest now is that given that we know there are various laws around the world that are, there's for example, in Europe, we have this law called the Energy Efficiency Directive.

I'll share a link to a post that we have published at the Green Web Foundation called Happy EE Day to those who celebrate. This is specifically about the fact that there's data that's been collected at a data center level, which is actually higher resolution than this kind of regional level that you might see here, which in my view, basically sets out a way for all the other, the providers who aren't just Microsoft, Google and Amazon to have a way of sharing the same kind of figures so that you actually start making comparable, you can start making comparisons between running a workload in one data center with one provider versus another one, for example, so that's some of the work that's going into this right now. And hopefully we'll see some more data come out because we now have this data being published. Or there are laws to get this data published in various places. And there should be a data set coming out, I think, early in 2025 for all of Europe, which is very extensive.

This will be any data center that uses, I think, more than half a megawatt of power. And given that most of the hyperscalers use tens of megawatts, that's going to be thousands upon thousands of data centers. So it'll be interesting to see what actually comes out from this. And what we'll find over the next month or two is what data has actually been shared by providers already, basically, because we are starting to get glimpses of this.

For example, in the Netherlands, we're seeing some submissions come to the public into the light of day. And I believe there's some stuff in Germany coming as well. So we will see, so you get some advance notice, and this might be stuff that can come into this. So we can start creating essentially an open resource of this information, so that if you're a operator of infrastructure, you've got an idea of what can actually measurably reduce some of the emissions on this. 

Asim Hussain: And did you say it's definitely going to be a public dataset? 

Chris Adams: I'll share a link to, again, a little bit of a kind of like wonkish thing, basically. With the Energy Efficiency Directive in Europe, which is, all of Europe, basically, that's 27 countries.

There's two things. If you operate your data center, you're mandated to publish a bunch of data points, like how much energy have you used? How clean is the power? How much of the power came from certificates versus on site generation and stuff like that? It's quite detailed. And...

Asim Hussain: When you say, sorry to interrupt, but when you say 'published', do you mean like in terms of a CDP where you have to publish to an authority who will then keep it secret? Or, 

Chris Adams: make available. Publicly, make publicly available. However, there is a kind of caveat which basically says, "if you're going to do this, you need to do this, except in the case where something is considered a trade secret." So what we've seen is that some providers are sharing this information, and some providers are not sharing this information.

This is the thing that we're seeing now. And

Asim Hussain: So, now we know which data centers MI6 uses. 

Chris Adams: Basically, or we're saying which companies are being, when companies talk about being really transparent, we have a way of seeing which one, which companies are walking the walk when it comes to talking about transparency and which ones are not being as transparent as their comms are saying so, basically.

That's one thing we're seeing. However, even if you don't publish the stuff, you still need to report to essentially the European Commission, and they will be creating an aggregated data set that they're publishing next year. So that won't be like "this data set run by Equinix is used this much energy last year," they'll have some figures so that we've got an idea of what how big this problem actually is, because it's 2024, we've been using computers and data centers for decades, but we have no real meaningful numbers at this level of detail. And even half a megawatt, 500 kilowatts of power, that's still quite a, that's a few racks at least, right? So that's still a bunch of data centers which aren't being included.

But this will give us an idea of, for the first time, just how much power is actually being used. And given that in Europe, they have binding legal targets to reduce emissions, they do need to know this because they said we need to halve emissions, more than halve emissions by 2030. And if you don't know how much power you're using and you don't know how clean that power is, it's very hard for you to tell if you're on track or off track. So that's one of the things that will be coming out next year and that's, I'll share a link to me diving into this, because I ended up having to make sense of it myself, and I found it quite difficult to read a bunch of these laws, but I've shared my understanding as I do this, and hopefully that should be useful to other people now, because, yeah, it turns out this data is actually being published, it's just, it's the first time people have done it, just like with other laws, where people are figuring out how to report on sustainability with legal teeth for the first time, and, yeah, it's a bit of a mess in certain countries right now.

Asim Hussain: I think the takeaway for people who might not understand is we do not know how much electricity data centers are using. And the amounts, and I've spoken to researchers who are doing kind of research for the US department of Energy and they're like, "look, we've just had to... It's guesswork. We've sent questionnaires and a bunch of people responded, a bunch of people didn't.

We inferred the rest and there's massive error bars." I mean, that's where we are. We don't even know, we can't project forward. So I think that's a really good thing just to have some solid data to even know where we will be in two, three years time. Yeah.

Chris Adams: And we're going to continue on this thread to come to the next story. So, that's what's happening in Europe, and that's, well, that's what we're seeing right now. But we know that the wind can change in politics all the time. And we've just seen a significant change in America. This is a story from Data Center Dynamics.

That's literally called Trump Won, Now What? And this is a bit of analysis about what we might see as a result with a new administration in the US because in the US we did see some things were looking actually quite positive in the world of AI. There was actually legislation around reporting on AI, just like Asim mentioned.

And there was unparalleled investment in cleaner forms of energy. And now we've got a new set of people who are in power who take more of a dim view on climate change and are unabashedly in favour of fossil fuels. Asim, I'll hand over to you because you said that you've got, there's a few things that caught your eye that to talk about here.

Asim Hussain: I think, I mean, I don't want to get too political and I'll try and keep it more to, I don't know if the facts is the right word. But yes. Okay. Look, when Trump came into power last time, it was quite a challenge for our community. He pulled out the Paris climate agreement. There's a lot of kind of negative rhetoric, which I don't know if he led or he was just, it gave a voice to, about climate change and the work that was being done. I think this time round though, the reason I'm a little bit more positive and it might be, I'll probably preface for this to everybody saying that I'm neither left nor right. I don't identify with a particular side. I identify with the side of the fight that we're fighting.

That's it. And so, like, I see Trump as somebody who is somewhat influenced by the people he hangs around with. and what he watches and what he hears and even though these are two people that I think the left are particularly critical of let's just look at it factually. There's Elon Musk which is part of the, we don't know how he's related to the place but he's going to be somewhat, he has a voice with Trump. And I mean this is a guy who, whether or not he, what his personal beliefs are in this space, he has a solar power company, he has an EV company, and we've seen Trump's rhetoric change to be more pro EV, at least up until, the election completed, so my hope is there's going to be a little bit of influence in that direction.

I mean, one of the things about Elon is he was part of the government committee on sustainability, I think it was. And he dropped in protest of Trump leaving the Paris commitment. So he was very against that whole process, and he is very pro climate, so there is some positivity there. I'm hoping that there'll be some influence to him in this.

This is all what I was thinking before yesterday. I'll tell what happened yesterday. This is what I was thinking before yesterday. That might be more of a positive influence there. And RFK, I think a lot of people know him for one aspect, but maybe people aren't aware of the fact that his entire career is as a environmental lawyer.

So that's what he spent his entire career at. He was a professor at I think Pace University teaching environmental law. He has litigated against many companies on a pollution perspective, including a big case against Monsanto. And he has been a big supporter of not just climate, but kind of environmentalism and caring for our planet for a while.

And I know he's got other opinions which are of a different spectrum as well. 

Chris Adams: Who needs vaccines? 

Asim Hussain: But let's just focus on what we want to focus on. So my hope was that Trump would listen, be influenced by this space. That was until yesterday when Trump announced some of his first appointments, which now turn out to be quite Warhawk-ish.

And so the whole story of "I'm going to, I'm the peacemaker. I'm going to bring peace to the world" is I think a little bit more up in question, but there's still some hope there. He has the other middle as appointments. So we'll see what happens. That's for me, what I'm looking at right now, is I'm looking to see what actual appointments he makes.

And I think that'll be the judge of what his tenancy is going to be like. And I think we're going to know pretty shortly in the next couple of weeks. Is it all, is what I'm saying nonsense and he just took advantage of these people and then will do whatever he wants now he's in power and listen to the people he used to listen to?

Or can it be swayed somewhat? So I'm probably not as depressed than most people. I'm still a little bit hopeful, but, yeah, that's just my very pragmatic viewpoint on this. That's where I'm thinking. 

Chris Adams: Or the environmental impact of digital services. So there's one thing. It looks, given the, you mentioned things like nuclear, for example, you have, and you actually mentioned specifically, Robert F. Kennedy, who's, one of his achievements, essentially, or policy achievements that he's touted before is being able to stop nuclear, or to get nuclear plants closed down.

That is at odds with some of the other work, or the other kind of commensive where there seems to be a lot of interest in nuclear as one of the ways to have a lower carbon form of energy. And I'm actually not sure which way this is going to go, to be honest, because one thing we've seen is that in the current like laws right now, which were passed under the previous administration, or the current administration, things like the Inflation Reduction Act, they work out to be really good for tech companies right now.

So if you, for example, right now, it's not actually that well known but let's say you see these announcements by Google or Microsoft and Amazon talking about "we're going to deploy a bunch of new data centers and they will be powered by renewable, by nuclear power" for example. If you look at how that's being paid you'll see that essentially half the cost of that is coming from the government themselves, right?

So you've got essentially your 50 percent subsidy to the largest companies in the world and you can imagine that there's going to be a lot of pressure to hold on to those particular, that kind of policy set up, because that is worth literally billions and billions to existing tech firms. So that might have some impact on like the environmental impact of the energy that we use, but you also have this thing where you've got someone who's very keen on expanding America's role as the largest producer of fossil gas right now.

So that has a flip side. I'm not quite sure which direction it's going to go in, but it does look like it's going to be bad news for, if nothing else, wind. It doesn't look like it's going to be great. So the idea that One of the knock on effects of this might be 

Asim Hussain: Yeah. Elon doesn't own a wind company, so I don't 

Chris Adams: but I said one thing that might bring up from a kind of operational point of view is do you end up with a slightly less volatile grid because you've got a bunch of huge amounts of money being thrown at stuff which is either fuel based so it can be dispatched internal all the time or nuclear regardless of your feelings of that which is a kind of a steady thing. That might have some implications of what you end up building as a result Because I could start talking about all the other kind of 

all the justice themes around that but we're coming to time and I don't want start on that because...

Asim Hussain: This is a rabbit hole. But yeah, I think probably you and I agree the next couple of weeks are going to be very interesting and we're going to, I think it's going to affect the next four or five years in quite meaningful ways 

Chris Adams: And the other thing the other flip side of this might be if you now have this scenario where you have tariffs which massively we've seen stories we'll share a link to some stories inside this where It looks like assume under this kind of tariff regime you would have basically laptops and electronics doubling in price. If you suddenly just doubled the cost of embodied stuff like of hardware that's going to create other drivers of what you actually then choose to do. For example you might hold on to hardware for longer because it's so expensive to replace now It's twice as expensive to replace. So that might change the steps that people end up being incentivized to do under this kind of regime for example. So there's some stuff there and it's also worth bearing in mind that's just America is obviously where one of the largest sectors but it's not the only one in the world. There's also China and there's all kinds of stuff happening in China for example as well. I'm gonna give you the last word actually if that's okay Asim because you were literally in China talking about green software and I wonder if there's any things that caught your eye that you might point people to or if there's any kind of things you saw there? 

Asim Hussain: I think you're talking about, I've got a few more minutes, I think you're talking about this, think, I can comment on that, but I think you've got to comment first on the actual, yeah, 

Chris Adams: it's a lot of the time when we talk about green software we talk about stuff that's happening either in Europe or North America because that's where the majority of the money is being spent in Western markets like Western markets right but China is also like the second largest source of emissions. Second largest economy in the world right now. There's a huge amount happening there and we tend not to see so much of that but there's actually someone who I've been starting to follow an analyst called David Fishman He's been showing some really interesting stuff because you basically see some in my view quite radical decisions being made in China to actually impact the environmental like consequences of using digital services. There was a thing published in May Basically this Energy Efficiency and Carbon Reduction Action Plan. And the short version of this is basically by the end of 2025 the goal is to have I think 80 percent of all the new data centers that are being created 80 percent of their energy has to be coming from fossil free sources, renewable energy which is, when China gets something done they move incredibly quickly and there's massive human rights consequences as a result of that, but in this one here this is something which is something that a lot of us are sleeping on. Like we talk about say my organization talks about fossil free internet by 2030 and we might see the UK government saying "we can achieve a carbon free grid by 2030." And in Germany there is a target for 80 percent renewable energy by 2030. And then in China for data centers they're talking about, yeah, 80 percent by the end of 2025. The caveats I need to share, this is stuff which is translated from the actual Chinese documentation, so it's going to be hard if you don't speak Chinese and that's why I'm pointing to the analysts who operate in this field and share some of this, but it's just so much more, moving so much faster than we are. And it's something that we don't really have that much visibility on. I think it's worth actually being aware of if we think about the kind of global picture for this because you can come away with the idea that "okay there's an election take place and that means there's never going to there's not going to be anything happening in green software." But yeah, the world is larger than just North America, for example, there's stuff happening in other parts of the world And in many ways they are being more aggressive and more ambitious on climate than we've seen before basically. 

Asim Hussain: So So my kind of one addition to this, and I want to acknowledge this is like an anecdotal single experience of a human being who went to China, but I went and I was part of an IEEE conference and there was a specific track, a specific room focused on, sustainable software. And so there were a number of players there who, you know, obviously in China actively involved in Tech sustainability in China, some data center operators, smaller data center operators, we didn't see Alibaba. Actually no, Alibaba were there. So we saw some like data center operators there. And I remember being part of a panel and the various questions. I remember just turning around, I think the, EU AI Act had just come into force or just, yeah, just come into force at that time.

And everybody was talking, in Europe, everybody's talking about the EU AI Act. If I was on panel and I was talking to a bunch of people in the tech sustainability sector, they might not have details of the EU AI Act to speak hand. But if I asked them, "what are some of the policies that the EU is instantiating which is driving some of this momentum?"

They'd be able to rattle off like EU AI Act CSRD, CSDDD, maybe they won't be able to double, triple click into kind of the nuance of it, but they'll be able to rattle it out. I remember being part of this panel and I asked, "what is the policies," I was thinking about the EU AI Act. I said, "what are the policies the Chinese government is enacting, which is driving all of your amazing work that you're all doing?" Nobody could mention one. It was silence across the panel. And so that one anecdotal state, and I did some Googling as well, and I couldn't find much, maybe that's my problem, I'm Googling in English, maybe it's all in Chinese, I will acknowledge that. But I think from the takeaway for me, that is, there does seem to be a disconnect between what is being stated at kind of the state level and what is being understood at the operator level. If this was Europe, and if this was the kind of my world, our world, soon as there's a hint of a regulation in this space, we're all talking about it, every person in this space is like using that as a reason for why you should invest more in the green space and you know this and that and the other, but that I didn't see that over there.

Could be a cultural difference, could be something else, or it could just be that maybe, and again, I don't know, and if 

someone's listening to this and actually has better information, I would love for you to reach out and to help educate me, but is, I mean, this could just be something that's said on the state level, which hasn't operationalized down to lower level. I don't know how it works over there, but what needs to happen for this kind of thing to operationalize? So we can have a conversation with Alibaba and all these other cloud players. I mean, what, where's their page on their websites talking about how they're going to help meet this thing?

There's some, there's something missing here and I don't quite know what it is, but it could be cultural. It could be anything. And I want somebody to educate me on this. 

Chris Adams: Do you know what, that reminds me of a piece of work we did last year with an organization called Wikirate, where we to track the top ten domains in the world which were, by traffic. Do they have sustainability policies, what are they doing there? And some Chinese providers did come up, and they do actually have something inside that.

I'll share a link to that, because it wasn't particularly easy for us to find this, but that was some information there, and they were some targets that were, they, it's weird. Like the thing you see in China is there's an idea very much of "do more before you say, rather than say before you do it." So inversion of what we often see in the West, for example, are like, this is the thing, like you, China can be referred to as a totalitarian state in many ways, but that means that when you see a policy decision taking place quickly, what do you want?

Do you want green neoliberalism? Do you want green totalitarianism? There's like a whole, you can have all these kinds of.

Asim Hussain: Maybe it's just the way, because of the way Europe's different. So I would expect the conversations, and I want to just, I want to make sure I add to this, the panel I was on and the people I was speaking to were doing amazing stuff. They were

Chris Adams: Was this the ACM one that you were referring to or 

Asim Hussain: IEEE 

Chris Adams: IEEE. So you were doing the talk for that.

Asim Hussain: There was great, there was some great, even some data center operators.

And I was like, "you need to talk more publicly about the work that you're doing." But yeah, there's some great work happening over there. 

Chris Adams: Well, maybe we'll see some stuff come out of COP 29, because COP 28, digital and green digital came on the radar for the first time, really, because when I, when Green Software Foundation sent me to COP 27 in Egypt, it was basically not on the radar in the slightest, no one was really talking about it.

So maybe we'll see something come out of COP 29 as a result from this, because there were some initial movements inside that. All right, Asim, I think we've gone over a little bit on time, but this has been fun to catch up and I hope the kind of takes here, or the links were at least maybe useful for people.

I guess what we'll normally do is we'll just make sure that the things we did speak about, we've got some links to follow up so that people who were curious can follow on from this. And yeah, mate, really lovely catching up again. Let's do this again next month or something like that, all right? Take of yourself, 

Asim Hussain: All right. Cheers, Chris. 

Chris Adams: Ta ra! 

Asim Hussain: Bye. 

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!

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]]> This Week in Green Software, host Chris Adams and Asim Hussain, Executive Director of the GSF discuss the latest developments in sustainable software, exploring topics like Microsoft's innovative use of cross-laminated timber in data centers to reduce embodied carbon, the environmental challenges of generative AI hardware, and the groundbreaking Real Time Cloud dataset. They delve into the impact of new international energy efficiency directives, the interplay between geopolitics and sustainability, and surprising developments in China's approach to sustainable technology. Packed with insights, this episode offers an in-depth look at the intersection of technology and climate action.

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TRANSCRIPT BELOW:

Asim Hussain: We do not know how much electricity data centers are using. And the amounts, 

and I've spoken to researchers who are doing kind of research for the US department of energy, and they're like, "look, we've just had to... it's guesswork." 

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to another episode of This Week in Green Software. And I probably should say welcome to Environment Variables, too, the parent name for this podcast, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Today, we're doing a quick check in with my friend and Executive Director of the Green Software Foundation, Asim Hussain.

Hi, Asim. 

Asim Hussain: Hi, how are you doing Chris? 

Chris Adams: And if you haven't heard this kind of format before, we are So, we are pretty much doing a roundup of interesting news or news that has caught our interest in the last few weeks in the world of sustainable software to basically share some of our takes and provide a bit of analysis or the things that we've seen, basically.

So that's pretty much it. We'll be sharing links to all the stories and all of the follow on links that we do from this. And I think that's it. That's pretty much it. Asim, before we start though, I know I've just said you're my friend and you work at the ED, but can I just give you a bit of space to introduce yourself about why you have something to do with Green Software?

Because I've just mentioned Executive Director, Green Software Foundation, but I suspect it might be a bit more useful to share a bit of background about where you came from in terms of professionally and where some of that credentials might be. And then I'll do mine and then we can dive into the story together.

Asim Hussain: Yeah. So Asim Hussain, based in the UK. Background and career throughout all the way to this point has been software engineering and about, six, seven years ago, really started to look at kind of sustainability space. That's when we met Chris, did a bunch of things, then launched the Green Software Foundation, helped co-found it three and a half years ago.

And I describe my job as, very luckily and very boringly being able to focus on answering one question and one question alone, which is "how can we have a future where software has zero harmful environmental impacts?" So I'm very excited to get on this call with Chris. 'Cause I get, I love getting wonky and there's not many people as refined in their thinking as Chris.

So we can really dive deep into some topics.

Chris Adams: That's very nice of you to say, and I assure you, I didn't put him up to that. Folks, if you're new to this podcast, my name is Chris Adams, I am the Director of Technology and Policy at the Green Web Foundation, which is not the same as the Green Software Foundation, but we're fellow travellers, we're a small Dutch non-profit focused on a fossil free, entirely fossil free internet, and we are members of the Green Software Foundation along with companies like Microsoft, GitHub, and Accenture, and so on.

I'm also the host of this podcast, and I actually met Asim in person for the first time ever at an unconference called OMG Climate back in 2019. So the connection is. 

Asim Hussain: I love that 

Chris Adams: I work at the Green Web Foundation. We publish open source software very similar to how the Green software Foundation publishes some open source software, but we also push out like reports and things like that. And I also work as one of the chairs of the policy working group inside the Green Software Foundation, because it makes sense to be working with other organizations with shared goals here.

If you are new to the format of this, it's literally a news roundup, so it should be, expect, hopefully some light entertainment and some interesting takes on this. We'll share links to all the stories that we discuss, and I think that's pretty much the general plan. Everything we share will be listed in these show notes.

So if you are listening using something like Spotify or YouTube, please remember to go to podcast.greensoftware.foundation to actually find the links that we start talking about. Alright then, I think that's pretty much it. Asim, are you sitting comfortably? 

Asim Hussain: Ah, yesss. 

Chris Adams: Okay, you look pretty happy, so I'm assuming we're going to go ahead with this.

Alright then, let's look at the first story. Does that sound okay? 

Asim Hussain: Yeah. Sounds good. 

Chris Adams: All right, the first story is, this is from the register, talking about Microsoft testing wooden data centers. So the headline for this is that Microsoft is starting to move into the world of what you might refer to as CLT, which is cross laminated timber.

This is a new building material that's used for construction in general. But because a significant chunk of Microsoft's emissions, their reported carbon emissions the last year or two actually came from the construction of data centers and the actual pouring of concrete.

This is, in my view, this is actually quite an interesting one because it's one potential lever for talking about the embodied carbon associated with using the kind of software that runs in these data centers. And there are some, honestly, quite impressive savings because the thing about wood compared to concrete is that wood can be a sink of CO2 rather than a source of CO2 emissions.

Asim, I'm pretty sure you've had a look at this and given that you used to work at Microsoft, you might have some kind of perspectives on this one, especially last time we spoke about data centers, we nerded out about the use of mushrooms in data center building materials as well, and you had some thoughts there.

So I'll hand over to you. When you read this, what kind of crossed your mind and was there anything you'd people's attention to on this one?

Asim Hussain: Yeah, I mean, I think one of the things I thought when I was reading it was, I don't think they did as good a job as they should have in communicating what the overall reason for using timber is. So basically this is all about mass timber and why is building things wood even a good thing when we're talking about actually growing trees is a good thing? Surely like chopping the tree down that you've just grown to sequester the carbon is a bad thing. And the rationale behind mass timber is that, that's basically the problem is that, when the durability of a tree I think is considered to be about a hundred years. I can't remember, don't quote me on that, I believe there is an official figure, which is when you grow a tree, like how long have you sequestered that carbon? And I think we've all agreed it's like a hundred years, whereas if you, and what happens, that tree then collapses, it goes to the ground, it decomposes, and it goes back into the atmosphere again.

So it's only sequestered it for a hundred years. Whereas the argument around mass timber is, well, let's take the tree before it's, So, let's take a carbon that's already dead and fallen to the ground and let's turn it into something else. It's a building, it's called mass timber. And through this process, we're effectively delaying the release of that carbon back into the atmosphere.

And I think some of the things I read in the past were like 300 years. So, it kind of goes that way.Now the theory there is it only really makes sense if you're building a building that is going to last for 300 years. I hope this data center is going to last for 300 years. That's a question in my mind. But Yeah, that is ultimately the argument on mass timber.

I think the argument here though is slightly different also because it's a relative difference between concrete versus timber as well, there's that aspect to it also, which as we know, concrete is a significant emitter of emissions, irrespective of the fact that, that tree just got felled. I mean, the relative difference is significant enough that the durability I don't think comes into play that much.

So it's an interesting direction. Just, I don't know if you saw this as well, Chris, but also OCP, Open Compute Project. Did you see they're doing, yeah, they're exploring, is it carbon negative concrete? I can't remember, it was, at the very least, it was less carbon intensive concrete, or it might be the kind of concrete which actually, as it cures, sucks carbon from the atmosphere.

Chris Adams: Yeah, few different ways you can make a cut or you can essentially use concrete or/and in particular, cement, which is a big source of emissions to reduce those ones there, because, for the story we're, linking to right now, when we look, talk about the wooden part, one of the things that's touted is, the use of timber instead of concrete can reduce the emissions associated something like two thirds lower emissions compared to steel, for example. That's one of the reasons why it actually is useful and if you're swapping, say, steel out for wood, there's also a saving around 30% around, is what is cited here. And that's largely because creating steel and creating concrete is incredibly energy intensive, but also just the actual process when you make concrete, for example, if you're using cement. If you're going to create cement when you're taking calcium carbonate, which is essentially limestone, there's a clue in the name, carbonate, you basically, you end up separating the carbon from the rest of it to actually make some of the materials, and that ends up being just emitted as part of the process, even if the cost of energy was entirely free, for example.

If you had entirely zero carbon heat, you'd still have that. So there's a few things that are problematic about using cement, but there is actually a lot of new work going into either creating low carbon cement or even using non different processes like using electrolysis, which isn't even relying on calcium carbonate, but using things like calcium silicate to basically precipitate out the necessary, essentially like compounds you would need it to actually create cement.

There's lots of things that are place right It's really interesting. And Microsoft is actually spending quite a lot of money in this field as well. We can share a few links to this because Microsoft is actually in many ways a complicated player in this role, because there's lots of things they're doing in terms of enabling emissions.

But when it comes to the actual construction of this, yes, they're building masses and masses of infrastructure, like in the order of tens of billions of dollars each quarter. But they're also one of the few organizations that's spending significant amounts on the different clean kinds of cement that we had inside this.

And that's the kind of thing I would respond to when you hand it over to me there.

Asim Hussain: Yeah. No, I think I'd also know that Bill Gates is also investing heavily, kind know he's not, officially leading the organization, 

"officially," 

he's not leading the organization anymore, but obviously there must be some influence there as well. So I didn't realize the carbon, so I actually assumed the carbon was from curing of the concrete, but you're saying it's from the construction, it's from actually generating the raw materials. 

Chris Adams: Yes, I'm very quickly leaving my domain of expertise, so I'll share a blog post that I wrote when I learned a bit about this, because there's a really interesting podcast by, okay, about as interesting as talking about cement actually gets, I suppose, that actually dives into this by a guy called David Roberts on the Volts podcast, where he does an interview about how this is made, how there are different approaches that you can use for doing this, how you can do it entirely fossil free.

so there are some options that you have here, but there's also, it brings up, raises all these other queries about, okay, if you are going to do this, how do you move cement around? Because in many cases, cement has a very, it sets in 90 minutes, right? So just like electricity is hard to store, things like cement can be very difficult to store.

So there is a whole question about, "okay, if someone wants to purchase this, how do they go about doing that?" And that is how some of this gets a bit more complicated. Complicated. So that's one of the options, and I think we should, if there's sufficient interest, it might be worth actually talking a little bit about reducing the embodied carbon and what levers are available, because this story is about wood, and we've gone down a kind of cementitious kind of sidebar, I suppose.

But there's a lot of interesting stuff happening with wood. For example, you mentioned the OCP, which that's the Open Compute Project. There're actually 3d models of wooden data center designs. There's one chap called Karl Rabe, who has been running the Wooden Data Center company for years, who's been doing this kind of work, and, they, I find it quite interesting and endearing at the same time, there's something pleasing about this, and you realize that this idea of cross laminated mass timber ends up being, it's getting increasing amounts of mindshare because it's one of the ways that you can build relatively large structures for a lower carbon footprint and the, for example, if you're in the UK and you ever go through London King's Cross, Google's shiny new headquarters uses all this kinds of CLT, this cross laminated timber well as one of their ways of reducing it. So it's very much seen as one of the trendy materials that you might actually use because it, aesthetically, is actually quite pleasing.

The thing, the elephant in the room when we talk about using wood for construction is "doesn't it burn down?" We had the Great Fire of London, which kind of redesigned London, as it were, right? And it turns out that the actual tooling that you have, the actual kind of material like CLT, it actually is surprisingly fire resistant.

It smoulders rather than burning in other ways. And this is actually one reason that there's actually something that you might initially worry about, but is actually not such a concern actually. So yeah, there's, I think we've come up with a bunch of links that we should add to this for people who are interested in this, because it's one of the levers.

And we know that this is increasingly, when you have organizations doing a massive build out of infrastructure, if they're going to build new buildings, Then This is one of the tools in the toolbox for reducing the embodied carbon, and Asim, what's the letter in the SCI that we have for this then?

Asim Hussain: I, intensity 

Chris Adams: yeah, so it'd be 

Asim Hussain: Oh no, it'd be the embodied. Yeah. Sorry. Yeah. 

Chris Adams: So that would be one, one lever you have for embodied intensity that is sometimes included.

Asim Hussain: And also I think we should probably like, correct me if I'm wrong, but just cement is, I believe, the last time I looked, 7 percent of global emissions. so yeah. So that's why this is such a.

So why are we talking about cement? It's 7 percent of global emissions, just cement. So it's a finding alternatives is not just an edge case. It's one of the main things that we need to do. 

Chris Adams: Absolutely. Yeah. So for context, that's about three to five times people's estimates of the digital sector. So this is one of the big ones that we need to be aware of. And it's essentially one of the most used materials in the world, basically, as well. We'll share some links to that because it's, when you start looking into it, it's surprisingly interesting if you care about reducing the emissions associated with digital, and you accept that a lot of these things take place in buildings, basically.

Alright, thanks for that, Asim! Shall we look at the next story? 

Asim Hussain: Yeah, let's do it. 

Chris Adams: The next one is, this is about the other part of embodied emissions actually. So this is a paper that was published, that initially published in Nature, but a preprint of it is available also on another website called NetworkDEE.Org. This was actually talking about the e-waste challenges associated with generative AI.

And essentially one of the things that comes up with AI. is that when you're using different kinds of hardware, while you can use existing hardware, like typical CPUs and things like that, a lot of the large providers and a lot of the kind of interest has gone into building new machines, or new kinds of specialized infrastructure, or specialized tooling, like NVIDIA's chips and stuff like that.

And this paper does a kind of material flow analysis of what this might look like if you project this forward over the next 5 to 10 years, for example. And this is actually, in my view, I think it's quite interesting because we often talk about the energy impact of AI and one of the reasons that it's quite difficult to actually even get a figure for the embodied impact of actually creating chips.

And when you move, if you're going to talk about, say, onshoring, moving the construction of chips to, say, the EU or in America, that has knock on implications on where the carbon emissions go and how visible they become, actually. Asim, I'll hand over to you for some 

Asim Hussain: I've, 

Chris Adams: here, actually.

Asim Hussain: I've not actually.

read this paper, so I can do what I normally do, which is to 

a

to wing, off the cuff. Yeah, it's interesting. A, you're right. The embodied carbon is still like the most stunningly hard thing to get. There's still only a couple of examples everybody kind of interpolates from to apply to everything else.

Sorry, just, so what this article is saying is that there is a faster, or should we say, what's the term in the cloud space? Not turnover, but the length of time.

Chris Adams: life of

Asim Hussain: Yeah. The life, is it saying that the useful life of AI chips is lower? 

Chris Adams: I think there's two things at work here. So one of them is just the fact that people are using this particular kind of hardware, which may be more energy intensive. That's one of the issues. And there are basically, I think there's two arguments being made here. One of them is that because the, 

because things are moving relatively quickly, a chip doesn't have the same kind of useful life cycle in the same data center, for example, so if you want to keep up, you're incentivized to buy the next chip because it's that much more efficient than the previous chips, right?

So, there's this idea that by doing that, because you have this kind of compressed hardware cycle in order to keep up with other providers, particularly in the kind of given space, then that is actually a driver of creation of much more in the way of electronics, particularly because there isn't much, this isn't a very circular sector right now.

So it's not like those chips are basically being broken down and then remanufactured into new feedstock for super efficient new chips. That's one of the things that is actually being, that's the argument that's being made here. And that has implications here. I think the figures that are used in the abstract is something in the region of 16 millions of cumulative waste by 2030, specifically for the tooling used for generative AI.

So that's like a, that's a non zero figure, that is something significant. I think when you look at this though, one thing it's worth actually, and one thing that I actually thought was, oh, this, if you've got this kind of endless treadmill. Does that mean that these chips are being thrown away?

And I'm actually, when I spoke to some people, I think it was, I actually asked Boris about this. Boris Gormazaychikov, who's the AI lead at Salesforce. Yeah, I asked him about this, and he said that is one of the factors. But one thing we're actually seeing is that given that there's, in the last couple of years, because it was just so hard to actually get hands on any of these chips, even things which were not the latest and greatest, they still were being used.

So it may be, it's not like these things are being thrown away entirely, right? It's not like they're going straight to landfill. But one of the problems we find is that there's almost, there's very little data on the circularity of these chips to see if they are being put to use, or if they are just going to essentially like landfill or not, basically.

This is one of the big problems that we do have and I would love to actually have some meaningful data on this because it's one of the big, it's one of the kind of generational shifts we're seeing in the sector right now. We've never seen so much money spent so quickly in this field with so little data being disclosed about what the knock on effects of this might actually be.

Asim Hussain: Two thoughts popped into my head as I was reading this, well, the abstract and what you said. And from other conversations I've had, especially in some of the places I've worked. The latest chips. Like, why do people want to use, why do Gen AI and AI startups and AI companies want to use the latest chips?

It's because dollar for dollar, it's cheaper for them from their perspective. They, it might cost more to rent those chips, but the AI job gets done sooner, so overall, it's just cheaper for them to use the latest generation of chips. And therefore it's more profitable for organizations.

But I wonder if there's this, almost this, and I don't know if this is because again, there's no data, but I wonder if there's this kind of constant battle then to get the latest chips. Is somebody left holding the bag with kind of slightly older chips, which aren't as profitable anymore, no one wants to use? And then that's just, know, it's not this perfectly free market where everything, all the information flows and the prices of the older chips, maybe it's just a mad battle for everybody to get the latest chips because that's the only thing anybody wants. And we're decommissioning, maybe we'll decommission these older chips sooner than the necessary just because of the incentives in the market.

That's one thought I had, and again, based off of nothing, so there's no data, so take that with the usual pinch of salt. But the other thought I had was just looking at some of the stuff that's happening in the space right now, like moving forward. I've forgotten the name of the project.

I will find it and I will get in the show notes, but there's, NVIDIA is now, they don't sell the boards, they don't sell like graphics cards, they sell entire vertically integrated units that you just like slot into a data center with everything baked in, the cooling baked in all these kinds of, because that's the only way you can get, The true levels of efficiency is if you, it's like the Apple ecosystem.

It's the only way to do it is everything is just owned by one company and it just does everything the way that product does it. so that I think will, A, make things more efficient, but B, potentially make things more complicated when it comes to e-waste, you know what I mean? if there's like a load of graphics cards.

Maybe I was thinking in my head, maybe in the future, everybody's going to have really great gaming experiences. Cause you'll have five year old, everybody's going to have a really amazing graphics card secondhand on their computer. But with these vertically integrated units, maybe that'll be a lot harder to do.

I don't know. I don't know what they look like inside. I don't know if it's a bunch of boards that you just unplug, or whether they're soldered together.

Chris Adams: so there are, you're right. So this is, maybe there's a couple of links that may be relevant for sharing this. So there's actually a really interesting piece by, I think the folks, the AI Now Institute actually spoke about the supply chains associated and where the concentration is for the creation of these chips.

Because when you have one or two extremely dominant players, then they're very much incentivized to not have too much stuff which is interoperable because that forces that, that means they end up being the people who get to set the prices, and we've seen NVIDIA, for example, briefly become the most valued, valuable company in the world, eclipsing both Amazon and Microsoft briefly over the summer before falling down to being in the top five position, for example.

So you do see that, and there is, that is one of the Issues that we do see is that you have this kind of vertical integration increasingly being pushed by this, but you also do see various other kind of pushes for this. For example, we know that like various hyperscalers have been looking to break this reliance on a single provider by having their own chips.

So Amazon has Published there, there's a, they have announced Trainium as an example. Google have been building their own chips, for example. Microsoft, I forget the name that they have for theirs as well, but set that you have and you do have AMD being in the kind of like low, far lower down compared to these other providers as well in terms of like sales and usage, I suppose.

'Cause they do have some options out there as well now, but yes, you're right. It's...

Asim Hussain: But the other argument, mean, you could argue it from that. I mean, I think that the thing to understand is like everything has 15 different arguments for it. So, you vertically alignment could be more efficient, but then you're right, it could be also vendor lock in.

It could be a bunch of these things all at same time. And the arguments for different like Amazon, everybody having their own chips is also pretty decent argument app I'm running on a MacBook, which has its own chip because once you vertically align, the efficiencies increase. And so the argument from Amazon, at least was like, "we build chips that are optimized for the exact workloads that we run.

And that's how we gain efficiencies." So there's multiple things in this, but you're right. That makes it much harder from an e-waste perspective, because then instead of having the same, even just, I don't even know if they're all x86, they must all be x86. actually, no, they're not now, not sure, but anyway, there's more, it's much more complicated when you come into e-waste when there's different protocols and different architectures. 

Chris Adams: About this whole idea of the cost of compute going down beneath a certain figure. There's a link that I've added to the show notes from the, actually, the publication, Latent Space, called the GPU Bubble, which does explicitly talk about how the cost of rented GPU compute has changed over the last few months, and what might happen as you have new clouds being available,

and what the introduction of new chips does to the cost of some of the other kinds of compute available, like you mentioned here, that's probably, in my view, I think that's one of the most interesting pieces of analysis about where the cost of this might actually go. And it may be the case that just how, in the 2000s, we end up with loads of dark fiber left around that we're still using now, as a result, when everyone had this massive build out for the initial dot com bubble.

You might see something comparable with AI, where we do have massively fast chips available for all this usage. So that could be the case, but yeah, it's a bit early now, we're not quite sure. I think there's a bunch we've got there. Well, we've added a few links, so if this, any of this is If this has been interesting to you, a listener, then there should be a bonanza of links available for you.

Thanks for that, Asim. Shall we move to the next one then? 

Asim Hussain: Yeah. Go on. 

Chris Adams: All right. So this one is, this is actually referring to the Green Software Foundation's, one of their projects called Real Time Cloud. This, I think sometime in October, the steering committee inside the Green Software Foundation pretty much merged in or approved the merging in of the yeah, ratified, the most recent work on Realtime Cloud.

Asim, you're in the steering committee, right? So maybe it might be useful for some of this to be explained a little bit to you, or like maybe you might talk about what this process looked like at your end, because I've been joining these meetings, but it's very much been the work of Adrian Cockcroft and Pindy Bhullar really pushing this to essentially create a datacenter-level list of all the carbon intensity figures for the three biggest clouds. So if you run a workload on Microsoft or Google or Amazon, you basically have, in my view, the most complete open data set available and where there has been information about the efficiency of a data center in the public domain, it's included.

Where there's information about water usage, it's also included. And this is, I'm really happy to actually finally see this kind of ratified because there is now something approaching a shared consensus view on what this data set, what this data should look like. And I think this is the thing I would.

Point people to, because I think this is actually used by some of the other software like Impact Framework and some tools like that actually. Maybe Asim, if I hand over to you to let you talk about what the process is when the steering committee inside the GSF ratify this and then maybe talk a little bit about the impact framework thing because there's some follow on things that I might talk about after that but I suspect that might be useful for people who are trying to figure out how they might use some of this information themselves when they're trying to quantify the environmental impact of the services they're operating.

Asim Hussain: Maybe I'll just start off with that. Actually. I'll just start off talking about how the Impact Framework team got involved. I think it was during the hackathon this year and we needed, what we wanted to do was a mechanism where we could help people like compute the carbon emissions of their cloud usage.

And so, the real time cloud was a data set at that point where it gave you regional information. So I forget all of it, but it was, PUE, power usage effectiveness. I think CFE was in there as well. It's. 

Chris Adams: Carbon free energy. Yeah.

Asim Hussain: Yeah, actually, it's for some reason, it's dropping from my head, which actual point of that data was needed for the carbon, for the hackathon.

But the impact framework team went, "wow, this is the data set that we need." And so they ended up building, I think all, the only thing we needed to add to actually was the geo location of the data center region so that then you could do good queries with Watttime and Electricity Maps and things like that.

So we ended up building a plugin for Real Time Cloud. We actually then turned into a baked in generic kind of CSV plugin, which was a good call. And so now you can use, now you can use this data set as one of the inputs to a computation, an end computation for your cloud usage and or, other end-to-end usage.

That's how the impact framework team got involved, which is, yeah, I think a really great example of kind of cross pollination of work inside the GSF. And just in terms of like how the process is here, I mean, this is driven through the standards working group. The RTC project is a standards working group project.

There, again, you've been part of it, Chris. It's a consensus-driven process. So the RTC group got together and said, we're happy with this dataset. We're happy with the specification associated with the columns. And that's the way we see it. This is a project which is not just data, but the specification for what the column should mean.

And this is our version of the data that we've published. Now, the goal, ultimately, is to get this into ISO, that if you wanted to disclose a dataset, similar dataset, and you were a different cloud provider, you could. As long as you exposed it with exactly the same columns, with exactly the same meanings. And that's the kind of power of standardization, is getting everybody to talk the same language. Right now I believe, Chris, that it's been ratified, but the process is that we're leaving it out there for six months for public feedback and broader review with an aim that in six months time, we'll take all the feedback, package it all together, and that's what we're going to publish into ISO, which will be another process. ISO is a process where you get reviewed by 175 country bodies, and then you've got to respond to their feedback and make adjustments, and if you have, and once you make those adjustments it gets accepted and you get into ISO.

Chris Adams: And when you say ISO, you're referring to the International Standards Organization, essentially the place where people standardize on stuff, basically, right?

Asim Hussain: One of the main ones, yeah. And one of the things we've noticed, as soon as, I mean, what does it mean? Why, is it important to get into ISO? It's important to get into ISO because what all that everybody's really looking for is, "oh, should I use this? God, is this the right thing to use?

Should I trust this?" And as soon you, the reason why people are really keen to use kind of ISO standards is they know it's going through an incredible consensus and review process, both internally at the GSF and then not only did the RTC team had to agree, then the whole standards work group had to agree, then we send it out to all of our members to see if they agree.

And if they don't, if they don't reject it, then it goes to the steering committee who have this ratification process. And in all honesty, I get very nervous at that process. Cause I'm like, yes, it is their right. And they do ratify things and they've never abused it. But it is kind of... I want people to have the conversation as part of the RTC team, rather than right at the end of that whole journey as a ratification process, but we see is more like ratification typically in the physical world is the actual process of everybody getting together and celebrating and signing a contract. It's actually more of a celebratory thing. It's ratified. So that's the way I think people should view kind of the ratification processes, the celebration from the steering committee that, "yeah, we signed this. We love it. We want it go ahead." So that's that.

Chris Adams: Okay, cool. Thank you for providing the extra context, because I work on one side where we're trying to figure out how to get the data into a dataset like this, but I wasn't so aware of okay, who gets to shoot this dataset down before it gets actually used in various places? And the thing I might share with you that What is probably of interest now is that given that we know there are various laws around the world that are, there's for example, in Europe, we have this law called the Energy Efficiency Directive.

I'll share a link to a post that we have published at the Green Web Foundation called Happy EE Day to those who celebrate. This is specifically about the fact that there's data that's been collected at a data center level, which is actually higher resolution than this kind of regional level that you might see here, which in my view, basically sets out a way for all the other, the providers who aren't just Microsoft, Google and Amazon to have a way of sharing the same kind of figures so that you actually start making comparable, you can start making comparisons between running a workload in one data center with one provider versus another one, for example, so that's some of the work that's going into this right now. And hopefully we'll see some more data come out because we now have this data being published. Or there are laws to get this data published in various places. And there should be a data set coming out, I think, early in 2025 for all of Europe, which is very extensive.

This will be any data center that uses, I think, more than half a megawatt of power. And given that most of the hyperscalers use tens of megawatts, that's going to be thousands upon thousands of data centers. So it'll be interesting to see what actually comes out from this. And what we'll find over the next month or two is what data has actually been shared by providers already, basically, because we are starting to get glimpses of this.

For example, in the Netherlands, we're seeing some submissions come to the public into the light of day. And I believe there's some stuff in Germany coming as well. So we will see, so you get some advance notice, and this might be stuff that can come into this. So we can start creating essentially an open resource of this information, so that if you're a operator of infrastructure, you've got an idea of what can actually measurably reduce some of the emissions on this. 

Asim Hussain: And did you say it's definitely going to be a public dataset? 

Chris Adams: I'll share a link to, again, a little bit of a kind of like wonkish thing, basically. With the Energy Efficiency Directive in Europe, which is, all of Europe, basically, that's 27 countries.

There's two things. If you operate your data center, you're mandated to publish a bunch of data points, like how much energy have you used? How clean is the power? How much of the power came from certificates versus on site generation and stuff like that? It's quite detailed. And...

Asim Hussain: When you say, sorry to interrupt, but when you say 'published', do you mean like in terms of a CDP where you have to publish to an authority who will then keep it secret? Or, 

Chris Adams: make available. Publicly, make publicly available. However, there is a kind of caveat which basically says, "if you're going to do this, you need to do this, except in the case where something is considered a trade secret." So what we've seen is that some providers are sharing this information, and some providers are not sharing this information.

This is the thing that we're seeing now. And

Asim Hussain: So, now we know which data centers MI6 uses. 

Chris Adams: Basically, or we're saying which companies are being, when companies talk about being really transparent, we have a way of seeing which one, which companies are walking the walk when it comes to talking about transparency and which ones are not being as transparent as their comms are saying so, basically.

That's one thing we're seeing. However, even if you don't publish the stuff, you still need to report to essentially the European Commission, and they will be creating an aggregated data set that they're publishing next year. So that won't be like "this data set run by Equinix is used this much energy last year," they'll have some figures so that we've got an idea of what how big this problem actually is, because it's 2024, we've been using computers and data centers for decades, but we have no real meaningful numbers at this level of detail. And even half a megawatt, 500 kilowatts of power, that's still quite a, that's a few racks at least, right? So that's still a bunch of data centers which aren't being included.

But this will give us an idea of, for the first time, just how much power is actually being used. And given that in Europe, they have binding legal targets to reduce emissions, they do need to know this because they said we need to halve emissions, more than halve emissions by 2030. And if you don't know how much power you're using and you don't know how clean that power is, it's very hard for you to tell if you're on track or off track. So that's one of the things that will be coming out next year and that's, I'll share a link to me diving into this, because I ended up having to make sense of it myself, and I found it quite difficult to read a bunch of these laws, but I've shared my understanding as I do this, and hopefully that should be useful to other people now, because, yeah, it turns out this data is actually being published, it's just, it's the first time people have done it, just like with other laws, where people are figuring out how to report on sustainability with legal teeth for the first time, and, yeah, it's a bit of a mess in certain countries right now.

Asim Hussain: I think the takeaway for people who might not understand is we do not know how much electricity data centers are using. And the amounts, and I've spoken to researchers who are doing kind of research for the US department of Energy and they're like, "look, we've just had to... It's guesswork. We've sent questionnaires and a bunch of people responded, a bunch of people didn't.

We inferred the rest and there's massive error bars." I mean, that's where we are. We don't even know, we can't project forward. So I think that's a really good thing just to have some solid data to even know where we will be in two, three years time. Yeah.

Chris Adams: And we're going to continue on this thread to come to the next story. So, that's what's happening in Europe, and that's, well, that's what we're seeing right now. But we know that the wind can change in politics all the time. And we've just seen a significant change in America. This is a story from Data Center Dynamics.

That's literally called Trump Won, Now What? And this is a bit of analysis about what we might see as a result with a new administration in the US because in the US we did see some things were looking actually quite positive in the world of AI. There was actually legislation around reporting on AI, just like Asim mentioned.

And there was unparalleled investment in cleaner forms of energy. And now we've got a new set of people who are in power who take more of a dim view on climate change and are unabashedly in favour of fossil fuels. Asim, I'll hand over to you because you said that you've got, there's a few things that caught your eye that to talk about here.

Asim Hussain: I think, I mean, I don't want to get too political and I'll try and keep it more to, I don't know if the facts is the right word. But yes. Okay. Look, when Trump came into power last time, it was quite a challenge for our community. He pulled out the Paris climate agreement. There's a lot of kind of negative rhetoric, which I don't know if he led or he was just, it gave a voice to, about climate change and the work that was being done. I think this time round though, the reason I'm a little bit more positive and it might be, I'll probably preface for this to everybody saying that I'm neither left nor right. I don't identify with a particular side. I identify with the side of the fight that we're fighting.

That's it. And so, like, I see Trump as somebody who is somewhat influenced by the people he hangs around with. and what he watches and what he hears and even though these are two people that I think the left are particularly critical of let's just look at it factually. There's Elon Musk which is part of the, we don't know how he's related to the place but he's going to be somewhat, he has a voice with Trump. And I mean this is a guy who, whether or not he, what his personal beliefs are in this space, he has a solar power company, he has an EV company, and we've seen Trump's rhetoric change to be more pro EV, at least up until, the election completed, so my hope is there's going to be a little bit of influence in that direction.

I mean, one of the things about Elon is he was part of the government committee on sustainability, I think it was. And he dropped in protest of Trump leaving the Paris commitment. So he was very against that whole process, and he is very pro climate, so there is some positivity there. I'm hoping that there'll be some influence to him in this.

This is all what I was thinking before yesterday. I'll tell what happened yesterday. This is what I was thinking before yesterday. That might be more of a positive influence there. And RFK, I think a lot of people know him for one aspect, but maybe people aren't aware of the fact that his entire career is as a environmental lawyer.

So that's what he spent his entire career at. He was a professor at I think Pace University teaching environmental law. He has litigated against many companies on a pollution perspective, including a big case against Monsanto. And he has been a big supporter of not just climate, but kind of environmentalism and caring for our planet for a while.

And I know he's got other opinions which are of a different spectrum as well. 

Chris Adams: Who needs vaccines? 

Asim Hussain: But let's just focus on what we want to focus on. So my hope was that Trump would listen, be influenced by this space. That was until yesterday when Trump announced some of his first appointments, which now turn out to be quite Warhawk-ish.

And so the whole story of "I'm going to, I'm the peacemaker. I'm going to bring peace to the world" is I think a little bit more up in question, but there's still some hope there. He has the other middle as appointments. So we'll see what happens. That's for me, what I'm looking at right now, is I'm looking to see what actual appointments he makes.

And I think that'll be the judge of what his tenancy is going to be like. And I think we're going to know pretty shortly in the next couple of weeks. Is it all, is what I'm saying nonsense and he just took advantage of these people and then will do whatever he wants now he's in power and listen to the people he used to listen to?

Or can it be swayed somewhat? So I'm probably not as depressed than most people. I'm still a little bit hopeful, but, yeah, that's just my very pragmatic viewpoint on this. That's where I'm thinking. 

Chris Adams: Or the environmental impact of digital services. So there's one thing. It looks, given the, you mentioned things like nuclear, for example, you have, and you actually mentioned specifically, Robert F. Kennedy, who's, one of his achievements, essentially, or policy achievements that he's touted before is being able to stop nuclear, or to get nuclear plants closed down.

That is at odds with some of the other work, or the other kind of commensive where there seems to be a lot of interest in nuclear as one of the ways to have a lower carbon form of energy. And I'm actually not sure which way this is going to go, to be honest, because one thing we've seen is that in the current like laws right now, which were passed under the previous administration, or the current administration, things like the Inflation Reduction Act, they work out to be really good for tech companies right now.

So if you, for example, right now, it's not actually that well known but let's say you see these announcements by Google or Microsoft and Amazon talking about "we're going to deploy a bunch of new data centers and they will be powered by renewable, by nuclear power" for example. If you look at how that's being paid you'll see that essentially half the cost of that is coming from the government themselves, right?

So you've got essentially your 50 percent subsidy to the largest companies in the world and you can imagine that there's going to be a lot of pressure to hold on to those particular, that kind of policy set up, because that is worth literally billions and billions to existing tech firms. So that might have some impact on like the environmental impact of the energy that we use, but you also have this thing where you've got someone who's very keen on expanding America's role as the largest producer of fossil gas right now.

So that has a flip side. I'm not quite sure which direction it's going to go in, but it does look like it's going to be bad news for, if nothing else, wind. It doesn't look like it's going to be great. So the idea that One of the knock on effects of this might be 

Asim Hussain: Yeah. Elon doesn't own a wind company, so I don't 

Chris Adams: but I said one thing that might bring up from a kind of operational point of view is do you end up with a slightly less volatile grid because you've got a bunch of huge amounts of money being thrown at stuff which is either fuel based so it can be dispatched internal all the time or nuclear regardless of your feelings of that which is a kind of a steady thing. That might have some implications of what you end up building as a result Because I could start talking about all the other kind of 

all the justice themes around that but we're coming to time and I don't want start on that because...

Asim Hussain: This is a rabbit hole. But yeah, I think probably you and I agree the next couple of weeks are going to be very interesting and we're going to, I think it's going to affect the next four or five years in quite meaningful ways 

Chris Adams: And the other thing the other flip side of this might be if you now have this scenario where you have tariffs which massively we've seen stories we'll share a link to some stories inside this where It looks like assume under this kind of tariff regime you would have basically laptops and electronics doubling in price. If you suddenly just doubled the cost of embodied stuff like of hardware that's going to create other drivers of what you actually then choose to do. For example you might hold on to hardware for longer because it's so expensive to replace now It's twice as expensive to replace. So that might change the steps that people end up being incentivized to do under this kind of regime for example. So there's some stuff there and it's also worth bearing in mind that's just America is obviously where one of the largest sectors but it's not the only one in the world. There's also China and there's all kinds of stuff happening in China for example as well. I'm gonna give you the last word actually if that's okay Asim because you were literally in China talking about green software and I wonder if there's any things that caught your eye that you might point people to or if there's any kind of things you saw there? 

Asim Hussain: I think you're talking about, I've got a few more minutes, I think you're talking about this, think, I can comment on that, but I think you've got to comment first on the actual, yeah, 

Chris Adams: it's a lot of the time when we talk about green software we talk about stuff that's happening either in Europe or North America because that's where the majority of the money is being spent in Western markets like Western markets right but China is also like the second largest source of emissions. Second largest economy in the world right now. There's a huge amount happening there and we tend not to see so much of that but there's actually someone who I've been starting to follow an analyst called David Fishman He's been showing some really interesting stuff because you basically see some in my view quite radical decisions being made in China to actually impact the environmental like consequences of using digital services. There was a thing published in May Basically this Energy Efficiency and Carbon Reduction Action Plan. And the short version of this is basically by the end of 2025 the goal is to have I think 80 percent of all the new data centers that are being created 80 percent of their energy has to be coming from fossil free sources, renewable energy which is, when China gets something done they move incredibly quickly and there's massive human rights consequences as a result of that, but in this one here this is something which is something that a lot of us are sleeping on. Like we talk about say my organization talks about fossil free internet by 2030 and we might see the UK government saying "we can achieve a carbon free grid by 2030." And in Germany there is a target for 80 percent renewable energy by 2030. And then in China for data centers they're talking about, yeah, 80 percent by the end of 2025. The caveats I need to share, this is stuff which is translated from the actual Chinese documentation, so it's going to be hard if you don't speak Chinese and that's why I'm pointing to the analysts who operate in this field and share some of this, but it's just so much more, moving so much faster than we are. And it's something that we don't really have that much visibility on. I think it's worth actually being aware of if we think about the kind of global picture for this because you can come away with the idea that "okay there's an election take place and that means there's never going to there's not going to be anything happening in green software." But yeah, the world is larger than just North America, for example, there's stuff happening in other parts of the world And in many ways they are being more aggressive and more ambitious on climate than we've seen before basically. 

Asim Hussain: So So my kind of one addition to this, and I want to acknowledge this is like an anecdotal single experience of a human being who went to China, but I went and I was part of an IEEE conference and there was a specific track, a specific room focused on, sustainable software. And so there were a number of players there who, you know, obviously in China actively involved in Tech sustainability in China, some data center operators, smaller data center operators, we didn't see Alibaba. Actually no, Alibaba were there. So we saw some like data center operators there. And I remember being part of a panel and the various questions. I remember just turning around, I think the, EU AI Act had just come into force or just, yeah, just come into force at that time.

And everybody was talking, in Europe, everybody's talking about the EU AI Act. If I was on panel and I was talking to a bunch of people in the tech sustainability sector, they might not have details of the EU AI Act to speak hand. But if I asked them, "what are some of the policies that the EU is instantiating which is driving some of this momentum?"

They'd be able to rattle off like EU AI Act CSRD, CSDDD, maybe they won't be able to double, triple click into kind of the nuance of it, but they'll be able to rattle it out. I remember being part of this panel and I asked, "what is the policies," I was thinking about the EU AI Act. I said, "what are the policies the Chinese government is enacting, which is driving all of your amazing work that you're all doing?" Nobody could mention one. It was silence across the panel. And so that one anecdotal state, and I did some Googling as well, and I couldn't find much, maybe that's my problem, I'm Googling in English, maybe it's all in Chinese, I will acknowledge that. But I think from the takeaway for me, that is, there does seem to be a disconnect between what is being stated at kind of the state level and what is being understood at the operator level. If this was Europe, and if this was the kind of my world, our world, soon as there's a hint of a regulation in this space, we're all talking about it, every person in this space is like using that as a reason for why you should invest more in the green space and you know this and that and the other, but that I didn't see that over there.

Could be a cultural difference, could be something else, or it could just be that maybe, and again, I don't know, and if 

someone's listening to this and actually has better information, I would love for you to reach out and to help educate me, but is, I mean, this could just be something that's said on the state level, which hasn't operationalized down to lower level. I don't know how it works over there, but what needs to happen for this kind of thing to operationalize? So we can have a conversation with Alibaba and all these other cloud players. I mean, what, where's their page on their websites talking about how they're going to help meet this thing?

There's some, there's something missing here and I don't quite know what it is, but it could be cultural. It could be anything. And I want somebody to educate me on this. 

Chris Adams: Do you know what, that reminds me of a piece of work we did last year with an organization called Wikirate, where we to track the top ten domains in the world which were, by traffic. Do they have sustainability policies, what are they doing there? And some Chinese providers did come up, and they do actually have something inside that.

I'll share a link to that, because it wasn't particularly easy for us to find this, but that was some information there, and they were some targets that were, they, it's weird. Like the thing you see in China is there's an idea very much of "do more before you say, rather than say before you do it." So inversion of what we often see in the West, for example, are like, this is the thing, like you, China can be referred to as a totalitarian state in many ways, but that means that when you see a policy decision taking place quickly, what do you want?

Do you want green neoliberalism? Do you want green totalitarianism? There's like a whole, you can have all these kinds of.

Asim Hussain: Maybe it's just the way, because of the way Europe's different. So I would expect the conversations, and I want to just, I want to make sure I add to this, the panel I was on and the people I was speaking to were doing amazing stuff. They were

Chris Adams: Was this the ACM one that you were referring to or 

Asim Hussain: IEEE 

Chris Adams: IEEE. So you were doing the talk for that.

Asim Hussain: There was great, there was some great, even some data center operators.

And I was like, "you need to talk more publicly about the work that you're doing." But yeah, there's some great work happening over there. 

Chris Adams: Well, maybe we'll see some stuff come out of COP 29, because COP 28, digital and green digital came on the radar for the first time, really, because when I, when Green Software Foundation sent me to COP 27 in Egypt, it was basically not on the radar in the slightest, no one was really talking about it.

So maybe we'll see something come out of COP 29 as a result from this, because there were some initial movements inside that. All right, Asim, I think we've gone over a little bit on time, but this has been fun to catch up and I hope the kind of takes here, or the links were at least maybe useful for people.

I guess what we'll normally do is we'll just make sure that the things we did speak about, we've got some links to follow up so that people who were curious can follow on from this. And yeah, mate, really lovely catching up again. Let's do this again next month or something like that, all right? Take of yourself, 

Asim Hussain: All right. Cheers, Chris. 

Chris Adams: Ta ra! 

Asim Hussain: Bye. 

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!

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]]> Shaping Web Sustainability with the W3C Shaping Web Sustainability with the W3C Thu, 14 Nov 2024 07:00:00 GMT 36:41 703prwq1 false https://podcasts.castplus.fm/e/xn1421y8-shaping-web-sustainability-with-the-w3c 68dc7d13d5c8a1f9b326f5ce 68dc7cef46a2532cdd8d1c23 In this episode of Environment Variables, host Chris Adams dives into the evolving landscape of sustainable web development with Alexander Dawson and Tzviya Siegman from the World Wide Web Consortium (W3C). Dawson and Siegman discuss the W3C’s efforts to develop Web Sustainability Guidelines (WSG), a comprehensive set of evidence-based practices aimed at reducing the environmental impact of web technologies. They explore the creation and potential impact of these guidelines, especially as global interest grows in embedding sustainable practices within web standards. The episode also covers the challenges of driving adoption across public and private sectors, the role of testability in sustainability guidelines, and future directions for standards that minimize digital carbon footprints. This engaging conversation provides listeners with insights into how W3C’s sustainability initiatives could shape the future of the web. 88 In this episode of Environment Variables, host Chris Adams dives into the evolving landscape of sustainable web development with Alexander Dawson and Tzviya Siegman from the World Wide Web Consortium (W3C). Dawson and Siegman discuss the W3C’s efforts to develop Web Sustainability Guidelines (WSG), a comprehensive set of evidence-based practices aimed at reducing the environmental impact of web technologies. They explore the creation and potential impact of these guidelines, especially as global interest grows in embedding sustainable practices within web standards. The episode also covers the challenges of driving adoption across public and private sectors, the role of testability in sustainability guidelines, and future directions for standards that minimize digital carbon footprints. This engaging conversation provides listeners with insights into how W3C’s sustainability initiatives could shape the future of the web.

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TRANSCRIPT BELOW:

Alexander Dawson: We have tried to, even at the point of being a community group, 

treated this as seriously as possible and made sure what we've created to be as evidence led as possible.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and insights from the world of sustainable software development. I'm your host, Chris Adams. Green software covers a lot of different kinds of software. And today, we're checking in on the work of a group of fellow travelers in the world of web development, and more specifically, the World Wide Web Consortium. If you're not familiar with the World Wide Web Consortium, the W3C, it's effectively the place where people who build the web define how the web works. And where standards like HTML works, how CSS works, and how they're made accessible, and so on. And long before the Green Software Foundation existed, there was the W3C Sustainable Web Design Community Group, chaired by Tim Frick at the agency Mighty Bytes, who was also the author of the O'Reilly book, Designing for Web Sustainability. And in the last few years, there's been a real uptick in activity in this group, with the creation of the W3C Web Sustainability Guidelines and regular monthly community calls to coordinate the work on developing more sustainable web development practices. So what exactly are the Web Sustainability Guidelines and who are they for? If you cared about sustainability in the digital realm, why would you try getting involved in a group like the W3C in the first place? How does this even change the way people build the web and consume web services? And how do we make them more sustainable? Joining us today are two key figures from the W3C to help us explore these themes. Alex Dawson, one of the editors of the Web Sustainability Guidelines at the W3C, and Tzviya Siegman, at the W3C's new sustainability lead. So in alphabetical order, Alex, you win here. So I'm just going to hand over to you to briefly introduce yourself before we hand over to Tzviya. Is that okay?

Alexander Dawson: Yep, that's fine. Hi, everyone. I'm Alex Dawson, and I'm an invited expert in the W3C Sustainable Web Interest Group. And as Chris has mentioned, I'm the editor of the Web Sustainability Guidelines. And I've also recently been involved in co-authoring the upcoming sustainability chapter in this year's Web Almanac.

Chris Adams: Cool. Thanks for that, Alex. Tzviya, can I give you the floor to do the same?

Cool. 

Tzviya Siegman: Sure. Thank you for having me. I'm Tzviya Siegman. I'm the sustainability leader at W3C. I recently joined the staff of W3C, but I've been involved here more than 10 years. I also do member engagement in North America. I recently been involved in setting up the interest group in W3C and getting used to being on the staff instead of being a member.

Chris Adams: Thanks, Tzviya. And if you're new to this podcast, my name is Chris Adams. I am the host of this podcast, but I'm also the executive director at the Green Web Foundation. I also work Inside the Green Software Foundation as one of the chairs of the policy working group as well. And I'm one of the maintainers of the library CO2.js, a tool that's used in a number of digital sustainability projects. Before we dive into this conversation, here's a quick reminder that we're going to share a transcript to this and links to all the projects that are just mentioned in our show notes. And, you'll need to look for that on podcast.greensoftware.foundation to find that. So if you're listening to this via Spotify or YouTube, you might not find them. So please remember to look that up there. Alright, so, with that, I think the two of you are sitting comfortably, right? Alex, maybe I'll just hand over to you. So, last time, around about this time last year, I believe you joined us with Anne Faubry, and you spoke to us about the second draft of the Web Sustainability Guidelines. And most recently, I saw you presenting at the Green IO London conference about, again, web sustainability, and specifically the process of making these guidelines for people to follow. So, as I understand it, you're now at, is it draft 8, or possibly draft 9? So, I'd like to ask a little bit about how they get built, but before we do that, could you just give a brief summary of what the Web Sustainability Guidelines are to the uninitiated?

Alexander Dawson: Yeah, absolutely. Essentially, the Web Sustainability Guidelines are an evidence-based set of advisory guidelines. Many people may be aware of the Web Content Accessibility Guidelines as our founding principles of accessibility. And we've kind of followed a similar set of principles, but designing and creating them for the sustainability community.

And they've been in development for about three years now. And obviously, They are to assist those who want to make digital products and services more sustainable through practical techniques. And obviously, the science is developing rapidly as is the sustainability field and of course, legislative compliance targets.

So it's a much needed feature that the web really requires. And, it's a sort of Global growing focus as climate change is becoming a ever more important topic of discussion.

Chris Adams: Cool, thanks. Alright, there's two forlorn questions I might ask. So, first of all, you spoke about the Web Sustainability Guidelines. That's quite a mouthful. Is it okay to call them like the WSGs? Is there a short term that we should use for that?

Alexander Dawson: Yep, WSGs is perfectly fine. We use WSG as our sort acronym.

Chris Adams: Got it, okay. So I use WSG, a bit like the kind of web content accessibility guidelines, or sometimes the WCAG, right? I'll call it. So WCAG and WSG. Got it. Okay. And also, I know that you're at Green.io, the conference, it's like one of the places to be going to if you're going to speak about digital sustainability. How was that? How did you find it?

Alexander Dawson: Yeah, it was amazing, really. I mean, you were there yourself. It was definitely the place to be. It was sort of like the Woodstock of sustainability, pretty much. Anyone and everyone who could be there managed to sort of cram in the place, standing remotely at the back of the conference room. There were sort of all the faces from all these sort of major players that could sort of get there.

Yeah, it was a pretty amazing event. all credit to sort of Gail and getting sort of everybody involved in the conference. It was pretty much the event of the year as far as digital sustainability goes.

Chris Adams: So, you enjoyed that. 

Alexander Dawson: Definitely, yeah.

Chris Adams: All right, okay, and Tzviya, if I hand over to you, as I understand it, you work the W3C, and you're the new sustainability lead, and part of your day job is essentially guiding the existing community group that created some of these guidelines. But there's also work to create a more formal, I think they're called interest groups, which is slightly different. We'll explore that a little bit later on. Before we do that, can I just backtrack to talk about how you ended up working as a sustainability lead in the W3C? Because I had no idea there was one until I heard about your involvement in this. And honestly, I was quite pleased because it's been something inside the W3Cs like, architectural guidelines, their TAG, they said the web should be sustainable.

And now they've actually got someone who's talking about this. So that was really nice to hear. So yeah, how'd you get into this? What's the story that this came about?

Tzviya Siegman: I'll give you a short history of my career. I started working in standards back in 2008. I worked for a publishing company, and I was doing the digital side of things, looking at XML, and all of a sudden, ebooks became the thing. And it was, "oh my goodness, Tzviya, we need somebody to work on this." And we created a hundred based on PDF and CR, and I was like, this doesn't really look right, maybe I can fix it. And I did. In order to fix it, you need to read standards. And the whole ebook program came under my leadership, and I was not very high up in the food chain at that point. Eventually, I started working on the standards with an organization called the International Digital Publishing Forum. Because that's where EPUB was at the time. And over the next six years, the IDPF merged with the W3C. And I became the chair of the publishing working group. And then I joined the W3C's advisory board. And then I became chair of the advisory board. And etc. And then I got laid off. And I worked with some people within the W3C to develop this role because I have become interested in sustainability. All along I've been working in accessibility, and the part of technology that's always really interested me is not necessarily creating faster products. That's, time to market was always intimidatingly working with the product managers and things like that. At my job in publishing, I was always frustrated when project issues like accessibility got thrown to the side because there was a new feature game developed. It's nice to have a new feature, but if it's not accessible, if he doesn't have pride, then we were, yeah. And so sustainability is in that area as well. I used to work on what a manager, a former manager of mine called the illities. Accessibility, sustainability, and now I'm still, this is living the dream for me. So, I was able to sculpt this job with the W3C, which is really a dream for me. And the fact that sustainability is mentioned in the W3C's vision, I'll take a little credit for that, because I was one of the people who wrote that. So, just a little credit, because there were a lot of people involved in writing that. But here we are, and we are really excited to be working on sustainability in an interest group, which you mentioned is not quite the same as the working group, because some of the differences now or later that we're working on a specification would, I can explain the different types of specifications, but I'm really excited to be leading the community group into something more formal than just a new file, even though we had a lot of uptake, where we were able to form a policy and an interest group to be through soon.

Thank you.

Chris Adams: I see, okay, so it sounds like this is not your first rodeo. You've been through the process of seeing how standards get developed, and this is why you being involved with this helps, because there may be a bunch of people who have, who might be curious about this and have all the things you kind of wish for, but you've seen how this works, you've seen how the sausage gets made, and that's, some of your role is to help steer some of that through so we end up with something that people actually can use and adopt in their work and things like that, then.

Tzviya Siegman: Yeah, absolutely. The work in W3C, when it's an interest group or a working group, it goes through a pretty rigorous process, and I can talk about that process in more detail in the literature.

Chris Adams: Thanks! Alright, okay, thanks for that, Tzviya. So hopefully we've caught up the audience to where we are today, November 2024. We spoke a little bit about the history. And I teased about the creation of this new group, like an interest group that has some more formal backing within the W3C. So for people who are not au fait with how web standards are formed, or what an interest group is compared to a community group, can you maybe just explain a little bit about why this is a big deal and what it makes possible that wasn't before?

Tzviya, I'll hand over to you, then maybe Alex will come in with some specific examples, because he was the author of the guidelines.

Tzviya Siegman: Yeah, thank you. I'll talk a little bit about what it is that we try to accomplish with standards and what that means in W3C. In W3C, standards require interoperable implementations. Interoperable is achieved by testable implementations where we test things to make sure that they work in at least two instances. Sometimes standards are codified by regulatory bodies. We see this with something like USBC in the EU. We also see this with WCAG, the Web Content Accessibility Guidelines being pointed to by various legislation. There are a lot of standards. Like WCAG, where this is pointed to, and the W3C, we have community groups, so anybody can start a community group, it doesn't have the formal structure of a W3C group, it's people get together, they work on things, and it's the incubation process, we call it, or just people get together because they need 

Chris Adams: Okay. 

Tzviya Siegman: An interest group is, has the authorization of the W3C's advisory committee. Everybody has to vote on the work. A member of each W3C member organization has the ability to vote yes or no or maybe. Yes, but I would like to see this then changed on the formation of an interest group. And an interest group can write statements. A working group is when the same came with the advisory committee, but they actually have the recommendation track, which is what W3C officially calls standards. The sustainability group is an interest group where they write in statements. Because the Web Sustainability Guidelines include a lot of things that are not necessarily testable. Although there are a lot of testable statements in there, and I'll turn it over to Alex to talk about how we'll proceed with that.

Chris Adams: Okay, thanks for that, Tzviya. So if I just paraphrase that for people, there's almost like an escalating layer of authority or formality that comes through this. And it may be that the bar is somewhat higher with the things that get published by an interest group compared to a community group. And like there's an escalating scale all the way up to having like full blown standards.

And this is like the midway point. And, like, Alex, if that's about right, I'll hand over to you, Alex, and maybe you can chime in on some of it, because, yeah, you are very hands on when writing some of these, perhaps.

Alexander Dawson: Yeah, obviously, we are trying to be as rigorous as possible because throughout the creation process, we have tried to, even at the point of being a community group, treated this as seriously as possible and made sure what we've created to be as evidence led as possible. And we are still in the process of trying to create as much measurability data as we can, which is something that we are going to be increasingly focused upon as an interest group.

So, with regards to testability, which is something that obviously interest groups, having testable implementations, it's not something that we have to be as strict upon, we still want to make sure our work is as rigorous and testable as possible. Because there may come a point in the future that we may want to have that testable compliance state for working what we have created in with legislative conformance and being able to do so may require at some point, obviously in the far future, taking our work from an interest group state to a working group state to get that recommendation status.

But that is something, in the far off distant future, as Veer will probably nod and agree with. But for the moment, obviously, we want to focus on making what we have created as rigorous as possible purely because anyone who is going to utilize our work, we want to have the best quality evidence for anything we are submitting as guidance.

We want to be able to. tell people using our work, this is the best way that you can implement something in your product or service because the evidence shows that this will have the greatest impact in terms of sustainability. And it's something that we've been very much focused upon when creating the Web Sustainability Guidelines from the offset, which is why we've got so many references within our work.

We've got quite a lot of material relating to things like GRI and such. So it's something that we've been very much focused upon from the offset.

Chris Adams: Okay, and by GRI, you're referring to the Global Reporting Initiative here, right?

So that's one of the other groups that talk about, okay, this is how you, set out what counts as sustainable, for example, and they've done a bunch of work that you're referring to with that one there.

Alexander Dawson: Yeah. It's one of the measures that we've been utilizing alongside obviously academic material and many other forms of tooling as well.

Chris Adams: Alright, thanks for that, Alex. So, I'd just like maybe touch on that a little bit more, because one thing that I know that we've seen, like, in France, for example, there are something like guidelines which are kind of mandated for government websites now, and it sounds like you might be looking for something like that on a more international scale that could be adopted in other parts of the world, and I mean, for folks who are new to this, the W3C has, like, a public mailing list, and if you look on there, you'll see, like, there are people from, say, I think, Singapore. The government of Singapore basically said, "hey, we're looking for some standards like this, or something for us to adopt," and I know there are people in other parts of the world who are basically saying, "I want to push for this, but I need some kind of something either like a standard or a set of guidelines I can ask my team to be following," and it sounds like that's the kind of push, because I know the WSGs have been in some kind of usable form now, but it feels like this seems to be a real push for adoption to get larger organizations to use them, who commissioned significant projects. And we spoke about how these might not be standards with a capital S, there might be a kind of guidance for sustainable software, but in terms of providing a reasonable expectation of what you can expect a responsible practitioner to do, that seems to be what they'd be shooting for.

So maybe we could talk a little bit about what adoption looks like here, and maybe we can hear some stories about organizations expressing interest in adopting these and what that process looks like, because, yeah, this is a new field, and I know that we've just spoken about how different places are looking for this. And Alex, I suspect you've been in some of these conversations, so maybe if I hand over to you, and then maybe let Tzviya chime in, because she's seen this happen a few times outside of just sustainability.

Alexander Dawson: Yeah, in terms of adoption, meeting the success criteria is rather open to interpretation at this point. I mean, we're, as I've mentioned, we're aiming to improve measurability data as an interest group, so we can better sort of lead by evidence. Regarding if a particular technique has a greater impact or not and that's sort of on the horizon.

Currently we sort of follow a similar practice to the Web Content Accessibility Guidelines with their accessibility techniques and much of that can be seen with, in our STAR supplement, Sustainable Tooling and Reporting, as that outlines optional techniques that are currently deemed, where appropriate, machine testable, so can be sort of repeated and used to showcase which success criteria can be repeatable and, implementable to meet certain success criteria within the particular guidelines.

Now, obviously, you don't have to follow them, they're not sort of gospel, and we're working to sort of improve them where we can along the pathway. But we've been sort of trying to improve upon this to sort of help people in terms of general adoption. And regarding sort of interest from the public and private sector, we've had quite a lot of interest definitely from various organizations.

I mean, I have been sort of trying to keep track wherever possible, and there have been a number of agencies globally where they mentioned that they have been starting to utilize and implement our work and there have been a lot of government departments who have got in touch who are openly interested in utilizing what we have created, even though it's in draft format, or have interest in actually for tracking our progress or getting on board with what we've created potentially for what could be future compliance, so it's good signs and sort of generally good feeling, if nothing else, that we're potentially on the right track with what we have been creating.

Chris Adams: Okay, all right, so if I understand that correctly, so I spoke about how France has, they've set up some standards themselves already. So like, "all our websites made by the public sector have to meet these standards." In other parts of the world, there might be people saying, "well, we're going to set some of this, and rather than write all this stuff ourselves, we'll refer to these things as like, we'll defer to that, as that's what we're going to hold as our bar that everything needs to meet," for example.

That's what some of this stuff is. It seems to be like the direction that's heading in. Okay, so that gives you some idea of how that might work. But, Tzviya, I am, I'll hand over to you because, obviously, it's a bit more complicated than that. And like, there's, we can ask for certain things, but that's not the same as people actually adopting this stuff for example. Like, if it was, well, we would see a probably more accessible web, for example. So, Tzviya, can I hand over to you to talk a little bit about, like, actually adoption and how this happens, or if there's any things that you've learned over the last few years that you might share that could help here?

Tzviya Siegman: So, adoption is a really complicated topic. Something that I think many people find shocking when they start looking at standards is that we can write standards in an incredibly idealistic way. But we can't actually make anybody use them. It's very frustrating. This is why the implementation tests were important. But even with testing, we can't promise adoption. And that's why it's important for everybody working on this to become evangelists. And I often say that my job is really evangelism, and a lot of people who have worked at, who are working on sustainability come from the world of accessibility because the role is very similar in terms of evangelism. And it's really important to make sure that the set of issues is a high priority. People who are working on browsers, servers, and in their organizations, like you might have an HR department who has a goal of, let's say, reducing carbon emissions, because that's what everybody says, because as Tim Frick likes to say, they have carbon tunnel vision. They'll say they're reducing carbon by 30 percent by 2030, so just pick a number. They don't know what that means. We need to get to the developers who are actually writing the code, and we need to get to the people who are building the servers, and so on. So that They can actually do the work because the people at HR or the CEO or whoever it is who's making these commitments don't have the tools to do this. Our guidelines give us some of the tools and the guidelines are divided into sections so that this touches a lot of different departments in an organization, and we're trying to break them down so that people can pull out the different sections that are relevant to them, but that adoption step is really important. I'll also say that what you were talking about, Chris, where different governments might be able to point to the guidelines, that's really where adoption is important. But it also comes down to being able to educate people, and we're also going to be working on different education materials. I love WCAG, I'm a big fan, but a lot of people are deeply critical of WCAG because it's so hard to understand. So we're trying to make ours a lot easier to understand, and hopefully we'll be able to be really successful with it.

Chris Adams: Okay, thanks for that, Tzviya. So, you, so there's two things that kind of came out of that. So, one of them was about, Alex, you spoke about these things being testable. And presumably, this is some of the stuff that you might see when people are using, say, web page tests, which has, like, Carbon control, or EcoGrid, or some of these things. If you can test for something, then you can provide some guidance as part of the analysis process, or you can automate this and check, like, has this got better or has it got worse? That's what some of the kind of sustainability things presumably would afford, right? And that's what you're kind of referring to with some of that.

And that might be something that you're essentially able to, like, enforce a standard on, or make sure it's meeting the standard that you've set out in some kind of agreement, right?

Alexander Dawson: Yeah, absolutely. If we can provide something that's testable or something that people can utilize, then it's something that toolmakers, people creating products and services, can implement with what they're creating. And that will obviously help us do our job because for people creating tools like, Website Carbon, Ecograder, they can filter that down into their processes so that rather than individuals having to read an extensive specification, with a few clicks of a button, it can provide feedback through reports, potentially tools that can be integrated within IDEs like Visual Studio Code, things that could be integrated within browsers such as the DevTools environments within, say, Google Chrome and Firefox and such. Lighthouse Project is a prime example.

These are things that if it's machine testable, it can be integrated within tooling and that will help take a lot of the friction out of people being able to quickly identify where the problems are occurring, how much of a problem it is, and how to identify what resolution needs to be,

Chris Adams: kind of remedial action you might take. All right, okay, thanks for that. Okay, so that helps summarize that, and Tzviya, you were talking a little bit about, okay, different groups might have different stakeholders, and you were referring to public sector as one group that might have legally binding targets.

For example, we know that in the UK, they have a target to reduce emissions by however much, and in Europe, it's reduced emissions by 55 percent by the end of, wow, in five and a half years time, for example, I mean, I'm speaking to you in the middle of an election, so there's a difference about, but like, up until yesterday, there was a whole thing about the federal government being the single biggest purchaser of green energy, which was forcing a kind of switch to cleaner energy, for example, things like that.

That seems to be what you're referring to, right?

Tzviya Siegman: Yeah, there's public and private, but there's also different stakeholders, such as product managers versus developers. Individuals within an organization.

Chris Adams: Ah, I see, so like when you've got product managers, if they know what to ask for, they're able to, that helps inform that conversation with the team they're working with and say, "can we please agree to meet these standards or have something like that." They don't need to be the specialists themselves to know that if there's a standard or set of guidelines out there, that at least gives a shared set of minimum standards that the work's going to be run at, basically.

Tzviya Siegman: Yeah, absolutely.

Chris Adams: Okay, cool. That provides a bit more guidance about, I guess, the theory of change behind some of this. So, we're just starting to come up to time, and I wondered if we might be able to look forward to, like, what's on the horizon in the world of sustainable web development, and if there's anything that you folks have, that maybe, if I was to ask you, What should be on an interested listener's radar if they were interested in web sustainability? If I hand over to you, Alex, first, and then over to you, Tzviya, is there anything, like, what should people be looking out for between now and the end of the year, for example, or perhaps in Q1 2025? What's on your list, Alex?

Alexander Dawson: I think the thing that's dominating everybody's discussion at the moment is just sort of AI, isn't it, really? Everyone's talking about it, it's causing a lot of discussions around sort of how big an impact it's going to have, and it's pretty much going to be the dominating discussion around, how we're going to sort of wrangle around it in terms of legislation and measurements and tolling and Optimization and such, so I think that's going to be a major sort of domineering factor, especially considering how much infrastructure it's currently absorbing in terms of building new facilities and such, so yeah.

Chris Adams: Okay, cool. And are there any particular projects or things that you're expecting to, any releases by projects you follow, or any big things like that you would direct people to? So, they've been listening to this and they might want to find like a follow on link or follow on project to look into. What would you direct people to if they found something that's interesting but wouldn't want to go somewhere else next?

Alexander Dawson: There's not a particular one especially that I've been following. I mean, if I find any useful links or materials, normally it gets sucked straight into our project into the Web Sustainability Guidelines because we have nicely organized references for any useful material that can help people make decisions and such.

So I always like to point people to the guidelines, to the latest release, because I, nicely guard in those links and keep them up to date with all the latest material relating to sustainability. We've got sort of about 1500 odd links, all nicely categorized for people interested in sustainability.

Yeah, it's probably the largest digital sustainability reference library on the web.

Chris Adams: Blimey. Okay. All right. And, Tzviya, is there anything that you might kind of direct people's attention to? Like, we've spoken all about interest groups and community groups, but like, they still run, right? Like, they happen in the, is it monthly? I mean, how, if someone wants to find out about this, what's the process like to start

getting involved in any of this stuff, for example?

Tzviya Siegman: I'll provide links for both the interest group and the community group in W3C. For the community group, you don't need to be a member. For the interest group, you need to be a member of W3C or be what's called an invitative expert, which means provide a reason that you should be invited and, like, get a review by me and the chairs of the interest group and then show that you'll do the work. Also, something I'll be working on in the next year or so is creating a horizontal review program for the W3C and sustainabilities. The W3C prides itself on doing horizontal review on all standards in the areas of accessibility, privacy, security, and internationalization, meaning, we'll say the example of accessibility, every, there's a review of every single specification to make sure that the specification itself is accessible and will introduce accessibility problems. So I have the job of creating this for sustainability.

Chris Adams: Sorry to interrupt, can I just check, because I'm not sure I'm familiar with the horizontal, is the idea there being that you've got three or four standards, and you want them to be consistent with each other? Is that what horizontal reviews, or is there something else?

Tzviya Siegman: Horizontal review means that, so, let's say you have a new CSS specification. There are dozens of those every year. There's an accessibility checklist for the people in the CSS working group to take a look at. And then we have the opportunity to speak with people who are experts in accessibility to make

Chris Adams: okay.

Tzviya Siegman: accessibility issues onto the web. So I will be working to create a sustainability horizontal review checklist. I don't know yet how I'll be doing this, but that's part of my job as sustainability lead. I'm very excited about this because this has not been done before. So we want to make sure that the W3C is not bringing new sustainability issues to the web.

Chris Adams: Oh, I see. Okay. So if I check if I understand that, and maybe this is one concrete example, I know that there are certain bits of CSS, like there are certain filters that if you run them, you can basically cook that CPU in your laptop. I think Florian at Mozilla, he's one of the people who built the Firefox profiler.

He has this kind of party trick where he basically takes you to a single web page and it triggers one thing which just cooks your machine, because it just runs all the cores really, really hard, basically, just because it's doing a kind of gentle group. It sounds like something a bit like that, like, if someone's going to introduce a feature, they're aware of some of the knock on impacts, or they might have, yeah, okay, I think I understand that now.

Tzviya Siegman: And I'm going to just remind Alex that he's working on the web almanac.

Chris Adams: Yeah,

Tzviya Siegman: And maybe you want to plug that.

Alexander Dawson: Yeah, absolutely. We've got the new edition of the Web Almanac, which will be coming out this month. I've contributed to the sustainability chapter, and anyone who's interested in sustainability will be wanting to check out the results of that. So, yeah.

Chris Adams: Cool, and for people who are not familiar with the Web Almanac, this is essentially a bit like a kind of state of the web. Every year it comes out, tells everyone what's going on. So you've been saying, essentially it's state of sustainability of the web, that's what your chapter's focusing on, right?

Alexander Dawson: Yeah, absolutely.

Chris Adams: Brilliant, well if that's visible, we'll share a link to that in the show notes, but anyone who's curious, Web Almanac seems like a fairly easy term to search in your chosen search engine. Brilliant. Okay, well, I think we've got a good few pointers, and we'll also link to some of the community groups that we spoke about here. I suspect there's a conversation to have about how the GSF should work with the W3C for some of the groups there, because I know there is, we spoke about AI, and there's a GSF, Green Software Foundation Working Group, specifically focused on AI. It may make sense to do something along those lines for web specifically, because it's another clear sector and a clear application of software.

So, just before we go, maybe I should ask, if people want to find out more about you and your work, where should we, where should people be looking? Tzviya, if I start with you, like, where should we go? Is there a LinkedIn page or something like that you'd direct people to?

Tzviya Siegman: Sure, I'll drop my LinkedIn page, it's linkedin.com/Tzviya, I think. And there's also my bio on the W3C staff page, which I don't remember the URL off the top of my head, but I'll find out.

Chris Adams: Okay, cool. And Alex, for you as well?

Alexander Dawson: Mine's pretty straightforward. You can find my details on my website, and my name is my URL, so it's alexanderdawson.com.

Chris Adams: Alright, that seems nice and easy to Google and look up, and Alexander and Dawson are spelt like you would expect them to be spelt as well.

Alexander Dawson: Absolutely.

Chris Adams: Brilliant. Well, folks, thank you so much for giving us a bit of a kind of deep dive into the world of the W3C, and how web standards get made, and how we can actually, how they can be adopted, and wish you best of luck.

Hopefully I'll see you at one of the other community groups, or the meetings every month, and yeah, hope you have a nice week, alright? Take care, folks. Ta ra.

Tzviya Siegman: Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.

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]]> In this episode of Environment Variables, host Chris Adams dives into the evolving landscape of sustainable web development with Alexander Dawson and Tzviya Siegman from the World Wide Web Consortium (W3C). Dawson and Siegman discuss the W3C’s efforts to develop Web Sustainability Guidelines (WSG), a comprehensive set of evidence-based practices aimed at reducing the environmental impact of web technologies. They explore the creation and potential impact of these guidelines, especially as global interest grows in embedding sustainable practices within web standards. The episode also covers the challenges of driving adoption across public and private sectors, the role of testability in sustainability guidelines, and future directions for standards that minimize digital carbon footprints. This engaging conversation provides listeners with insights into how W3C’s sustainability initiatives could shape the future of the web.

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TRANSCRIPT BELOW:

Alexander Dawson: We have tried to, even at the point of being a community group, 

treated this as seriously as possible and made sure what we've created to be as evidence led as possible.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and insights from the world of sustainable software development. I'm your host, Chris Adams. Green software covers a lot of different kinds of software. And today, we're checking in on the work of a group of fellow travelers in the world of web development, and more specifically, the World Wide Web Consortium. If you're not familiar with the World Wide Web Consortium, the W3C, it's effectively the place where people who build the web define how the web works. And where standards like HTML works, how CSS works, and how they're made accessible, and so on. And long before the Green Software Foundation existed, there was the W3C Sustainable Web Design Community Group, chaired by Tim Frick at the agency Mighty Bytes, who was also the author of the O'Reilly book, Designing for Web Sustainability. And in the last few years, there's been a real uptick in activity in this group, with the creation of the W3C Web Sustainability Guidelines and regular monthly community calls to coordinate the work on developing more sustainable web development practices. So what exactly are the Web Sustainability Guidelines and who are they for? If you cared about sustainability in the digital realm, why would you try getting involved in a group like the W3C in the first place? How does this even change the way people build the web and consume web services? And how do we make them more sustainable? Joining us today are two key figures from the W3C to help us explore these themes. Alex Dawson, one of the editors of the Web Sustainability Guidelines at the W3C, and Tzviya Siegman, at the W3C's new sustainability lead. So in alphabetical order, Alex, you win here. So I'm just going to hand over to you to briefly introduce yourself before we hand over to Tzviya. Is that okay?

Alexander Dawson: Yep, that's fine. Hi, everyone. I'm Alex Dawson, and I'm an invited expert in the W3C Sustainable Web Interest Group. And as Chris has mentioned, I'm the editor of the Web Sustainability Guidelines. And I've also recently been involved in co-authoring the upcoming sustainability chapter in this year's Web Almanac.

Chris Adams: Cool. Thanks for that, Alex. Tzviya, can I give you the floor to do the same?

Cool. 

Tzviya Siegman: Sure. Thank you for having me. I'm Tzviya Siegman. I'm the sustainability leader at W3C. I recently joined the staff of W3C, but I've been involved here more than 10 years. I also do member engagement in North America. I recently been involved in setting up the interest group in W3C and getting used to being on the staff instead of being a member.

Chris Adams: Thanks, Tzviya. And if you're new to this podcast, my name is Chris Adams. I am the host of this podcast, but I'm also the executive director at the Green Web Foundation. I also work Inside the Green Software Foundation as one of the chairs of the policy working group as well. And I'm one of the maintainers of the library CO2.js, a tool that's used in a number of digital sustainability projects. Before we dive into this conversation, here's a quick reminder that we're going to share a transcript to this and links to all the projects that are just mentioned in our show notes. And, you'll need to look for that on podcast.greensoftware.foundation to find that. So if you're listening to this via Spotify or YouTube, you might not find them. So please remember to look that up there. Alright, so, with that, I think the two of you are sitting comfortably, right? Alex, maybe I'll just hand over to you. So, last time, around about this time last year, I believe you joined us with Anne Faubry, and you spoke to us about the second draft of the Web Sustainability Guidelines. And most recently, I saw you presenting at the Green IO London conference about, again, web sustainability, and specifically the process of making these guidelines for people to follow. So, as I understand it, you're now at, is it draft 8, or possibly draft 9? So, I'd like to ask a little bit about how they get built, but before we do that, could you just give a brief summary of what the Web Sustainability Guidelines are to the uninitiated?

Alexander Dawson: Yeah, absolutely. Essentially, the Web Sustainability Guidelines are an evidence-based set of advisory guidelines. Many people may be aware of the Web Content Accessibility Guidelines as our founding principles of accessibility. And we've kind of followed a similar set of principles, but designing and creating them for the sustainability community.

And they've been in development for about three years now. And obviously, They are to assist those who want to make digital products and services more sustainable through practical techniques. And obviously, the science is developing rapidly as is the sustainability field and of course, legislative compliance targets.

So it's a much needed feature that the web really requires. And, it's a sort of Global growing focus as climate change is becoming a ever more important topic of discussion.

Chris Adams: Cool, thanks. Alright, there's two forlorn questions I might ask. So, first of all, you spoke about the Web Sustainability Guidelines. That's quite a mouthful. Is it okay to call them like the WSGs? Is there a short term that we should use for that?

Alexander Dawson: Yep, WSGs is perfectly fine. We use WSG as our sort acronym.

Chris Adams: Got it, okay. So I use WSG, a bit like the kind of web content accessibility guidelines, or sometimes the WCAG, right? I'll call it. So WCAG and WSG. Got it. Okay. And also, I know that you're at Green.io, the conference, it's like one of the places to be going to if you're going to speak about digital sustainability. How was that? How did you find it?

Alexander Dawson: Yeah, it was amazing, really. I mean, you were there yourself. It was definitely the place to be. It was sort of like the Woodstock of sustainability, pretty much. Anyone and everyone who could be there managed to sort of cram in the place, standing remotely at the back of the conference room. There were sort of all the faces from all these sort of major players that could sort of get there.

Yeah, it was a pretty amazing event. all credit to sort of Gail and getting sort of everybody involved in the conference. It was pretty much the event of the year as far as digital sustainability goes.

Chris Adams: So, you enjoyed that. 

Alexander Dawson: Definitely, yeah.

Chris Adams: All right, okay, and Tzviya, if I hand over to you, as I understand it, you work the W3C, and you're the new sustainability lead, and part of your day job is essentially guiding the existing community group that created some of these guidelines. But there's also work to create a more formal, I think they're called interest groups, which is slightly different. We'll explore that a little bit later on. Before we do that, can I just backtrack to talk about how you ended up working as a sustainability lead in the W3C? Because I had no idea there was one until I heard about your involvement in this. And honestly, I was quite pleased because it's been something inside the W3Cs like, architectural guidelines, their TAG, they said the web should be sustainable.

And now they've actually got someone who's talking about this. So that was really nice to hear. So yeah, how'd you get into this? What's the story that this came about?

Tzviya Siegman: I'll give you a short history of my career. I started working in standards back in 2008. I worked for a publishing company, and I was doing the digital side of things, looking at XML, and all of a sudden, ebooks became the thing. And it was, "oh my goodness, Tzviya, we need somebody to work on this." And we created a hundred based on PDF and CR, and I was like, this doesn't really look right, maybe I can fix it. And I did. In order to fix it, you need to read standards. And the whole ebook program came under my leadership, and I was not very high up in the food chain at that point. Eventually, I started working on the standards with an organization called the International Digital Publishing Forum. Because that's where EPUB was at the time. And over the next six years, the IDPF merged with the W3C. And I became the chair of the publishing working group. And then I joined the W3C's advisory board. And then I became chair of the advisory board. And etc. And then I got laid off. And I worked with some people within the W3C to develop this role because I have become interested in sustainability. All along I've been working in accessibility, and the part of technology that's always really interested me is not necessarily creating faster products. That's, time to market was always intimidatingly working with the product managers and things like that. At my job in publishing, I was always frustrated when project issues like accessibility got thrown to the side because there was a new feature game developed. It's nice to have a new feature, but if it's not accessible, if he doesn't have pride, then we were, yeah. And so sustainability is in that area as well. I used to work on what a manager, a former manager of mine called the illities. Accessibility, sustainability, and now I'm still, this is living the dream for me. So, I was able to sculpt this job with the W3C, which is really a dream for me. And the fact that sustainability is mentioned in the W3C's vision, I'll take a little credit for that, because I was one of the people who wrote that. So, just a little credit, because there were a lot of people involved in writing that. But here we are, and we are really excited to be working on sustainability in an interest group, which you mentioned is not quite the same as the working group, because some of the differences now or later that we're working on a specification would, I can explain the different types of specifications, but I'm really excited to be leading the community group into something more formal than just a new file, even though we had a lot of uptake, where we were able to form a policy and an interest group to be through soon.

Thank you.

Chris Adams: I see, okay, so it sounds like this is not your first rodeo. You've been through the process of seeing how standards get developed, and this is why you being involved with this helps, because there may be a bunch of people who have, who might be curious about this and have all the things you kind of wish for, but you've seen how this works, you've seen how the sausage gets made, and that's, some of your role is to help steer some of that through so we end up with something that people actually can use and adopt in their work and things like that, then.

Tzviya Siegman: Yeah, absolutely. The work in W3C, when it's an interest group or a working group, it goes through a pretty rigorous process, and I can talk about that process in more detail in the literature.

Chris Adams: Thanks! Alright, okay, thanks for that, Tzviya. So hopefully we've caught up the audience to where we are today, November 2024. We spoke a little bit about the history. And I teased about the creation of this new group, like an interest group that has some more formal backing within the W3C. So for people who are not au fait with how web standards are formed, or what an interest group is compared to a community group, can you maybe just explain a little bit about why this is a big deal and what it makes possible that wasn't before?

Tzviya, I'll hand over to you, then maybe Alex will come in with some specific examples, because he was the author of the guidelines.

Tzviya Siegman: Yeah, thank you. I'll talk a little bit about what it is that we try to accomplish with standards and what that means in W3C. In W3C, standards require interoperable implementations. Interoperable is achieved by testable implementations where we test things to make sure that they work in at least two instances. Sometimes standards are codified by regulatory bodies. We see this with something like USBC in the EU. We also see this with WCAG, the Web Content Accessibility Guidelines being pointed to by various legislation. There are a lot of standards. Like WCAG, where this is pointed to, and the W3C, we have community groups, so anybody can start a community group, it doesn't have the formal structure of a W3C group, it's people get together, they work on things, and it's the incubation process, we call it, or just people get together because they need 

Chris Adams: Okay. 

Tzviya Siegman: An interest group is, has the authorization of the W3C's advisory committee. Everybody has to vote on the work. A member of each W3C member organization has the ability to vote yes or no or maybe. Yes, but I would like to see this then changed on the formation of an interest group. And an interest group can write statements. A working group is when the same came with the advisory committee, but they actually have the recommendation track, which is what W3C officially calls standards. The sustainability group is an interest group where they write in statements. Because the Web Sustainability Guidelines include a lot of things that are not necessarily testable. Although there are a lot of testable statements in there, and I'll turn it over to Alex to talk about how we'll proceed with that.

Chris Adams: Okay, thanks for that, Tzviya. So if I just paraphrase that for people, there's almost like an escalating layer of authority or formality that comes through this. And it may be that the bar is somewhat higher with the things that get published by an interest group compared to a community group. And like there's an escalating scale all the way up to having like full blown standards.

And this is like the midway point. And, like, Alex, if that's about right, I'll hand over to you, Alex, and maybe you can chime in on some of it, because, yeah, you are very hands on when writing some of these, perhaps.

Alexander Dawson: Yeah, obviously, we are trying to be as rigorous as possible because throughout the creation process, we have tried to, even at the point of being a community group, treated this as seriously as possible and made sure what we've created to be as evidence led as possible. And we are still in the process of trying to create as much measurability data as we can, which is something that we are going to be increasingly focused upon as an interest group.

So, with regards to testability, which is something that obviously interest groups, having testable implementations, it's not something that we have to be as strict upon, we still want to make sure our work is as rigorous and testable as possible. Because there may come a point in the future that we may want to have that testable compliance state for working what we have created in with legislative conformance and being able to do so may require at some point, obviously in the far future, taking our work from an interest group state to a working group state to get that recommendation status.

But that is something, in the far off distant future, as Veer will probably nod and agree with. But for the moment, obviously, we want to focus on making what we have created as rigorous as possible purely because anyone who is going to utilize our work, we want to have the best quality evidence for anything we are submitting as guidance.

We want to be able to. tell people using our work, this is the best way that you can implement something in your product or service because the evidence shows that this will have the greatest impact in terms of sustainability. And it's something that we've been very much focused upon when creating the Web Sustainability Guidelines from the offset, which is why we've got so many references within our work.

We've got quite a lot of material relating to things like GRI and such. So it's something that we've been very much focused upon from the offset.

Chris Adams: Okay, and by GRI, you're referring to the Global Reporting Initiative here, right?

So that's one of the other groups that talk about, okay, this is how you, set out what counts as sustainable, for example, and they've done a bunch of work that you're referring to with that one there.

Alexander Dawson: Yeah. It's one of the measures that we've been utilizing alongside obviously academic material and many other forms of tooling as well.

Chris Adams: Alright, thanks for that, Alex. So, I'd just like maybe touch on that a little bit more, because one thing that I know that we've seen, like, in France, for example, there are something like guidelines which are kind of mandated for government websites now, and it sounds like you might be looking for something like that on a more international scale that could be adopted in other parts of the world, and I mean, for folks who are new to this, the W3C has, like, a public mailing list, and if you look on there, you'll see, like, there are people from, say, I think, Singapore. The government of Singapore basically said, "hey, we're looking for some standards like this, or something for us to adopt," and I know there are people in other parts of the world who are basically saying, "I want to push for this, but I need some kind of something either like a standard or a set of guidelines I can ask my team to be following," and it sounds like that's the kind of push, because I know the WSGs have been in some kind of usable form now, but it feels like this seems to be a real push for adoption to get larger organizations to use them, who commissioned significant projects. And we spoke about how these might not be standards with a capital S, there might be a kind of guidance for sustainable software, but in terms of providing a reasonable expectation of what you can expect a responsible practitioner to do, that seems to be what they'd be shooting for.

So maybe we could talk a little bit about what adoption looks like here, and maybe we can hear some stories about organizations expressing interest in adopting these and what that process looks like, because, yeah, this is a new field, and I know that we've just spoken about how different places are looking for this. And Alex, I suspect you've been in some of these conversations, so maybe if I hand over to you, and then maybe let Tzviya chime in, because she's seen this happen a few times outside of just sustainability.

Alexander Dawson: Yeah, in terms of adoption, meeting the success criteria is rather open to interpretation at this point. I mean, we're, as I've mentioned, we're aiming to improve measurability data as an interest group, so we can better sort of lead by evidence. Regarding if a particular technique has a greater impact or not and that's sort of on the horizon.

Currently we sort of follow a similar practice to the Web Content Accessibility Guidelines with their accessibility techniques and much of that can be seen with, in our STAR supplement, Sustainable Tooling and Reporting, as that outlines optional techniques that are currently deemed, where appropriate, machine testable, so can be sort of repeated and used to showcase which success criteria can be repeatable and, implementable to meet certain success criteria within the particular guidelines.

Now, obviously, you don't have to follow them, they're not sort of gospel, and we're working to sort of improve them where we can along the pathway. But we've been sort of trying to improve upon this to sort of help people in terms of general adoption. And regarding sort of interest from the public and private sector, we've had quite a lot of interest definitely from various organizations.

I mean, I have been sort of trying to keep track wherever possible, and there have been a number of agencies globally where they mentioned that they have been starting to utilize and implement our work and there have been a lot of government departments who have got in touch who are openly interested in utilizing what we have created, even though it's in draft format, or have interest in actually for tracking our progress or getting on board with what we've created potentially for what could be future compliance, so it's good signs and sort of generally good feeling, if nothing else, that we're potentially on the right track with what we have been creating.

Chris Adams: Okay, all right, so if I understand that correctly, so I spoke about how France has, they've set up some standards themselves already. So like, "all our websites made by the public sector have to meet these standards." In other parts of the world, there might be people saying, "well, we're going to set some of this, and rather than write all this stuff ourselves, we'll refer to these things as like, we'll defer to that, as that's what we're going to hold as our bar that everything needs to meet," for example.

That's what some of this stuff is. It seems to be like the direction that's heading in. Okay, so that gives you some idea of how that might work. But, Tzviya, I am, I'll hand over to you because, obviously, it's a bit more complicated than that. And like, there's, we can ask for certain things, but that's not the same as people actually adopting this stuff for example. Like, if it was, well, we would see a probably more accessible web, for example. So, Tzviya, can I hand over to you to talk a little bit about, like, actually adoption and how this happens, or if there's any things that you've learned over the last few years that you might share that could help here?

Tzviya Siegman: So, adoption is a really complicated topic. Something that I think many people find shocking when they start looking at standards is that we can write standards in an incredibly idealistic way. But we can't actually make anybody use them. It's very frustrating. This is why the implementation tests were important. But even with testing, we can't promise adoption. And that's why it's important for everybody working on this to become evangelists. And I often say that my job is really evangelism, and a lot of people who have worked at, who are working on sustainability come from the world of accessibility because the role is very similar in terms of evangelism. And it's really important to make sure that the set of issues is a high priority. People who are working on browsers, servers, and in their organizations, like you might have an HR department who has a goal of, let's say, reducing carbon emissions, because that's what everybody says, because as Tim Frick likes to say, they have carbon tunnel vision. They'll say they're reducing carbon by 30 percent by 2030, so just pick a number. They don't know what that means. We need to get to the developers who are actually writing the code, and we need to get to the people who are building the servers, and so on. So that They can actually do the work because the people at HR or the CEO or whoever it is who's making these commitments don't have the tools to do this. Our guidelines give us some of the tools and the guidelines are divided into sections so that this touches a lot of different departments in an organization, and we're trying to break them down so that people can pull out the different sections that are relevant to them, but that adoption step is really important. I'll also say that what you were talking about, Chris, where different governments might be able to point to the guidelines, that's really where adoption is important. But it also comes down to being able to educate people, and we're also going to be working on different education materials. I love WCAG, I'm a big fan, but a lot of people are deeply critical of WCAG because it's so hard to understand. So we're trying to make ours a lot easier to understand, and hopefully we'll be able to be really successful with it.

Chris Adams: Okay, thanks for that, Tzviya. So, you, so there's two things that kind of came out of that. So, one of them was about, Alex, you spoke about these things being testable. And presumably, this is some of the stuff that you might see when people are using, say, web page tests, which has, like, Carbon control, or EcoGrid, or some of these things. If you can test for something, then you can provide some guidance as part of the analysis process, or you can automate this and check, like, has this got better or has it got worse? That's what some of the kind of sustainability things presumably would afford, right? And that's what you're kind of referring to with some of that.

And that might be something that you're essentially able to, like, enforce a standard on, or make sure it's meeting the standard that you've set out in some kind of agreement, right?

Alexander Dawson: Yeah, absolutely. If we can provide something that's testable or something that people can utilize, then it's something that toolmakers, people creating products and services, can implement with what they're creating. And that will obviously help us do our job because for people creating tools like, Website Carbon, Ecograder, they can filter that down into their processes so that rather than individuals having to read an extensive specification, with a few clicks of a button, it can provide feedback through reports, potentially tools that can be integrated within IDEs like Visual Studio Code, things that could be integrated within browsers such as the DevTools environments within, say, Google Chrome and Firefox and such. Lighthouse Project is a prime example.

These are things that if it's machine testable, it can be integrated within tooling and that will help take a lot of the friction out of people being able to quickly identify where the problems are occurring, how much of a problem it is, and how to identify what resolution needs to be,

Chris Adams: kind of remedial action you might take. All right, okay, thanks for that. Okay, so that helps summarize that, and Tzviya, you were talking a little bit about, okay, different groups might have different stakeholders, and you were referring to public sector as one group that might have legally binding targets.

For example, we know that in the UK, they have a target to reduce emissions by however much, and in Europe, it's reduced emissions by 55 percent by the end of, wow, in five and a half years time, for example, I mean, I'm speaking to you in the middle of an election, so there's a difference about, but like, up until yesterday, there was a whole thing about the federal government being the single biggest purchaser of green energy, which was forcing a kind of switch to cleaner energy, for example, things like that.

That seems to be what you're referring to, right?

Tzviya Siegman: Yeah, there's public and private, but there's also different stakeholders, such as product managers versus developers. Individuals within an organization.

Chris Adams: Ah, I see, so like when you've got product managers, if they know what to ask for, they're able to, that helps inform that conversation with the team they're working with and say, "can we please agree to meet these standards or have something like that." They don't need to be the specialists themselves to know that if there's a standard or set of guidelines out there, that at least gives a shared set of minimum standards that the work's going to be run at, basically.

Tzviya Siegman: Yeah, absolutely.

Chris Adams: Okay, cool. That provides a bit more guidance about, I guess, the theory of change behind some of this. So, we're just starting to come up to time, and I wondered if we might be able to look forward to, like, what's on the horizon in the world of sustainable web development, and if there's anything that you folks have, that maybe, if I was to ask you, What should be on an interested listener's radar if they were interested in web sustainability? If I hand over to you, Alex, first, and then over to you, Tzviya, is there anything, like, what should people be looking out for between now and the end of the year, for example, or perhaps in Q1 2025? What's on your list, Alex?

Alexander Dawson: I think the thing that's dominating everybody's discussion at the moment is just sort of AI, isn't it, really? Everyone's talking about it, it's causing a lot of discussions around sort of how big an impact it's going to have, and it's pretty much going to be the dominating discussion around, how we're going to sort of wrangle around it in terms of legislation and measurements and tolling and Optimization and such, so I think that's going to be a major sort of domineering factor, especially considering how much infrastructure it's currently absorbing in terms of building new facilities and such, so yeah.

Chris Adams: Okay, cool. And are there any particular projects or things that you're expecting to, any releases by projects you follow, or any big things like that you would direct people to? So, they've been listening to this and they might want to find like a follow on link or follow on project to look into. What would you direct people to if they found something that's interesting but wouldn't want to go somewhere else next?

Alexander Dawson: There's not a particular one especially that I've been following. I mean, if I find any useful links or materials, normally it gets sucked straight into our project into the Web Sustainability Guidelines because we have nicely organized references for any useful material that can help people make decisions and such.

So I always like to point people to the guidelines, to the latest release, because I, nicely guard in those links and keep them up to date with all the latest material relating to sustainability. We've got sort of about 1500 odd links, all nicely categorized for people interested in sustainability.

Yeah, it's probably the largest digital sustainability reference library on the web.

Chris Adams: Blimey. Okay. All right. And, Tzviya, is there anything that you might kind of direct people's attention to? Like, we've spoken all about interest groups and community groups, but like, they still run, right? Like, they happen in the, is it monthly? I mean, how, if someone wants to find out about this, what's the process like to start

getting involved in any of this stuff, for example?

Tzviya Siegman: I'll provide links for both the interest group and the community group in W3C. For the community group, you don't need to be a member. For the interest group, you need to be a member of W3C or be what's called an invitative expert, which means provide a reason that you should be invited and, like, get a review by me and the chairs of the interest group and then show that you'll do the work. Also, something I'll be working on in the next year or so is creating a horizontal review program for the W3C and sustainabilities. The W3C prides itself on doing horizontal review on all standards in the areas of accessibility, privacy, security, and internationalization, meaning, we'll say the example of accessibility, every, there's a review of every single specification to make sure that the specification itself is accessible and will introduce accessibility problems. So I have the job of creating this for sustainability.

Chris Adams: Sorry to interrupt, can I just check, because I'm not sure I'm familiar with the horizontal, is the idea there being that you've got three or four standards, and you want them to be consistent with each other? Is that what horizontal reviews, or is there something else?

Tzviya Siegman: Horizontal review means that, so, let's say you have a new CSS specification. There are dozens of those every year. There's an accessibility checklist for the people in the CSS working group to take a look at. And then we have the opportunity to speak with people who are experts in accessibility to make

Chris Adams: okay.

Tzviya Siegman: accessibility issues onto the web. So I will be working to create a sustainability horizontal review checklist. I don't know yet how I'll be doing this, but that's part of my job as sustainability lead. I'm very excited about this because this has not been done before. So we want to make sure that the W3C is not bringing new sustainability issues to the web.

Chris Adams: Oh, I see. Okay. So if I check if I understand that, and maybe this is one concrete example, I know that there are certain bits of CSS, like there are certain filters that if you run them, you can basically cook that CPU in your laptop. I think Florian at Mozilla, he's one of the people who built the Firefox profiler.

He has this kind of party trick where he basically takes you to a single web page and it triggers one thing which just cooks your machine, because it just runs all the cores really, really hard, basically, just because it's doing a kind of gentle group. It sounds like something a bit like that, like, if someone's going to introduce a feature, they're aware of some of the knock on impacts, or they might have, yeah, okay, I think I understand that now.

Tzviya Siegman: And I'm going to just remind Alex that he's working on the web almanac.

Chris Adams: Yeah,

Tzviya Siegman: And maybe you want to plug that.

Alexander Dawson: Yeah, absolutely. We've got the new edition of the Web Almanac, which will be coming out this month. I've contributed to the sustainability chapter, and anyone who's interested in sustainability will be wanting to check out the results of that. So, yeah.

Chris Adams: Cool, and for people who are not familiar with the Web Almanac, this is essentially a bit like a kind of state of the web. Every year it comes out, tells everyone what's going on. So you've been saying, essentially it's state of sustainability of the web, that's what your chapter's focusing on, right?

Alexander Dawson: Yeah, absolutely.

Chris Adams: Brilliant, well if that's visible, we'll share a link to that in the show notes, but anyone who's curious, Web Almanac seems like a fairly easy term to search in your chosen search engine. Brilliant. Okay, well, I think we've got a good few pointers, and we'll also link to some of the community groups that we spoke about here. I suspect there's a conversation to have about how the GSF should work with the W3C for some of the groups there, because I know there is, we spoke about AI, and there's a GSF, Green Software Foundation Working Group, specifically focused on AI. It may make sense to do something along those lines for web specifically, because it's another clear sector and a clear application of software.

So, just before we go, maybe I should ask, if people want to find out more about you and your work, where should we, where should people be looking? Tzviya, if I start with you, like, where should we go? Is there a LinkedIn page or something like that you'd direct people to?

Tzviya Siegman: Sure, I'll drop my LinkedIn page, it's linkedin.com/Tzviya, I think. And there's also my bio on the W3C staff page, which I don't remember the URL off the top of my head, but I'll find out.

Chris Adams: Okay, cool. And Alex, for you as well?

Alexander Dawson: Mine's pretty straightforward. You can find my details on my website, and my name is my URL, so it's alexanderdawson.com.

Chris Adams: Alright, that seems nice and easy to Google and look up, and Alexander and Dawson are spelt like you would expect them to be spelt as well.

Alexander Dawson: Absolutely.

Chris Adams: Brilliant. Well, folks, thank you so much for giving us a bit of a kind of deep dive into the world of the W3C, and how web standards get made, and how we can actually, how they can be adopted, and wish you best of luck.

Hopefully I'll see you at one of the other community groups, or the meetings every month, and yeah, hope you have a nice week, alright? Take care, folks. Ta ra.

Tzviya Siegman: Thank you.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.

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]]> Backstage: Awesome Green Software Backstage: Awesome Green Software Thu, 07 Nov 2024 08:00:00 GMT 9:28 81qq3k91 false https://podcasts.castplus.fm/e/v855jzr8-backstage-awesome-green-software 68dc7d0a46a2532cdd8d24cb 68dc7cef46a2532cdd8d1c23 In this episode of Backstage, we go backstage with the Awesome Green Software (AGS) project, a pioneering initiative from the Green Software Foundation’s Open Source Working Group. Project leads Ahmad Antar and Abid Ali, along with contributor Josh Rauvola, share how AGS is building a centralized platform for developers to access tools, libraries, and frameworks to create greener, more sustainable software. They discuss the innovative tech behind AGS, the project’s progress toward launch, and its potential to make green software development accessible to organizations worldwide. 87 In this episode of Backstage, we go backstage with the Awesome Green Software (AGS) project, a pioneering initiative from the Green Software Foundation’s Open Source Working Group. Project leads Ahmad Antar and Abid Ali, along with contributor Josh Rauvola, share how AGS is building a centralized platform for developers to access tools, libraries, and frameworks to create greener, more sustainable software. They discuss the innovative tech behind AGS, the project’s progress toward launch, and its potential to make green software development accessible to organizations worldwide.

Learn more about the Awesome Green Software Project:

Learn more about our people:

Find out more about the GSF:

Resources:

Events:

If you enjoyed this episode then please either:

TRANSCRIPT BELOW:
Chris Skipper: Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Skipper. On this episode, we have another edition of Backstage. Here, we'll take you behind the scenes at the Green Software Foundation to highlight ongoing projects and share insights from the members directly involved.

Today, we're diving into the Awesome Green Software Project, an exciting initiative curated by the Green Software Foundation's open source working group. Today, project leads Ahmad Antar and Abid Ali have provided us with some informative content and along with some soundbites from contributor Josh Rauvola to give us the lowdown on the collection of tools, libraries, and frameworks designed to help developers create greener, more sustainable software.

So, what makes this amazing, nay awesome, collection of green software resources tick? Let's explore that in this backstage episode. The GSF is all about building software that emits less carbon. Awesome Green Software fulfills that by creating a one stop shop which compiles research, tools, code, libraries, and training from volunteers that will be used for building applications that emit less carbon into our atmosphere.

There aren't too many tools available today that organizations can use to ensure their software is following sustainability principles. But, Awesome Green Software aims to fill that void. The GSF has the Impact Framework and the Software Carbon Intensity Specification, the SCI. And now, Awesome Green Software to provide that support.

As previously mentioned, Awesome Green Software brings research and resources that will be available at one place for organizations to access, hence reducing their efforts and time to find these tools and rather focus their efforts on building impactful software. Awesome Green Software was an idea that the GSF wanted to start for a while.

GSF member Digital Emissions took the initiative and is bringing it to reality. They have made a lot of progress over the last few months. The front end and back end are almost ready, and they are getting ready to start testing soon. Awesome Green Software is still in the developmental phases, but once it's ready to launch, We are expecting many contributors from the GSF and beyond to start contributing impactful code, tools, and utilities.

It will have a great impact on the overall community because currently it is not easy to find such tools in one place. Next, I'd like to play you a soundbite from Josh. We asked him the question, what are some of the key challenges you've encountered while developing the Awesome Green Software project?

And how did you and your team work to overcome them?

Josh Rauvola: Well, one of the main challenges we've faced is consolidating a vast amount of information and resources into a single user friendly platform. There's so much valuable content out there, but it's scattered across various sources. Organizing it in a way that's easily accessible to developers and other people was a significant task.

We also had to ensure that integrating technologies like Notion for content management and Algolia for search, which we can get into later, would provide a seamless user experience. To overcome these challenges, our team focused on building a solid backend and frontend infrastructure. We've been meticulous in our approach, and we're excited to get this project out into the hands of people that truly care about the environment.

Chris Skipper: With such a comprehensive list of tools and resources, the Awesome Green Software Project provides a bird's eye view of everything related to sustainable software out there on the internet. Can you explain some of the methods that you used to compile such a comprehensive list?

Josh Rauvola: Certainly. One innovative approach we're taking is leveraging powerful AI search capabilities through Algolia. This allows users to quickly find the tools and resources they need to build sustainable software. We're also using Notion as a content management system, which simplifies the process, for contributors to add and update resources in the directory.

By combining these technologies with modern front end frameworks, we're creating a responsive and efficient platform that not only compiles existing resources, but also encourages community engagement and contributions. We're all about being as efficient as possible, so we utilized some of the tools provided on our platform to align better with our awesome green software goals.

Chris Skipper: Could you highlight some of the specific tools, technologies, or frameworks included in the Awesome Green Software Project? What are some of your favorites? And how do they support the project's sustainability goals?

Josh Rauvola: That's a great question. The project includes a variety of tools like the Carbon Aware SDK, which helps developers make their applications carbon aware by optimizing energy consumption based on grid carbon intensity. We also feature tools like Scaphandre, a power monitoring agent that helps measure the energy consumption of servers.

Personally, I'm a big fan of the CodeCarbon.io library. It allows developers to track and reduce CO2 emissions from their computing by integrating seamlessly within their code. These tools support our sustainability goals by providing practical solutions that developers can implement to reduce the carbon footprint of their software.

Chris Skipper: The final question we put to Josh was, throughout the development of the Awesome Green Software project, What valuable lessons or insights have you gained that could benefit other organizations aiming to promote sustainable software?

Josh Rauvola: Well, one of the most valuable lessons we've learned is the importance of accessibility and community engagement. Only by the amazing support of the Green Software Foundation and the team at Digital Emissions have we been able to create such a wonderful platform. Creating a centralized platform where resources are shared.

Green software, easily accessible, significantly lowers the barrier for organizations to adopt sustainable practices. By making it easier for developers to find and use these tools, we encourage wider adoption of green software practices. Now all we need to do is get the word out. I'm sure this podcast will go a long way in doing so.

Chris Skipper: Yes, hopefully it does. I'd like to extend a big thank you to Ahmad, Abid, and Josh for sharing their valuable insights on the Awesome Green Software project. It's evident that this initiative goes beyond just providing resources. Thank you. It's about creating a comprehensive platform where developers can easily access tools, libraries, and frameworks to build greener, more sustainable software.

From their use of innovative technologies like Algolia and Notion to the thoughtful selection of carbon tracking tools, Ahmad, Abid, and Josh have shown us how AGS will make a real difference for organizations like yours. Aiming to reduce their software's carbon footprint. Their dedication to making the Awesome Green Software project a one stop shop is inspiring.

And it's exciting to hear about the milestones reached so far. With the front and back end nearly ready, and testing just around the corner, the team's focus on accessibility and community engagement will undoubtedly drive broader adoption of sustainable software practices as more contributors join after the launch.

We can't wait to see how AGS will shape the green software landscape and empower developers worldwide. slide. Now, before we finish this episode of Backstage, we have a few events to read out. Firstly, a free in person event, Green Gaming with Strategies, on November the 9th at 9am CET in Katowice, Poland.

Learn how the gaming industry contributes to and can mitigate its environmental footprint. Next up, we have Implications of AI related data center electricity use and emissions. A hybrid event happening on November the 12th to the 13th in Washington DC. Explore how to map, measure and mitigate the impacts of AI data center electricity usage.

Next up, a virtual event titled Digital Impact Pitch on November the 20th at 6. 30pm CET. This virtual event will help you discover what is the impact of our daily use of digital technologies. And finally, the Green Waves Hackathon closing day on November the 22nd at 8. 30am CET at TU Delft in the Netherlands.

Network, get talks and celebrate the ideas created during the hackathon. This event is your opportunity to experience the most innovative solutions developed by the Green Waves Hackathon participants. Whether you're passionate about sustainable IT or water research, This closing day will showcase the cutting edge ideas that can help reduce environmental impact.

Join us for a day packed with insightful presentations, live demos, and the excitement of the awards ceremony. Links to all the events and the resources mentioned in the show will be in the show notes below this podcast episode. So, we've reached the end of this special backstage episode. I hope you enjoyed the podcast.

To listen to more podcasts about Green Software, please visit podcast. greensoftware.foundation and we'll see you on the next episode. Bye for now!



Hosted on Acast. See acast.com/privacy for more information.

]]> In this episode of Backstage, we go backstage with the Awesome Green Software (AGS) project, a pioneering initiative from the Green Software Foundation’s Open Source Working Group. Project leads Ahmad Antar and Abid Ali, along with contributor Josh Rauvola, share how AGS is building a centralized platform for developers to access tools, libraries, and frameworks to create greener, more sustainable software. They discuss the innovative tech behind AGS, the project’s progress toward launch, and its potential to make green software development accessible to organizations worldwide.

Learn more about the Awesome Green Software Project:

Learn more about our people:

Find out more about the GSF:

Resources:

Events:

If you enjoyed this episode then please either:

TRANSCRIPT BELOW:
Chris Skipper: Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Skipper. On this episode, we have another edition of Backstage. Here, we'll take you behind the scenes at the Green Software Foundation to highlight ongoing projects and share insights from the members directly involved.

Today, we're diving into the Awesome Green Software Project, an exciting initiative curated by the Green Software Foundation's open source working group. Today, project leads Ahmad Antar and Abid Ali have provided us with some informative content and along with some soundbites from contributor Josh Rauvola to give us the lowdown on the collection of tools, libraries, and frameworks designed to help developers create greener, more sustainable software.

So, what makes this amazing, nay awesome, collection of green software resources tick? Let's explore that in this backstage episode. The GSF is all about building software that emits less carbon. Awesome Green Software fulfills that by creating a one stop shop which compiles research, tools, code, libraries, and training from volunteers that will be used for building applications that emit less carbon into our atmosphere.

There aren't too many tools available today that organizations can use to ensure their software is following sustainability principles. But, Awesome Green Software aims to fill that void. The GSF has the Impact Framework and the Software Carbon Intensity Specification, the SCI. And now, Awesome Green Software to provide that support.

As previously mentioned, Awesome Green Software brings research and resources that will be available at one place for organizations to access, hence reducing their efforts and time to find these tools and rather focus their efforts on building impactful software. Awesome Green Software was an idea that the GSF wanted to start for a while.

GSF member Digital Emissions took the initiative and is bringing it to reality. They have made a lot of progress over the last few months. The front end and back end are almost ready, and they are getting ready to start testing soon. Awesome Green Software is still in the developmental phases, but once it's ready to launch, We are expecting many contributors from the GSF and beyond to start contributing impactful code, tools, and utilities.

It will have a great impact on the overall community because currently it is not easy to find such tools in one place. Next, I'd like to play you a soundbite from Josh. We asked him the question, what are some of the key challenges you've encountered while developing the Awesome Green Software project?

And how did you and your team work to overcome them?

Josh Rauvola: Well, one of the main challenges we've faced is consolidating a vast amount of information and resources into a single user friendly platform. There's so much valuable content out there, but it's scattered across various sources. Organizing it in a way that's easily accessible to developers and other people was a significant task.

We also had to ensure that integrating technologies like Notion for content management and Algolia for search, which we can get into later, would provide a seamless user experience. To overcome these challenges, our team focused on building a solid backend and frontend infrastructure. We've been meticulous in our approach, and we're excited to get this project out into the hands of people that truly care about the environment.

Chris Skipper: With such a comprehensive list of tools and resources, the Awesome Green Software Project provides a bird's eye view of everything related to sustainable software out there on the internet. Can you explain some of the methods that you used to compile such a comprehensive list?

Josh Rauvola: Certainly. One innovative approach we're taking is leveraging powerful AI search capabilities through Algolia. This allows users to quickly find the tools and resources they need to build sustainable software. We're also using Notion as a content management system, which simplifies the process, for contributors to add and update resources in the directory.

By combining these technologies with modern front end frameworks, we're creating a responsive and efficient platform that not only compiles existing resources, but also encourages community engagement and contributions. We're all about being as efficient as possible, so we utilized some of the tools provided on our platform to align better with our awesome green software goals.

Chris Skipper: Could you highlight some of the specific tools, technologies, or frameworks included in the Awesome Green Software Project? What are some of your favorites? And how do they support the project's sustainability goals?

Josh Rauvola: That's a great question. The project includes a variety of tools like the Carbon Aware SDK, which helps developers make their applications carbon aware by optimizing energy consumption based on grid carbon intensity. We also feature tools like Scaphandre, a power monitoring agent that helps measure the energy consumption of servers.

Personally, I'm a big fan of the CodeCarbon.io library. It allows developers to track and reduce CO2 emissions from their computing by integrating seamlessly within their code. These tools support our sustainability goals by providing practical solutions that developers can implement to reduce the carbon footprint of their software.

Chris Skipper: The final question we put to Josh was, throughout the development of the Awesome Green Software project, What valuable lessons or insights have you gained that could benefit other organizations aiming to promote sustainable software?

Josh Rauvola: Well, one of the most valuable lessons we've learned is the importance of accessibility and community engagement. Only by the amazing support of the Green Software Foundation and the team at Digital Emissions have we been able to create such a wonderful platform. Creating a centralized platform where resources are shared.

Green software, easily accessible, significantly lowers the barrier for organizations to adopt sustainable practices. By making it easier for developers to find and use these tools, we encourage wider adoption of green software practices. Now all we need to do is get the word out. I'm sure this podcast will go a long way in doing so.

Chris Skipper: Yes, hopefully it does. I'd like to extend a big thank you to Ahmad, Abid, and Josh for sharing their valuable insights on the Awesome Green Software project. It's evident that this initiative goes beyond just providing resources. Thank you. It's about creating a comprehensive platform where developers can easily access tools, libraries, and frameworks to build greener, more sustainable software.

From their use of innovative technologies like Algolia and Notion to the thoughtful selection of carbon tracking tools, Ahmad, Abid, and Josh have shown us how AGS will make a real difference for organizations like yours. Aiming to reduce their software's carbon footprint. Their dedication to making the Awesome Green Software project a one stop shop is inspiring.

And it's exciting to hear about the milestones reached so far. With the front and back end nearly ready, and testing just around the corner, the team's focus on accessibility and community engagement will undoubtedly drive broader adoption of sustainable software practices as more contributors join after the launch.

We can't wait to see how AGS will shape the green software landscape and empower developers worldwide. slide. Now, before we finish this episode of Backstage, we have a few events to read out. Firstly, a free in person event, Green Gaming with Strategies, on November the 9th at 9am CET in Katowice, Poland.

Learn how the gaming industry contributes to and can mitigate its environmental footprint. Next up, we have Implications of AI related data center electricity use and emissions. A hybrid event happening on November the 12th to the 13th in Washington DC. Explore how to map, measure and mitigate the impacts of AI data center electricity usage.

Next up, a virtual event titled Digital Impact Pitch on November the 20th at 6. 30pm CET. This virtual event will help you discover what is the impact of our daily use of digital technologies. And finally, the Green Waves Hackathon closing day on November the 22nd at 8. 30am CET at TU Delft in the Netherlands.

Network, get talks and celebrate the ideas created during the hackathon. This event is your opportunity to experience the most innovative solutions developed by the Green Waves Hackathon participants. Whether you're passionate about sustainable IT or water research, This closing day will showcase the cutting edge ideas that can help reduce environmental impact.

Join us for a day packed with insightful presentations, live demos, and the excitement of the awards ceremony. Links to all the events and the resources mentioned in the show will be in the show notes below this podcast episode. So, we've reached the end of this special backstage episode. I hope you enjoyed the podcast.

To listen to more podcasts about Green Software, please visit podcast. greensoftware.foundation and we'll see you on the next episode. Bye for now!



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]]> Remembering Abhishek Gupta: How does AI and ML Impact Climate Change? Remembering Abhishek Gupta: How does AI and ML Impact Climate Change? Thu, 17 Oct 2024 07:00:00 GMT 43:07 40pmmj21 false https://podcasts.castplus.fm/e/0nj0031n-remembering-abhishek-gupta-how-does-ai-and-ml-impact-climate-change 68dc7d0e7be17a7f01356915 68dc7cef46a2532cdd8d1c23 86 In this episode, we honor the memory of Abhishek Gupta, who was an instrumental figure in the Green Software Foundation and a Co-Chair of the Standards Working Group. Abhishek's work was pivotal in the development of the Software Carbon Intensity (SCI) Specification, now adopted globally. His tireless efforts shaped the future of green software, leaving an indelible mark on the industry. As we remember Abhishek, we reflect on his legacy of sustainability, leadership, and friendship, celebrating the remarkable impact he had on both his colleagues and the world. We are airing an old episode that featured Abhishek Gupta, Episode 5 of Environment Variables. Where host Chris Adams is joined by Will Buchanan of Azure ML (Microsoft), Abhishek Gupta; the chair of the Standards Working Group for the Green Software Foundation and Lynn Kaack, assistant professor at the Hertie School in Berlin to discuss how artificial intelligence and machine learning impact climate change. They discuss boundaries, Jevons paradox, the EU AI Act, inferencing and supplying us with a plethora of materials regarding ML and AI and the climate!

In Memoriam: Abhishek Gupta:

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TRANSCRIPT BELOW:

Chris Skipper: This week on Environment Variables, we have some sad news.

We have to report the untimely passing of Abhishek Gupta. Abhishek was a key part of the Green Software Foundation. He was the co chair of the Standards Working Group from the GSF's formation to his passing on September 30th of this year. I would like to read out an in memoriam that was posted by Asim Hussain, honoring the great man, his legacy of leadership, his collaboration, and the heart that he put into the Green Software Foundation.

When the Green Software Foundation was formed over three years ago, Abhishek offered to help. The standards working group was looking for a co chair, and he jumped in to help without hesitation. He led the standards working group for over three years, with the full support of everyone involved. Leading a work group isn't about imposing your will on others, It's about finding common ground, nurturing discussions, and searching for the truth.

Abhishek excelled in all of those areas. He leaves a powerful legacy with the Software Carbon Intensity SCI specification. His tireless efforts over the years led to a consensus on the specification, which was later published to ISO in late 2023, and is now being adopted globally.

The impact of the SCI is truly global, with academics and enterprises worldwide adopting it. This widespread adoption is a testament to Abhishek's vision and dedication and his influence will be felt in every software product that bears an SCI score. His legacy is not just a memory, but a living testament to his work.

He has left an unforgettable mark on the community. And will be remembered for his contributions for years to come. Aho brother, I'll see you on the other side, Asim Hussain. To honor Abhishek, we're going to revisit an older episode of Environment Variables from the very start of the podcast, episode five, How Does AI and ML Impact Climate Change?

In this episode, host Chris Adams is joined by Abhishek Gupta, Lynne Kaack, and Will Buchanan to discuss these topics. So, without further ado, here's the episode.

Abhishek Gupta: We're not just doing all of this accounting to produce reports and to, you know, spill ink, but it's to concretely drive change in behavior. And this was coming from folks who are a part of the standards working group, including Will and myself, who are practitioners who are itching to get something that helps us change our behavior, change our team's behaviors when it comes to building greener software.

Asim Hussain: Hello and welcome to Environment Variables brought to you by the Green Software Foundation. In each episode we discuss the latest news and events surrounding green software. On our show you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host Asim Hussain.

Chris Adams: Hello there and welcome to the Environment Variables podcast, the podcast about green software. I'm Chris Adams filling in for Asim Hussain, the regular host while he's on paternity leave with a brand new baby. I met Asim on climateaction.tech, an online community for climate aware techies. And I work for the Green Web Foundation where we work towards a fossil free internet by 2030, as well as working as the co chair for the Green Software Foundation's policy group. Today, we're talking about climate change, AI, and green software. And I'm joined by Lynn, Will, and Abhishek.

Will Buchanan: Thanks for having me. My name is Will. I'm a product manager on the Azure Machine Learning team. also a member of the Green Software Foundation's Standards and Innovation Working Group. Within Microsoft, I foster the Green AI community, which now has a few hundred members, and I'm also a climate activist that's focused on pragmatic solutions to complex environmental issues.

Recently, I shipped energy consumption metrics within Azure Machine Learning, and we are about to publish a paper titled Measuring Carbon Intensity of AI in Cloud Instances, which I think we'll touch on today.

Abhishek Gupta: Well, thanks for having me. I'm Abhishek Gupta. I'm the founder and principal researcher at the Montreal AI Ethics Institute. I also work as a senior responsible AI leader and expert at the Boston Consulting Group, BCG, and I serve as the chair for the Standards Working Group at the Green Software Foundation.

So I've got a few hats on there. Most of my work, as it relates to what we're going to talk about today, It runs at the intersection of responsible AI and green software. In particular, what's of interest to me is looking at how the intersections of social responsibility, the environmental impacts of software systems, in particular AI systems, can be thought about when we're looking to make a positive impact on the world while using technology in a responsible fashion. I also, as a part of the Green Software Foundation, help through the standards working group, come up with the software carbon intensity specification, where we're trying to create an actionable way for developers and consumers of software systems to better assess and mitigate the environmental impacts of their work.

Chris Adams: Okay. And Lynn, last but not least, joining us from Berlin. Thank you very much for joining us. 

Lynn Kaack: Yeah thank you so much. I am an assistant professor at a policy school, public policy school, called Hertie School in Berlin. And I am also a co founder and a chair of an organization called Climate Change AI. with Climate Change AI, we facilitate work at the intersection of machine learning and And different kinds of climate domains, focusing on climate change mitigation and adaptation. And in my work, in my research, I am looking at how we can use machine learning as a tool to address different problems related to energy and climate policy. I'm also interested in the policy of AI and climate change. And today, actually, since we're talking about papers, I have a paper coming up. That is called Aligning Artificial Intelligence with Climate Change Mitigation, where we look at the different impacts from machine learning and how they affect greenhouse gas emissions.

Chris Adams: So we actually have some decent deep domain expertise and I'll try to keep this quite accessible, but we might drop into a little bits of like data science nerder on here, but the podcast has done that previously and it turns out to be something that we've got some decent feedback from because there aren't that many podcasts covering this. Okay so let's, uh, get into this topic of green AI and climate change. As we we know, IT is a significant driver of emissions in its own right. When we think about the climate crisis this year, the IPCC, which is the Intergovernmental Panel on climate Change in their big reports, which synthesized literally thousands of papers explicitly called out digital as a thing we, we should be talking about and thinking about.

And if you're a responsible technologist, it seems like a thing that we should be taking into account here. Now, I found it helpful to think about IT IT a little bit like how we think about the shipping industry, partly because they're similar in terms of emissions, which is around between 1 and 3%, depending on what you look at it, but also in that both of these acts like kind of connective tissue for society.

We also think of IT as a kind of force multiplier for The existing forms of activity. So if you use it, which is in line with the recommendations of the science, that's a good thing. But if you use it to do something, which is kind of rejecting some of the science, it might not be such a good thing. And within technology, AI and machine learning in particular is one of the fastest growing sectors and often seen as one of the biggest levers of all. So we're going to highlight some interesting projects we'll start off with. And out of that, we'll probably dive into some specifics about that or some other things you might want to take into account. If you're a technologist wanting to incorporate an awareness of climate into how you work and build greener software, then finally, we'll hopefully leave you with some actionable tips and techniques or projects that you may contribute to or use in your daily practice.

There's another term that we might be touching on here when you're making AI greener, and that's specifically Green AI. Is that the case, Will?

Will Buchanan: Correct. And that actually was coined by researchers a few years ago, Roy Schwartz, Emma Struble, Jesse Dodge. And it's really focused on making the development of the AI system itself more sustainable. And it's to be disambiguated from the term using AI for sustainability.

Chris Adams: Okay, so that's something we'll touch on both today. We'll talk about some of the external impacts and some of the internal impacts. We're going to start with something quite easy first because, well, why not? I'm just going to ask each of the people here to kind of point to maybe one project that they've seen that's using ML in quite an interesting fashion to ideally come up with some kind of measurable win. Well, if there was one project you'd actually look to that you think is kind of embodying these ideas of like green AI something which is really helping us essentially face some of the challenges, maybe you could tell us about what's catching your eye at the moment or what you'd look at.

Will Buchanan: I've been thinking a lot about natural capital recently, and I learned about a startup called Pachama, which combines remote sensing data with machine learning to help measure and monitor the carbon stored in a forest. I think it's really, really valuable because they're providing verification and insurance of carbon credits at scale. They've protected about a million hectares of forest. That's really when you have IoT and remote sensing and machine learning combining to help nature restore itself.

Chris Adams: Okay, cool. So if I understand that, they're using satellites to basically track forests and track deforestation. Is that the idea that they're doing there? 

Will Buchanan: Yes, Yes, and and also to verify the amount of carbon that a forest can sequester.

Chris Adams: Okay, cool. All right. I know there's a few other projects related to this. If I just hand over to Abhishek, can you let us know what's caught your eyes recently, and then we'll see what other projects come out of this.

Abhishek Gupta: Yeah, absolutely. I think one of the projects, and I don't know, I mean, what the measurable impact has been so far. In fact, it's, it's something that's come out of MILA, which is, or, you know, called the Montreal Institute for Learning Algorithms, which is Dr. Bengio's lab in, in Montreal. In fact, one of the people who led that project is a part of Climate Change AI as well, who I'm sure Lynn can talk more about too, which is SASA. And she's done this project called This Climate Does Not Exist, which I think was a fascinating use of machine learning to visualize the impact climate change will have on, you know, places around you in a very arresting and visually capturing fashion, which I think when we think about.

What impact climate change is going to have around us. Sometimes it, it feels quite distant because it's a, it's a slow rolling thing that's coming our way. And this puts it in, in, in a way that's quite immediate, quite visually arresting. And I think stirs people to action. I, as I said, I'm not sure what the measurable impact of that has been yet, but I, I certainly feel that those are the kinds of creative users of AI when we want to galvanize people into action around climate change.

Lynn Kaack: I'm happy to also talk about a application, which is also kind of difficult in terms of measuring impact, but I think it's an another interesting component of what AI can do. And this is something that researchers at the Austrian Institute of Technology do on a project called Infrared. And they use machine learning to help design new districts and cities.

And especially at the moment in many countries, a lot of new urban districts are being built and how we build these has a huge impact on energy consumption in cities, both in terms of transportation, but also how buildings are heated or cooled. And by the use of machine learning, they can drastically improve design choices because now they can approximate their very computationally heavy models and run them much faster. which means that they can also have more runs and can try out more design configurations. So this is a rather indirect application, but it has huge implications also on emissions for, for many decades to come.

Chris Adams: So essentially it's using of housing policy as climate policy there, because there's just a huge amount of emissions built into how people live and whether they need to drive everywhere in a car and stuff like that. That's some of the stuff that it's doing and making that part easier?

Lynn Kaack: So it's not really looking at housing policy, but it's looking at how districts are designed. So they take a group of, of houses, like if the new district is to be built and then they simulate the wind flow going through the cities, which are very expensive simulation models. they take the outputs of their model and approximate it with a machine learning model, which makes it much, much faster.

So from hours or days, you go to milliseconds or below seconds for one run. And then you can try out different design configurations and understand better how The built infrastructure affects natural cooling, for example, in cities or walkability, energy impacts generally of the microclimate on, on the built environment.

Chris Adams: Wow, I had no idea that was, it was actually possible. That's really, really cool.

Will Buchanan: That's very cool. That's similar to generative design.

Chris Adams: design. This is a phrase I haven't heard actually, Will. Maybe you could elucidate or share something there, actually. 

Will Buchanan: It's similar similar to to some software that Autodesk has built where you can try out many different iterations of a design and come up with optimum solutions. I think it's really cool that you're just consolidating it and running these models more efficiently.

Chris Adams: cool. And that's a bit like following, say, a fitness function saying, I have a chair, or, you know, I want to have something that works like a chair. It needs four legs and a seating pattern. Then it essentially comes up with some of the designs or iterates through some of the possibilities, something like that?

Will Buchanan: Exactly. 

Chris Adams: Oh, wow. Okay. That's cool. All right. And so we've spoken about AI, and there's a few exciting, interesting ones that we can add to the show notes and list from, and for people to look into and see how that might relate to what they do, I suppose. I want to ask a little bit about measuring impact from these projects, because quite a few different ways that you can actually measure impact here.

And in many times, it can be quite a difficult thing to kind of pin down. And this is continually a thing that's come up. When I know that. So, people have tried to come up with specs like the Software Carbon Intensity, and I'm sure Abhishek, you've had some experiences here. Will, you've mentioned a little bit about, um, Actually measuring impact internally, and it sounds like you've just had to do a bunch of this work on the ML team right now and expose some of these numbers to people consuming these services in the first place. Could you talk about some of that part a bit, perhaps?

Will Buchanan: Certainly. And so, as I mentioned, we have shipped energy consumption metrics for both training and inference within Azure machine learning. And that's really complex when you think of the infrastructure required to just report that,but that doesn't necessarily account for the additional power that's consumed in the data center, such as the idle power for devices or for the utilization of your servers.

There's so many different factors there. So you really, you could encounter scope creep when you come to your measurement methodology. So it's really necessary to put boundaries around that.

Chris Adams: and when you use the term boundaries here, you're saying, I'm going to measure the environmental impact of the servers, but not the environmental impact of building the building to put the servers in. Is that the idea of when you're referring to a boundary here? Yeah.

Will Buchanan: Yes, that's a great example.

Chris Adams: Okay. All right. I think this is actually something we've come across quite a few times in other places as well, actually, because maybe it's worth asking about this kind of boundary issue that we have had here, because automatically that sounds complicated here.

And I know that Abhishek, you've had some issues at your end as well with defining this style for deciding what's in or out, because I think this is one thing that we've had to explicitly do for the software carbon intensity spec, right?

Abhishek Gupta: Exactly. And, and I think when we talk about boundaries, it's, it's, it's trying to get a sense for what are the actual pieces that are consumed, right? From an operational standpoint, from an embodied emission standpoint, and how you make those, you know, allocations across you know, what, what your system is consuming.

And I use the word system because I think again, when we talk about software, we're not just talking about a specific piece, but we're talking about really everything that it touches, be that, you know, network, be that bandwidth consumption, be that, you know, as, as Will was saying, idle power. Even when we're looking at cloud computing, it becomes even more complicated when you have your pieces of software that are sharing tenancy across the pieces of hardware and how different consumers are perhaps sharing that piece of hardware with you and thinking about whether you've booked the resource ahead of time or not, whether it's hot or cold in terms of its availability and what implications that has.

I mean, there are so many different facets to it. And each of those decisions, what I want to highlight here is that it comes with a trade off, right? So we also don't have any standards in terms of how we should go about measuring that and what should be included, what shouldn't be included. And so the way people report out these numbers today also doesn't really make it actionable for folks who are consuming or who want to consume these reports, these metrics in taking decisions as to, you know, whether something is green or not.

And I think that's one of the places that The Software Carbon Intensity Specification is trying to help folks is to help standardize it first and foremost, but also to make it actionable so that if you're someone who's environmentally conscious, you can make the right choice by being informed about what the actual impacts are.

Chris Adams: Okay, this is question that I'm curious here, because so far we've only been speaking internally about, okay, what is the environmental impact of IT itself, like it's direct emissions. But the assumption that I have here is that there are ways we might talk about the impact that it has on the outside world in terms of activity we're speeding up or accelerating or supporting there.

Is that the only issue that we need to think about, or are there any other things to take into account about like this system boundary part that we've just been talking 

Lynn Kaack: Yeah. So the system effects are really important to look at and to consider. Maybe just to give an example, like if you use machine learning in, let's say the oil and gas sector to make a small part of the operation is more energy efficient, that first sight looks like something that could be considered sustainable and green, but you also have to realize that often then you are reducing costs as well, and that might change the way that oil and gas in this particular example is competitive, or the particular company is competitive, and that actually might shift also how much oil and gas we are able to use in the short run, how the price is changed.

So, these indirect effects can actually then have much larger impacts than the immediate effects of such an application. So drawing boundaries is really important and also opening this up to to have a broader system level view and really try to understand how does the technology also change then, then to larger consumption and production patterns. It's important.

Chris Adams: So if I understand that correctly, that's talking almost like the consequences of an intervention that we might make here. So even though we've might have reduced the emissions of say the drilling part by putting a wind turbine on an oil rig, for example, that might change the economics and make people more likely to use the oil, in which many cases they might burn, for example, or stuff like that.

Is that basically what you're saying there?

Lynn Kaack: Essentially what I'm saying is that efficiency improvements in particular, and often they can be done with data science or with machine learning or AI systems. They often come with cost reductions and then those cost reductions do something and change something. And often this is also considered under rebound effects, but it's not only rebound effects.

So there's systemic, the system level impacts that come from more small scale applications that need to be considered.

Will Buchanan: That's such a good good point, and I think I've also heard it called Jevon's paradox, too.

Chris Adams: Yes. Jevons paradox. This is stuff from like the 1800s with steam engines, right? Like my understanding of the Jevons paradox was when people had steam engines and they made steam engines more efficient, this led to people basically burning more coal because it suddenly became more accessible to more people and you end up using them in a greater number of factories.

So there's a kind of rebound, I think, that we need to take into account. This is something I think has been quite difficult to actually capture with existing ways of tracking the environment impact of particular projects. We have like an idea of, say, an attribution based approach and a consequence based approach. And maybe it's worth actually talking about here, about how Some of the complexities we might need to wrestle with when you're designing a system here. I mean, Abhishek, I think this was one of the things that was an early decision with the software carbon intensity part to not try to have an attribution approach versus a marginal approach. And if we're not diving too deeply into jargon here, maybe you might be able to kind of share a bit more information on that part there, because It sounds like it's worth expanding or explaining to people, to the audience, a bit better here.

Abhishek Gupta: Indeed, you know, the reason for making that choice was again, our emphasis on being action oriented, right? So as we had started to develop the software carbon intensity specification, one of the early debates that we had to wrestle with and, and, you know, Will and Will was of course a crucial part of that as well as whether folks who were a part of the standards working group was figuring out how.

For example, the GHG way of going about doing that, you know, accounting doesn't really translate all that well for software systems and how perhaps adopting a slightly different approach would lead to more better. More actionable outcomes for the folks who want to use this ultimately to change behavior because, you know, without getting into specifics of, you know, what marginal is and what consequential approaches are, and, and if we want, I mean, I'm, I'm sure you know, will, would, would be happy to dive into all of those details as would I.

But the, the, the thing that we were seeing was that we're doing all of this great work around, you know, talking about scope one, two, three emissions, et cetera, but it's not really helping to drive behavior change. And that's really the crux of all of this, right, is that we're not just doing all of this accounting to produce reports and to, you know, spill ink, but it's to concretely drive change in behavior.

And that's where we found that adopting a consequential, adopting a marginal approach actually helped make it more actionable. And this was coming from folks who are a part of the standards working group, including Will and myself, who are practitioners, who, who are itching to get something that helps, helps us change our behavior, change our team's behaviors when it comes to building greener software, broadly speaking.

Chris Adams: Okay. So that helps with explaining the difference between a consequential approach and a marginal approach, as in the consequences of me building this thing will mean that this is more likely to happen. And if I understand it, the GHG protocol that you mentioned, which is the greenhouse gas protocol, and this scoped emissions approach, this is the kind of standard way that an organization might report It's kind of climate responsibility, as it were, when, and when you say scoped emissions, that's like scope one, which is burning burning say that's, that's emissions from fossil fuels burned on site or in your car.

For example, scope two is electricity and scope three is your supply chain. If I understand what you're saying, there's like a kind of gap there that doesn't account for the impacts of this, perhaps. I mean, some people who've referred to this as scope zero or scope four, which might be, what are the impacts an organization is happening to. Essentially, we mentioned before, do something around this systemic change, or as Lynn mentioned, like this is changing the price of a particular commodity to make it more likely to be used or less likely to be used. And this is what I understand the SCI is actually trying to do. It's trying to address some of this consequential approach, because the current approach doesn't capture all of the impacts an organization might actually have at the moment. Right?

Will Buchanan: Yeah, that's a good summary. One challenge that I have noticed is that until it's required in reporting structures like the greenhouse gas protocol, then organizations don't have an incentive to really take the action that they need to avoid climate disaster. Something I encounter on a daily basis, and I think broadly we need to bring this into the public discourse.

Chris Adams: I think you're right. I think it's worth actually, Lynn, I think that when I've seen some of the work that you've done, you've done previously, this is something that's come into some of the briefings that I think that you've shared previously with the climate change AI work and some of the policy briefings for governments as well. Is there something that you might be able to add on here?

Lynn Kaack: Yeah, so something that comes to mind is, for example, like a concrete legislation that's currently being developed is the EU AI Act. And that's a place where, for the first time, AI systems are being regulated also that scale. And climate change almost didn't play a role for that regulation in the first draft.

So here it's also really evident that if we don't write in climate change now as a criterion for evaluating AI systems. It will probably be ignored for the next years to come. So the way that legislation works is by classifying certain AI systems as high risk and also just outright banning some other systems, but as high risk systems could, the original legislation stood, weren't really explicitly classified as high risk, even if they had like a huge environmental or climate change impact. And that's that I talked about a lot with policymakers and trying to encourage them to more explicitly make environmental factors and climate change a factor for evaluating systems. So that'd be a very concrete case where making climate change more explicit in the AI context is important also in terms of legislation.

Abhishek Gupta: So there's, there's a lot to be said about the EU AI Act, right? And a ton of ink has been spilled everywhere, I think, as, as, you know, it's, it's, it's, it's called the Brussels effect for a reason, right? Where the, whatever happens in the EU is taken as gospel and sort of spread across the world, which I think, as already Lynn has pointed out there, It's not, it's not perfect, right?

I think one of the things that I've seen being particularly problematic is, is the rigid categorization of what, you know, high risk use cases are and, and whether the EU AI act, as we'll see, hopefully with some, with some you know, revisions that are coming down the pipe is whether we'll have the ability to add new categories and, and not just update subcategories within the existing identified high risk categories.

And I think that's where things like considerations for environmental impacts and really tying that to this you know, societal impacts of AI, where we're talking about bias, privacy, and all the other areas, is going to be particularly important because we need multiple levers to, to try to account for, or to push on getting people to consider the environmental impacts.

And given that there is such a great momentum already in terms of privacy considerations, bias considerations. I think now is the time where we really push hard to make environmental considerations an equally first class citizen when it comes to, you know, thinking about the societal impacts of AI.

Will Buchanan: This is something I'm incredibly passionate about. I think needs to encompass the full scope of harms that are caused by an AI system. So that could be the hidden environmental impacts of either the development or the application. The application could vastly outweigh the good that you're doing, even just expanding oil and gas production by a certain percentage amount. I think it just must account for all of the harms for both ecosystems and people.

Chris Adams: And there's this idea of like a risk thing. Does this categorization actually include this stuff right now? What counts as a high risk use case, for example, when mentioned here?

Lynn Kaack: So I haven't seen the latest iteration. I think that there has been some update on, there's been a lot of feedback on the version that was published in April last year. I haven't seen the latest iteration. I think a lot of things changed in, yeah, in the first version, there was It's high risk systems where when, that affect personal safety, like human rights in the sense of, of personal wellbeing, but the completely overlooked environmental protection aspects aspects of human rights.

Chris Adams: Wow, that's quite a large one, especially when you take into account the human rights. Okay. We've spoken about the external impact, but I am led to believe there is also an internal impact from this as well. Like the AI has, has some direct impact that we might want to talk about as well. As I understand it, we spoke about 2 to 3 percent of emissions here, but if we know there's an external impact, why would we care about any of the internal impacts of AI we might be doing or why you might want to care about the internal impacts of AI as well, for example, like the direct emissions.

Will Buchanan: So by direct emissions, you're talking about, let's say, the scope, too, of the operational cost of the model.

Chris Adams: Yeah, there'll be things that we have, there's an external impact or there is a, we use this phrase scope 4, for example, to talk about all the other things that induce in the world. But there is a, a kind of stuff which happens inside the system boundary that we've spoken about. And presumably that's something we should be caring about as well, right? So there'll be steps that we can take to make the, the use of AI, particularly like say the, the models more efficient and more effective and more, all these parts here, this is something that we should be looking at as well, presumably right?

Will Buchanan: Totally. And so in our paper, which is going to be published, I think on Monday, we've calculated the emissions of several different models. And one of them was a 6 billion parameter transformer model. And the operational carbon footprint was equivalent to about a rail car of coal. And that's just for training. So it's really imperative that we address this and provide transparency this

Lynn Kaack: Is that Is that for developing a model or for training at once? I mean, is that with grid search, architecture search?

Will Buchanan: For for a single training run. So it does not account for sweeps or deployments.

Chris Adams: All right, so there's a, there's some language that we haven't heard for here, so, but maybe it might be worth asking, maybe Will, could you maybe talk about, just briefly, you said a rail car full of coal, and I don't actually know what that is, I mean, in metric terms, what does that look like?

Will Buchanan: A hundred million grams. I don't have the conversion handy, but we took the U.S EPA greenhouse gas equivalencies. And I should add the methodology that we applied was the Green Software Foundation's SCI. So we calculated the energy consumed by the model and multiplied it by the carbon intensity of the grid that powers that data center.

Chris Adams: Okay, cool, and that was per training run? So that wasn't the, in the, the equation of the entire model, is that correct?

Will Buchanan: Correct. 

Abhishek Gupta: That's the other interesting part as well, right? When you're thinking about the life cycle is, or life cycle of the model, I should say, because life cycle has multiple meanings here, which is that once that model is out there, what are the inference costs, right? And are we, if this is something that's going to be used you know, hundreds, thousands, tens of thousands of times, if it's something, you know, if it's, if it's a large model that's, you know, now being used as a pre trained model and is going to be fine tuned on by, by other folks downstream, are we able to then, you know, talk about amortization of that cost across all of those use cases?

And again, I think what becomes interesting and, and is how do we account for that stuff as well, right? Because we, we don't have complete visibility on that as well. And, and I know Lynn's nodding here because her paper that's, I think coming out, being released in an hour and a half, actually the embargo gets lifted on our paper, actually talks about some of those system level impacts.

So maybe, maybe Lynn, you want to chime in and talk a little bit about that as well?

Lynn Kaack: Yeah, thank you so much. Exactly. So I think what's a crucial number that we're currently still missing is not what is emitted from a single model in a well known setting, but what is emitted overall from applying machine learning? So what are the usage patterns and practice? Like how often do people develop models from scratch?

How often do they train or retrain them? People I mean, of course, organizations and typically large organizations and companies. And how do they perform inference on how much data, how frequently? And there are some numbers out there from Facebook and Google and in their large scale applications actually inference outweighs their training and development costs in terms of greenhouse gas emissions.

So inference might become a bigger share depending on the application. So we really need to understand better how machine learning is being used in practice also to understand the direct emissions that come from it.

Chris Adams: An inference is a use of a model once it's in the wild. Is that what an inference is in this case? So there's an environment, so you could think of the making part, and then there is the usage part from the inference, right? So is that how that part works?

Lynn Kaack: Exactly. So if you use a model on a data point, we call that inference. So you feed in the data and it gives you a result. Then training means you sort of train a single configuration of the model once on your training data set, and then development is what I refer to as if you search over different configurations of the model.

So there are lots of hyperparameters that you can adjust to achieve better performance. And if new models are being developed, then there's an extensive search over those hyperparameters and architecture configurations that then of course gets really energy intensive because we are training the model thousands of times essentially.

Will Buchanan: Yeah, one of the figures that really resonated with me, I think Nvidia posted on their blog that inferencing for about 80 to 90 percent of the carbon cost of a model. think Lynn on one of your papers, it was, Amazon had also claimed around 90 percent. So these are really non trivial costs, and I'm not aware of any framework to measure this.

Lynn Kaack: Yeah. So that Amazon number just to be clear is costs or monetary costs that came from a talk, but there are numbers now published by Google and Facebook, but they look at some applications of theirs where inference outweighs training in terms of energy consumption. They're not exact numbers. It's not entirely clear which applications those are, but there is some data at least that shows that.

And I think it just highly depends on the application that you're looking at. And sometimes, you know, you build a model and then you do inference once and you have the data set that you, and then in other types, you build a model and then you apply it billion times a day. So, of course, that can then add up to a lot more energy consumption.

Chris Adams: Wow, I didn't realize that was actually an issue, because most of the numbers I've seen have been focusing on the training part. So, Will, I think this is something we spoke about before, that training, there's a kind of trend in the use, in the energy use from training already. Is this something, because I've seen figures from OpenAI, but my assumption was that basically computers are generally getting more efficient. About twice as efficient every two years, or so with like Moore's Law or Kumi's Law or things like that. But if you're seeing an uptick in usage here, is, does that mean that they're staying about the same? Or is there, is there a trend that we should be taking into account there?

Will Buchanan: So I think the computational costs of training have been doubling every 3.4 months or so, and so I think the trend is only accelerating. The models are just getting larger and larger, and you've got, I think, GPT 3 is one of the largest ones around at this point. We might challenge Moore's Law.

Chris Adams: Okay. So if Moore's Law is doubling every once every two years, I mean, what is the impact of doubling every 3.4 months? I mean, over a few years, what does that work out to be? Because I don't think I could do the exponential numbers, the exponential math, but it sounds like it's, it sounds like a pretty big number, basically, if something is doubling on every three or four months, right?

Will Buchanan: I also don't have the math handy, but I think it's important to note here, and Abhishek was talking about this earlier, models are very flexible, so you can train them once and then provide some fine tuning or transfer learning approach on top of them, and then repurpose these models for a number of different applications. And then you can even compress them, let's say using ONNX Runtime. You can be very efficient. You can really amortize the cost of that model.

Abhishek Gupta: So yeah, just building on Will's point, there's a lot of work on quantizing the weights of a trained network, applying distillation approaches, using teacher student model approaches that actually helps to shrink down the model quite a bit, especially with the whole push for TinyML, trying to shrink down models so that they can be deployed on edge devices has been something that's helped to manage to a great extent, the computational impacts.

One of the other things that I wanted to highlight as, as, you know, Will was talking about more models getting larger is, there's this almost fetish in the world today to continuously scale and keep pushing for ever larger models and in chasing SOTA, as they would say, so chasing state of the art, you know, which is great for academic publications where you get to show, "Hey, I improved state of the art performance on this benchmark data set by 0.5 percent or whatever," right? And in performance, I think what's being ignored is that that has a tremendous, tremendous computational cost. In fact, one of the hidden costs that I think doesn't get talked about enough is there's this statistic out there that, you know, 90 percent of the models don't make it into production.

And that kind of relates to things like, you know, neural architecture search and, you know, hyper parameter tuning, where you're constantly trying to refine a model to achieve better performance. A lot of that actually goes to waste because that stuff doesn't make it into production. So it's actually not even used.

And so there's a whole bunch of computational expenditure that is done that actually never sees the light of day, never becomes useful. That obviously has environmental impacts, right? Because of the operational and embodied carbon impacts, but none of that actually gets talked about, reported, documented anywhere because, well, who wants to know that, hey, I trained, you know, 73 different, you know, combinations to get to where I'm at.

You just talk about the final results.

Chris Adams: Okay, let's say that if you don't want to go down one of those rabbit holes, what should you be using or where would you start if you wanted to start applying some of these ideas about greener AI in your work on a daily basis? Does anyone have anything that they would lead with, for example?

Will Buchanan: Bigger is not always better. Sometimes you really should choose the right tool for the job. We've had some really great graduate student projects from the University of Washington's Information School, and they built some case studies and samples around green AI. As an example, a project led by Daniel Chin was comparing a sparse or a dense model of a green AI model to a dense model for an anomaly detection setting.

And they found that using sparse, meaning less trees and a shallow, smaller depth per tree, random forest would save a massive amount of carbon and provide the equivalent accuracy. So I think it saved about 98 percent in terms of the monetary cost and energy.

Chris Adams: Okay, wow, that's bigger than I was expecting. What would you say to people if they're in production, they're trying to do something now?

Lynn Kaack: I think a big goal should be to not only develop more energy efficient machine learning models, but then also ensure that those are actually being used. And surprisingly, even sometimes within the same company, certain model developments are not being passed on to other parts of the company. So really trying to develop standard models that then are also being used in practice is important.

So interoperability of energy efficient machine learning models.

Chris Adams: If people, someone does want to look at this stuff, and they do want to apply some of these ideas, you spoke a little bit about using some other models. would you suggest people look if they wanted to operationalize some of the kind of wins or some of the better ways to make green software greener, for example?

I realize you've got a paper coming out and you work on this day to day. So yeah, what would you point us to?

Lynn Kaack: So, I mean, as I understand, there's a lot of ongoing research in the machine learning community for more energy efficient machine learning. So I don't have any names on top of my head in terms of workshops or community resources where one can see what are the most energy efficient model types. For a specific application.

I know that there are some very comprehensive papers also that summarize all the different research approaches that are being taken, but I would encourage if you are looking for using like a deep learning model of some kind, just inform yourself quickly if there's also a leaner version of it. So much of the like widely used models like BERT, for example, smaller versions that can almost do the same thing.

And maybe your performance doesn't suffer much. If you're using a much lighter model.

Chris Adams: Okay, so light up models and looking around what we have there. And Will, is there a paper or a source you might point to?

Will Buchanan: I was actually going to talk about the Carbon Aware paper that we're about to publish. I think that's a slightly different track.

Chris Adams: That's up next week, right? So that'll be the 13th or 14th of June. That's when that'll be visible, correct?

Will Buchanan: Exactly. 

Chris Adams: Okay, cool. All right, then. There's a load more that we could dive into. We've got copious, copious, copious show notes here. So what I'm gonna do is I'm gonna say thank you everyone for coming in and sharing your wisdom and your experiences with us, and hopefully we'll have more conversations about green software in future. Thank you folks.

Asim Hussain: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show and of course we want more listeners.

find out more about the Green Software Foundation, please visit greensoftware. foundation. Thanks again and see you in the next episode.



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]]> In this episode, we honor the memory of Abhishek Gupta, who was an instrumental figure in the Green Software Foundation and a Co-Chair of the Standards Working Group. Abhishek's work was pivotal in the development of the Software Carbon Intensity (SCI) Specification, now adopted globally. His tireless efforts shaped the future of green software, leaving an indelible mark on the industry. As we remember Abhishek, we reflect on his legacy of sustainability, leadership, and friendship, celebrating the remarkable impact he had on both his colleagues and the world. We are airing an old episode that featured Abhishek Gupta, Episode 5 of Environment Variables. Where host Chris Adams is joined by Will Buchanan of Azure ML (Microsoft), Abhishek Gupta; the chair of the Standards Working Group for the Green Software Foundation and Lynn Kaack, assistant professor at the Hertie School in Berlin to discuss how artificial intelligence and machine learning impact climate change. They discuss boundaries, Jevons paradox, the EU AI Act, inferencing and supplying us with a plethora of materials regarding ML and AI and the climate!

In Memoriam: Abhishek Gupta:

Learn more about our guests:


Find out more about the GSF:

Episode resources:


If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Chris Skipper: This week on Environment Variables, we have some sad news.

We have to report the untimely passing of Abhishek Gupta. Abhishek was a key part of the Green Software Foundation. He was the co chair of the Standards Working Group from the GSF's formation to his passing on September 30th of this year. I would like to read out an in memoriam that was posted by Asim Hussain, honoring the great man, his legacy of leadership, his collaboration, and the heart that he put into the Green Software Foundation.

When the Green Software Foundation was formed over three years ago, Abhishek offered to help. The standards working group was looking for a co chair, and he jumped in to help without hesitation. He led the standards working group for over three years, with the full support of everyone involved. Leading a work group isn't about imposing your will on others, It's about finding common ground, nurturing discussions, and searching for the truth.

Abhishek excelled in all of those areas. He leaves a powerful legacy with the Software Carbon Intensity SCI specification. His tireless efforts over the years led to a consensus on the specification, which was later published to ISO in late 2023, and is now being adopted globally.

The impact of the SCI is truly global, with academics and enterprises worldwide adopting it. This widespread adoption is a testament to Abhishek's vision and dedication and his influence will be felt in every software product that bears an SCI score. His legacy is not just a memory, but a living testament to his work.

He has left an unforgettable mark on the community. And will be remembered for his contributions for years to come. Aho brother, I'll see you on the other side, Asim Hussain. To honor Abhishek, we're going to revisit an older episode of Environment Variables from the very start of the podcast, episode five, How Does AI and ML Impact Climate Change?

In this episode, host Chris Adams is joined by Abhishek Gupta, Lynne Kaack, and Will Buchanan to discuss these topics. So, without further ado, here's the episode.

Abhishek Gupta: We're not just doing all of this accounting to produce reports and to, you know, spill ink, but it's to concretely drive change in behavior. And this was coming from folks who are a part of the standards working group, including Will and myself, who are practitioners who are itching to get something that helps us change our behavior, change our team's behaviors when it comes to building greener software.

Asim Hussain: Hello and welcome to Environment Variables brought to you by the Green Software Foundation. In each episode we discuss the latest news and events surrounding green software. On our show you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host Asim Hussain.

Chris Adams: Hello there and welcome to the Environment Variables podcast, the podcast about green software. I'm Chris Adams filling in for Asim Hussain, the regular host while he's on paternity leave with a brand new baby. I met Asim on climateaction.tech, an online community for climate aware techies. And I work for the Green Web Foundation where we work towards a fossil free internet by 2030, as well as working as the co chair for the Green Software Foundation's policy group. Today, we're talking about climate change, AI, and green software. And I'm joined by Lynn, Will, and Abhishek.

Will Buchanan: Thanks for having me. My name is Will. I'm a product manager on the Azure Machine Learning team. also a member of the Green Software Foundation's Standards and Innovation Working Group. Within Microsoft, I foster the Green AI community, which now has a few hundred members, and I'm also a climate activist that's focused on pragmatic solutions to complex environmental issues.

Recently, I shipped energy consumption metrics within Azure Machine Learning, and we are about to publish a paper titled Measuring Carbon Intensity of AI in Cloud Instances, which I think we'll touch on today.

Abhishek Gupta: Well, thanks for having me. I'm Abhishek Gupta. I'm the founder and principal researcher at the Montreal AI Ethics Institute. I also work as a senior responsible AI leader and expert at the Boston Consulting Group, BCG, and I serve as the chair for the Standards Working Group at the Green Software Foundation.

So I've got a few hats on there. Most of my work, as it relates to what we're going to talk about today, It runs at the intersection of responsible AI and green software. In particular, what's of interest to me is looking at how the intersections of social responsibility, the environmental impacts of software systems, in particular AI systems, can be thought about when we're looking to make a positive impact on the world while using technology in a responsible fashion. I also, as a part of the Green Software Foundation, help through the standards working group, come up with the software carbon intensity specification, where we're trying to create an actionable way for developers and consumers of software systems to better assess and mitigate the environmental impacts of their work.

Chris Adams: Okay. And Lynn, last but not least, joining us from Berlin. Thank you very much for joining us. 

Lynn Kaack: Yeah thank you so much. I am an assistant professor at a policy school, public policy school, called Hertie School in Berlin. And I am also a co founder and a chair of an organization called Climate Change AI. with Climate Change AI, we facilitate work at the intersection of machine learning and And different kinds of climate domains, focusing on climate change mitigation and adaptation. And in my work, in my research, I am looking at how we can use machine learning as a tool to address different problems related to energy and climate policy. I'm also interested in the policy of AI and climate change. And today, actually, since we're talking about papers, I have a paper coming up. That is called Aligning Artificial Intelligence with Climate Change Mitigation, where we look at the different impacts from machine learning and how they affect greenhouse gas emissions.

Chris Adams: So we actually have some decent deep domain expertise and I'll try to keep this quite accessible, but we might drop into a little bits of like data science nerder on here, but the podcast has done that previously and it turns out to be something that we've got some decent feedback from because there aren't that many podcasts covering this. Okay so let's, uh, get into this topic of green AI and climate change. As we we know, IT is a significant driver of emissions in its own right. When we think about the climate crisis this year, the IPCC, which is the Intergovernmental Panel on climate Change in their big reports, which synthesized literally thousands of papers explicitly called out digital as a thing we, we should be talking about and thinking about.

And if you're a responsible technologist, it seems like a thing that we should be taking into account here. Now, I found it helpful to think about IT IT a little bit like how we think about the shipping industry, partly because they're similar in terms of emissions, which is around between 1 and 3%, depending on what you look at it, but also in that both of these acts like kind of connective tissue for society.

We also think of IT as a kind of force multiplier for The existing forms of activity. So if you use it, which is in line with the recommendations of the science, that's a good thing. But if you use it to do something, which is kind of rejecting some of the science, it might not be such a good thing. And within technology, AI and machine learning in particular is one of the fastest growing sectors and often seen as one of the biggest levers of all. So we're going to highlight some interesting projects we'll start off with. And out of that, we'll probably dive into some specifics about that or some other things you might want to take into account. If you're a technologist wanting to incorporate an awareness of climate into how you work and build greener software, then finally, we'll hopefully leave you with some actionable tips and techniques or projects that you may contribute to or use in your daily practice.

There's another term that we might be touching on here when you're making AI greener, and that's specifically Green AI. Is that the case, Will?

Will Buchanan: Correct. And that actually was coined by researchers a few years ago, Roy Schwartz, Emma Struble, Jesse Dodge. And it's really focused on making the development of the AI system itself more sustainable. And it's to be disambiguated from the term using AI for sustainability.

Chris Adams: Okay, so that's something we'll touch on both today. We'll talk about some of the external impacts and some of the internal impacts. We're going to start with something quite easy first because, well, why not? I'm just going to ask each of the people here to kind of point to maybe one project that they've seen that's using ML in quite an interesting fashion to ideally come up with some kind of measurable win. Well, if there was one project you'd actually look to that you think is kind of embodying these ideas of like green AI something which is really helping us essentially face some of the challenges, maybe you could tell us about what's catching your eye at the moment or what you'd look at.

Will Buchanan: I've been thinking a lot about natural capital recently, and I learned about a startup called Pachama, which combines remote sensing data with machine learning to help measure and monitor the carbon stored in a forest. I think it's really, really valuable because they're providing verification and insurance of carbon credits at scale. They've protected about a million hectares of forest. That's really when you have IoT and remote sensing and machine learning combining to help nature restore itself.

Chris Adams: Okay, cool. So if I understand that, they're using satellites to basically track forests and track deforestation. Is that the idea that they're doing there? 

Will Buchanan: Yes, Yes, and and also to verify the amount of carbon that a forest can sequester.

Chris Adams: Okay, cool. All right. I know there's a few other projects related to this. If I just hand over to Abhishek, can you let us know what's caught your eyes recently, and then we'll see what other projects come out of this.

Abhishek Gupta: Yeah, absolutely. I think one of the projects, and I don't know, I mean, what the measurable impact has been so far. In fact, it's, it's something that's come out of MILA, which is, or, you know, called the Montreal Institute for Learning Algorithms, which is Dr. Bengio's lab in, in Montreal. In fact, one of the people who led that project is a part of Climate Change AI as well, who I'm sure Lynn can talk more about too, which is SASA. And she's done this project called This Climate Does Not Exist, which I think was a fascinating use of machine learning to visualize the impact climate change will have on, you know, places around you in a very arresting and visually capturing fashion, which I think when we think about.

What impact climate change is going to have around us. Sometimes it, it feels quite distant because it's a, it's a slow rolling thing that's coming our way. And this puts it in, in, in a way that's quite immediate, quite visually arresting. And I think stirs people to action. I, as I said, I'm not sure what the measurable impact of that has been yet, but I, I certainly feel that those are the kinds of creative users of AI when we want to galvanize people into action around climate change.

Lynn Kaack: I'm happy to also talk about a application, which is also kind of difficult in terms of measuring impact, but I think it's an another interesting component of what AI can do. And this is something that researchers at the Austrian Institute of Technology do on a project called Infrared. And they use machine learning to help design new districts and cities.

And especially at the moment in many countries, a lot of new urban districts are being built and how we build these has a huge impact on energy consumption in cities, both in terms of transportation, but also how buildings are heated or cooled. And by the use of machine learning, they can drastically improve design choices because now they can approximate their very computationally heavy models and run them much faster. which means that they can also have more runs and can try out more design configurations. So this is a rather indirect application, but it has huge implications also on emissions for, for many decades to come.

Chris Adams: So essentially it's using of housing policy as climate policy there, because there's just a huge amount of emissions built into how people live and whether they need to drive everywhere in a car and stuff like that. That's some of the stuff that it's doing and making that part easier?

Lynn Kaack: So it's not really looking at housing policy, but it's looking at how districts are designed. So they take a group of, of houses, like if the new district is to be built and then they simulate the wind flow going through the cities, which are very expensive simulation models. they take the outputs of their model and approximate it with a machine learning model, which makes it much, much faster.

So from hours or days, you go to milliseconds or below seconds for one run. And then you can try out different design configurations and understand better how The built infrastructure affects natural cooling, for example, in cities or walkability, energy impacts generally of the microclimate on, on the built environment.

Chris Adams: Wow, I had no idea that was, it was actually possible. That's really, really cool.

Will Buchanan: That's very cool. That's similar to generative design.

Chris Adams: design. This is a phrase I haven't heard actually, Will. Maybe you could elucidate or share something there, actually. 

Will Buchanan: It's similar similar to to some software that Autodesk has built where you can try out many different iterations of a design and come up with optimum solutions. I think it's really cool that you're just consolidating it and running these models more efficiently.

Chris Adams: cool. And that's a bit like following, say, a fitness function saying, I have a chair, or, you know, I want to have something that works like a chair. It needs four legs and a seating pattern. Then it essentially comes up with some of the designs or iterates through some of the possibilities, something like that?

Will Buchanan: Exactly. 

Chris Adams: Oh, wow. Okay. That's cool. All right. And so we've spoken about AI, and there's a few exciting, interesting ones that we can add to the show notes and list from, and for people to look into and see how that might relate to what they do, I suppose. I want to ask a little bit about measuring impact from these projects, because quite a few different ways that you can actually measure impact here.

And in many times, it can be quite a difficult thing to kind of pin down. And this is continually a thing that's come up. When I know that. So, people have tried to come up with specs like the Software Carbon Intensity, and I'm sure Abhishek, you've had some experiences here. Will, you've mentioned a little bit about, um, Actually measuring impact internally, and it sounds like you've just had to do a bunch of this work on the ML team right now and expose some of these numbers to people consuming these services in the first place. Could you talk about some of that part a bit, perhaps?

Will Buchanan: Certainly. And so, as I mentioned, we have shipped energy consumption metrics for both training and inference within Azure machine learning. And that's really complex when you think of the infrastructure required to just report that,but that doesn't necessarily account for the additional power that's consumed in the data center, such as the idle power for devices or for the utilization of your servers.

There's so many different factors there. So you really, you could encounter scope creep when you come to your measurement methodology. So it's really necessary to put boundaries around that.

Chris Adams: and when you use the term boundaries here, you're saying, I'm going to measure the environmental impact of the servers, but not the environmental impact of building the building to put the servers in. Is that the idea of when you're referring to a boundary here? Yeah.

Will Buchanan: Yes, that's a great example.

Chris Adams: Okay. All right. I think this is actually something we've come across quite a few times in other places as well, actually, because maybe it's worth asking about this kind of boundary issue that we have had here, because automatically that sounds complicated here.

And I know that Abhishek, you've had some issues at your end as well with defining this style for deciding what's in or out, because I think this is one thing that we've had to explicitly do for the software carbon intensity spec, right?

Abhishek Gupta: Exactly. And, and I think when we talk about boundaries, it's, it's, it's trying to get a sense for what are the actual pieces that are consumed, right? From an operational standpoint, from an embodied emission standpoint, and how you make those, you know, allocations across you know, what, what your system is consuming.

And I use the word system because I think again, when we talk about software, we're not just talking about a specific piece, but we're talking about really everything that it touches, be that, you know, network, be that bandwidth consumption, be that, you know, as, as Will was saying, idle power. Even when we're looking at cloud computing, it becomes even more complicated when you have your pieces of software that are sharing tenancy across the pieces of hardware and how different consumers are perhaps sharing that piece of hardware with you and thinking about whether you've booked the resource ahead of time or not, whether it's hot or cold in terms of its availability and what implications that has.

I mean, there are so many different facets to it. And each of those decisions, what I want to highlight here is that it comes with a trade off, right? So we also don't have any standards in terms of how we should go about measuring that and what should be included, what shouldn't be included. And so the way people report out these numbers today also doesn't really make it actionable for folks who are consuming or who want to consume these reports, these metrics in taking decisions as to, you know, whether something is green or not.

And I think that's one of the places that The Software Carbon Intensity Specification is trying to help folks is to help standardize it first and foremost, but also to make it actionable so that if you're someone who's environmentally conscious, you can make the right choice by being informed about what the actual impacts are.

Chris Adams: Okay, this is question that I'm curious here, because so far we've only been speaking internally about, okay, what is the environmental impact of IT itself, like it's direct emissions. But the assumption that I have here is that there are ways we might talk about the impact that it has on the outside world in terms of activity we're speeding up or accelerating or supporting there.

Is that the only issue that we need to think about, or are there any other things to take into account about like this system boundary part that we've just been talking 

Lynn Kaack: Yeah. So the system effects are really important to look at and to consider. Maybe just to give an example, like if you use machine learning in, let's say the oil and gas sector to make a small part of the operation is more energy efficient, that first sight looks like something that could be considered sustainable and green, but you also have to realize that often then you are reducing costs as well, and that might change the way that oil and gas in this particular example is competitive, or the particular company is competitive, and that actually might shift also how much oil and gas we are able to use in the short run, how the price is changed.

So, these indirect effects can actually then have much larger impacts than the immediate effects of such an application. So drawing boundaries is really important and also opening this up to to have a broader system level view and really try to understand how does the technology also change then, then to larger consumption and production patterns. It's important.

Chris Adams: So if I understand that correctly, that's talking almost like the consequences of an intervention that we might make here. So even though we've might have reduced the emissions of say the drilling part by putting a wind turbine on an oil rig, for example, that might change the economics and make people more likely to use the oil, in which many cases they might burn, for example, or stuff like that.

Is that basically what you're saying there?

Lynn Kaack: Essentially what I'm saying is that efficiency improvements in particular, and often they can be done with data science or with machine learning or AI systems. They often come with cost reductions and then those cost reductions do something and change something. And often this is also considered under rebound effects, but it's not only rebound effects.

So there's systemic, the system level impacts that come from more small scale applications that need to be considered.

Will Buchanan: That's such a good good point, and I think I've also heard it called Jevon's paradox, too.

Chris Adams: Yes. Jevons paradox. This is stuff from like the 1800s with steam engines, right? Like my understanding of the Jevons paradox was when people had steam engines and they made steam engines more efficient, this led to people basically burning more coal because it suddenly became more accessible to more people and you end up using them in a greater number of factories.

So there's a kind of rebound, I think, that we need to take into account. This is something I think has been quite difficult to actually capture with existing ways of tracking the environment impact of particular projects. We have like an idea of, say, an attribution based approach and a consequence based approach. And maybe it's worth actually talking about here, about how Some of the complexities we might need to wrestle with when you're designing a system here. I mean, Abhishek, I think this was one of the things that was an early decision with the software carbon intensity part to not try to have an attribution approach versus a marginal approach. And if we're not diving too deeply into jargon here, maybe you might be able to kind of share a bit more information on that part there, because It sounds like it's worth expanding or explaining to people, to the audience, a bit better here.

Abhishek Gupta: Indeed, you know, the reason for making that choice was again, our emphasis on being action oriented, right? So as we had started to develop the software carbon intensity specification, one of the early debates that we had to wrestle with and, and, you know, Will and Will was of course a crucial part of that as well as whether folks who were a part of the standards working group was figuring out how.

For example, the GHG way of going about doing that, you know, accounting doesn't really translate all that well for software systems and how perhaps adopting a slightly different approach would lead to more better. More actionable outcomes for the folks who want to use this ultimately to change behavior because, you know, without getting into specifics of, you know, what marginal is and what consequential approaches are, and, and if we want, I mean, I'm, I'm sure you know, will, would, would be happy to dive into all of those details as would I.

But the, the, the thing that we were seeing was that we're doing all of this great work around, you know, talking about scope one, two, three emissions, et cetera, but it's not really helping to drive behavior change. And that's really the crux of all of this, right, is that we're not just doing all of this accounting to produce reports and to, you know, spill ink, but it's to concretely drive change in behavior.

And that's where we found that adopting a consequential, adopting a marginal approach actually helped make it more actionable. And this was coming from folks who are a part of the standards working group, including Will and myself, who are practitioners, who, who are itching to get something that helps, helps us change our behavior, change our team's behaviors when it comes to building greener software, broadly speaking.

Chris Adams: Okay. So that helps with explaining the difference between a consequential approach and a marginal approach, as in the consequences of me building this thing will mean that this is more likely to happen. And if I understand it, the GHG protocol that you mentioned, which is the greenhouse gas protocol, and this scoped emissions approach, this is the kind of standard way that an organization might report It's kind of climate responsibility, as it were, when, and when you say scoped emissions, that's like scope one, which is burning burning say that's, that's emissions from fossil fuels burned on site or in your car.

For example, scope two is electricity and scope three is your supply chain. If I understand what you're saying, there's like a kind of gap there that doesn't account for the impacts of this, perhaps. I mean, some people who've referred to this as scope zero or scope four, which might be, what are the impacts an organization is happening to. Essentially, we mentioned before, do something around this systemic change, or as Lynn mentioned, like this is changing the price of a particular commodity to make it more likely to be used or less likely to be used. And this is what I understand the SCI is actually trying to do. It's trying to address some of this consequential approach, because the current approach doesn't capture all of the impacts an organization might actually have at the moment. Right?

Will Buchanan: Yeah, that's a good summary. One challenge that I have noticed is that until it's required in reporting structures like the greenhouse gas protocol, then organizations don't have an incentive to really take the action that they need to avoid climate disaster. Something I encounter on a daily basis, and I think broadly we need to bring this into the public discourse.

Chris Adams: I think you're right. I think it's worth actually, Lynn, I think that when I've seen some of the work that you've done, you've done previously, this is something that's come into some of the briefings that I think that you've shared previously with the climate change AI work and some of the policy briefings for governments as well. Is there something that you might be able to add on here?

Lynn Kaack: Yeah, so something that comes to mind is, for example, like a concrete legislation that's currently being developed is the EU AI Act. And that's a place where, for the first time, AI systems are being regulated also that scale. And climate change almost didn't play a role for that regulation in the first draft.

So here it's also really evident that if we don't write in climate change now as a criterion for evaluating AI systems. It will probably be ignored for the next years to come. So the way that legislation works is by classifying certain AI systems as high risk and also just outright banning some other systems, but as high risk systems could, the original legislation stood, weren't really explicitly classified as high risk, even if they had like a huge environmental or climate change impact. And that's that I talked about a lot with policymakers and trying to encourage them to more explicitly make environmental factors and climate change a factor for evaluating systems. So that'd be a very concrete case where making climate change more explicit in the AI context is important also in terms of legislation.

Abhishek Gupta: So there's, there's a lot to be said about the EU AI Act, right? And a ton of ink has been spilled everywhere, I think, as, as, you know, it's, it's, it's, it's called the Brussels effect for a reason, right? Where the, whatever happens in the EU is taken as gospel and sort of spread across the world, which I think, as already Lynn has pointed out there, It's not, it's not perfect, right?

I think one of the things that I've seen being particularly problematic is, is the rigid categorization of what, you know, high risk use cases are and, and whether the EU AI act, as we'll see, hopefully with some, with some you know, revisions that are coming down the pipe is whether we'll have the ability to add new categories and, and not just update subcategories within the existing identified high risk categories.

And I think that's where things like considerations for environmental impacts and really tying that to this you know, societal impacts of AI, where we're talking about bias, privacy, and all the other areas, is going to be particularly important because we need multiple levers to, to try to account for, or to push on getting people to consider the environmental impacts.

And given that there is such a great momentum already in terms of privacy considerations, bias considerations. I think now is the time where we really push hard to make environmental considerations an equally first class citizen when it comes to, you know, thinking about the societal impacts of AI.

Will Buchanan: This is something I'm incredibly passionate about. I think needs to encompass the full scope of harms that are caused by an AI system. So that could be the hidden environmental impacts of either the development or the application. The application could vastly outweigh the good that you're doing, even just expanding oil and gas production by a certain percentage amount. I think it just must account for all of the harms for both ecosystems and people.

Chris Adams: And there's this idea of like a risk thing. Does this categorization actually include this stuff right now? What counts as a high risk use case, for example, when mentioned here?

Lynn Kaack: So I haven't seen the latest iteration. I think that there has been some update on, there's been a lot of feedback on the version that was published in April last year. I haven't seen the latest iteration. I think a lot of things changed in, yeah, in the first version, there was It's high risk systems where when, that affect personal safety, like human rights in the sense of, of personal wellbeing, but the completely overlooked environmental protection aspects aspects of human rights.

Chris Adams: Wow, that's quite a large one, especially when you take into account the human rights. Okay. We've spoken about the external impact, but I am led to believe there is also an internal impact from this as well. Like the AI has, has some direct impact that we might want to talk about as well. As I understand it, we spoke about 2 to 3 percent of emissions here, but if we know there's an external impact, why would we care about any of the internal impacts of AI we might be doing or why you might want to care about the internal impacts of AI as well, for example, like the direct emissions.

Will Buchanan: So by direct emissions, you're talking about, let's say, the scope, too, of the operational cost of the model.

Chris Adams: Yeah, there'll be things that we have, there's an external impact or there is a, we use this phrase scope 4, for example, to talk about all the other things that induce in the world. But there is a, a kind of stuff which happens inside the system boundary that we've spoken about. And presumably that's something we should be caring about as well, right? So there'll be steps that we can take to make the, the use of AI, particularly like say the, the models more efficient and more effective and more, all these parts here, this is something that we should be looking at as well, presumably right?

Will Buchanan: Totally. And so in our paper, which is going to be published, I think on Monday, we've calculated the emissions of several different models. And one of them was a 6 billion parameter transformer model. And the operational carbon footprint was equivalent to about a rail car of coal. And that's just for training. So it's really imperative that we address this and provide transparency this

Lynn Kaack: Is that Is that for developing a model or for training at once? I mean, is that with grid search, architecture search?

Will Buchanan: For for a single training run. So it does not account for sweeps or deployments.

Chris Adams: All right, so there's a, there's some language that we haven't heard for here, so, but maybe it might be worth asking, maybe Will, could you maybe talk about, just briefly, you said a rail car full of coal, and I don't actually know what that is, I mean, in metric terms, what does that look like?

Will Buchanan: A hundred million grams. I don't have the conversion handy, but we took the U.S EPA greenhouse gas equivalencies. And I should add the methodology that we applied was the Green Software Foundation's SCI. So we calculated the energy consumed by the model and multiplied it by the carbon intensity of the grid that powers that data center.

Chris Adams: Okay, cool, and that was per training run? So that wasn't the, in the, the equation of the entire model, is that correct?

Will Buchanan: Correct. 

Abhishek Gupta: That's the other interesting part as well, right? When you're thinking about the life cycle is, or life cycle of the model, I should say, because life cycle has multiple meanings here, which is that once that model is out there, what are the inference costs, right? And are we, if this is something that's going to be used you know, hundreds, thousands, tens of thousands of times, if it's something, you know, if it's, if it's a large model that's, you know, now being used as a pre trained model and is going to be fine tuned on by, by other folks downstream, are we able to then, you know, talk about amortization of that cost across all of those use cases?

And again, I think what becomes interesting and, and is how do we account for that stuff as well, right? Because we, we don't have complete visibility on that as well. And, and I know Lynn's nodding here because her paper that's, I think coming out, being released in an hour and a half, actually the embargo gets lifted on our paper, actually talks about some of those system level impacts.

So maybe, maybe Lynn, you want to chime in and talk a little bit about that as well?

Lynn Kaack: Yeah, thank you so much. Exactly. So I think what's a crucial number that we're currently still missing is not what is emitted from a single model in a well known setting, but what is emitted overall from applying machine learning? So what are the usage patterns and practice? Like how often do people develop models from scratch?

How often do they train or retrain them? People I mean, of course, organizations and typically large organizations and companies. And how do they perform inference on how much data, how frequently? And there are some numbers out there from Facebook and Google and in their large scale applications actually inference outweighs their training and development costs in terms of greenhouse gas emissions.

So inference might become a bigger share depending on the application. So we really need to understand better how machine learning is being used in practice also to understand the direct emissions that come from it.

Chris Adams: An inference is a use of a model once it's in the wild. Is that what an inference is in this case? So there's an environment, so you could think of the making part, and then there is the usage part from the inference, right? So is that how that part works?

Lynn Kaack: Exactly. So if you use a model on a data point, we call that inference. So you feed in the data and it gives you a result. Then training means you sort of train a single configuration of the model once on your training data set, and then development is what I refer to as if you search over different configurations of the model.

So there are lots of hyperparameters that you can adjust to achieve better performance. And if new models are being developed, then there's an extensive search over those hyperparameters and architecture configurations that then of course gets really energy intensive because we are training the model thousands of times essentially.

Will Buchanan: Yeah, one of the figures that really resonated with me, I think Nvidia posted on their blog that inferencing for about 80 to 90 percent of the carbon cost of a model. think Lynn on one of your papers, it was, Amazon had also claimed around 90 percent. So these are really non trivial costs, and I'm not aware of any framework to measure this.

Lynn Kaack: Yeah. So that Amazon number just to be clear is costs or monetary costs that came from a talk, but there are numbers now published by Google and Facebook, but they look at some applications of theirs where inference outweighs training in terms of energy consumption. They're not exact numbers. It's not entirely clear which applications those are, but there is some data at least that shows that.

And I think it just highly depends on the application that you're looking at. And sometimes, you know, you build a model and then you do inference once and you have the data set that you, and then in other types, you build a model and then you apply it billion times a day. So, of course, that can then add up to a lot more energy consumption.

Chris Adams: Wow, I didn't realize that was actually an issue, because most of the numbers I've seen have been focusing on the training part. So, Will, I think this is something we spoke about before, that training, there's a kind of trend in the use, in the energy use from training already. Is this something, because I've seen figures from OpenAI, but my assumption was that basically computers are generally getting more efficient. About twice as efficient every two years, or so with like Moore's Law or Kumi's Law or things like that. But if you're seeing an uptick in usage here, is, does that mean that they're staying about the same? Or is there, is there a trend that we should be taking into account there?

Will Buchanan: So I think the computational costs of training have been doubling every 3.4 months or so, and so I think the trend is only accelerating. The models are just getting larger and larger, and you've got, I think, GPT 3 is one of the largest ones around at this point. We might challenge Moore's Law.

Chris Adams: Okay. So if Moore's Law is doubling every once every two years, I mean, what is the impact of doubling every 3.4 months? I mean, over a few years, what does that work out to be? Because I don't think I could do the exponential numbers, the exponential math, but it sounds like it's, it sounds like a pretty big number, basically, if something is doubling on every three or four months, right?

Will Buchanan: I also don't have the math handy, but I think it's important to note here, and Abhishek was talking about this earlier, models are very flexible, so you can train them once and then provide some fine tuning or transfer learning approach on top of them, and then repurpose these models for a number of different applications. And then you can even compress them, let's say using ONNX Runtime. You can be very efficient. You can really amortize the cost of that model.

Abhishek Gupta: So yeah, just building on Will's point, there's a lot of work on quantizing the weights of a trained network, applying distillation approaches, using teacher student model approaches that actually helps to shrink down the model quite a bit, especially with the whole push for TinyML, trying to shrink down models so that they can be deployed on edge devices has been something that's helped to manage to a great extent, the computational impacts.

One of the other things that I wanted to highlight as, as, you know, Will was talking about more models getting larger is, there's this almost fetish in the world today to continuously scale and keep pushing for ever larger models and in chasing SOTA, as they would say, so chasing state of the art, you know, which is great for academic publications where you get to show, "Hey, I improved state of the art performance on this benchmark data set by 0.5 percent or whatever," right? And in performance, I think what's being ignored is that that has a tremendous, tremendous computational cost. In fact, one of the hidden costs that I think doesn't get talked about enough is there's this statistic out there that, you know, 90 percent of the models don't make it into production.

And that kind of relates to things like, you know, neural architecture search and, you know, hyper parameter tuning, where you're constantly trying to refine a model to achieve better performance. A lot of that actually goes to waste because that stuff doesn't make it into production. So it's actually not even used.

And so there's a whole bunch of computational expenditure that is done that actually never sees the light of day, never becomes useful. That obviously has environmental impacts, right? Because of the operational and embodied carbon impacts, but none of that actually gets talked about, reported, documented anywhere because, well, who wants to know that, hey, I trained, you know, 73 different, you know, combinations to get to where I'm at.

You just talk about the final results.

Chris Adams: Okay, let's say that if you don't want to go down one of those rabbit holes, what should you be using or where would you start if you wanted to start applying some of these ideas about greener AI in your work on a daily basis? Does anyone have anything that they would lead with, for example?

Will Buchanan: Bigger is not always better. Sometimes you really should choose the right tool for the job. We've had some really great graduate student projects from the University of Washington's Information School, and they built some case studies and samples around green AI. As an example, a project led by Daniel Chin was comparing a sparse or a dense model of a green AI model to a dense model for an anomaly detection setting.

And they found that using sparse, meaning less trees and a shallow, smaller depth per tree, random forest would save a massive amount of carbon and provide the equivalent accuracy. So I think it saved about 98 percent in terms of the monetary cost and energy.

Chris Adams: Okay, wow, that's bigger than I was expecting. What would you say to people if they're in production, they're trying to do something now?

Lynn Kaack: I think a big goal should be to not only develop more energy efficient machine learning models, but then also ensure that those are actually being used. And surprisingly, even sometimes within the same company, certain model developments are not being passed on to other parts of the company. So really trying to develop standard models that then are also being used in practice is important.

So interoperability of energy efficient machine learning models.

Chris Adams: If people, someone does want to look at this stuff, and they do want to apply some of these ideas, you spoke a little bit about using some other models. would you suggest people look if they wanted to operationalize some of the kind of wins or some of the better ways to make green software greener, for example?

I realize you've got a paper coming out and you work on this day to day. So yeah, what would you point us to?

Lynn Kaack: So, I mean, as I understand, there's a lot of ongoing research in the machine learning community for more energy efficient machine learning. So I don't have any names on top of my head in terms of workshops or community resources where one can see what are the most energy efficient model types. For a specific application.

I know that there are some very comprehensive papers also that summarize all the different research approaches that are being taken, but I would encourage if you are looking for using like a deep learning model of some kind, just inform yourself quickly if there's also a leaner version of it. So much of the like widely used models like BERT, for example, smaller versions that can almost do the same thing.

And maybe your performance doesn't suffer much. If you're using a much lighter model.

Chris Adams: Okay, so light up models and looking around what we have there. And Will, is there a paper or a source you might point to?

Will Buchanan: I was actually going to talk about the Carbon Aware paper that we're about to publish. I think that's a slightly different track.

Chris Adams: That's up next week, right? So that'll be the 13th or 14th of June. That's when that'll be visible, correct?

Will Buchanan: Exactly. 

Chris Adams: Okay, cool. All right, then. There's a load more that we could dive into. We've got copious, copious, copious show notes here. So what I'm gonna do is I'm gonna say thank you everyone for coming in and sharing your wisdom and your experiences with us, and hopefully we'll have more conversations about green software in future. Thank you folks.

Asim Hussain: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please do leave a rating and review if you like what we're doing. It helps other people discover the show and of course we want more listeners.

find out more about the Green Software Foundation, please visit greensoftware. foundation. Thanks again and see you in the next episode.



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]]> Backstage: TOSS Project Backstage: TOSS Project Thu, 10 Oct 2024 07:00:00 GMT 14:20 z1r33zz0 false https://podcasts.castplus.fm/e/18pvvll8-backstage-toss-project 68dc7d027be17a7f01356451 68dc7cef46a2532cdd8d1c23 85 In this episode of Environment Variables, host Chris Skipper introduces "Backstage," a new segment offering behind-the-scenes insights into the Green Software Foundation's key projects. The episode highlights the "Transforming Organizations for Sustainable Software" (TOSS) project, led by Pindy Bhullar, which aims to embed sustainable practices across all aspects of business operations. The TOSS framework, divided into four pillars—strategy, implementation, operations, and compliance—provides organizations with a decision tree approach to drive sustainability in software development and IT. With collaborative efforts, workshops, and real-time feedback, the project is set to release version 1.0, with the ultimate goal of reducing carbon emissions across the industry. 

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TRANSCRIPT BELOW: 

Chris Skipper: Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Skipper. Today, we're launching an exciting new segment: 

Backstage. Here, we'll take you behind the scenes at the Green Software Foundation to highlight ongoing projects and share insights from the members directly involved.

In the first episode of Backstage, you'll hear about innovative projects shaping the future of green software and the hard work behind it all. It's easy to overlook the complexity and dedication required to drive sustainability in the tech industry. But today, you'll hear more about the passion, thoughtfulness, and expertise that fuels these initiatives.

From collaborative projects to cutting edge research on sustainable computing, the Green Software Foundation is the conversation you need to be part of, and we're bringing you Deep Inside. Join us as we look into the stories, challenges, and triumphs of the people making a real impact on our environment through software.

We're no longer gatekeeping what it takes to set new standards and norms to green software. So, what does it take to inject sustainability into every aspect of software development? How are organizations and passionate climate activists in software turning ambitious goals into practical, actionable results?

Let's find out on this special Backstage episode. For this initial episode, we'll be looking at the Transforming Organizations for Sustainable Software project, better known as the TOSS project. Leading this project is the inimitable Pindy Bhullar, who provided us with some soundbites. This initiative is really important in the Green Software Foundation's broader mission to reduce carbon emissions through software.

It's all about providing changemakers in organizations with the tools, methodologies, and frameworks they need to embed green software practices and drive real, measurable change. Firstly, we asked Pindy to give us an overview of the TOSS project. It's goals and why it's so crucial for the foundation.

Pindy Bhullar: Transforming Organizations for Sustainable Software is the acronym for TOSS. Businesses will be able to utilize the TOSS framework as a guide to lay the groundwork for managing change and also improving software operations in the future. Software practices within organizations can be integrated with sustainability in a cohesive and agile manner, rather than addressing green software practices in an isolated approach.

For a company to fully benefit from sustainable transformation of their software development processes, we need to review all aspects of technology. The TOSS framework is designed to be embedded across multiple aspects of its business operations. Dividing the task framework along four pillars has allowed for simultaneous top down and bottom up reinforcement of sustainable practices, as well as the integration of new tools, processes, and regulations that emerge over time.

The four pillars include aim to foster a dynamic foundation for companies to understand where to act now, to adjust later, and expand with an organizational sustainable software transformation. The four pillars are strategy, implementation, operational, compliance, and regulations. And within each of the pillars, we have designed a decision tree that will be constructed to guide organizations in transforming their software journey. 

 The Green Software Foundation has created the specification to calculate carbon emissions from software, the SCI, and also developed the Impact Framework to enable firms to calculate their emissions. But in order for firms to transform their technology operations, we need to embed best practices across the wider organization.

Chris Skipper: It's clear that by integrating sustainability across multiple levels of business operations, the TOSS project is setting up organizations for long term success in their green transformation journey. Now, let's talk about the impact this project can have. Reducing the carbon footprint in software development is a huge focus for many companies today.

So, how exactly does the TOSS project help achieve that, and what kind of sustainability improvements can organizations expect by adopting this framework? 

Pindy Bhullar: The TOSS project views sustainability in software development and IT operations as a holistic approach, and the primary goal is not to view software and IT as an isolated function, but integrate the carbon footprint into the functions across the organization.

The framework will help organizations to ask the questions and adapt their responses based on their needs, but also connect strategy, implementation, operations, and compliance and regulations. 

Chris Skipper: It's insightful to see how the TOSS framework fosters a more integrated approach, making sustainability a core part of a company's strategy, operations, and compliance.

Now, as with any large scale initiative, developing a framework like TOSS must come with its challenges. We asked Pindy to share some of the key obstacles the team has encountered while bringing this project to life, and how have they navigated these challenges to keep the project moving forward. 

Pindy Bhullar: We're all busy in our day jobs, so just being able to focus on the TOSS framework has been a challenge.

We've been having regular calls since the project inception January this year, which has enabled us to keep the discussions moving. We have overcome obstacles by building a version 0.5 of the framework and then asking volunteers from the GSF member organizations to join a series of workshops to review, question, challenge, and brainstorm. It enabled us to develop the framework further by incorporating all the great feedback. 

Chris Skipper: It's great to hear how the team has managed to build the toss framework through collaborative efforts and feedback from GSF member organizations. It sounds like the workshops have played a crucial role in refining the framework.

Now, Pindy mentioned that the TOSS project incorporates a decision tree strategy. We asked Pindy to walk us through how this approach is helping organizations embed green software practices more effectively. How does this decision tree guide organizations in making sustainable choices throughout their software development and IT operations?

Pindy Bhullar: We use the decision tree approach for the TOSS project for a number of reasons, particularly in contexts where decisions involve multiple options and varying outcomes. It's also a great way to visually represent complex decisions into manageable parts, and it helps decision makers visualize the entire process in a structured, analogical format.

It also enables the evaluation of multiple potential paths and their respective outcomes. So each decision point can have different branches, each representing a different course of action. One of the benefits of decision trees is the ability to easily update or expand as new information becomes available.

This flexibility is critical in the space of green IT and green software, as this topic is constantly evolving. 

Chris Skipper: It's clear that the decision tree approach adds flexibility and adaptability, making it a perfect fit for an evolving field like green IT and software. Now, let's dive into the specific tools and technologies that are supporting the TOSS project.

We asked Pindy to highlight some of the key tools or frameworks being used and explain how they align with the project's sustainability goals. How do they contribute to helping organizations achieve lower emissions and more sustainable software practices? 

Pindy Bhullar: To build the framework of decision trees, we are using mirrorboards.

Initially, it helped us to brainstorm and use the ideation tools to create mind maps to generate and organize our ideas in an interactive way. We also benefited from the real time collaboration and, more importantly, the ability to create the visual charts. The tool is enabling us to progress with the TOSS framework and build the sustainable software decision trees.

Chris Skipper: Now, let's explore the collaborative side of this initiative. We asked Pindy if there have been any significant partnerships or collaborations with other organizations or teams that have enhanced the success of the TOSS project. How have these partnerships contributed to its development and progression?

Pindy Bhullar: There have been excellent collaborations during this project. The workshop sessions were critical in partnering with organizations because it enabled us to further build the TOSS decision trees. It also helped us to make faster progress, while allowing the group to discuss new ideas, make revisions, and also to bring new insights.

Chris Skipper: It's clear that collaboration has played a crucial role in pushing the TOSS project forward, with the workshops really driving the development of those decision trees. Now, let's shift focus to the progress the TOSS project has made over time. We asked Pindy to walk us through some of the significant achievements or milestones the project has achieved so far, and how has it evolved since its inception earlier this year?

Pindy Bhullar: At the start of the project, we created a business case which outlined the TOSS project and what we wanted to achieve. Then we moved into a phase of brainstorming and mind mapping, really trying to capture key points of how we wanted to develop the framework and decision trees. We started then to break down the functions that would be involved in organizations and we created the four pillars that I mentioned earlier.

And out of that, then we started to get into the detail and start to create the decision trees. We have progressed since the workshops, and now we're working towards version 1.0 of the decision trees. We have redeveloped the strategy and implementation pillar, and we're still working on the operational and compliance and regulation pillars.

From the initial ideas, we've added further content and details to the framework, and it's constantly evolving. 

Chris Skipper: It's truly fascinating to hear how the TOSS project has progressed. From the initial business case to the detailed decision trees, and the continual refinement of the strategy and implementation pillars.

Now, as we look to the future, we'd love to know more about what's on the horizon for TOSS. What are the next steps in the project, and what long term impact will it have on the widespread adoption of green software practices across the industry? 

Pindy Bhullar: Over the course of the next few months, we want to complete version 1. 0 of the four decision trees. Also, create the guidance documentation that will sit alongside it. And in addition, create a white paper documenting the framework. We want the long term impact of the framework to help organizations implement tools, methods for calculating their emissions, processes, ensuring that we link the strategic level of the organization all the way through to software development and operations.

Chris Skipper: I'd like to thank Pindy for sharing such valuable insights into the TOSS project. It's clear that this framework is not only about integrating sustainable practices, but transforming the entire approach organizations take to software operations. From her detailed explanation of the four pillars, strategy, implementation, operations, and compliance, it's evident that TOSS provides a holistic solution that embeds sustainability at every level.

The collaborative workshops and iterative feedback process she described have played a crucial role in refining the decision trees, and it's exciting to hear that version 1.0 is on the horizon. As the project evolves, it's clear TOSS will have a lasting impact, empowering organizations to seamlessly incorporate green software practices and meet their carbon reduction goals.

We truly look forward to seeing how the framework will continue to grow and shape the future of sustainable software. Now, we've come to the end of the episode, but before we finish off, we have a few events to read out. Firstly, we have Sustainable AI, Myth vs Reality, Gain Insights and Actionable Strategies on the Intersection of IT and Environmental Responsibility at this in-person event hosted by Sustainable IT Netherlands at Accenture in Amsterdam on October 15th, starting at 6pm Central European Standard Time.

Next up, we have CNCF Sustainability Week, Cloud Native for Green Software. Share and explore ideas, patterns, best practices, and tools in the context of the green software principles. This hybrid event is hosted by Green Software development, Karlsruhe at Blue Hands in Karlsruhe on October the 15th at 6:30 PM CEST.

And finally, we have Code Green London October Meetup. Learn about the latest in green software and network with London's green tech community. Also on October the 15th at 7:00 PM BST. This in-person event at the Red Badger in London will feature past environment variables guest Tereze Gail. Who will be talking about Salesforce's sustainable AI approach.

Hear how the world's largest enterprise software company is balancing AI innovation with environmental responsibility. So, we've reached the end of this special backstage episode. I hope you enjoyed the podcast. To listen to more episodes about Green Software, please visit podcast.greensoftware.foundation and we'll see you on the next episode. Bye for now.



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]]> In this episode of Environment Variables, host Chris Skipper introduces "Backstage," a new segment offering behind-the-scenes insights into the Green Software Foundation's key projects. The episode highlights the "Transforming Organizations for Sustainable Software" (TOSS) project, led by Pindy Bhullar, which aims to embed sustainable practices across all aspects of business operations. The TOSS framework, divided into four pillars—strategy, implementation, operations, and compliance—provides organizations with a decision tree approach to drive sustainability in software development and IT. With collaborative efforts, workshops, and real-time feedback, the project is set to release version 1.0, with the ultimate goal of reducing carbon emissions across the industry. 

Learn more about our people:

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If you enjoyed this episode then please either:
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TRANSCRIPT BELOW: 

Chris Skipper: Welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Skipper. Today, we're launching an exciting new segment: 

Backstage. Here, we'll take you behind the scenes at the Green Software Foundation to highlight ongoing projects and share insights from the members directly involved.

In the first episode of Backstage, you'll hear about innovative projects shaping the future of green software and the hard work behind it all. It's easy to overlook the complexity and dedication required to drive sustainability in the tech industry. But today, you'll hear more about the passion, thoughtfulness, and expertise that fuels these initiatives.

From collaborative projects to cutting edge research on sustainable computing, the Green Software Foundation is the conversation you need to be part of, and we're bringing you Deep Inside. Join us as we look into the stories, challenges, and triumphs of the people making a real impact on our environment through software.

We're no longer gatekeeping what it takes to set new standards and norms to green software. So, what does it take to inject sustainability into every aspect of software development? How are organizations and passionate climate activists in software turning ambitious goals into practical, actionable results?

Let's find out on this special Backstage episode. For this initial episode, we'll be looking at the Transforming Organizations for Sustainable Software project, better known as the TOSS project. Leading this project is the inimitable Pindy Bhullar, who provided us with some soundbites. This initiative is really important in the Green Software Foundation's broader mission to reduce carbon emissions through software.

It's all about providing changemakers in organizations with the tools, methodologies, and frameworks they need to embed green software practices and drive real, measurable change. Firstly, we asked Pindy to give us an overview of the TOSS project. It's goals and why it's so crucial for the foundation.

Pindy Bhullar: Transforming Organizations for Sustainable Software is the acronym for TOSS. Businesses will be able to utilize the TOSS framework as a guide to lay the groundwork for managing change and also improving software operations in the future. Software practices within organizations can be integrated with sustainability in a cohesive and agile manner, rather than addressing green software practices in an isolated approach.

For a company to fully benefit from sustainable transformation of their software development processes, we need to review all aspects of technology. The TOSS framework is designed to be embedded across multiple aspects of its business operations. Dividing the task framework along four pillars has allowed for simultaneous top down and bottom up reinforcement of sustainable practices, as well as the integration of new tools, processes, and regulations that emerge over time.

The four pillars include aim to foster a dynamic foundation for companies to understand where to act now, to adjust later, and expand with an organizational sustainable software transformation. The four pillars are strategy, implementation, operational, compliance, and regulations. And within each of the pillars, we have designed a decision tree that will be constructed to guide organizations in transforming their software journey. 

 The Green Software Foundation has created the specification to calculate carbon emissions from software, the SCI, and also developed the Impact Framework to enable firms to calculate their emissions. But in order for firms to transform their technology operations, we need to embed best practices across the wider organization.

Chris Skipper: It's clear that by integrating sustainability across multiple levels of business operations, the TOSS project is setting up organizations for long term success in their green transformation journey. Now, let's talk about the impact this project can have. Reducing the carbon footprint in software development is a huge focus for many companies today.

So, how exactly does the TOSS project help achieve that, and what kind of sustainability improvements can organizations expect by adopting this framework? 

Pindy Bhullar: The TOSS project views sustainability in software development and IT operations as a holistic approach, and the primary goal is not to view software and IT as an isolated function, but integrate the carbon footprint into the functions across the organization.

The framework will help organizations to ask the questions and adapt their responses based on their needs, but also connect strategy, implementation, operations, and compliance and regulations. 

Chris Skipper: It's insightful to see how the TOSS framework fosters a more integrated approach, making sustainability a core part of a company's strategy, operations, and compliance.

Now, as with any large scale initiative, developing a framework like TOSS must come with its challenges. We asked Pindy to share some of the key obstacles the team has encountered while bringing this project to life, and how have they navigated these challenges to keep the project moving forward. 

Pindy Bhullar: We're all busy in our day jobs, so just being able to focus on the TOSS framework has been a challenge.

We've been having regular calls since the project inception January this year, which has enabled us to keep the discussions moving. We have overcome obstacles by building a version 0.5 of the framework and then asking volunteers from the GSF member organizations to join a series of workshops to review, question, challenge, and brainstorm. It enabled us to develop the framework further by incorporating all the great feedback. 

Chris Skipper: It's great to hear how the team has managed to build the toss framework through collaborative efforts and feedback from GSF member organizations. It sounds like the workshops have played a crucial role in refining the framework.

Now, Pindy mentioned that the TOSS project incorporates a decision tree strategy. We asked Pindy to walk us through how this approach is helping organizations embed green software practices more effectively. How does this decision tree guide organizations in making sustainable choices throughout their software development and IT operations?

Pindy Bhullar: We use the decision tree approach for the TOSS project for a number of reasons, particularly in contexts where decisions involve multiple options and varying outcomes. It's also a great way to visually represent complex decisions into manageable parts, and it helps decision makers visualize the entire process in a structured, analogical format.

It also enables the evaluation of multiple potential paths and their respective outcomes. So each decision point can have different branches, each representing a different course of action. One of the benefits of decision trees is the ability to easily update or expand as new information becomes available.

This flexibility is critical in the space of green IT and green software, as this topic is constantly evolving. 

Chris Skipper: It's clear that the decision tree approach adds flexibility and adaptability, making it a perfect fit for an evolving field like green IT and software. Now, let's dive into the specific tools and technologies that are supporting the TOSS project.

We asked Pindy to highlight some of the key tools or frameworks being used and explain how they align with the project's sustainability goals. How do they contribute to helping organizations achieve lower emissions and more sustainable software practices? 

Pindy Bhullar: To build the framework of decision trees, we are using mirrorboards.

Initially, it helped us to brainstorm and use the ideation tools to create mind maps to generate and organize our ideas in an interactive way. We also benefited from the real time collaboration and, more importantly, the ability to create the visual charts. The tool is enabling us to progress with the TOSS framework and build the sustainable software decision trees.

Chris Skipper: Now, let's explore the collaborative side of this initiative. We asked Pindy if there have been any significant partnerships or collaborations with other organizations or teams that have enhanced the success of the TOSS project. How have these partnerships contributed to its development and progression?

Pindy Bhullar: There have been excellent collaborations during this project. The workshop sessions were critical in partnering with organizations because it enabled us to further build the TOSS decision trees. It also helped us to make faster progress, while allowing the group to discuss new ideas, make revisions, and also to bring new insights.

Chris Skipper: It's clear that collaboration has played a crucial role in pushing the TOSS project forward, with the workshops really driving the development of those decision trees. Now, let's shift focus to the progress the TOSS project has made over time. We asked Pindy to walk us through some of the significant achievements or milestones the project has achieved so far, and how has it evolved since its inception earlier this year?

Pindy Bhullar: At the start of the project, we created a business case which outlined the TOSS project and what we wanted to achieve. Then we moved into a phase of brainstorming and mind mapping, really trying to capture key points of how we wanted to develop the framework and decision trees. We started then to break down the functions that would be involved in organizations and we created the four pillars that I mentioned earlier.

And out of that, then we started to get into the detail and start to create the decision trees. We have progressed since the workshops, and now we're working towards version 1.0 of the decision trees. We have redeveloped the strategy and implementation pillar, and we're still working on the operational and compliance and regulation pillars.

From the initial ideas, we've added further content and details to the framework, and it's constantly evolving. 

Chris Skipper: It's truly fascinating to hear how the TOSS project has progressed. From the initial business case to the detailed decision trees, and the continual refinement of the strategy and implementation pillars.

Now, as we look to the future, we'd love to know more about what's on the horizon for TOSS. What are the next steps in the project, and what long term impact will it have on the widespread adoption of green software practices across the industry? 

Pindy Bhullar: Over the course of the next few months, we want to complete version 1. 0 of the four decision trees. Also, create the guidance documentation that will sit alongside it. And in addition, create a white paper documenting the framework. We want the long term impact of the framework to help organizations implement tools, methods for calculating their emissions, processes, ensuring that we link the strategic level of the organization all the way through to software development and operations.

Chris Skipper: I'd like to thank Pindy for sharing such valuable insights into the TOSS project. It's clear that this framework is not only about integrating sustainable practices, but transforming the entire approach organizations take to software operations. From her detailed explanation of the four pillars, strategy, implementation, operations, and compliance, it's evident that TOSS provides a holistic solution that embeds sustainability at every level.

The collaborative workshops and iterative feedback process she described have played a crucial role in refining the decision trees, and it's exciting to hear that version 1.0 is on the horizon. As the project evolves, it's clear TOSS will have a lasting impact, empowering organizations to seamlessly incorporate green software practices and meet their carbon reduction goals.

We truly look forward to seeing how the framework will continue to grow and shape the future of sustainable software. Now, we've come to the end of the episode, but before we finish off, we have a few events to read out. Firstly, we have Sustainable AI, Myth vs Reality, Gain Insights and Actionable Strategies on the Intersection of IT and Environmental Responsibility at this in-person event hosted by Sustainable IT Netherlands at Accenture in Amsterdam on October 15th, starting at 6pm Central European Standard Time.

Next up, we have CNCF Sustainability Week, Cloud Native for Green Software. Share and explore ideas, patterns, best practices, and tools in the context of the green software principles. This hybrid event is hosted by Green Software development, Karlsruhe at Blue Hands in Karlsruhe on October the 15th at 6:30 PM CEST.

And finally, we have Code Green London October Meetup. Learn about the latest in green software and network with London's green tech community. Also on October the 15th at 7:00 PM BST. This in-person event at the Red Badger in London will feature past environment variables guest Tereze Gail. Who will be talking about Salesforce's sustainable AI approach.

Hear how the world's largest enterprise software company is balancing AI innovation with environmental responsibility. So, we've reached the end of this special backstage episode. I hope you enjoyed the podcast. To listen to more episodes about Green Software, please visit podcast.greensoftware.foundation and we'll see you on the next episode. Bye for now.



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]]> The Week in Green Software: The Sustainable Data Paradox The Week in Green Software: The Sustainable Data Paradox Thu, 03 Oct 2024 07:00:00 GMT 42:45 70w77jp0 false https://podcasts.castplus.fm/e/rnkmml0n-the-week-in-green-software-the-sustainable-data-paradox 68dc7d0409b1c365e49ab1a1 68dc7cef46a2532cdd8d1c23 84 This Week in Green Software, the affable Anne Currie is joined by Sara Bergman, Senior Software Engineer at Microsoft and co-author of Building Green Software. Together, they dive into the complexities of sustainable data in relation to AI and cloud computing. They explore the environmental impact of managing and storing vast quantities of data, and question the feasibility of making these processes more eco-friendly. The discussion touches on cloud providers' carbon reporting, the importance of using AI responsibly, and how businesses can optimize their cloud use to minimize their environmental footprint. Tune in for an insightful conversation on balancing technological advancements with sustainability in the age of AI.

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Find out more about the GSF:

News:

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TRANSCRIPT BELOW:

Sara Bergman: What data do we need to take in order to take meaningful action? Like, what is the level that, of course, yeah, if I could get minute by minute, like there's tons of stuff we could do and correlations we could draw, but what is the level of data that we would need to start taking meaningful action? And I think that could unlock a lot of good things.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne Currie: Hello, and my name is Anne and welcome to 

The Week in Green Software. So this week, you won't be hearing the usual dulcet tones of Chris Adams. I'll be joining you, Anne Currie, and we'll be delving into the tricky, but interesting world of sustainable data, whether it's possible to store and manage huge quantities of data, which we will need now, particularly for AI, in a way that's eco friendly.

Or is that impossible? Well, look, I'm going to leap through to the end and say, we have to do it. And therefore it is not impossible. It just has to be done. We'll find a way of doing it. And there are ways of doing it, which we'll be talking about today. we'll also be exploring why big cloud providers' carbon reporting isn't really telling us the full story.

Or, well, we are not interpreting it in the way that it was designed to be interpreted. And we need to be more careful about that. We're reading more into it than is true. And we need to be much, much more explicit about what the carbon reports from people like AWS and Azure actually mean.

We'll also be talking about data centers and AI. And that's something that my guest today is an expert in. And talking about my guest, joining me today is my co-author on Building Green Software, the book on what we need to do to make the tech industry green, and also Environment Variables regular, the lovely Sara Bergman.

So Sara, do you want to introduce yourself?

Sara Bergman: Yeah, hi. Thanks for having me on again. Always lovely to be here. I'm so excited to have a chat with you, Anne. Yeah, my name is Sara Bergman. I am a senior software engineer at Watttime. Microsoft, author of Building Green Software. And like here, I'm always asked, what have you been up to recently? Nowadays, I'm like, what have I been up to recently?

I did a fun thing, though. I had the talk for the Norwegian, because I live in Norway, and the Norwegian tax authorities about green software. And that was really fun. I love it when Because they are actually very far ahead in their journey, they're one of the most innovative companies when it comes to IT, so it was really fun to come out and have a chat with them.

Anne Currie: That's great. And I should introduce myself as well. My name is Anne Currie and I am co-author of O'Reilly's new Building Green Software along with Sara and our other co-author, Sara Hsu. And I also do a lot of training. So I've been busy at the moment doing loads of training courses. So, workshops on building green software and also an experts training course, which is all quite good.

So, if you want to get involved in any of that stuff, you can follow me on LinkedIn. So, as usual, today, we will be talking about a couple of interesting articles, publications that have come out over the past week around green software, all things green, and as usual, all the links to the articles will be in the show notes, so you'll be able to read them yourselves afterwards.

But I'll give you a little bit of a summary about what they say. So the first article we'll be talking about today is from the Green Web Foundation. And it was written by our normal Environment Variables host, Chris Adams. So that's where he is, or that's what he's been up to today in his work at the Green Web Foundation.

And he wrote it with his colleague, Hannah Smith. And the report is all around AI's environmental impacts. And it's got some interesting figures in there. Basically, AI uses a load of electricity and at the moment, as we don't yet have a completely green grid, that means that a lot more CO2 is being emitted into the atmosphere as a result of the fact that we're training a lot of models, doing a lot of inference.

So it is an interesting report and it's, I'm going to kind of summarize, they have some actionable things, some questions for you to ask yourself at the end of the report. So I will go over those now and then Sara and I can discuss them. The first is that you should always question your use of AI.

That's kind of part of using the right tool for the job. Is AI the right tool for what you're doing? Is it overkill? Could you use a spreadsheet? That's If you are using AI and you decide it is the right tool for the job, are you using it properly? Are you using it well? Are you using the right AI tools for the job?

And the third is to try and get ideas of your footprint, of the footprints of the work that you're doing with the AI, so you actually have an idea about what impact it is having and what you're going to need to do about it in the future. So Sara, this is kind of your area. Did you enjoy the report? What did you think?

Sara Bergman: I did. It's a very long report. It's very well written. Obviously, I mean, with Chris and Hannah, you're going to get something that's well written, of course. So no surprises there. No, it's good. And I think also for people who are maybe newer to the field of green software and green AI in general, there was a lot of good background to like really help understand the intricacies of this area. And something that I particularly find interesting in the shift we are now is that they talk about different phases where your emissions kind of stem from, it's like manufacturing, training and inference. And now, like you said, we talk a lot about inferencing, like using AI, that the use phase is what we talk about.

But back when I started, sounds like I'm really old, only four years ago, not that long ago, but when I first started talking about green AI. Yeah, a lot of, a few people, not so many were talking about green AI. A lot of people were researching, but not so many people were like discussing it. And then it was a lot of focus on the training.

There was a lot of great research being done on how to minimize the impact on training. I think in the research community, that's maybe the easiest, not the easiest thing, but a good first thing to research, right? And now we're seeing more focus on. on the production side, not like inferencing. And I think that shift has been very interesting to follow.

Anne Currie: But yeah, it is fascinating, isn't it? Cause there's loads, and we've had this conversation offline, because obviously we a book together and therefore we talk together quite a lot. But we've had this conversation a lot in that it feels like there's a load of stuff to be learned about inference.

So how you actually get the answers back as a user for models from the world of things like CDNs, how do you get fast answers and easy answers to things all over the world from data that is not necessarily by default, wasn't created close to the user who's querying it?

So yeah, there's, loads of prior art there to learn from it. It's a really interesting field.

Sara Bergman: It is a very interesting field and I think an additional like spiciness of this is that now a lot of people equate AI to large language models. That's not all of the AI that exists in the world, it's just the right now the most popular and what we think maybe will be the most interesting use case, or for like productivity and stuff.

And for that we require a lot of specialized hardware, like GPUs or TPUs on CPUs, et cetera, which is another thing to kind of reason over. And, maybe if you're very good at handling your production workloads on a CPU, like this might be a new area for you where you, we need to learn as an industry. I thought that was an interesting part of the article.

Anne Currie: Yes. Yeah, it is. It is all very interesting, isn't it? it's like something that all three of us say in the book a lot, is that there's a load of clever stuff going on in the tech industry, and it's usually, there's a desire to make things efficient because it's cheaper. Otherwise, everything gets very expensive.

So if you use things as they were intended to be used and use the right tools for the job, usually that's significantly more energy efficient and therefore greener. And the same is very much true of AI, isn't it? If you use the right tools, the right hardware, the right models,

Sara Bergman: Yeah, exactly. Exactly. So that and then I think that ties into right, that was it number two on their like, use AI responsibly, which was interesting. I also like the first question that they had. Now I'm scrolling and losing it was it think about when you should use AI? Was that the phrasing?

Yeah, question your use, know when to use it. Like, do you have a favorite use case?

Anne Currie: Well, I have to say, I do love generative AI. 

Sara Bergman: Yeah. 

Anne Currie: it's, an interesting one, cause quite often... well, it's amazing how often it comes up where people think that it's kind of like you're either doing AI or you're green. That's it. Those are the two, two options. And they cannot be the two options.

It can't be either don't do AI and therefore it's green or do AI and therefore it's not green. They have be brought together because if there's one thing that we know, two things we know are coming in the future for humanity. One is climate change. The other is AI and use of AI and AI systems.

They cannot be mutually exclusive. They have to be brought together. I mean, and AI is just computing. It's the same kind of things that we do when we talk about in Building Green Software for all computing just needs to be applied to AI. It's not a, it's not a new thing on its own, but

Sara Bergman: and I agree. And I think that's also so interesting with this, like, larger discourse, as you say, I think not only that it exists in other areas of life, where there are some people who would be like, "no, we should just stop doing this, like, stop advancing technology, and then everything will solve itself," but that doesn't work. We cannot and does not want to go back to like a farmer society where everyone grows your own food. Like if you're into growing your own food, that's fantastic. I'm happy for you. I grow radishes on my balcony and I enjoy that thoroughly. But there's so much advantage and good for humanity and our planet that comes from technology as well.

So we need to do both. We must do both. Like, it's non negotiable. So it's more like how and when? And 'when' is hopefully starting now.

Anne Currie: Cause the trouble is, if you have that conversation, if you say, "Oh, well, I want to do AI, therefore I can't do green." People will choose AI because the businesses, if they don't, if they don't try these new tools and services as they come available, they will go out of business. So, if you say, "Oh, well, if you're going to be green, you can't use AI," then what you're telling people is don't be green.

And it's crazy. You're just making the wrong arguments. But anyway.

Sara Bergman: I fully agree for sure. And I think it was something we were in a different, or like this podcast together, but another episode and you said something that I've been carrying with me for so long. Sometimes people say something and it just get like something clicks. And what you said, and I'm going to maybe paraphrase, but things that are limited are sort of less concerning. Like, for example, if you build a super efficient washing machine, I still have only so much washing up to do. There is an end to how much I will wash it. Like this is in response to Jevons paradox, of course. And the same goes for this. Like, yeah, AI will make us more efficient, more productive.

Okay. But we'll then just do more. It's like, yeah, but the working day is sort of eight hours and we are only so many people on the planet. There is a limit to when we'll be done, whereas for crypto, for example, which always comes up when we talk about this, of course, there's no limit. I will never say I have enough money, I'm done mining. But there is a limit where I say, thank you, co-pilot for GitHub, I don't need more code suggestions.

I'm happy with my feature now, sort of.

Anne Currie: Oh, yeah. That's an issue, because, I always tend to think that one of the, one of the things about AI is that, that we're not,, we haven't yet reached the limits of what we want to do with it. But, you're right. There's, it's nowhere near. I mean, we've already come up with the thing that is the most limitless.

You've heard the use of CPU and it's CPU use that's really the problem that's, that uses a lot of electricity, is crypto. It is literally boundless how much we might want to do with it. So we've kind of already created the worst 30.

Sara Bergman: yes.

Anne Currie: AI is so

Sara Bergman: not it.

Anne Currie: it's a comparison. At least there's some people, 

I don't know, we shouldn't be controversial because there are a whole load of people who say, well, there's loads of benefits of cryptos, but I personally do not, I'm not a crypto bro.

Sara Bergman: Same. No, I did write my master's thesis on blockchain. I think blockchain is a cool invention. Like, it's a cool technology. And there are use cases that are, of course, bounded. And then there are use cases that are unbounded. And I think people can use their own head to figure out what's what.

Anne Currie: We've now got ourselves into enough trouble talking about good and bad uses of technology. And to a certain extent, it's very hard to say what's a good or bad use of technology. I tend in my head to think it was a bad use of technology, I, not everyone agrees with me, but we will move on to the next article, which is one, now...

So this one, cloud provider Greenhouse Gas reporting isn't enough. The case for product level accountability. Which is, again, the link is included in the show notes. Now I know, because this talks about Azure, Sara, you, might be in a position where I'm saying things to you and you have to say, you may say that "Anne, but I cannot possibly comment."

So in which case you'll just have to leave me to talk, but that's fine. Cause I can talk endlessly about this stuff because I find it really, I think this is absolutely fascinating and there's loads of stuff to learn from this article. So the article is all about, it's highlighting the limits of cloud providers and data, the carbon footprint reports.

And it emphasizes the need for companies to track emissions at product, at data product level. And it argues that these broader reports mask the true environmental costs of specific processes like data and that things would be better if you broke the emissions down to product level. And I think that's great, but I don't think that's actually the problem.

I think it's well worth reading the article, but I think it skips over, I think, what is the more significant problem that we're having, that we're seeing, because I'm out there talking to people a lot and I know this is a significant problem at the moment, that the cloud providers' reports are doing something, a lot of the new cloud providers' reports, particularly the recent AWS releases are doing something which I can't really shout at them for because I demanded it myself some years ago and I am somewhat hoist by my own petard for this.

So many years ago, me and a fellow, a collaborator called Paul Johnston ran a, back in 2018, we ran a campaign called Sustainable Servers by 2024. And what we were campaigning for was that all of the cloud providers would commit, and this was some time ago, would commit to being carbon neutral by 2024.

And it was really aimed entirely at AWS because Google and Azure were already carbon neutral at that point and AWS was not. So, we ran this big campaign and we had petitions and all kinds of things for saying that they should be carbon neutral, collateral and tradeful. And of course, carbon neutrality is quite a limited demand.

It's basically saying, "I want you to be carbon neutral. I want you to know how much your carbon emissions are. And then buy offsets that match the carbon emissions." Now, we all know that offsets are good in the past, but they're not time matched. They don't mean that the carbon savings that you've created are time matched with the carbon emissions of your systems.

So your systems can still be throwing off carbon dioxide into the atmosphere whilst you are carbon neutral. So it was a useful, a useful measure in 2018, it was still useful. Now in 2024, it's actually not as good as it could, it's not enough. it's the bare bones. It's the least we can ask for.

But AWS have done it on time, 2024, and they are producing these reports that they're giving to AWS customers that say, "you're carbon neutral, your systems are net, not producing any carbon." But the problem is that a report that says, and that's great, but it is nowhere near enough.

It's not enough. It's great information for your finance team because your finance team don't want that data. They need that data, particularly if you're going to be, if you're in the EU or you're selling into the EU. Because the EU is now demanding carbon taxes, and carbon taxes are another form of offsetting, really.

You pay for the carbon you're emitting into the atmosphere. So if AWS produced a report saying, look, there's no net carbon you're producing into the atmosphere at the moment, which they are generally at the moment for most AWS systems, it seems. That is really a report for your finance team, so they don't have to, so they can go, "oh, that's great.

The offsetting has already been done for me." That is not a report for your tech team because they're not saying that no carbon is actually being produced by your systems. Your systems are still producing loads of carbon and you still need to adjust your systems. to reduce carbon, which you can do, but the report is kind of giving you the impression that you don't need to.

So is that something you can comment on or?

Sara Bergman: Yeah, this gets me going, right? Cause I'm all about enabling like people with feet on the ground, hands on the code is what I say. So I do agree a lot of the reporting that comes out, it's much better than it used to be. This difference, and maybe now we're slipping into the next paper a little bit, but there, of course, difference between market-based and location-based reporting in the Greenhouse Gas Protocol.

That is a feature, a fundamental feature of the Greenhouse Gas Protocol, which every single industry makes use of. Now, not only, 

Anne Currie: I'm going to interrupt you at this point, just because this is something that I realized when talking to people. One of the issues is that people don't understand the difference between market-based and location-based. What does that mean? So market-based, I think that the word that people really understand is offset.

If you're market-based, it's saying you're still putting carbon into the atmosphere, but we are offsetting it, which was great 10 years ago, nowhere near good enough nowadays. location-based means actually, it's about reducing, it's about not putting carbon into the atmosphere. So that's what we want. Market offsets are a step to getting there, but they are not there.

So sorry, go on.

Sara Bergman: yeah, no, but that's, good. I also like it's like the electrons you pay for versus the electrons you use, if you want to get really down to the socket level. So yeah, I think that's something to talk about. Also, something that I kept thinking about after reading this article, and I just want to read the comment from it because I think the comment was amazing.

And the comment is, "conceptually, I agree. More data is better. However, I've never met anyone advocating for product level data who has actual operational experiences of running shared service platforms and therefore would understand the complexity of delivering these metrics. Because yes, if we're talking about, we could have one report for the finance team, that's fine, but should there be an additional one for, the people who write the code, who maintain the service? And then the question becomes, okay, but what data do we need to take in order to take meaningful action? Like, what is the level that, of course, yeah, if I could get minute by minute, like, there's tons of stuff we could do, correlations we could draw, but what is the level of data that We would need to start taking meaningful action? And I think that could unlock a lot of good things.

Maybe we don't need the world. Do you know what I mean?

Anne Currie: Yeah. I know what you mean. I totally know what you mean. I think we should step down our emissions. Well, having said that, within the Green Software Foundation, there is a project, the Real Time Metrics Project. And that is looking forward to when we can have second, millisecond by millisecond data about, so we can tune applications to, to get there.

But then we also have another project, which I run, so their real time metrics project is run by Adrian Cockcroft and I run a project called the Maturity Matrix Project, which, comes out of our book. So it's, the penultimate chapter in our book. And the Maturity Matrix Project is start, is, it says that all that real time stuff is really quite advanced.

It's way further advanced than pretty much anybody in the world currently is. What we actually need at the moment is quite simple stuff, like "just turn off machines." Turn off machines when you're not using them. And you can, you don't need real time metrics for that. The other thing that we can do that doesn't require real time metrics is, so one of the things that kind of annoys me about the new AWS Cloud Footprint report, which is, it's fine, it's not a lie, it's just giving you offsets.

It's just telling you what your offsets are. It's totally fine. But people are misusing it and misreading it as thinking it's about carbon emissions. There's another thing that AWS have said, which I really like, which is that, "look, we all do a load of work."

It's called the shared responsibility model. "We will take responsibility for the sustainability of the cloud, if you take responsibility for the sustainability in the cloud. Now" I like it, except that it's also very confusing. What they are saying is that "we will build tools that can be aligned with actual location-based zero carbon operations.

We will build those tools." Those tools in the book, we call them green platforms. They're things like serverless, spot instances, really clever instance types. You see, it kind of aligned with their whole modernization strategy, how you get into the cloud and use it well, as it was intended to be used.

So they're saying that, "but it's your responsibility to use those tools. We can build those tools, but if you don't use them, you won't be green. If you just sit in dedicated instances, you will, there's nothing we can do about it. You will never be green. We'll offset you," which is why, the reports say, "well, we'll pay for your bad behavior.

But it is still bad behavior." You'll get to carbon neutral, but you'll never get to carbon zero. And I think that those messages, which are quite complicated, can both exist, but they need to be quite clearly communicated. And at the moment, I don't think we are being so clearly communicated. What's your thoughts?

Sara Bergman: Yeah, this is also, I think this is also something I thought a lot about reading this article. Like, where is the line? Because many cloud providers, they do have clearly green ambition. They are financially incentivized in many ways to be more energy efficient, use less resources of course, because all of those things cost money for any cloud provider, even if that cloud provider is your local on-prem.

So that is one thing, they wanna make cheaper stuff that are often greener, almost always greener, but it is your responsibility to get on them. And how do you do that? Now, all three of the cloud providers do have architected frameworks that have a dedicated sustainability section.

And there for some scenarios, it's actually incredibly good. Like it's very detailed. So you can go and just like, "Oh, my scenario fits into this." I would like those sections to be longer for all of the big three and for the smaller ones, maybe to include more. But I also now maybe skipping randomly ahead, back and forth, something, a mental image that I think helps because sometimes I think the key message is getting lost.

"Okay. But the cloud is green. Why do I need to take action?" and that is a tricky conversation to have because Yeah, if you're selling the cloud, of course, you don't want to say "no, but it's not green." It's like, it just becomes a bit messy. So a mental model I like to use, which I heard from another wonderful woman here in Norway, which is if you have a car and you have a ski box, because right, we have lots of skis here.

So they can think of the car as the cloud and it's the responsibility of the car manufacturer to make that as efficient and green as possible. And they do, right? And but you, as a user, you choose how do I transport my skis in the car? Do I transport them inside the car? Yeah, maybe they that would be greener right because then there's no additional wind catch of the ski box on top, but maybe they don't fit, so you put them on the ski box on the roof. Fine, you accept that additional wind cost and thus increase the energy. But, once ski season is over, and you don't need the skis anymore, what does eeveryone do? You've remove the ski box. I don't see anyone riding around with the ski box in May, just because, right?

And the same goes for the cloud. So yeah, the cloud has lots of work, a lot of stuff in it to make it greener. But if you choose to use a ski box, that's fine. But once the ski season is over, remove the ski box. So once you're not using your test environment, remove it, shut it down.

Anne Currie: Yeah. Yeah. I like the analogy. It's very Scandinavian.

Sara Bergman: It's very Scandinavian, I'm sorry, but I am very Scandinavian, so that's what you get. I guess the same applies if you have a surfing board, I suppose you put them on the roof as well, 

or a canoe,

Anne Currie: A canoe!

Sara Bergman: yeah, or a kayak, yeah. They have longer seasons though, but, so yeah, to try to translate, I feel like I was very, poor Chris who has to edit this, all my ramblings.

But yeah, as a cloud user, you have a responsibility and I think there could be two reports of showing this is what we as a cloud provider, I think all cloud providers are pretty good at this. This is what we took responsibility for. That's awesome. So much better than it was like 10, 5 years ago.

But then also how do we enable people using the cloud to take green actions? Because they want to take green actions, we want them to take green actions, like how do we enable that at the same time? Yeah.

Anne Currie: Yeah. I mean, it's, kind of like "is the cloud green?" The trouble is the, answer is as always in tech, it depends. Oh yeah. It's like, it depends. Are you using it as it was intended to be used? And if you, and the, one of the reasons why you sometimes see Gartner reports and things saying that the cloud is really poorly used,

it's because there's two stages in moving into the cloud. One, is you just kind of lift and shift. For most people, they lift and shift. Although you might argue that's actually never a really good way to go into the cloud. I've written books about this. It's not, but it's, it is a way, a common way for people to go into the cloud.

But once you've lifted and shifted, you have to then actually use the cloud, as it were, you see, you have to go beyond that. If you stop at lift and shift, it's really ungreen. It's worse than being on prem', because in the cloud, it's so easy to over provision. So you have to actually, you have to adopt the cloud, it's called cloud native and it's not really cloud native, but then you have to adopt the cloud way of doing things because otherwise you will over provision and you will then make more carbon than you did on prem', because it's harder to over provision on prem'.

Sara Bergman: Yeah, And that's a journey. And that's something I think all cloud providers are pretty good at supporting, right? That's their bread and butter in many ways.

Anne Currie: It is, they just want people to do it, but still it's hard because it's really so hard to lift and shift into the cloud that once people get there, they go, "thank God for that." And they don't want to go and look at what happens next, which is kind of one of the issues of lift and shift as opposed to kind of just slicing up and moving bits and bits one by one into the cloud, is that

these big projects, they're so painful that once you've done them, you never want to, I've done so many big projects in the past, and you always think you're going to go on and do the next stage, but you're so destroyed by that project. You just think, "Oh my God, we're all burned out. We don't even want to think about it again."

It's, yeah. Yes.

Sara Bergman: We need to glamorize DevOps or operations. It needs to be something there, more and more afterworks, I don't know. Something to make them more glamorous.

Anne Currie: These big projects are awful. I never want to do one again. But anyway. Yeah. And they're, ungreen because you then go on to the next bit where you start to actually refine and improve. So yeah, it's, we need to step back and think about how we're doing that. So, which is, I think a lot, I was signing books at a conference about kind of, DevOps and, CICD and team topologies and moving faster releases, last week.

And I think that's really green because if you can't move, you can't adopt these better tools and services that remain.

Sara Bergman: Yeah, exactly. Yeah, I also said someone, or heard someone who said like, what we need now is like performance engineering, we need to go back to basics in many senses, right? And because a lot of these learnings are not new. It's the same when people are like, "ooh, if I," now I'm going on a tangent here, but if people want to be greener, they're like, "oh, I should just rewrite, like, make my code more efficient."

And like, by all means do. But if you haven't had like a reason to do that yet, I don't think you're going to convince your management chain that sustainability is going to be the reason why you go implement it because high performance computing is not new at all. Like we've known about all these things for a very long time.

So if you haven't done it yet, I mean, kudos to you if sustainability is the thing that makes you implement it. Kudos to you. I just think it's going to be a tough sell.

Anne Currie: I've mentioned a few times on environment variables, but it always interests me, is on that note of efficient code is expensive and we don't do it anymore, sara now works on Microsoft Exchange and 25 years ago, I also worked on Microsoft Exchange, but in Microsoft Exchange 25 years ago, we had to have everything written in C because the hardware was, it was just not possible to do it using anything other than the most optimized C code, which meant that it was really expensive to do things.

It took a long time. Yeah. The world has changed. we've got better hardware and we use it. We use it to go, to not have to write everything in C.

Sara Bergman: Exactly. And we see the same on the mobile industry, right? They have more apps that do more things now because their phones can handle it. They couldn't when the first smart modes came out and just, again, going back to what we talked about earlier, I don't think we can stop technology progressing in order to be greener.

We need it to progress and be green at the same time.

Anne Currie: Yeah. Yeah. We need it all. 

Sara Bergman: People are going to call us greedy.

Anne Currie: We want it all. So we're going to zip onto our final article link today, which is, 

Is Sustainable Data Storage a Paradox? So it's a piece in TechRadar by Jon Howes about environmental challenges posed by cloud storage and AI, and the rising energy demands of data centers that are associated with AI and storage. So thoughts? Is green data storage possible? Sarah?

Sara Bergman: To quote you, it has to be. No, but I, okay. So there are many things here, but I think also he said that cloud is the least wasteful storage solution because it is highly optimized, blah, blah, blah, blah. So I don't know if the paradox is. Could there be other ways? Or if the paradox is, I don't know, it was a catchy title, and I'm not sure I understood what the paradox was.

But also, what I really kept thinking about, is storage really what we should be concerned about here? I (naively, maybe) would guess that compute would be much more resource intensive than the actual, like, store at rest. Then, of course, training the model, you would need to access the storage.

But again, I would be, in these days, more concerned about inference. And then, again, naively maybe, I would expect the CPU and GPU usage to be what we're worried about, not our disks.

Anne Currie: Yeah, I tend to agree. I would say with data, the opportunity there is there's quite a lot of low hanging fruit. And it's a really, good example of the shared responsibility model as well, that it's up to you, to us as users to not be wasteful in our use of data. And by waste, I don't mean storing more than we need, because I think that telling people to throw the stuff away in the world of AI is just crazy talk.

No one's going to do it. You're going to keep everything, right? We just need to see if the general AI can make use of it. The wastage is having it in a medium that's where it can be accessed more quickly than it ever needs to be accessed. So that the longer, if you say, look, I'm going to access this once a year, you can stick it all on tape and it's practically free in terms of carbon.

You can save as long as you like, there's this, that kind of like never underestimate the bandwidth of a station wagon full of tapes driving down the freeway. Tapes are pretty good. You can put a load of stuff on tapes and most data, especially data for AI, it doesn't matter if it takes you a couple of hours or even a couple of days to get it back.

You can, so you don't need to keep it on sSD, where it's just really much more carbon intensive than tape. So it's just, it's quite easy. You just need to think about it and not store things in the wrong medium. Use the tools that are best suited to the job.

Sara Bergman: yeah, be diligent about hot, warm, and cold storage. Like, what do you need, when, and how much? And yeah, also people say, "should I delete all my photos and emails?" I'm like, "no, you want them, right?" You're going to look at your photos again, you're going to maybe search or read your emails again. So don't remove them.

Maybe you know, unsubscribe to the email you never look at. That's like an option if you always get an email from your build pipeline, but you never look at it. Yeah, maybe don't need to get those emails even. Again, low hanging fruits exists. But yeah, storage mediums are not energy intensive if they're cold.

Anne Currie: Yes. Yeah. Don't keep it hot when it can be cold. Cold is the new hot. It's the new hot.

Sara Bergman: and the new green.

Anne Currie: And the new green, yeah. Right, so yeah, we've talked through all of the things. So is there anything else you want to say, coming out of what we've talked about today?

Sara Bergman: Oh, I don't think so. I think it was an interesting discussion. We are, what I love about this industry is that we're constantly on our learning curve. New things always come up, we have to adapt and adjust, and I get to put on my engineering thinking hat. And I love that. I think, we should be excited about that.

Like we have opportunities to be green, "ooh, I have opportunities to learn, opportunities to explore," not like, "ooh, this dreadful thing I have to go do." It can be fun. I mean, yeah, make it glamorous.

Anne Currie: Well, on that incredibly positive note, I will, sign us off. That's fantastic. So before I sign off today, just a reminder, all the links are in the show notes, so have a read through the articles yourself. And to tell you that this week, it is the Green Software Foundation Global Summit event.

So there are live events in quite a few cities. There's one on the first in London. There are ones in Munich and Hamburg, and there's one in Dublin, there's Berlin and Singapore. And, do feel free, I can't actually make, it's a real shame because I do really love to meet people in person.

And I think flying is one of those things where we, flying is an incredibly valuable thing. I don't tell people not to fly, but for me, the reason to not fly all the way over to London if you're in the US, for example, to go and meet people in person is, that you should set up your own local meetups, which are in person, which people can get to and from.

And because having people locally that you can talk with is incredibly useful. So set up your own summits if you can't make any of the summits that are out there.

Sara Bergman: Or your own meetup, if a summit seems a bit much, then set up your own GSF meetup.

Anne Currie: Yeah, that is an excellent idea. You'll meet a load of lovely, like-minded people, and you can chat away about saving the world, which is always a good thing to chat about. So one of the nice things about the green community is we're all aligned on trying to make positive change without turning everything off and going back to the stage.

So we've come to the end of our podcast and oh, it looks like Chris, our editor, has given me a final fun question for you, which is, if you were to design an AI that had zero environmental mental impact for a totally non-serious purpose, what would it do?

Sara Bergman: I'm embarrassed to say that I thought so much about this, because my mind went, ooh, what would I do? And then I'm like, would I optimize my life? Would I make something silly? Would I make something useful? And then I just, there were so many options that I don't know. But some of the things that like, popped up was like, organize all my photos.

I have a small child. I take a lot of photos of him. I would like for there to be some nice organization going on there. That would be very nice. Also, an AI that designed cute nails that I could do at home with like the stuff I have. Because I am not artsy, but I like fun nails. So yeah, like, like, if I am bad at doing my nails and I have four colors, what can I do that's cute?

Anne Currie: And of course, all of those things could be done zero impact because the, key thing about them is that they are not latency insensitive. You could say, well, actually, I'll wait to run my models and do my inference till the sun's shining and the wind's blowing and there's excess electricity on the grid.

The, I don't, I would, to make it so it's there, I wouldn't say, well, it's in the middle of the night and it's still night, but I'm really desperate to get my fancy nails done.

Sara Bergman: Yeah. 

Anne Currie: Just wait. Just say, well, I'll, find out tomorrow morning what the AI is storming. It's all about making things less on demand, so we align with when the sun's shining and the wind's blowing. 

Sara Bergman: Thanks for taking me back to technical.

Anne Currie: So thank you very much for being on. It's been, as always, delightful to talk to you.

Sara Bergman: Likewise. I had a blast. Thank you so much.

Anne Currie: It's nice. And we don't say, obviously, while we're writing the book, we talked together all the time, but now that the book's finished, then we talk together less often. So for all listeners today, links are in the show notes. And if you haven't read Building Green Software from O'Reilly, you are missing a trick because that really, it's a fun read.

Everybody seems to be enjoying it.

Sara Bergman: Yeah. I think the top thing people are like, "Oh, it's fun." I'm like, "Yeah, it's funny. It's good."

Anne Currie: So if you haven't read it, crazy! Go out and read it, or join one of my training courses, which are also fun. So thank you very much. And I'm sure we will both be back on the Environment Variables sometime in the not-too-distant future. So goodbye from me. 

Sara Bergman: Bye. 

Anne Currie: Cheerio.

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To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode!

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]]> This Week in Green Software, the affable Anne Currie is joined by Sara Bergman, Senior Software Engineer at Microsoft and co-author of Building Green Software. Together, they dive into the complexities of sustainable data in relation to AI and cloud computing. They explore the environmental impact of managing and storing vast quantities of data, and question the feasibility of making these processes more eco-friendly. The discussion touches on cloud providers' carbon reporting, the importance of using AI responsibly, and how businesses can optimize their cloud use to minimize their environmental footprint. Tune in for an insightful conversation on balancing technological advancements with sustainability in the age of AI.

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TRANSCRIPT BELOW:

Sara Bergman: What data do we need to take in order to take meaningful action? Like, what is the level that, of course, yeah, if I could get minute by minute, like there's tons of stuff we could do and correlations we could draw, but what is the level of data that we would need to start taking meaningful action? And I think that could unlock a lot of good things.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne Currie: Hello, and my name is Anne and welcome to 

The Week in Green Software. So this week, you won't be hearing the usual dulcet tones of Chris Adams. I'll be joining you, Anne Currie, and we'll be delving into the tricky, but interesting world of sustainable data, whether it's possible to store and manage huge quantities of data, which we will need now, particularly for AI, in a way that's eco friendly.

Or is that impossible? Well, look, I'm going to leap through to the end and say, we have to do it. And therefore it is not impossible. It just has to be done. We'll find a way of doing it. And there are ways of doing it, which we'll be talking about today. we'll also be exploring why big cloud providers' carbon reporting isn't really telling us the full story.

Or, well, we are not interpreting it in the way that it was designed to be interpreted. And we need to be more careful about that. We're reading more into it than is true. And we need to be much, much more explicit about what the carbon reports from people like AWS and Azure actually mean.

We'll also be talking about data centers and AI. And that's something that my guest today is an expert in. And talking about my guest, joining me today is my co-author on Building Green Software, the book on what we need to do to make the tech industry green, and also Environment Variables regular, the lovely Sara Bergman.

So Sara, do you want to introduce yourself?

Sara Bergman: Yeah, hi. Thanks for having me on again. Always lovely to be here. I'm so excited to have a chat with you, Anne. Yeah, my name is Sara Bergman. I am a senior software engineer at Watttime. Microsoft, author of Building Green Software. And like here, I'm always asked, what have you been up to recently? Nowadays, I'm like, what have I been up to recently?

I did a fun thing, though. I had the talk for the Norwegian, because I live in Norway, and the Norwegian tax authorities about green software. And that was really fun. I love it when Because they are actually very far ahead in their journey, they're one of the most innovative companies when it comes to IT, so it was really fun to come out and have a chat with them.

Anne Currie: That's great. And I should introduce myself as well. My name is Anne Currie and I am co-author of O'Reilly's new Building Green Software along with Sara and our other co-author, Sara Hsu. And I also do a lot of training. So I've been busy at the moment doing loads of training courses. So, workshops on building green software and also an experts training course, which is all quite good.

So, if you want to get involved in any of that stuff, you can follow me on LinkedIn. So, as usual, today, we will be talking about a couple of interesting articles, publications that have come out over the past week around green software, all things green, and as usual, all the links to the articles will be in the show notes, so you'll be able to read them yourselves afterwards.

But I'll give you a little bit of a summary about what they say. So the first article we'll be talking about today is from the Green Web Foundation. And it was written by our normal Environment Variables host, Chris Adams. So that's where he is, or that's what he's been up to today in his work at the Green Web Foundation.

And he wrote it with his colleague, Hannah Smith. And the report is all around AI's environmental impacts. And it's got some interesting figures in there. Basically, AI uses a load of electricity and at the moment, as we don't yet have a completely green grid, that means that a lot more CO2 is being emitted into the atmosphere as a result of the fact that we're training a lot of models, doing a lot of inference.

So it is an interesting report and it's, I'm going to kind of summarize, they have some actionable things, some questions for you to ask yourself at the end of the report. So I will go over those now and then Sara and I can discuss them. The first is that you should always question your use of AI.

That's kind of part of using the right tool for the job. Is AI the right tool for what you're doing? Is it overkill? Could you use a spreadsheet? That's If you are using AI and you decide it is the right tool for the job, are you using it properly? Are you using it well? Are you using the right AI tools for the job?

And the third is to try and get ideas of your footprint, of the footprints of the work that you're doing with the AI, so you actually have an idea about what impact it is having and what you're going to need to do about it in the future. So Sara, this is kind of your area. Did you enjoy the report? What did you think?

Sara Bergman: I did. It's a very long report. It's very well written. Obviously, I mean, with Chris and Hannah, you're going to get something that's well written, of course. So no surprises there. No, it's good. And I think also for people who are maybe newer to the field of green software and green AI in general, there was a lot of good background to like really help understand the intricacies of this area. And something that I particularly find interesting in the shift we are now is that they talk about different phases where your emissions kind of stem from, it's like manufacturing, training and inference. And now, like you said, we talk a lot about inferencing, like using AI, that the use phase is what we talk about.

But back when I started, sounds like I'm really old, only four years ago, not that long ago, but when I first started talking about green AI. Yeah, a lot of, a few people, not so many were talking about green AI. A lot of people were researching, but not so many people were like discussing it. And then it was a lot of focus on the training.

There was a lot of great research being done on how to minimize the impact on training. I think in the research community, that's maybe the easiest, not the easiest thing, but a good first thing to research, right? And now we're seeing more focus on. on the production side, not like inferencing. And I think that shift has been very interesting to follow.

Anne Currie: But yeah, it is fascinating, isn't it? Cause there's loads, and we've had this conversation offline, because obviously we a book together and therefore we talk together quite a lot. But we've had this conversation a lot in that it feels like there's a load of stuff to be learned about inference.

So how you actually get the answers back as a user for models from the world of things like CDNs, how do you get fast answers and easy answers to things all over the world from data that is not necessarily by default, wasn't created close to the user who's querying it?

So yeah, there's, loads of prior art there to learn from it. It's a really interesting field.

Sara Bergman: It is a very interesting field and I think an additional like spiciness of this is that now a lot of people equate AI to large language models. That's not all of the AI that exists in the world, it's just the right now the most popular and what we think maybe will be the most interesting use case, or for like productivity and stuff.

And for that we require a lot of specialized hardware, like GPUs or TPUs on CPUs, et cetera, which is another thing to kind of reason over. And, maybe if you're very good at handling your production workloads on a CPU, like this might be a new area for you where you, we need to learn as an industry. I thought that was an interesting part of the article.

Anne Currie: Yes. Yeah, it is. It is all very interesting, isn't it? it's like something that all three of us say in the book a lot, is that there's a load of clever stuff going on in the tech industry, and it's usually, there's a desire to make things efficient because it's cheaper. Otherwise, everything gets very expensive.

So if you use things as they were intended to be used and use the right tools for the job, usually that's significantly more energy efficient and therefore greener. And the same is very much true of AI, isn't it? If you use the right tools, the right hardware, the right models,

Sara Bergman: Yeah, exactly. Exactly. So that and then I think that ties into right, that was it number two on their like, use AI responsibly, which was interesting. I also like the first question that they had. Now I'm scrolling and losing it was it think about when you should use AI? Was that the phrasing?

Yeah, question your use, know when to use it. Like, do you have a favorite use case?

Anne Currie: Well, I have to say, I do love generative AI. 

Sara Bergman: Yeah. 

Anne Currie: it's, an interesting one, cause quite often... well, it's amazing how often it comes up where people think that it's kind of like you're either doing AI or you're green. That's it. Those are the two, two options. And they cannot be the two options.

It can't be either don't do AI and therefore it's green or do AI and therefore it's not green. They have be brought together because if there's one thing that we know, two things we know are coming in the future for humanity. One is climate change. The other is AI and use of AI and AI systems.

They cannot be mutually exclusive. They have to be brought together. I mean, and AI is just computing. It's the same kind of things that we do when we talk about in Building Green Software for all computing just needs to be applied to AI. It's not a, it's not a new thing on its own, but

Sara Bergman: and I agree. And I think that's also so interesting with this, like, larger discourse, as you say, I think not only that it exists in other areas of life, where there are some people who would be like, "no, we should just stop doing this, like, stop advancing technology, and then everything will solve itself," but that doesn't work. We cannot and does not want to go back to like a farmer society where everyone grows your own food. Like if you're into growing your own food, that's fantastic. I'm happy for you. I grow radishes on my balcony and I enjoy that thoroughly. But there's so much advantage and good for humanity and our planet that comes from technology as well.

So we need to do both. We must do both. Like, it's non negotiable. So it's more like how and when? And 'when' is hopefully starting now.

Anne Currie: Cause the trouble is, if you have that conversation, if you say, "Oh, well, I want to do AI, therefore I can't do green." People will choose AI because the businesses, if they don't, if they don't try these new tools and services as they come available, they will go out of business. So, if you say, "Oh, well, if you're going to be green, you can't use AI," then what you're telling people is don't be green.

And it's crazy. You're just making the wrong arguments. But anyway.

Sara Bergman: I fully agree for sure. And I think it was something we were in a different, or like this podcast together, but another episode and you said something that I've been carrying with me for so long. Sometimes people say something and it just get like something clicks. And what you said, and I'm going to maybe paraphrase, but things that are limited are sort of less concerning. Like, for example, if you build a super efficient washing machine, I still have only so much washing up to do. There is an end to how much I will wash it. Like this is in response to Jevons paradox, of course. And the same goes for this. Like, yeah, AI will make us more efficient, more productive.

Okay. But we'll then just do more. It's like, yeah, but the working day is sort of eight hours and we are only so many people on the planet. There is a limit to when we'll be done, whereas for crypto, for example, which always comes up when we talk about this, of course, there's no limit. I will never say I have enough money, I'm done mining. But there is a limit where I say, thank you, co-pilot for GitHub, I don't need more code suggestions.

I'm happy with my feature now, sort of.

Anne Currie: Oh, yeah. That's an issue, because, I always tend to think that one of the, one of the things about AI is that, that we're not,, we haven't yet reached the limits of what we want to do with it. But, you're right. There's, it's nowhere near. I mean, we've already come up with the thing that is the most limitless.

You've heard the use of CPU and it's CPU use that's really the problem that's, that uses a lot of electricity, is crypto. It is literally boundless how much we might want to do with it. So we've kind of already created the worst 30.

Sara Bergman: yes.

Anne Currie: AI is so

Sara Bergman: not it.

Anne Currie: it's a comparison. At least there's some people, 

I don't know, we shouldn't be controversial because there are a whole load of people who say, well, there's loads of benefits of cryptos, but I personally do not, I'm not a crypto bro.

Sara Bergman: Same. No, I did write my master's thesis on blockchain. I think blockchain is a cool invention. Like, it's a cool technology. And there are use cases that are, of course, bounded. And then there are use cases that are unbounded. And I think people can use their own head to figure out what's what.

Anne Currie: We've now got ourselves into enough trouble talking about good and bad uses of technology. And to a certain extent, it's very hard to say what's a good or bad use of technology. I tend in my head to think it was a bad use of technology, I, not everyone agrees with me, but we will move on to the next article, which is one, now...

So this one, cloud provider Greenhouse Gas reporting isn't enough. The case for product level accountability. Which is, again, the link is included in the show notes. Now I know, because this talks about Azure, Sara, you, might be in a position where I'm saying things to you and you have to say, you may say that "Anne, but I cannot possibly comment."

So in which case you'll just have to leave me to talk, but that's fine. Cause I can talk endlessly about this stuff because I find it really, I think this is absolutely fascinating and there's loads of stuff to learn from this article. So the article is all about, it's highlighting the limits of cloud providers and data, the carbon footprint reports.

And it emphasizes the need for companies to track emissions at product, at data product level. And it argues that these broader reports mask the true environmental costs of specific processes like data and that things would be better if you broke the emissions down to product level. And I think that's great, but I don't think that's actually the problem.

I think it's well worth reading the article, but I think it skips over, I think, what is the more significant problem that we're having, that we're seeing, because I'm out there talking to people a lot and I know this is a significant problem at the moment, that the cloud providers' reports are doing something, a lot of the new cloud providers' reports, particularly the recent AWS releases are doing something which I can't really shout at them for because I demanded it myself some years ago and I am somewhat hoist by my own petard for this.

So many years ago, me and a fellow, a collaborator called Paul Johnston ran a, back in 2018, we ran a campaign called Sustainable Servers by 2024. And what we were campaigning for was that all of the cloud providers would commit, and this was some time ago, would commit to being carbon neutral by 2024.

And it was really aimed entirely at AWS because Google and Azure were already carbon neutral at that point and AWS was not. So, we ran this big campaign and we had petitions and all kinds of things for saying that they should be carbon neutral, collateral and tradeful. And of course, carbon neutrality is quite a limited demand.

It's basically saying, "I want you to be carbon neutral. I want you to know how much your carbon emissions are. And then buy offsets that match the carbon emissions." Now, we all know that offsets are good in the past, but they're not time matched. They don't mean that the carbon savings that you've created are time matched with the carbon emissions of your systems.

So your systems can still be throwing off carbon dioxide into the atmosphere whilst you are carbon neutral. So it was a useful, a useful measure in 2018, it was still useful. Now in 2024, it's actually not as good as it could, it's not enough. it's the bare bones. It's the least we can ask for.

But AWS have done it on time, 2024, and they are producing these reports that they're giving to AWS customers that say, "you're carbon neutral, your systems are net, not producing any carbon." But the problem is that a report that says, and that's great, but it is nowhere near enough.

It's not enough. It's great information for your finance team because your finance team don't want that data. They need that data, particularly if you're going to be, if you're in the EU or you're selling into the EU. Because the EU is now demanding carbon taxes, and carbon taxes are another form of offsetting, really.

You pay for the carbon you're emitting into the atmosphere. So if AWS produced a report saying, look, there's no net carbon you're producing into the atmosphere at the moment, which they are generally at the moment for most AWS systems, it seems. That is really a report for your finance team, so they don't have to, so they can go, "oh, that's great.

The offsetting has already been done for me." That is not a report for your tech team because they're not saying that no carbon is actually being produced by your systems. Your systems are still producing loads of carbon and you still need to adjust your systems. to reduce carbon, which you can do, but the report is kind of giving you the impression that you don't need to.

So is that something you can comment on or?

Sara Bergman: Yeah, this gets me going, right? Cause I'm all about enabling like people with feet on the ground, hands on the code is what I say. So I do agree a lot of the reporting that comes out, it's much better than it used to be. This difference, and maybe now we're slipping into the next paper a little bit, but there, of course, difference between market-based and location-based reporting in the Greenhouse Gas Protocol.

That is a feature, a fundamental feature of the Greenhouse Gas Protocol, which every single industry makes use of. Now, not only, 

Anne Currie: I'm going to interrupt you at this point, just because this is something that I realized when talking to people. One of the issues is that people don't understand the difference between market-based and location-based. What does that mean? So market-based, I think that the word that people really understand is offset.

If you're market-based, it's saying you're still putting carbon into the atmosphere, but we are offsetting it, which was great 10 years ago, nowhere near good enough nowadays. location-based means actually, it's about reducing, it's about not putting carbon into the atmosphere. So that's what we want. Market offsets are a step to getting there, but they are not there.

So sorry, go on.

Sara Bergman: yeah, no, but that's, good. I also like it's like the electrons you pay for versus the electrons you use, if you want to get really down to the socket level. So yeah, I think that's something to talk about. Also, something that I kept thinking about after reading this article, and I just want to read the comment from it because I think the comment was amazing.

And the comment is, "conceptually, I agree. More data is better. However, I've never met anyone advocating for product level data who has actual operational experiences of running shared service platforms and therefore would understand the complexity of delivering these metrics. Because yes, if we're talking about, we could have one report for the finance team, that's fine, but should there be an additional one for, the people who write the code, who maintain the service? And then the question becomes, okay, but what data do we need to take in order to take meaningful action? Like, what is the level that, of course, yeah, if I could get minute by minute, like, there's tons of stuff we could do, correlations we could draw, but what is the level of data that We would need to start taking meaningful action? And I think that could unlock a lot of good things.

Maybe we don't need the world. Do you know what I mean?

Anne Currie: Yeah. I know what you mean. I totally know what you mean. I think we should step down our emissions. Well, having said that, within the Green Software Foundation, there is a project, the Real Time Metrics Project. And that is looking forward to when we can have second, millisecond by millisecond data about, so we can tune applications to, to get there.

But then we also have another project, which I run, so their real time metrics project is run by Adrian Cockcroft and I run a project called the Maturity Matrix Project, which, comes out of our book. So it's, the penultimate chapter in our book. And the Maturity Matrix Project is start, is, it says that all that real time stuff is really quite advanced.

It's way further advanced than pretty much anybody in the world currently is. What we actually need at the moment is quite simple stuff, like "just turn off machines." Turn off machines when you're not using them. And you can, you don't need real time metrics for that. The other thing that we can do that doesn't require real time metrics is, so one of the things that kind of annoys me about the new AWS Cloud Footprint report, which is, it's fine, it's not a lie, it's just giving you offsets.

It's just telling you what your offsets are. It's totally fine. But people are misusing it and misreading it as thinking it's about carbon emissions. There's another thing that AWS have said, which I really like, which is that, "look, we all do a load of work."

It's called the shared responsibility model. "We will take responsibility for the sustainability of the cloud, if you take responsibility for the sustainability in the cloud. Now" I like it, except that it's also very confusing. What they are saying is that "we will build tools that can be aligned with actual location-based zero carbon operations.

We will build those tools." Those tools in the book, we call them green platforms. They're things like serverless, spot instances, really clever instance types. You see, it kind of aligned with their whole modernization strategy, how you get into the cloud and use it well, as it was intended to be used.

So they're saying that, "but it's your responsibility to use those tools. We can build those tools, but if you don't use them, you won't be green. If you just sit in dedicated instances, you will, there's nothing we can do about it. You will never be green. We'll offset you," which is why, the reports say, "well, we'll pay for your bad behavior.

But it is still bad behavior." You'll get to carbon neutral, but you'll never get to carbon zero. And I think that those messages, which are quite complicated, can both exist, but they need to be quite clearly communicated. And at the moment, I don't think we are being so clearly communicated. What's your thoughts?

Sara Bergman: Yeah, this is also, I think this is also something I thought a lot about reading this article. Like, where is the line? Because many cloud providers, they do have clearly green ambition. They are financially incentivized in many ways to be more energy efficient, use less resources of course, because all of those things cost money for any cloud provider, even if that cloud provider is your local on-prem.

So that is one thing, they wanna make cheaper stuff that are often greener, almost always greener, but it is your responsibility to get on them. And how do you do that? Now, all three of the cloud providers do have architected frameworks that have a dedicated sustainability section.

And there for some scenarios, it's actually incredibly good. Like it's very detailed. So you can go and just like, "Oh, my scenario fits into this." I would like those sections to be longer for all of the big three and for the smaller ones, maybe to include more. But I also now maybe skipping randomly ahead, back and forth, something, a mental image that I think helps because sometimes I think the key message is getting lost.

"Okay. But the cloud is green. Why do I need to take action?" and that is a tricky conversation to have because Yeah, if you're selling the cloud, of course, you don't want to say "no, but it's not green." It's like, it just becomes a bit messy. So a mental model I like to use, which I heard from another wonderful woman here in Norway, which is if you have a car and you have a ski box, because right, we have lots of skis here.

So they can think of the car as the cloud and it's the responsibility of the car manufacturer to make that as efficient and green as possible. And they do, right? And but you, as a user, you choose how do I transport my skis in the car? Do I transport them inside the car? Yeah, maybe they that would be greener right because then there's no additional wind catch of the ski box on top, but maybe they don't fit, so you put them on the ski box on the roof. Fine, you accept that additional wind cost and thus increase the energy. But, once ski season is over, and you don't need the skis anymore, what does eeveryone do? You've remove the ski box. I don't see anyone riding around with the ski box in May, just because, right?

And the same goes for the cloud. So yeah, the cloud has lots of work, a lot of stuff in it to make it greener. But if you choose to use a ski box, that's fine. But once the ski season is over, remove the ski box. So once you're not using your test environment, remove it, shut it down.

Anne Currie: Yeah. Yeah. I like the analogy. It's very Scandinavian.

Sara Bergman: It's very Scandinavian, I'm sorry, but I am very Scandinavian, so that's what you get. I guess the same applies if you have a surfing board, I suppose you put them on the roof as well, 

or a canoe,

Anne Currie: A canoe!

Sara Bergman: yeah, or a kayak, yeah. They have longer seasons though, but, so yeah, to try to translate, I feel like I was very, poor Chris who has to edit this, all my ramblings.

But yeah, as a cloud user, you have a responsibility and I think there could be two reports of showing this is what we as a cloud provider, I think all cloud providers are pretty good at this. This is what we took responsibility for. That's awesome. So much better than it was like 10, 5 years ago.

But then also how do we enable people using the cloud to take green actions? Because they want to take green actions, we want them to take green actions, like how do we enable that at the same time? Yeah.

Anne Currie: Yeah. I mean, it's, kind of like "is the cloud green?" The trouble is the, answer is as always in tech, it depends. Oh yeah. It's like, it depends. Are you using it as it was intended to be used? And if you, and the, one of the reasons why you sometimes see Gartner reports and things saying that the cloud is really poorly used,

it's because there's two stages in moving into the cloud. One, is you just kind of lift and shift. For most people, they lift and shift. Although you might argue that's actually never a really good way to go into the cloud. I've written books about this. It's not, but it's, it is a way, a common way for people to go into the cloud.

But once you've lifted and shifted, you have to then actually use the cloud, as it were, you see, you have to go beyond that. If you stop at lift and shift, it's really ungreen. It's worse than being on prem', because in the cloud, it's so easy to over provision. So you have to actually, you have to adopt the cloud, it's called cloud native and it's not really cloud native, but then you have to adopt the cloud way of doing things because otherwise you will over provision and you will then make more carbon than you did on prem', because it's harder to over provision on prem'.

Sara Bergman: Yeah, And that's a journey. And that's something I think all cloud providers are pretty good at supporting, right? That's their bread and butter in many ways.

Anne Currie: It is, they just want people to do it, but still it's hard because it's really so hard to lift and shift into the cloud that once people get there, they go, "thank God for that." And they don't want to go and look at what happens next, which is kind of one of the issues of lift and shift as opposed to kind of just slicing up and moving bits and bits one by one into the cloud, is that

these big projects, they're so painful that once you've done them, you never want to, I've done so many big projects in the past, and you always think you're going to go on and do the next stage, but you're so destroyed by that project. You just think, "Oh my God, we're all burned out. We don't even want to think about it again."

It's, yeah. Yes.

Sara Bergman: We need to glamorize DevOps or operations. It needs to be something there, more and more afterworks, I don't know. Something to make them more glamorous.

Anne Currie: These big projects are awful. I never want to do one again. But anyway. Yeah. And they're, ungreen because you then go on to the next bit where you start to actually refine and improve. So yeah, it's, we need to step back and think about how we're doing that. So, which is, I think a lot, I was signing books at a conference about kind of, DevOps and, CICD and team topologies and moving faster releases, last week.

And I think that's really green because if you can't move, you can't adopt these better tools and services that remain.

Sara Bergman: Yeah, exactly. Yeah, I also said someone, or heard someone who said like, what we need now is like performance engineering, we need to go back to basics in many senses, right? And because a lot of these learnings are not new. It's the same when people are like, "ooh, if I," now I'm going on a tangent here, but if people want to be greener, they're like, "oh, I should just rewrite, like, make my code more efficient."

And like, by all means do. But if you haven't had like a reason to do that yet, I don't think you're going to convince your management chain that sustainability is going to be the reason why you go implement it because high performance computing is not new at all. Like we've known about all these things for a very long time.

So if you haven't done it yet, I mean, kudos to you if sustainability is the thing that makes you implement it. Kudos to you. I just think it's going to be a tough sell.

Anne Currie: I've mentioned a few times on environment variables, but it always interests me, is on that note of efficient code is expensive and we don't do it anymore, sara now works on Microsoft Exchange and 25 years ago, I also worked on Microsoft Exchange, but in Microsoft Exchange 25 years ago, we had to have everything written in C because the hardware was, it was just not possible to do it using anything other than the most optimized C code, which meant that it was really expensive to do things.

It took a long time. Yeah. The world has changed. we've got better hardware and we use it. We use it to go, to not have to write everything in C.

Sara Bergman: Exactly. And we see the same on the mobile industry, right? They have more apps that do more things now because their phones can handle it. They couldn't when the first smart modes came out and just, again, going back to what we talked about earlier, I don't think we can stop technology progressing in order to be greener.

We need it to progress and be green at the same time.

Anne Currie: Yeah. Yeah. We need it all. 

Sara Bergman: People are going to call us greedy.

Anne Currie: We want it all. So we're going to zip onto our final article link today, which is, 

Is Sustainable Data Storage a Paradox? So it's a piece in TechRadar by Jon Howes about environmental challenges posed by cloud storage and AI, and the rising energy demands of data centers that are associated with AI and storage. So thoughts? Is green data storage possible? Sarah?

Sara Bergman: To quote you, it has to be. No, but I, okay. So there are many things here, but I think also he said that cloud is the least wasteful storage solution because it is highly optimized, blah, blah, blah, blah. So I don't know if the paradox is. Could there be other ways? Or if the paradox is, I don't know, it was a catchy title, and I'm not sure I understood what the paradox was.

But also, what I really kept thinking about, is storage really what we should be concerned about here? I (naively, maybe) would guess that compute would be much more resource intensive than the actual, like, store at rest. Then, of course, training the model, you would need to access the storage.

But again, I would be, in these days, more concerned about inference. And then, again, naively maybe, I would expect the CPU and GPU usage to be what we're worried about, not our disks.

Anne Currie: Yeah, I tend to agree. I would say with data, the opportunity there is there's quite a lot of low hanging fruit. And it's a really, good example of the shared responsibility model as well, that it's up to you, to us as users to not be wasteful in our use of data. And by waste, I don't mean storing more than we need, because I think that telling people to throw the stuff away in the world of AI is just crazy talk.

No one's going to do it. You're going to keep everything, right? We just need to see if the general AI can make use of it. The wastage is having it in a medium that's where it can be accessed more quickly than it ever needs to be accessed. So that the longer, if you say, look, I'm going to access this once a year, you can stick it all on tape and it's practically free in terms of carbon.

You can save as long as you like, there's this, that kind of like never underestimate the bandwidth of a station wagon full of tapes driving down the freeway. Tapes are pretty good. You can put a load of stuff on tapes and most data, especially data for AI, it doesn't matter if it takes you a couple of hours or even a couple of days to get it back.

You can, so you don't need to keep it on sSD, where it's just really much more carbon intensive than tape. So it's just, it's quite easy. You just need to think about it and not store things in the wrong medium. Use the tools that are best suited to the job.

Sara Bergman: yeah, be diligent about hot, warm, and cold storage. Like, what do you need, when, and how much? And yeah, also people say, "should I delete all my photos and emails?" I'm like, "no, you want them, right?" You're going to look at your photos again, you're going to maybe search or read your emails again. So don't remove them.

Maybe you know, unsubscribe to the email you never look at. That's like an option if you always get an email from your build pipeline, but you never look at it. Yeah, maybe don't need to get those emails even. Again, low hanging fruits exists. But yeah, storage mediums are not energy intensive if they're cold.

Anne Currie: Yes. Yeah. Don't keep it hot when it can be cold. Cold is the new hot. It's the new hot.

Sara Bergman: and the new green.

Anne Currie: And the new green, yeah. Right, so yeah, we've talked through all of the things. So is there anything else you want to say, coming out of what we've talked about today?

Sara Bergman: Oh, I don't think so. I think it was an interesting discussion. We are, what I love about this industry is that we're constantly on our learning curve. New things always come up, we have to adapt and adjust, and I get to put on my engineering thinking hat. And I love that. I think, we should be excited about that.

Like we have opportunities to be green, "ooh, I have opportunities to learn, opportunities to explore," not like, "ooh, this dreadful thing I have to go do." It can be fun. I mean, yeah, make it glamorous.

Anne Currie: Well, on that incredibly positive note, I will, sign us off. That's fantastic. So before I sign off today, just a reminder, all the links are in the show notes, so have a read through the articles yourself. And to tell you that this week, it is the Green Software Foundation Global Summit event.

So there are live events in quite a few cities. There's one on the first in London. There are ones in Munich and Hamburg, and there's one in Dublin, there's Berlin and Singapore. And, do feel free, I can't actually make, it's a real shame because I do really love to meet people in person.

And I think flying is one of those things where we, flying is an incredibly valuable thing. I don't tell people not to fly, but for me, the reason to not fly all the way over to London if you're in the US, for example, to go and meet people in person is, that you should set up your own local meetups, which are in person, which people can get to and from.

And because having people locally that you can talk with is incredibly useful. So set up your own summits if you can't make any of the summits that are out there.

Sara Bergman: Or your own meetup, if a summit seems a bit much, then set up your own GSF meetup.

Anne Currie: Yeah, that is an excellent idea. You'll meet a load of lovely, like-minded people, and you can chat away about saving the world, which is always a good thing to chat about. So one of the nice things about the green community is we're all aligned on trying to make positive change without turning everything off and going back to the stage.

So we've come to the end of our podcast and oh, it looks like Chris, our editor, has given me a final fun question for you, which is, if you were to design an AI that had zero environmental mental impact for a totally non-serious purpose, what would it do?

Sara Bergman: I'm embarrassed to say that I thought so much about this, because my mind went, ooh, what would I do? And then I'm like, would I optimize my life? Would I make something silly? Would I make something useful? And then I just, there were so many options that I don't know. But some of the things that like, popped up was like, organize all my photos.

I have a small child. I take a lot of photos of him. I would like for there to be some nice organization going on there. That would be very nice. Also, an AI that designed cute nails that I could do at home with like the stuff I have. Because I am not artsy, but I like fun nails. So yeah, like, like, if I am bad at doing my nails and I have four colors, what can I do that's cute?

Anne Currie: And of course, all of those things could be done zero impact because the, key thing about them is that they are not latency insensitive. You could say, well, actually, I'll wait to run my models and do my inference till the sun's shining and the wind's blowing and there's excess electricity on the grid.

The, I don't, I would, to make it so it's there, I wouldn't say, well, it's in the middle of the night and it's still night, but I'm really desperate to get my fancy nails done.

Sara Bergman: Yeah. 

Anne Currie: Just wait. Just say, well, I'll, find out tomorrow morning what the AI is storming. It's all about making things less on demand, so we align with when the sun's shining and the wind's blowing. 

Sara Bergman: Thanks for taking me back to technical.

Anne Currie: So thank you very much for being on. It's been, as always, delightful to talk to you.

Sara Bergman: Likewise. I had a blast. Thank you so much.

Anne Currie: It's nice. And we don't say, obviously, while we're writing the book, we talked together all the time, but now that the book's finished, then we talk together less often. So for all listeners today, links are in the show notes. And if you haven't read Building Green Software from O'Reilly, you are missing a trick because that really, it's a fun read.

Everybody seems to be enjoying it.

Sara Bergman: Yeah. I think the top thing people are like, "Oh, it's fun." I'm like, "Yeah, it's funny. It's good."

Anne Currie: So if you haven't read it, crazy! Go out and read it, or join one of my training courses, which are also fun. So thank you very much. And I'm sure we will both be back on the Environment Variables sometime in the not-too-distant future. So goodbye from me. 

Sara Bergman: Bye. 

Anne Currie: Cheerio.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode!

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]]> Electricity Maps Electricity Maps Thu, 26 Sep 2024 07:00:00 GMT 50:14 m0j22lx0 false https://podcasts.castplus.fm/e/183mmp98-electricity-maps 68dc7d116d92c33f9c88f685 68dc7cef46a2532cdd8d1c23 Host Chris Adams sits down with CEO Olivier Corradi and Tech Lead Íngrid Munné Collado of Electricity Maps, a company that leverages data to enable decarbonization of electricity grids. They discuss the complexities of carbon intensity data, the role of accurate forecasting in renewable energy, and how this data helps optimize electricity usage for sustainability. Olivier explains the origins of Electricity Maps, their goal of providing real-time carbon footprint insights, and their theory of change—targeting citizens, corporations, and institutions to create a greener future. Tune in to hear about the history, frontiers, and future of this engaging field. 83 Host Chris Adams sits down with CEO Olivier Corradi and Tech Lead Íngrid Munné Collado of Electricity Maps, a company that leverages data to enable decarbonization of electricity grids. They discuss the complexities of carbon intensity data, the role of accurate forecasting in renewable energy, and how this data helps optimize electricity usage for sustainability. Olivier explains the origins of Electricity Maps, their goal of providing real-time carbon footprint insights, and their theory of change—targeting citizens, corporations, and institutions to create a greener future. Tune in to hear about the history, frontiers, and future of this engaging field.

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TRANSCRIPT BELOW:

Olivier Corradi: The dream is to get to street-level granularity, and this is why flow tracing, by the way, is so important, because as you increase the resolution of what you're looking at, then obviously if I'm looking at just a city, most of the electricity is actually produced outside of the city, and that's also why, by the way, the whole forecasting challenge has just become massive. Try to forecast how clean the electricity is going to be at every location on the planet. Obviously, you need really good renewable forecasts as well. And that's what civilization needs if it wants to rely significantly on these renewables that are intermittent.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. We often talk about carbon intensity, or how clean electricity is on this podcast, as one of the levers for making software more sustainable.

To know this, you need to get the data from somewhere, in a form that's easy to understand and consume. So today we're sitting down with two people who live and breathe this data from Electricity Maps. Electricity Maps is a company at the forefront of enabling a data-driven decarbonization of electricity and today we're joined by both Íngrid and Olivier from the firm.

Hey folks, how's it going?

Íngrid Munné Collado: Hi.

Olivier Corradi: Hi, Chris.

Chris Adams: Cool. Folks, we'll talk about the origins of Electricity Maps in a second, but before I do, I just want to give you a bit of space to introduce the two of you, actually. We normally go by surname first, so I think Olivier Corradi, you're ahead of Íngrid, so can I give you some space to introduce yourself first, and then we'll do the same thing for Íngrid, alright?

Olivier Corradi: Of course, and thank you so much for the invitation and for inviting us to the show. So, my name is Olivier, I'm originally a machine learning engineer and data scientist. My background has been academic, I've worked as well in the research industry with IBM Research and Simulating Electricity Grids. And I would say the most relevant thing that I'm doing now is Electricity Maps that I founded eight years ago.

Chris Adams: Okay, thanks for that, Olivier. And Íngrid, over to you, I suppose.

Íngrid Munné Collado: Hi. Hi, Chris. I'm Íngrid. I'm the Tech Lead at Electricity Maps and I'm very happy to be here. More specifically, I'm part of the grid forecast team where we built, as we have said, forecast models for renewable energy sources and other signals such as carbon intensity. I've been recently involved in replacing and improving the weather forecast data that we use at Electricity Maps to achieve better forecasts.

And yeah, outside of work, when I'm not working, I'm either going to CrossFit where I'm trying to learn how to do a pull up, which I don't know, or knitting and just making sweaters. So quite, quite contrast.

Chris Adams: Cool, thank you for telling me. I'm glad you mentioned weather, actually. I mean, as an English person, we talk about weather the same way that we breathe, I suppose, and as I understand it, you folks are both calling from Denmark, right? We're a little bit chilly today than it otherwise would be, right?

Íngrid Munné Collado: Yeah, it is quite cold for September. I think it's quite usual, but it would be a bit on the colder side.

Chris Adams: Yeah, same here. We, it's, Berlin has, someone has switched off the summer, so we now had to, like, I've cycled in with my coat for the first time today, and did not enjoy that. Okay, so I should introduce myself, actually, folks, if you're new to this podcast. So my name is Chris. I work at the Green Web Foundation as the executive director there.

I also work in the Green Software Foundation as one of the co chairs of the policy working group where we Basically, work on policy to see about coming laws and things that we might actually want to respond to or help members understand what the implications of might actually be. The other thing I'll share with you is that we try our best to have quite helpful show notes for this.

So we're going to mention various projects and papers along, and if you are viewing this in Spotify or YouTube podcasts or something, you might not see them. So be sure to look up podcast. greensoftware.foundation to see the full links and the transcript. Okay, you two folks sitting comfortably?

Íngrid Munné Collado: Yeah.

Olivier Corradi: We are, sure.

Chris Adams: Brilliant, okay, then I'll begin.

Okay, first question I'll put to you, Olivier, because I've actually been a fan of Electricity Maps for quite a while, and when I first heard about Electricity Maps back in 2017, it wasn't Electricity Maps, it was electricitymap.org. And this is something that is reserved for non profit organizations. So it's 2024 now.

And I now know that the same kind of cool map that I started talking about at conferences and so on, is now available under electricitymaps.com. And as someone working in a non profit, I end up talking and thinking about theories of change all the time. So there was a really interesting post I saw on the blog, on the Electricity Maps blog post, particularly talking about this theory of inaction.

And it's rare to see start ups talk about this kind of stuff with a fluency, really. So, Olivier, can you tell me a little bit about basically, what this is, and how this informs you spending all the time on Electricity Maps, because I know there's a few other things that you've worked on before.

Olivier Corradi: Yeah, I'm happy that you actually give me the opportunity to articulate this. So in the early days when Electricity Map was started in 2016, there was this question that remained for a couple of years afterwards, which was, "should we actually be an NGO or should it be a for profit company?" And honestly, the conclusion is that it depends on how you want to impact the world and it depends on how you want to be funded and so on.

But the way that we've been looking at things is basically to say when a company will pay you for something you're delivering, that represents a change that is happening in the world that is sufficiently valuable that someone will pay you to do it. And people are taking you seriously when they're implementing it.

So this was a little bit what tipped it over towards more of the, let's say, for profit world. But in order to make sure that we never lost track of the actual impact we want to have, we started having like this framework that we stole from someone else, honestly, and adapted afterwards. But first of all the vision we have here is to imagine a world where we have low carbon electricity that's delivered everywhere across the world, every hour of the year.

And this was really why Electricity Maps was created, out of almost a frustration of folks looking at the electricity grid from a yearly perspective, instead of looking at it hour by hour. And of course it made sense before we had renewables. Now that we, the wind doesn't always blow and the sun doesn't always shine, we need that.

And the angle we're taking here is data-driven decarbonization, as you mentioned, because we're a mixture of folks who come from academia, who know how to build machine learning systems and tech. So this is our angle. So with that vision in mind, providing clean electricity every hour of the day, we realized there's three pressure points that we need to apply if we want to create that transformation.

The first one is, we identify them as citizens. Second one, corporations. And the third one, institutions. And there is this framework that, that depicts a triangle of inaction where each of these corners are pointing at the other as an excuse to not change. For example, citizens are saying, "well I actually would love to be greener, but the government is just not putting the right incentives for me to be cleaner," like the public infrastructure is not helping. Another piece of it is the citizens are pointing at corporations and saying, "well, I, too bad, like, flying is actually cheaper than taking the train. Like, I don't have a clean offering here."

And then the corporations on the other side will say, "well, but everyone wants to fly, right? I mean, the citizens are not ready for that change." They'll say, "well, I'll just keep operating like I am." Corporations will point at institutions as well and say, "well, the right tax schemes are not here for us to make flying cheaper, actually, so we'll just continue what we're doing."

And then institutions will be, "we don't have" like the, let's say, "social acceptance from the citizens to actually, exactly, to put these green tariffs. And on the other hand we're just a small organization and institution, sorry," and you have corporations like Microsoft, OpenAI, Google, that are just like, more powerful than governments.

And so you can basically map these out in a small triangle. And we said, okay, so how are we going to impact this? And then most of our initiatives can actually be mapped to this triangle, where on the citizen side, the app that we have of Electricity Maps is creating this awareness, creating the debates as well to make sure we have a factual understanding and can challenge our politicians in the right way.

There's the piece on corporations where we are working with them in order to ensure that green offerings can actually hit the ground, like that we have electric vehicles that can use electricity at the optimal time, all these things. And it's a win because we get a financial cut of this and we can grow our company and grow the impact.

And then finally, on the institutional side of things, we're trying to make sure that the right carbon accounting methodologies and so on are being pushed. We basically want to live in a world where we have an accounting that represents what's physically happening in the world. And one example is we have a data portal where our historical data can be used by any company who wants to do granular carbon accounting.

Again, moving away from this world where you're doing it on a yearly basis to an hourly basis. So this is the framework we've put in place and the way we're articulating internally as well how we allocate resources and prioritize and make sure that what we do leads to impact ultimately.

Chris Adams: Cool. Thank you for that. I have one question following up from this, because when I was doing a bit of research, I looked up this triangle of inaction, and I think I might need one of your help, one of your help in pronouncing the person's name. Is Pierre, is it Peyretou?

Olivier Corradi: Not too bad. Yeah. Well done.

Chris Adams: Okay, so for people who are listening, what we'll do is we'll share a link to some of the theory behind it.

He's actually got an online course. I think there is, there's a French school of business which does actually have some online resources to understand this kind of theory. So if you're Curious and you want to think about, okay, how does this work? Or if you'd like to see an actual triangle rather than have us describe it, follow the link and you'll see some of it there.

Okay, thanks for that, Olivier. Okay, so we've spoken a little bit about data, and you mentioned about data being in the open, Olivier, and I can actually confirm that when we spoke to Toby before, Toby is one, sorry, Tony is one of the colleagues at Literacy Maps. We did an interview with him around about November last year, actually, and he was talking all about this, Open Data Portal.

And back then we were like, "oh, sweet, there's all this data being published." And I'll be honest, you're a startup. I was like, "are they really going to publish the next year?" And then January came around and it actually did get published. I was like, "wow, this almost never happens." So I was really pleased to actually see a startup and a company follow through with making some of this data available because it's so, so, so useful and this is so hard to find otherwise.

I can speak as someone in a non profit who's been trying to find this data. It's so, so handy to have some of that available now. So yeah, thank you on that one. Okay, so the question, I'll leave some space because I think there was a response coming up there actually, Olivier.

Olivier Corradi: Oh, thank you so much for the shout out. You know, it's important for us as well to feel like what we're doing leads to direct impact and that we have folks that are demanding this data. And sometimes these feedback loops are not always present. So I appreciate you giving us the shout out.

Chris Adams: Yeah, so if anyone is listening, this is open data, so it's something that you're able to build on and we'll share a link to the actual website that makes it very clear how you can use this information. So if you're trying to build something and you're looking at historical stuff, it's totally there.

Have at it. Okay, so we spoke a little bit about carbon intensity at different parts of the world and how you need to think about it on an hourly basis rather than just an annual basis. And we might talk about why when you talk about green energy saying green energy with certificates that came from solar and then saying, using that to make usage at night be counted as green might be conceptually a little bit challenging, we might say.

So that's some of the stuff we spoke about. Now, the thing I want to ask a little bit about, and Íngrid, if I can hand this over to you. When you're working as an engineer and you start thinking about carbon intensity. It's when you use, say, APIs, you just see a single number that goes up and down.

And like, it's fun. And it's very easy to underestimate just how much work can go into this and all the complexities around this. Because as I understand it, it's more than just like, looking at what the power will look at looking at generation from a single plant. Íngrid, can I just give you a chance to actually talk about what goes into sharing some of these current and historical figures? Because I know there's some forecasting work, and we'll talk about that later, but we've just spoken about open data and some of the historical stuff, and some of the context there might be useful for people who are considering downloading some of this or messing around with it themselves.

Íngrid Munné Collado: Yeah, absolutely. And I would say that without this amazing work of collecting data, any forecast, like forecast would not be possible at Electricity Maps because we need this data to be able to produce our forecast. So let's take a look at how we collect data. This is currently done by one of our teams at Electricity Maps, the Grid Modeling and Methodology team.

And our work starts by first trying to get as much real-time data and historical data from, that is available publicly. And here we're talking about governmental institutions, transmission system operators, that for people without electrical engineering background, those are the folks that manage the electricity grid at high voltage, and that make sure that demand and production is actually matches at every single second.

So we try to collect this data and now we have data for 228 zones. And when we think about it, we might say, okay, we just collect CSVs and everything is standardized and nice, but the data is really messy. So we might get data from like in PDF files, in TXT files. We have sometimes, I can tell you a bit of an experience.

When I joined Electricity Maps, I had to build the parsers for Japan. And I'm originally from Spain, so I know a lot about electrical system in, in Spain, but when I had to dig into the Japanese electrical system, I didn't know that they have eight different organizations that collect data and each and single one of them is in a different format.

And sometimes we even had to do some image recognition to get the power of the nuclear power plants that they have in Japan. So data is very messy. It contains outliers. We have missing values, wrong values. Some production modes might be missing as well, so there's a lot of work that we have to do behind the scenes to make sure that we collect this data every hour or even in lower resolutions to make sure that we can use this data and process it.

So once we have this data in the raw format, then we process it to make sure that we don't have these outliers or missing values. And that we have a complete power breakdown. That means that if Spain, for example, has nuclear, gas, coal, and like in total eight production modes, that the data we get has eight production modes because otherwise the carbon intensity values that we might show on the app or on the API, they are not going to make any sense.

So once we have this done, then we can actually go into maybe one of the core pipelines we have at Electricity Maps. That is the flow tracing pipeline. And Tony did an amazing job in the previous episode where he explained how this works. But for those who don't know, the flow tracing pipeline makes sure that we are able to trace back the origin of electricity.

Because if I plug my laptop here in Denmark, the electricity that I'm consuming is not only the one that is being produced in Denmark, but that one that is being produced in Denmark all the exchanges that happen between Denmark and the neighboring countries. So, we have this pipeline that makes sure that we can take into account all the neighboring countries and what happens at every single hour of the day to make sure that we can know the exact mix at a given hour of the day.

That's actually when we get the origin of electricity, the power breakdown, and then we can translate this number into carbon intensity by using emission factors that we update recurrently and that this actually helps us understand one, what's the carbon footprint of one kilowatt hour that we consume at a given country.

So emission factors are different based on the source we use and based on the country we are at. And that's what you see on the map.

Chris Adams: Wow, okay, so that's, let me just run through some of that then. Just to make sure I'm understanding it. So you're essentially getting a bunch of data. You're having to do, clean some of it up. Like, that's quite common. And most of us might be used to like, working with maybe text or CSVs. But I think you said that you're essentially like doing OCR, like optical character recognition in some on like, gIFs or pictures rather than actually having to read an actual number. Okay, and then once you've got that, you've got an idea of what the generation might be, but then you then need to do a bit of, like, working out where data is, where energy is being traded across borders, essentially, because in some ways the grid does have all this stuff, you just can't look at the production, for example. Because, like, I mean, the UK has, like, new connectors to other countries all the time, and Germany uses loads and loads of France's nuclear power, for example.

So there's all the stuff like that, and then once you've got this idea that, okay, there's probably this much coming from these places, you then need to think about, okay, well, what is the carbon intensity of power from a coal fired power station, or a new coal fired power station versus an old one, and stuff like that.

So there's all that other depth as well. Okay, and that all goes into a single number.

Íngrid Munné Collado: Exactly.

Chris Adams: Alright, I am kind of, so, I see why people do this now, because when I've looked at this before, I've looked at numbers saying, "oh yeah, it's just like generation," but no, there is, generation and production are two totally different things, and you do need to take into account some of this if you want a meaningful number.

Íngrid Munné Collado: And if I can say something just to give you a rough number, a country like France, it has interconnectors like with eight different countries. So imagine if you only consider the production or generation in France without considering all the interconnectors around, you might get a completely different picture.

Chris Adams: Ah, okay, alright, thanks for providing extra context because, yeah, I can see how complicated this gets very quickly in that case. Alright, okay, one thing that you mentioned before was that there are all these complicated scrapers, and when I looked at Electricity Maps a few years ago, I was surprised by there's quite a lot of it which is open source, so like, don't believe me?

Look at the repo, for example. There's some of this out there so you can see just how messy the data might actually be or possibly contribute if there's a gap, right? 

Íngrid Munné Collado: Yeah, actually, for example, when I had to work in the Japanese parsers, I got help from people in Japan. That they actually would point me to the right CSV file. Oh, and at some point the CSV changed the URL where it was stored. And it was not until someone, a contributor, helped me out and said, "Hey, check this link instead."

So we have, so all our parsers are open source and they are hosted on GitHub, on the contrib repo. And we are extremely happy about all the contributors that help us out, like be able to get this 228 zones that we have now, on the map.

Chris Adams: Okay, go yeah, Olivier, I was going to ask if you anything you wanted to add, because I believe you did a bit work on some of the academic underpinnings for some of this.

Olivier Corradi: I just wanted to add as well that if we tie it back a little bit to the triangle of inaction, what is happening here and why this is so exciting is that we're basically enabling some of the citizens to also act on climate change by being able to contribute with their unique expertise, which helps us.

There's no way Electricity Maps with, we're a bit more than 20 people now based in Copenhagen. There's no way we would have been able to cover the world if not for the help of all these wonderful contributors that have helped us all along, and it gives them also a way to contribute something meaningful where maybe before they didn't have that opportunity.

Chris Adams: Cool, and Olivier, we spoke a little bit about flow tracing, and I understand this is the you published a paper about this years and years ago, and that was one of the things to basically, the methodology that you're talking about, yes, there's some proprietary code, but the general approach that's being taken, it's in the public for people to understand, so they can challenge it and interrogate it and say, well, yeah, this is, I disagree with this thing and this is why I think this might need to be changed in the future, right?

Olivier Corradi: Absolutely and our philosophy has always been if there's something where we think we can move faster with the help of others, then let's open it up because then we can allow for contributions and so on. If it's something where we know that if we open it up, it's going to take us a lot of efforts to handle the contributions, then we don't open it up, and that's why some of the internal pipelines that Íngrid described are proprietary, because it's just faster for us to change a couple of things and not worry about what will happen if we open it up. But in general, we always try to be open, because we are trying to create a global consensus on how we account for things, so that can only be achieved through openness.

Chris Adams: Okay, you're singing from the same hymn sheet as us, like, we, the Green Software Foundation has a big thing about Open, the software carbon intensity metric is Open. If you want to pay to download it, you can buy it from the ISO for 63 Swiss francs. I don't know why you would, but that's available for people, and me, working at the Green Web Foundation, we use open as a lever, so we publish almost everything we can, either under open source or under open licenses. So, Íngrid, if I may, can I just come back to you, because we spoke a little bit about historical stuff, and you mentioned that forecasting is now a big thing, because the thing about renewable energy that we kind of alluded to before is that it changes over time. The sun goes up, sometimes the wind blows, storms move around, stuff like that.

So, can you just tell me a little bit about why this idea of forecasting is maybe more of a focus for you folks now and maybe explain a little bit about how, I guess, the sausage gets made and how that can be difficult.

Íngrid Munné Collado: Yeah, of course. So, as Olivier mentioned in the beginning, we want our data to be as actionable as possible. And now imagine that we are software developers and we run cloud jobs. And at the same time, we want to be aware of our carbon footprint. And we know that our cloud jobs might last five, six hours.

So, real-time data and historical data does not really help us achieve, like, be aware of what's the carbon footprint of our cloud job in the future, like, when is the right time to schedule this? And EV chargers might also think the same, like, when is the right time to charge? So, we saw that real-time and historical data would not help us achieve that.

Then we realized, okay, then we need to provide them with forecasts, and we know that we are providing a global API with data from all over the world for all bidding zones. Then, if we, let's go back to this software developer who wants to schedule a cloud job, because I think that's going to help the audience understand.

I can choose a data center in Sweden, but I can also choose a data center in Texas. So we can, we have the power of choosing which data center we want to run our cloud job. But at the same time, how do I know which one is the best? I don't have any idea. And at the same time, okay, but is it better to run my cloud job at 9 in the morning or 9 in the evening?

And then that's when we realized, okay, forecasts can actually solve that, but we need to provide it globally for all the zones in the world. So that's why we decided to focus on forecasting carbon intensity for the next 24 hours. That means, like, day-ahead. So we run the pipeline every hour and we provide forecast for the next 24 hours.

But then the next challenge came up. When we realized, "wait a second, is carbon intensity a metric that everybody can understand?" If I tell you that your cloud job used, I don't know, 200 grams of CO2, you don't, it's very difficult to relate. But if I tell you that if you schedule your cloud job at 9 am,

you're gonna run on 90 percent of renewable energy share. Instead of running 9 pm and then the renewable energy share is going to be 10%, it's very easy to understand and say, "oh, you know what, I'm going to do it at 9 in the morning because the renewable energy share is 90%." That's when we realized, okay, we can't just focus on carbon intensity forecasts, we need to do that with renewable energy forecasts and more specifically, wind and solar power forecasts.

Because that increases user engagement and the actionability of our forecasts.

Chris Adams: Ah, okay, that's quite a subtle change then, so, and I understand that because intuitively it's something that I just become so, kind of, you take it for granted, right? If someone is coming to this new, yes, explaining carbon intensity is conceptually quite complicated, but how much is running on clean energy is quite a bit easier to understand very quickly.

Íngrid Munné Collado: Exactly. if I can add on that, then when we said, let's start on renewable energy forecasts, it's a problem that has been not solved maybe because research evolves very fast, but it's a problem that has been laying around for years. I've worked on that field for many years. Because energy is traded on this day-ahead market, so people need this forecast to be able to trade energy in day-ahead markets or intra-day markets.

So when I started working at Electricity Maps on that field, I came from my previous job, and I came in, completely biased and thinking that this challenge would be easy to solve, because in my previous job, we had to do renewable energy forecast of specific assets. And then I would have the perfect setup for a machine learning engineer.

That means I knew the location of the wind turbines. I knew all the data regarding the wind turbines, like the blades, the installed capacity, if it was under maintenance or not. I also had access to multiple weather forecast data and I only had to build models for Denmark or the UK or Netherlands, so it was very focused, but when I joined Electricity Maps, the problem is completely different.

Here we focus on building a renewable energy forecast at country level or, bidding zone level without knowing the location of the assets, without knowing what's installed capacity, if the turbines are under maintenance or not, and that makes the problem very difficult because we know that renewable energy forecast is very linked to weather.

And if you don't know the exact location, then that's another challenge. So, I think in the grid forecasting, we did a really good job on finding a solution that generalizes well enough, and that allows us to generate this forecast for wind and solar for all zones in the world.

Chris Adams: Ah, okay, thanks for providing that extra context. So, I'm just going to check if I understand some of the terminology you used there. You said like a day-ahead market and an intraday market. So, so basically, as I understand it, if you maybe run a wind farm, for example, the day-ahead, you're going to say, "well, I reckon we can sell this many kilowatt hours or megawatt hours of power tomorrow," and that's what you'll make a bid in, and that's, there's consequences for either underbidding or overbidding for that kind of number, so that's why you'd care.

And the intraday is a bit like the kind of shorter term thing, so, you might make one big bit but then you might say there's a little bit of flexibility or you say well okay I, okay cool. And you mentioned this term bidding zone. Now bidding zone is a little bit like a country but it's not always a country.

So like America has all these different bidding zones because it has different grids and there's, that's the kind of, when you folks have mentioned the word zone that's kind of what you're referring to. It's not quite a country but it's more related to like, is there a kind of a unit of carbon intensity for a particular grid region, right?

Íngrid Munné Collado: Exactly. I mean, if we focus in Spain, for example, Spain, Portugal, France, Germany, a zone is the same as a country. But if we look at Sweden, it's split into four zones, into four bidding zones, and Norway into three. So if you check out our data, then you can compare between zone and country.

Chris Adams: Okay, and within a given country you can have radically different carbon intensity, and we might talk a little bit about some of that a little bit later then. Okay, cool.

Olivier Corradi: The dream is to get to street level granularity. And this is why flow tracing, by the way, is so important, because as you increase the resolution of what you're looking at, then obviously, if I'm looking at just a city, most of the electricity is actually produced outside of the city. And that's also why, by the way, the whole forecasting challenge just becomes massive.

Try to forecast how clean the electricity is going to be at every location on the planet. Obviously, you need really good renewable forecasts as well, and that's what civilization needs if it wants to rely significantly on these renewables that are intermittent.

Chris Adams: Wow, okay, I didn't realize street level was the dream for this, blimey. Okay, so we spoke a little bit about software and cloud jobs and stuff like that, but it's also worth just briefly touching, like, this is used outside of the cloud world. And I think one of you mentioned EVs as one of the examples here.

Could we just briefly touch on that? Because that might be one of the things which is concrete that lots of people might experience or might know someone who might have something which is, like, impacting them. Because yeah, EVs are becoming more popular now and it's probably one of the biggest new large uses of electricity in most people, in houses for example.

So yeah, Íngrid, maybe you could talk a little bit about that and then we can move on to some of the other questions.

Íngrid Munné Collado: Yeah, of course. So one of our clients is Monta. And we have a very great success story with them. Monta, for those who don't know them, is a global operating platform for EV. And one of the solutions is the smart charging feature, where they offer users to shift their charging according to the carbon intensity of the grid, the share of renewables, and that's by using our forecasts, or the price of the electricity grid.

And users are completely empowered to choose and to prioritize which signal they want to follow, if it's mostly price or low carbon or high share of renewables, and by doing that, those users took action in decarbonizing the grid. And the results are quite impressive because 70, 000 charging sessions were optimized for low carbon or high renewable share, and there was a 48 percent growth in user engagement, according to Monta, and that means that they optimized for low carbon charging, and in this process, 200 grams of CO2 were avoided for each charge on average.

So these are the numbers and this is the feedback that we're always willing to get because when we create, when we produce this forecast and we spend so much time building these forecast models is because we really want to know that people use our data and that they really use our data for this use cases.

So we were really happy to know that.

Chris Adams: Ah, thanks. Okay, so you mentioned one thing that I think was quite interesting. You mentioned, like, the cost being a thing that might change at different times of day and, broadly speaking, this is because, as I understand it, when energy is really when there's lots of green energy, renewable energy on the grid, it will be relatively cheap and somewhat green.

So, in the UK, for example, we have something like this Octopus Energy. I'm not in the UK, I'm in Germany, but I grew up in the UK. So, Octopus Energy is one company that's been doing a bunch of stuff like this about having agile and intelligent tariffs. So there's essentially a financial reason as well as a kind of basically an environmental reason for doing some of this.

And I think what I've heard in the UK, for example, I believe on an interview recently I heard just by doing some of this, essentially, when controlling some EVs, for example, I think the figure was something like 1.2 gigawatts of demand they had control over. Now that's basically the size of a nuclear power station in many cases, so that's like a significant amount of flexibility on the grid that would otherwise have to come from burning loads of fossil fuels.

Right? Okay, so maybe I can allow us to talk a little bit more about the cost thing because I know that when we talk about this, when we talk about green software, you can make an argument that yeah, you should do it because it's good for the planet, but there's also a real cost fact which comes into this that I think is actually growing and maybe this is something that I understand that you folks have been looking into as well as one of the ways to address more this triangle of inaction and align some of the incentives for more kind of grid complementary activity perhaps.

Íngrid Munné Collado: Yeah. So one of the issues we face is that we know that we want, we are implementing climate action and we want people to use our data for, in order to decarbonize the grid. But sometimes the entry barrier can be quite high when we just go there with, "hey, you have to save CO2." But sometimes it might be easier for users to understand that, "hey, if you look at the electricity prices, that you have also seen that they are not constant, you might be able to save money and CO2."

And that's very, that's a very good entry point as we mentioned in the Triangle of Inaction to increase user awareness because the main, for people it's very easy to understand price and how much they are going to pay at the end of the month and if they are going to save money and therefore it's also nice for us to get to them by I'm Price and then explain that, "hey, by doing that, you might also have an impact on CO2," but we have also a blog post where we mentioned that just optimizing for price doesn't mean that you're also saving CO2.

It's you need to implement some what we call co-optimization, where you want to either prioritize one or the other. And at the same time, maybe you might be able to save more CO2 if you optimize for higher renewable share, and at the same time, you can also save money by doing that. So that's why we think that price is a problem worth solving and that we also need to explore that area. 

Chris Adams: Ah, I see. And if I understand it, and if we follow the kind of path from before, we spoke about, like, a lot of us might be used to just paying a single price for power, but the price might change depending on where you are geographically as well, and that has some implications too, like, this is one thing that I guess, this is probably the newer world we're moving into, in that, yes, there might be, like, night time tariffs, but it feels like there's a lot more dynamism, both temporally and spatially for some of this.

Íngrid Munné Collado: Absolutely. And we saw that when the war in Ukraine started, that as soon as we saw that there were less gas resources, prices skyrocketed. And for example, in Spain, where there was a price cap

The situation was solved also because Spain has other sources of getting natural gas. But countries like Denmark, Germany, they really struggled with that. And the prices really doubled for some months. And it's not only political conflicts. It's also availability of the power plants. I it was two weeks ago where Finland had some unexpected maintenance in one of the, of their, of the largest nuclear power plants.

And that cost their head prices to double. And also in Texas, we have seen extreme weather events that causes prices to change from

Chris Adams: More than double,

Íngrid Munné Collado: yeah, more than double from negative prices at some point because there, there's one area in Texas that has a lot of renewables and another area in Texas that doesn't rely on renewables and due to those extreme weather events, they have to turn off some of the power plants and they, then this affects the prices.

And something I want to mention, I'm an electrical engineering at heart. So I want to talk about the power of interconnectors, and we might think that just by increasing renewables, this is going to make prices to be cheaper, but the problem is not going to be solved, and it's not going to make prices cheaper if we don't have nice interconnectors that make these flows between countries possible.

That's why prices are just changing so much because there's so much happening and going on right now, both on installing new energy sources, weather events that we're seeing, unfortunately, due to climate change and also the lack of interconnections at the moment.

Chris Adams: Okay, so you mentioned two things that I think are interesting there. So one thing was this idea of, okay, the price can change massively. Like the Texas example, I think if we look at, like, the cost of power, right, it might be between 20 to maybe 60 US dollars per megawatt hour in Texas. For example, you're one, you mentioned, like, there's a bunch of wind in the panhandle in the kind of northwest, and then there's around Houston, there's loads and loads of demand where people use all the power, right?

And I think the figures I've heard were something in the region of negative two and a half thousand dollars at one point, and then 30 minutes drive away, the cost is three and a half thousand positive. So like, you got a almost four, five, thousand dollar swing in the pricing here. And one of the reasons is just because it's a bit like network connectivity.

You know, the pipe isn't big enough. And this is one thing that we have to kind of work around, essentially, thinking about this.

Íngrid Munné Collado: And the same issue is happening in Germany, and like, Germany doesn't have these price mechanisms that we have in the US, but in Germany, there are also, like, huge problems due to transmission capacity, because the, most of the solar power production is, takes place in one part of the country, while the demand is concentrated in the completely other opposite.

And they are now experiencing with curtailments and extreme negative prices because they don't have this transmission capacity. They also don't have storage capacity, and there are no market mechanisms that control how we increase this solar power production in the grid and how we just export this to other areas.

Chris Adams: Ah, okay. All right, thanks for the extra context. For people who are still with us, we'll share a link to the previous episode, because we spoke with chap Philipp Wiesner, who was building a bunch of this work to simulate these kind of grids to give an idea of what the pricing might actually be with different services.

And if you have data centers, how adding some storage might actually change the cost and the carbon of running various software services. So we'll add a link for that. Olivier, while you're here, I want to ask one question, if I may, about basically, optimising for carbon intensity, because that's what lots of engineers are kind of trained to do somewhat.

And I, inside the Green Software Foundation, there is a kind of standard called Software Carbon Intensity. It's the thing that you're supposed to, or that you might optimise for, essentially. And it basically lets you use two different ways of thinking about the carbon intensity of electricity.

And so one of these is called a average carbon intensity and another one is called marginal intensity and they are slightly different. And it can be quite counterintuitive when you're first coming to this because it can give you somewhat different answers or incentivize different kinds of actions.

For someone who's coming to this for the first time, can you provide a little bit of like background on how to navigate some of this and how to think about some of this? Because it's something that. I think a lot of people come to and they scratch their head quite a lot because it can be a bit confusing having two numbers which can suggest you do totally different things sometimes.

Olivier Corradi: Yeah, well, we might get into some of the weeds, so I'll try to keep the, sort of, the discussion a little bit high level so we don't get too technical. But I think the way that I'm trying to explain this, so, on the average side of things, the first thing that I will say, actually, is that the word average can be a little bit misleading, because it sounds like, like you're taking an average over a period of time, which you're not.

Actually, what we're doing here is computing what we call the flow trace signal. So it's like taking the production locally, looking at what's imported, and then concluding on what is the constitution of the electricity that I'm getting at. So it's a representation of all the power plants on the system, basically.

So that's what we'll refer to as average. And on the marginal side of things, the story goes that if you're plugging an electric vehicle to charge at a particular time, then it's not all the power plants in that system that will ramp up to give you that additional electricity. It's the one that's called the marginal.

And a loose way to define it is to say it's the cheapest power plant that still has capacity to ramp up, to produce more. So, in theory, that marginal concept makes a lot of sense, because we're saying that seems to reflect what is physically happening on the system. If you actually go a little bit more into the details, and that's where the differences start to pop up quite starkly.

Well, if you are plugging, for example, your iPhone on this, on, to charge right now. Then it's not this marginal power plant that will ramp up. In all likelihood, nothing will happen on the grid. It's just the frequency of the system that will change a little bit. But you're not shoveling a little bit more coal in the coal power plant to burn off a little bit more, right?

So, emission factor would be zero. Like, no impact on the grid, you could say. If you go the other way, and you say, now I plug, imagine a metaphorical plug on a data center that's using like 90 percent of the consumption in the grid, Then you can't have only that small power plant that has spare capacity, it's just not going to be enough to ramp up this data center.

You would need all the power plants on the grid to ramp up in this what if scenario where the data center didn't exist. And then the last example is, if you have an electric vehicle and you're plugging it in, then the electricity will be delivered instantaneously to you. And that's just not a change that a coal power plant or a gas turbine can react on.

And that starts to create some additional complexity when you think about, okay, but can this be predicted? Can the plugging in of my electric vehicle, could that have been predicted by the market? And if it's already predicted, then it's part of the business as usual scenario. It's not marginal anymore.

So when you actually try to go down to all the details, it becomes hyper complicated. And we've tried really hard to talk to all the power system engineers, the electrical engineers and the TSOs in Europe as well. And one of the most fruitful discussions we had was with 50 Hertz that concluded that they don't think you can actually reliably identify or verify what the marginal power plant is because depending on what market you're looking at, if it's day-ahead, intraday, real-time, depending on the magnitude of the changes, depending on all the interconnectivity, the marginal power plant is just a concept that philosophically makes sense, but from a data perspective, it's just hard to measure, and that's also, I believe, why most of the regulations recently on the hydrogen regulation in both the US and in Europe that is documenting what is it, what signal should you be using, whatwhen you want to prove that your hydrogen is clean, then they are settling on an average signal. So if you take a step back from all of this, you're having an argument that philosophically for me makes sense. Of course, you want to make sure that the short term impact of what you're doing is minimized.

When you start looking at the data, you have a signal that is difficult to audit. And we've been working on this for eight years now, six years, sorry, creating a marginal signal and trying to verify it. And I've just seen enough that it can be manipulated in many ways. And that's why it's a little bit difficult.

So to get back and conclude a little bit and to answer your first question, which is how would we navigate this thing? I think the advice we're generally giving is get both signals, plot the data next to each other, and depend on the use cases, ask yourself "Is user acceptance important?" Are you going to show this in an interface?

Because if you are going to show it in the dashboard of an electric vehicle, in a country like France, which is majority nuclear, or in Ontario, lots of decarbonized electricity already, it's going to be a tough sell to tell them, look, your EV is being charged on gas, which is on the margin. So this user acceptance is important.

The second piece is auditability. Are you thinking of being generally directionally correct? Or do you need an auditor to prove that the data is correct from a scope 2, scope 3 perspective? And these are typically things they're going to inform depending on if you're willing to have something that is philosophically more accurate, but in practice more wobbly or if you want to have something that's just simple to explain and sort of abides by the regulation.

Chris Adams: Ah, okay, all right, so if I try to kind of summarize it, it sounds like the marginal thing it's conceptually attractive and kind of fun and it might give me some and in very ways it's basically gives me this idea that I can make relatively small changes rather than some of the systemic changes that might be needed for some of this.

But from an audit, from an auditing point of view, because you're comparing some of this to essentially a counterfactual which might not exist, it's actually very difficult to say, "well, yeah, I definitely made this impact and if it weren't for me doing this these people wouldn't have switched this stuff on," for example.

So you kind of need to have a degree of kind of clairvoyant level of information for this to actually really check this. Okay, cool. Speaking of clairvoyant, I've just realized the reason I've used this term is it makes me think of, there is a paper by a research student, I think her name, Tammy, I'm so sorry if I pronounce her name incorrectly, Thanathorn Sukprasert, she wrote all about using the SCI and using these different signals and how they, when they agree and when they do not agree, and if you are curious about this as someone listening to this, we'll share a link to that paper because it's a really good paper, she's also doing another one which is all about, okay, what are the possible savings from carbon-aware computing?

And that is also a really fun paper to read, and maybe, Tammy, if you're free, we'd love to have you on the podcast to talk about some of that, because there's quite a lot of fun to read there. Okay, cool. Thank you for that, Olivier. I realize that was quite detailed, but it, I appreciate you talking about the fact that in other sectors, there's stuff we can look at, because, as I understand it, this whole shift towards clean or green hydrogen is somewhat comparable to data centers in the fact that you have a very concentrated amount of energy being drawn in one place. So, in the same way a data center might be tens or hundreds of megawatts, you might see something similar with, like, creating hydrogen.

Is that the idea behind some of that?

Olivier Corradi: Absolutely, and if if the way that I'm thinking about this if you really want to simplify it, if we take a step back, the opportunity we have ahead of us is large, abundant renewables that are going to be the cheapest and the fastest way for us to expand the system. I mean, nuclear is great as well, and hydro and so on, but it's just, it takes more time, and it's a little bit more expensive when you put it in directly.

And so, if we put all this renewable in the system, we better well make sure that every flexible appliance out there is aligning their consumption to the time at which the renewables are creating that electricity, because else we're just hindering their deployment. You know, it's less batteries that we need.

And I think that's generally also the thinking behind the hydrogen. We want to make sure we're electrolyzing the hydrogen at the times where all these abundant renewables are producing electricity. And so that's the simplest heuristic and the fastest way for me to explain the systemic change we're undergoing and also why we're focusing on these renewable power forecasts.

There's a lot of value there. And by the way, why they also typically align with the price of electricity.

Chris Adams: Ah, okay, thank you. I do hope we still have some people with us, because that was a bit of a deep dive. But as sometimes when we're coming to new fields, working in software engineering, you do need to kind of engage with the details sometimes. Folks, I think we're coming up to the time that we had allocated for this.

And this has been loads and loads of fun. I've really enjoyed this. If people do want to know more about what either of you are doing, can I just give you a bit of space to talk about, like, Follow me on either Twitter, LinkedIn, Mastodon, whatever, like yourselves personally. And then if there's anything you would direct people's attention to, yeah please do, and then I think we'll wrap up actually, so maybe Íngrid if I hand over to you and then you Olivier, we'll wrap up with you okay?

Íngrid Munné Collado: Absolutely, so if people want to know what I do, they can check my LinkedIn, and it's Íngrid Munne, M U N N E, and I'm also on X or Twitter with the same name. So looking forward to that.

Chris Adams: Thank you, Íngrid. And Olivier, for you.

Olivier Corradi: Likewise, like Íngrid, you can reach me on LinkedIn and Twitter. If you search for my name, you'll find me. And I think in terms of resources to watch, we try to publish blog posts that are going deep into the topic, that are thoughtful. We don't publish a lot, but when we do, it's like We, we at least try to have serious research there, so check out our website, check out our blog, and we have a couple of guides on carbon accounting as well if you want to go deeper on the topic, and a few videos on YouTube if you want to nerd out more on some of the things we just discussed.

Chris Adams: Okay, we'll add a few links for all of those and the thing I'll also share is that in the Green Software Foundation, there are ongoing discussions in the standards working groups to discuss all this stuff. So you can see all the kind of back and forth around this, so if you do want to engage with this and possibly join to actually take part in that conversation, there is that available.

So you can see what discussions have come before and how people arrive at deciding which carbon signal to be following. Alright, I think that takes us to our time. Folks, I really enjoyed this trip, and we'll make sure that everything, or as many things as we remember, are in the show notes for people who want to continue this quest to learn more about building more sustainable software. Thanks a lot, folks, and have a lovely time in Denmark. Ta ra!

Íngrid Munné Collado: Thank you. Bye!

Olivier Corradi: Bye-bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode! 



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]]> Host Chris Adams sits down with CEO Olivier Corradi and Tech Lead Íngrid Munné Collado of Electricity Maps, a company that leverages data to enable decarbonization of electricity grids. They discuss the complexities of carbon intensity data, the role of accurate forecasting in renewable energy, and how this data helps optimize electricity usage for sustainability. Olivier explains the origins of Electricity Maps, their goal of providing real-time carbon footprint insights, and their theory of change—targeting citizens, corporations, and institutions to create a greener future. Tune in to hear about the history, frontiers, and future of this engaging field.

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TRANSCRIPT BELOW:

Olivier Corradi: The dream is to get to street-level granularity, and this is why flow tracing, by the way, is so important, because as you increase the resolution of what you're looking at, then obviously if I'm looking at just a city, most of the electricity is actually produced outside of the city, and that's also why, by the way, the whole forecasting challenge has just become massive. Try to forecast how clean the electricity is going to be at every location on the planet. Obviously, you need really good renewable forecasts as well. And that's what civilization needs if it wants to rely significantly on these renewables that are intermittent.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. We often talk about carbon intensity, or how clean electricity is on this podcast, as one of the levers for making software more sustainable.

To know this, you need to get the data from somewhere, in a form that's easy to understand and consume. So today we're sitting down with two people who live and breathe this data from Electricity Maps. Electricity Maps is a company at the forefront of enabling a data-driven decarbonization of electricity and today we're joined by both Íngrid and Olivier from the firm.

Hey folks, how's it going?

Íngrid Munné Collado: Hi.

Olivier Corradi: Hi, Chris.

Chris Adams: Cool. Folks, we'll talk about the origins of Electricity Maps in a second, but before I do, I just want to give you a bit of space to introduce the two of you, actually. We normally go by surname first, so I think Olivier Corradi, you're ahead of Íngrid, so can I give you some space to introduce yourself first, and then we'll do the same thing for Íngrid, alright?

Olivier Corradi: Of course, and thank you so much for the invitation and for inviting us to the show. So, my name is Olivier, I'm originally a machine learning engineer and data scientist. My background has been academic, I've worked as well in the research industry with IBM Research and Simulating Electricity Grids. And I would say the most relevant thing that I'm doing now is Electricity Maps that I founded eight years ago.

Chris Adams: Okay, thanks for that, Olivier. And Íngrid, over to you, I suppose.

Íngrid Munné Collado: Hi. Hi, Chris. I'm Íngrid. I'm the Tech Lead at Electricity Maps and I'm very happy to be here. More specifically, I'm part of the grid forecast team where we built, as we have said, forecast models for renewable energy sources and other signals such as carbon intensity. I've been recently involved in replacing and improving the weather forecast data that we use at Electricity Maps to achieve better forecasts.

And yeah, outside of work, when I'm not working, I'm either going to CrossFit where I'm trying to learn how to do a pull up, which I don't know, or knitting and just making sweaters. So quite, quite contrast.

Chris Adams: Cool, thank you for telling me. I'm glad you mentioned weather, actually. I mean, as an English person, we talk about weather the same way that we breathe, I suppose, and as I understand it, you folks are both calling from Denmark, right? We're a little bit chilly today than it otherwise would be, right?

Íngrid Munné Collado: Yeah, it is quite cold for September. I think it's quite usual, but it would be a bit on the colder side.

Chris Adams: Yeah, same here. We, it's, Berlin has, someone has switched off the summer, so we now had to, like, I've cycled in with my coat for the first time today, and did not enjoy that. Okay, so I should introduce myself, actually, folks, if you're new to this podcast. So my name is Chris. I work at the Green Web Foundation as the executive director there.

I also work in the Green Software Foundation as one of the co chairs of the policy working group where we Basically, work on policy to see about coming laws and things that we might actually want to respond to or help members understand what the implications of might actually be. The other thing I'll share with you is that we try our best to have quite helpful show notes for this.

So we're going to mention various projects and papers along, and if you are viewing this in Spotify or YouTube podcasts or something, you might not see them. So be sure to look up podcast. greensoftware.foundation to see the full links and the transcript. Okay, you two folks sitting comfortably?

Íngrid Munné Collado: Yeah.

Olivier Corradi: We are, sure.

Chris Adams: Brilliant, okay, then I'll begin.

Okay, first question I'll put to you, Olivier, because I've actually been a fan of Electricity Maps for quite a while, and when I first heard about Electricity Maps back in 2017, it wasn't Electricity Maps, it was electricitymap.org. And this is something that is reserved for non profit organizations. So it's 2024 now.

And I now know that the same kind of cool map that I started talking about at conferences and so on, is now available under electricitymaps.com. And as someone working in a non profit, I end up talking and thinking about theories of change all the time. So there was a really interesting post I saw on the blog, on the Electricity Maps blog post, particularly talking about this theory of inaction.

And it's rare to see start ups talk about this kind of stuff with a fluency, really. So, Olivier, can you tell me a little bit about basically, what this is, and how this informs you spending all the time on Electricity Maps, because I know there's a few other things that you've worked on before.

Olivier Corradi: Yeah, I'm happy that you actually give me the opportunity to articulate this. So in the early days when Electricity Map was started in 2016, there was this question that remained for a couple of years afterwards, which was, "should we actually be an NGO or should it be a for profit company?" And honestly, the conclusion is that it depends on how you want to impact the world and it depends on how you want to be funded and so on.

But the way that we've been looking at things is basically to say when a company will pay you for something you're delivering, that represents a change that is happening in the world that is sufficiently valuable that someone will pay you to do it. And people are taking you seriously when they're implementing it.

So this was a little bit what tipped it over towards more of the, let's say, for profit world. But in order to make sure that we never lost track of the actual impact we want to have, we started having like this framework that we stole from someone else, honestly, and adapted afterwards. But first of all the vision we have here is to imagine a world where we have low carbon electricity that's delivered everywhere across the world, every hour of the year.

And this was really why Electricity Maps was created, out of almost a frustration of folks looking at the electricity grid from a yearly perspective, instead of looking at it hour by hour. And of course it made sense before we had renewables. Now that we, the wind doesn't always blow and the sun doesn't always shine, we need that.

And the angle we're taking here is data-driven decarbonization, as you mentioned, because we're a mixture of folks who come from academia, who know how to build machine learning systems and tech. So this is our angle. So with that vision in mind, providing clean electricity every hour of the day, we realized there's three pressure points that we need to apply if we want to create that transformation.

The first one is, we identify them as citizens. Second one, corporations. And the third one, institutions. And there is this framework that, that depicts a triangle of inaction where each of these corners are pointing at the other as an excuse to not change. For example, citizens are saying, "well I actually would love to be greener, but the government is just not putting the right incentives for me to be cleaner," like the public infrastructure is not helping. Another piece of it is the citizens are pointing at corporations and saying, "well, I, too bad, like, flying is actually cheaper than taking the train. Like, I don't have a clean offering here."

And then the corporations on the other side will say, "well, but everyone wants to fly, right? I mean, the citizens are not ready for that change." They'll say, "well, I'll just keep operating like I am." Corporations will point at institutions as well and say, "well, the right tax schemes are not here for us to make flying cheaper, actually, so we'll just continue what we're doing."

And then institutions will be, "we don't have" like the, let's say, "social acceptance from the citizens to actually, exactly, to put these green tariffs. And on the other hand we're just a small organization and institution, sorry," and you have corporations like Microsoft, OpenAI, Google, that are just like, more powerful than governments.

And so you can basically map these out in a small triangle. And we said, okay, so how are we going to impact this? And then most of our initiatives can actually be mapped to this triangle, where on the citizen side, the app that we have of Electricity Maps is creating this awareness, creating the debates as well to make sure we have a factual understanding and can challenge our politicians in the right way.

There's the piece on corporations where we are working with them in order to ensure that green offerings can actually hit the ground, like that we have electric vehicles that can use electricity at the optimal time, all these things. And it's a win because we get a financial cut of this and we can grow our company and grow the impact.

And then finally, on the institutional side of things, we're trying to make sure that the right carbon accounting methodologies and so on are being pushed. We basically want to live in a world where we have an accounting that represents what's physically happening in the world. And one example is we have a data portal where our historical data can be used by any company who wants to do granular carbon accounting.

Again, moving away from this world where you're doing it on a yearly basis to an hourly basis. So this is the framework we've put in place and the way we're articulating internally as well how we allocate resources and prioritize and make sure that what we do leads to impact ultimately.

Chris Adams: Cool. Thank you for that. I have one question following up from this, because when I was doing a bit of research, I looked up this triangle of inaction, and I think I might need one of your help, one of your help in pronouncing the person's name. Is Pierre, is it Peyretou?

Olivier Corradi: Not too bad. Yeah. Well done.

Chris Adams: Okay, so for people who are listening, what we'll do is we'll share a link to some of the theory behind it.

He's actually got an online course. I think there is, there's a French school of business which does actually have some online resources to understand this kind of theory. So if you're Curious and you want to think about, okay, how does this work? Or if you'd like to see an actual triangle rather than have us describe it, follow the link and you'll see some of it there.

Okay, thanks for that, Olivier. Okay, so we've spoken a little bit about data, and you mentioned about data being in the open, Olivier, and I can actually confirm that when we spoke to Toby before, Toby is one, sorry, Tony is one of the colleagues at Literacy Maps. We did an interview with him around about November last year, actually, and he was talking all about this, Open Data Portal.

And back then we were like, "oh, sweet, there's all this data being published." And I'll be honest, you're a startup. I was like, "are they really going to publish the next year?" And then January came around and it actually did get published. I was like, "wow, this almost never happens." So I was really pleased to actually see a startup and a company follow through with making some of this data available because it's so, so, so useful and this is so hard to find otherwise.

I can speak as someone in a non profit who's been trying to find this data. It's so, so handy to have some of that available now. So yeah, thank you on that one. Okay, so the question, I'll leave some space because I think there was a response coming up there actually, Olivier.

Olivier Corradi: Oh, thank you so much for the shout out. You know, it's important for us as well to feel like what we're doing leads to direct impact and that we have folks that are demanding this data. And sometimes these feedback loops are not always present. So I appreciate you giving us the shout out.

Chris Adams: Yeah, so if anyone is listening, this is open data, so it's something that you're able to build on and we'll share a link to the actual website that makes it very clear how you can use this information. So if you're trying to build something and you're looking at historical stuff, it's totally there.

Have at it. Okay, so we spoke a little bit about carbon intensity at different parts of the world and how you need to think about it on an hourly basis rather than just an annual basis. And we might talk about why when you talk about green energy saying green energy with certificates that came from solar and then saying, using that to make usage at night be counted as green might be conceptually a little bit challenging, we might say.

So that's some of the stuff we spoke about. Now, the thing I want to ask a little bit about, and Íngrid, if I can hand this over to you. When you're working as an engineer and you start thinking about carbon intensity. It's when you use, say, APIs, you just see a single number that goes up and down.

And like, it's fun. And it's very easy to underestimate just how much work can go into this and all the complexities around this. Because as I understand it, it's more than just like, looking at what the power will look at looking at generation from a single plant. Íngrid, can I just give you a chance to actually talk about what goes into sharing some of these current and historical figures? Because I know there's some forecasting work, and we'll talk about that later, but we've just spoken about open data and some of the historical stuff, and some of the context there might be useful for people who are considering downloading some of this or messing around with it themselves.

Íngrid Munné Collado: Yeah, absolutely. And I would say that without this amazing work of collecting data, any forecast, like forecast would not be possible at Electricity Maps because we need this data to be able to produce our forecast. So let's take a look at how we collect data. This is currently done by one of our teams at Electricity Maps, the Grid Modeling and Methodology team.

And our work starts by first trying to get as much real-time data and historical data from, that is available publicly. And here we're talking about governmental institutions, transmission system operators, that for people without electrical engineering background, those are the folks that manage the electricity grid at high voltage, and that make sure that demand and production is actually matches at every single second.

So we try to collect this data and now we have data for 228 zones. And when we think about it, we might say, okay, we just collect CSVs and everything is standardized and nice, but the data is really messy. So we might get data from like in PDF files, in TXT files. We have sometimes, I can tell you a bit of an experience.

When I joined Electricity Maps, I had to build the parsers for Japan. And I'm originally from Spain, so I know a lot about electrical system in, in Spain, but when I had to dig into the Japanese electrical system, I didn't know that they have eight different organizations that collect data and each and single one of them is in a different format.

And sometimes we even had to do some image recognition to get the power of the nuclear power plants that they have in Japan. So data is very messy. It contains outliers. We have missing values, wrong values. Some production modes might be missing as well, so there's a lot of work that we have to do behind the scenes to make sure that we collect this data every hour or even in lower resolutions to make sure that we can use this data and process it.

So once we have this data in the raw format, then we process it to make sure that we don't have these outliers or missing values. And that we have a complete power breakdown. That means that if Spain, for example, has nuclear, gas, coal, and like in total eight production modes, that the data we get has eight production modes because otherwise the carbon intensity values that we might show on the app or on the API, they are not going to make any sense.

So once we have this done, then we can actually go into maybe one of the core pipelines we have at Electricity Maps. That is the flow tracing pipeline. And Tony did an amazing job in the previous episode where he explained how this works. But for those who don't know, the flow tracing pipeline makes sure that we are able to trace back the origin of electricity.

Because if I plug my laptop here in Denmark, the electricity that I'm consuming is not only the one that is being produced in Denmark, but that one that is being produced in Denmark all the exchanges that happen between Denmark and the neighboring countries. So, we have this pipeline that makes sure that we can take into account all the neighboring countries and what happens at every single hour of the day to make sure that we can know the exact mix at a given hour of the day.

That's actually when we get the origin of electricity, the power breakdown, and then we can translate this number into carbon intensity by using emission factors that we update recurrently and that this actually helps us understand one, what's the carbon footprint of one kilowatt hour that we consume at a given country.

So emission factors are different based on the source we use and based on the country we are at. And that's what you see on the map.

Chris Adams: Wow, okay, so that's, let me just run through some of that then. Just to make sure I'm understanding it. So you're essentially getting a bunch of data. You're having to do, clean some of it up. Like, that's quite common. And most of us might be used to like, working with maybe text or CSVs. But I think you said that you're essentially like doing OCR, like optical character recognition in some on like, gIFs or pictures rather than actually having to read an actual number. Okay, and then once you've got that, you've got an idea of what the generation might be, but then you then need to do a bit of, like, working out where data is, where energy is being traded across borders, essentially, because in some ways the grid does have all this stuff, you just can't look at the production, for example. Because, like, I mean, the UK has, like, new connectors to other countries all the time, and Germany uses loads and loads of France's nuclear power, for example.

So there's all the stuff like that, and then once you've got this idea that, okay, there's probably this much coming from these places, you then need to think about, okay, well, what is the carbon intensity of power from a coal fired power station, or a new coal fired power station versus an old one, and stuff like that.

So there's all that other depth as well. Okay, and that all goes into a single number.

Íngrid Munné Collado: Exactly.

Chris Adams: Alright, I am kind of, so, I see why people do this now, because when I've looked at this before, I've looked at numbers saying, "oh yeah, it's just like generation," but no, there is, generation and production are two totally different things, and you do need to take into account some of this if you want a meaningful number.

Íngrid Munné Collado: And if I can say something just to give you a rough number, a country like France, it has interconnectors like with eight different countries. So imagine if you only consider the production or generation in France without considering all the interconnectors around, you might get a completely different picture.

Chris Adams: Ah, okay, alright, thanks for providing extra context because, yeah, I can see how complicated this gets very quickly in that case. Alright, okay, one thing that you mentioned before was that there are all these complicated scrapers, and when I looked at Electricity Maps a few years ago, I was surprised by there's quite a lot of it which is open source, so like, don't believe me?

Look at the repo, for example. There's some of this out there so you can see just how messy the data might actually be or possibly contribute if there's a gap, right? 

Íngrid Munné Collado: Yeah, actually, for example, when I had to work in the Japanese parsers, I got help from people in Japan. That they actually would point me to the right CSV file. Oh, and at some point the CSV changed the URL where it was stored. And it was not until someone, a contributor, helped me out and said, "Hey, check this link instead."

So we have, so all our parsers are open source and they are hosted on GitHub, on the contrib repo. And we are extremely happy about all the contributors that help us out, like be able to get this 228 zones that we have now, on the map.

Chris Adams: Okay, go yeah, Olivier, I was going to ask if you anything you wanted to add, because I believe you did a bit work on some of the academic underpinnings for some of this.

Olivier Corradi: I just wanted to add as well that if we tie it back a little bit to the triangle of inaction, what is happening here and why this is so exciting is that we're basically enabling some of the citizens to also act on climate change by being able to contribute with their unique expertise, which helps us.

There's no way Electricity Maps with, we're a bit more than 20 people now based in Copenhagen. There's no way we would have been able to cover the world if not for the help of all these wonderful contributors that have helped us all along, and it gives them also a way to contribute something meaningful where maybe before they didn't have that opportunity.

Chris Adams: Cool, and Olivier, we spoke a little bit about flow tracing, and I understand this is the you published a paper about this years and years ago, and that was one of the things to basically, the methodology that you're talking about, yes, there's some proprietary code, but the general approach that's being taken, it's in the public for people to understand, so they can challenge it and interrogate it and say, well, yeah, this is, I disagree with this thing and this is why I think this might need to be changed in the future, right?

Olivier Corradi: Absolutely and our philosophy has always been if there's something where we think we can move faster with the help of others, then let's open it up because then we can allow for contributions and so on. If it's something where we know that if we open it up, it's going to take us a lot of efforts to handle the contributions, then we don't open it up, and that's why some of the internal pipelines that Íngrid described are proprietary, because it's just faster for us to change a couple of things and not worry about what will happen if we open it up. But in general, we always try to be open, because we are trying to create a global consensus on how we account for things, so that can only be achieved through openness.

Chris Adams: Okay, you're singing from the same hymn sheet as us, like, we, the Green Software Foundation has a big thing about Open, the software carbon intensity metric is Open. If you want to pay to download it, you can buy it from the ISO for 63 Swiss francs. I don't know why you would, but that's available for people, and me, working at the Green Web Foundation, we use open as a lever, so we publish almost everything we can, either under open source or under open licenses. So, Íngrid, if I may, can I just come back to you, because we spoke a little bit about historical stuff, and you mentioned that forecasting is now a big thing, because the thing about renewable energy that we kind of alluded to before is that it changes over time. The sun goes up, sometimes the wind blows, storms move around, stuff like that.

So, can you just tell me a little bit about why this idea of forecasting is maybe more of a focus for you folks now and maybe explain a little bit about how, I guess, the sausage gets made and how that can be difficult.

Íngrid Munné Collado: Yeah, of course. So, as Olivier mentioned in the beginning, we want our data to be as actionable as possible. And now imagine that we are software developers and we run cloud jobs. And at the same time, we want to be aware of our carbon footprint. And we know that our cloud jobs might last five, six hours.

So, real-time data and historical data does not really help us achieve, like, be aware of what's the carbon footprint of our cloud job in the future, like, when is the right time to schedule this? And EV chargers might also think the same, like, when is the right time to charge? So, we saw that real-time and historical data would not help us achieve that.

Then we realized, okay, then we need to provide them with forecasts, and we know that we are providing a global API with data from all over the world for all bidding zones. Then, if we, let's go back to this software developer who wants to schedule a cloud job, because I think that's going to help the audience understand.

I can choose a data center in Sweden, but I can also choose a data center in Texas. So we can, we have the power of choosing which data center we want to run our cloud job. But at the same time, how do I know which one is the best? I don't have any idea. And at the same time, okay, but is it better to run my cloud job at 9 in the morning or 9 in the evening?

And then that's when we realized, okay, forecasts can actually solve that, but we need to provide it globally for all the zones in the world. So that's why we decided to focus on forecasting carbon intensity for the next 24 hours. That means, like, day-ahead. So we run the pipeline every hour and we provide forecast for the next 24 hours.

But then the next challenge came up. When we realized, "wait a second, is carbon intensity a metric that everybody can understand?" If I tell you that your cloud job used, I don't know, 200 grams of CO2, you don't, it's very difficult to relate. But if I tell you that if you schedule your cloud job at 9 am,

you're gonna run on 90 percent of renewable energy share. Instead of running 9 pm and then the renewable energy share is going to be 10%, it's very easy to understand and say, "oh, you know what, I'm going to do it at 9 in the morning because the renewable energy share is 90%." That's when we realized, okay, we can't just focus on carbon intensity forecasts, we need to do that with renewable energy forecasts and more specifically, wind and solar power forecasts.

Because that increases user engagement and the actionability of our forecasts.

Chris Adams: Ah, okay, that's quite a subtle change then, so, and I understand that because intuitively it's something that I just become so, kind of, you take it for granted, right? If someone is coming to this new, yes, explaining carbon intensity is conceptually quite complicated, but how much is running on clean energy is quite a bit easier to understand very quickly.

Íngrid Munné Collado: Exactly. if I can add on that, then when we said, let's start on renewable energy forecasts, it's a problem that has been not solved maybe because research evolves very fast, but it's a problem that has been laying around for years. I've worked on that field for many years. Because energy is traded on this day-ahead market, so people need this forecast to be able to trade energy in day-ahead markets or intra-day markets.

So when I started working at Electricity Maps on that field, I came from my previous job, and I came in, completely biased and thinking that this challenge would be easy to solve, because in my previous job, we had to do renewable energy forecast of specific assets. And then I would have the perfect setup for a machine learning engineer.

That means I knew the location of the wind turbines. I knew all the data regarding the wind turbines, like the blades, the installed capacity, if it was under maintenance or not. I also had access to multiple weather forecast data and I only had to build models for Denmark or the UK or Netherlands, so it was very focused, but when I joined Electricity Maps, the problem is completely different.

Here we focus on building a renewable energy forecast at country level or, bidding zone level without knowing the location of the assets, without knowing what's installed capacity, if the turbines are under maintenance or not, and that makes the problem very difficult because we know that renewable energy forecast is very linked to weather.

And if you don't know the exact location, then that's another challenge. So, I think in the grid forecasting, we did a really good job on finding a solution that generalizes well enough, and that allows us to generate this forecast for wind and solar for all zones in the world.

Chris Adams: Ah, okay, thanks for providing that extra context. So, I'm just going to check if I understand some of the terminology you used there. You said like a day-ahead market and an intraday market. So, so basically, as I understand it, if you maybe run a wind farm, for example, the day-ahead, you're going to say, "well, I reckon we can sell this many kilowatt hours or megawatt hours of power tomorrow," and that's what you'll make a bid in, and that's, there's consequences for either underbidding or overbidding for that kind of number, so that's why you'd care.

And the intraday is a bit like the kind of shorter term thing, so, you might make one big bit but then you might say there's a little bit of flexibility or you say well okay I, okay cool. And you mentioned this term bidding zone. Now bidding zone is a little bit like a country but it's not always a country.

So like America has all these different bidding zones because it has different grids and there's, that's the kind of, when you folks have mentioned the word zone that's kind of what you're referring to. It's not quite a country but it's more related to like, is there a kind of a unit of carbon intensity for a particular grid region, right?

Íngrid Munné Collado: Exactly. I mean, if we focus in Spain, for example, Spain, Portugal, France, Germany, a zone is the same as a country. But if we look at Sweden, it's split into four zones, into four bidding zones, and Norway into three. So if you check out our data, then you can compare between zone and country.

Chris Adams: Okay, and within a given country you can have radically different carbon intensity, and we might talk a little bit about some of that a little bit later then. Okay, cool.

Olivier Corradi: The dream is to get to street level granularity. And this is why flow tracing, by the way, is so important, because as you increase the resolution of what you're looking at, then obviously, if I'm looking at just a city, most of the electricity is actually produced outside of the city. And that's also why, by the way, the whole forecasting challenge just becomes massive.

Try to forecast how clean the electricity is going to be at every location on the planet. Obviously, you need really good renewable forecasts as well, and that's what civilization needs if it wants to rely significantly on these renewables that are intermittent.

Chris Adams: Wow, okay, I didn't realize street level was the dream for this, blimey. Okay, so we spoke a little bit about software and cloud jobs and stuff like that, but it's also worth just briefly touching, like, this is used outside of the cloud world. And I think one of you mentioned EVs as one of the examples here.

Could we just briefly touch on that? Because that might be one of the things which is concrete that lots of people might experience or might know someone who might have something which is, like, impacting them. Because yeah, EVs are becoming more popular now and it's probably one of the biggest new large uses of electricity in most people, in houses for example.

So yeah, Íngrid, maybe you could talk a little bit about that and then we can move on to some of the other questions.

Íngrid Munné Collado: Yeah, of course. So one of our clients is Monta. And we have a very great success story with them. Monta, for those who don't know them, is a global operating platform for EV. And one of the solutions is the smart charging feature, where they offer users to shift their charging according to the carbon intensity of the grid, the share of renewables, and that's by using our forecasts, or the price of the electricity grid.

And users are completely empowered to choose and to prioritize which signal they want to follow, if it's mostly price or low carbon or high share of renewables, and by doing that, those users took action in decarbonizing the grid. And the results are quite impressive because 70, 000 charging sessions were optimized for low carbon or high renewable share, and there was a 48 percent growth in user engagement, according to Monta, and that means that they optimized for low carbon charging, and in this process, 200 grams of CO2 were avoided for each charge on average.

So these are the numbers and this is the feedback that we're always willing to get because when we create, when we produce this forecast and we spend so much time building these forecast models is because we really want to know that people use our data and that they really use our data for this use cases.

So we were really happy to know that.

Chris Adams: Ah, thanks. Okay, so you mentioned one thing that I think was quite interesting. You mentioned, like, the cost being a thing that might change at different times of day and, broadly speaking, this is because, as I understand it, when energy is really when there's lots of green energy, renewable energy on the grid, it will be relatively cheap and somewhat green.

So, in the UK, for example, we have something like this Octopus Energy. I'm not in the UK, I'm in Germany, but I grew up in the UK. So, Octopus Energy is one company that's been doing a bunch of stuff like this about having agile and intelligent tariffs. So there's essentially a financial reason as well as a kind of basically an environmental reason for doing some of this.

And I think what I've heard in the UK, for example, I believe on an interview recently I heard just by doing some of this, essentially, when controlling some EVs, for example, I think the figure was something like 1.2 gigawatts of demand they had control over. Now that's basically the size of a nuclear power station in many cases, so that's like a significant amount of flexibility on the grid that would otherwise have to come from burning loads of fossil fuels.

Right? Okay, so maybe I can allow us to talk a little bit more about the cost thing because I know that when we talk about this, when we talk about green software, you can make an argument that yeah, you should do it because it's good for the planet, but there's also a real cost fact which comes into this that I think is actually growing and maybe this is something that I understand that you folks have been looking into as well as one of the ways to address more this triangle of inaction and align some of the incentives for more kind of grid complementary activity perhaps.

Íngrid Munné Collado: Yeah. So one of the issues we face is that we know that we want, we are implementing climate action and we want people to use our data for, in order to decarbonize the grid. But sometimes the entry barrier can be quite high when we just go there with, "hey, you have to save CO2." But sometimes it might be easier for users to understand that, "hey, if you look at the electricity prices, that you have also seen that they are not constant, you might be able to save money and CO2."

And that's very, that's a very good entry point as we mentioned in the Triangle of Inaction to increase user awareness because the main, for people it's very easy to understand price and how much they are going to pay at the end of the month and if they are going to save money and therefore it's also nice for us to get to them by I'm Price and then explain that, "hey, by doing that, you might also have an impact on CO2," but we have also a blog post where we mentioned that just optimizing for price doesn't mean that you're also saving CO2.

It's you need to implement some what we call co-optimization, where you want to either prioritize one or the other. And at the same time, maybe you might be able to save more CO2 if you optimize for higher renewable share, and at the same time, you can also save money by doing that. So that's why we think that price is a problem worth solving and that we also need to explore that area. 

Chris Adams: Ah, I see. And if I understand it, and if we follow the kind of path from before, we spoke about, like, a lot of us might be used to just paying a single price for power, but the price might change depending on where you are geographically as well, and that has some implications too, like, this is one thing that I guess, this is probably the newer world we're moving into, in that, yes, there might be, like, night time tariffs, but it feels like there's a lot more dynamism, both temporally and spatially for some of this.

Íngrid Munné Collado: Absolutely. And we saw that when the war in Ukraine started, that as soon as we saw that there were less gas resources, prices skyrocketed. And for example, in Spain, where there was a price cap

The situation was solved also because Spain has other sources of getting natural gas. But countries like Denmark, Germany, they really struggled with that. And the prices really doubled for some months. And it's not only political conflicts. It's also availability of the power plants. I it was two weeks ago where Finland had some unexpected maintenance in one of the, of their, of the largest nuclear power plants.

And that cost their head prices to double. And also in Texas, we have seen extreme weather events that causes prices to change from

Chris Adams: More than double,

Íngrid Munné Collado: yeah, more than double from negative prices at some point because there, there's one area in Texas that has a lot of renewables and another area in Texas that doesn't rely on renewables and due to those extreme weather events, they have to turn off some of the power plants and they, then this affects the prices.

And something I want to mention, I'm an electrical engineering at heart. So I want to talk about the power of interconnectors, and we might think that just by increasing renewables, this is going to make prices to be cheaper, but the problem is not going to be solved, and it's not going to make prices cheaper if we don't have nice interconnectors that make these flows between countries possible.

That's why prices are just changing so much because there's so much happening and going on right now, both on installing new energy sources, weather events that we're seeing, unfortunately, due to climate change and also the lack of interconnections at the moment.

Chris Adams: Okay, so you mentioned two things that I think are interesting there. So one thing was this idea of, okay, the price can change massively. Like the Texas example, I think if we look at, like, the cost of power, right, it might be between 20 to maybe 60 US dollars per megawatt hour in Texas. For example, you're one, you mentioned, like, there's a bunch of wind in the panhandle in the kind of northwest, and then there's around Houston, there's loads and loads of demand where people use all the power, right?

And I think the figures I've heard were something in the region of negative two and a half thousand dollars at one point, and then 30 minutes drive away, the cost is three and a half thousand positive. So like, you got a almost four, five, thousand dollar swing in the pricing here. And one of the reasons is just because it's a bit like network connectivity.

You know, the pipe isn't big enough. And this is one thing that we have to kind of work around, essentially, thinking about this.

Íngrid Munné Collado: And the same issue is happening in Germany, and like, Germany doesn't have these price mechanisms that we have in the US, but in Germany, there are also, like, huge problems due to transmission capacity, because the, most of the solar power production is, takes place in one part of the country, while the demand is concentrated in the completely other opposite.

And they are now experiencing with curtailments and extreme negative prices because they don't have this transmission capacity. They also don't have storage capacity, and there are no market mechanisms that control how we increase this solar power production in the grid and how we just export this to other areas.

Chris Adams: Ah, okay. All right, thanks for the extra context. For people who are still with us, we'll share a link to the previous episode, because we spoke with chap Philipp Wiesner, who was building a bunch of this work to simulate these kind of grids to give an idea of what the pricing might actually be with different services.

And if you have data centers, how adding some storage might actually change the cost and the carbon of running various software services. So we'll add a link for that. Olivier, while you're here, I want to ask one question, if I may, about basically, optimising for carbon intensity, because that's what lots of engineers are kind of trained to do somewhat.

And I, inside the Green Software Foundation, there is a kind of standard called Software Carbon Intensity. It's the thing that you're supposed to, or that you might optimise for, essentially. And it basically lets you use two different ways of thinking about the carbon intensity of electricity.

And so one of these is called a average carbon intensity and another one is called marginal intensity and they are slightly different. And it can be quite counterintuitive when you're first coming to this because it can give you somewhat different answers or incentivize different kinds of actions.

For someone who's coming to this for the first time, can you provide a little bit of like background on how to navigate some of this and how to think about some of this? Because it's something that. I think a lot of people come to and they scratch their head quite a lot because it can be a bit confusing having two numbers which can suggest you do totally different things sometimes.

Olivier Corradi: Yeah, well, we might get into some of the weeds, so I'll try to keep the, sort of, the discussion a little bit high level so we don't get too technical. But I think the way that I'm trying to explain this, so, on the average side of things, the first thing that I will say, actually, is that the word average can be a little bit misleading, because it sounds like, like you're taking an average over a period of time, which you're not.

Actually, what we're doing here is computing what we call the flow trace signal. So it's like taking the production locally, looking at what's imported, and then concluding on what is the constitution of the electricity that I'm getting at. So it's a representation of all the power plants on the system, basically.

So that's what we'll refer to as average. And on the marginal side of things, the story goes that if you're plugging an electric vehicle to charge at a particular time, then it's not all the power plants in that system that will ramp up to give you that additional electricity. It's the one that's called the marginal.

And a loose way to define it is to say it's the cheapest power plant that still has capacity to ramp up, to produce more. So, in theory, that marginal concept makes a lot of sense, because we're saying that seems to reflect what is physically happening on the system. If you actually go a little bit more into the details, and that's where the differences start to pop up quite starkly.

Well, if you are plugging, for example, your iPhone on this, on, to charge right now. Then it's not this marginal power plant that will ramp up. In all likelihood, nothing will happen on the grid. It's just the frequency of the system that will change a little bit. But you're not shoveling a little bit more coal in the coal power plant to burn off a little bit more, right?

So, emission factor would be zero. Like, no impact on the grid, you could say. If you go the other way, and you say, now I plug, imagine a metaphorical plug on a data center that's using like 90 percent of the consumption in the grid, Then you can't have only that small power plant that has spare capacity, it's just not going to be enough to ramp up this data center.

You would need all the power plants on the grid to ramp up in this what if scenario where the data center didn't exist. And then the last example is, if you have an electric vehicle and you're plugging it in, then the electricity will be delivered instantaneously to you. And that's just not a change that a coal power plant or a gas turbine can react on.

And that starts to create some additional complexity when you think about, okay, but can this be predicted? Can the plugging in of my electric vehicle, could that have been predicted by the market? And if it's already predicted, then it's part of the business as usual scenario. It's not marginal anymore.

So when you actually try to go down to all the details, it becomes hyper complicated. And we've tried really hard to talk to all the power system engineers, the electrical engineers and the TSOs in Europe as well. And one of the most fruitful discussions we had was with 50 Hertz that concluded that they don't think you can actually reliably identify or verify what the marginal power plant is because depending on what market you're looking at, if it's day-ahead, intraday, real-time, depending on the magnitude of the changes, depending on all the interconnectivity, the marginal power plant is just a concept that philosophically makes sense, but from a data perspective, it's just hard to measure, and that's also, I believe, why most of the regulations recently on the hydrogen regulation in both the US and in Europe that is documenting what is it, what signal should you be using, whatwhen you want to prove that your hydrogen is clean, then they are settling on an average signal. So if you take a step back from all of this, you're having an argument that philosophically for me makes sense. Of course, you want to make sure that the short term impact of what you're doing is minimized.

When you start looking at the data, you have a signal that is difficult to audit. And we've been working on this for eight years now, six years, sorry, creating a marginal signal and trying to verify it. And I've just seen enough that it can be manipulated in many ways. And that's why it's a little bit difficult.

So to get back and conclude a little bit and to answer your first question, which is how would we navigate this thing? I think the advice we're generally giving is get both signals, plot the data next to each other, and depend on the use cases, ask yourself "Is user acceptance important?" Are you going to show this in an interface?

Because if you are going to show it in the dashboard of an electric vehicle, in a country like France, which is majority nuclear, or in Ontario, lots of decarbonized electricity already, it's going to be a tough sell to tell them, look, your EV is being charged on gas, which is on the margin. So this user acceptance is important.

The second piece is auditability. Are you thinking of being generally directionally correct? Or do you need an auditor to prove that the data is correct from a scope 2, scope 3 perspective? And these are typically things they're going to inform depending on if you're willing to have something that is philosophically more accurate, but in practice more wobbly or if you want to have something that's just simple to explain and sort of abides by the regulation.

Chris Adams: Ah, okay, all right, so if I try to kind of summarize it, it sounds like the marginal thing it's conceptually attractive and kind of fun and it might give me some and in very ways it's basically gives me this idea that I can make relatively small changes rather than some of the systemic changes that might be needed for some of this.

But from an audit, from an auditing point of view, because you're comparing some of this to essentially a counterfactual which might not exist, it's actually very difficult to say, "well, yeah, I definitely made this impact and if it weren't for me doing this these people wouldn't have switched this stuff on," for example.

So you kind of need to have a degree of kind of clairvoyant level of information for this to actually really check this. Okay, cool. Speaking of clairvoyant, I've just realized the reason I've used this term is it makes me think of, there is a paper by a research student, I think her name, Tammy, I'm so sorry if I pronounce her name incorrectly, Thanathorn Sukprasert, she wrote all about using the SCI and using these different signals and how they, when they agree and when they do not agree, and if you are curious about this as someone listening to this, we'll share a link to that paper because it's a really good paper, she's also doing another one which is all about, okay, what are the possible savings from carbon-aware computing?

And that is also a really fun paper to read, and maybe, Tammy, if you're free, we'd love to have you on the podcast to talk about some of that, because there's quite a lot of fun to read there. Okay, cool. Thank you for that, Olivier. I realize that was quite detailed, but it, I appreciate you talking about the fact that in other sectors, there's stuff we can look at, because, as I understand it, this whole shift towards clean or green hydrogen is somewhat comparable to data centers in the fact that you have a very concentrated amount of energy being drawn in one place. So, in the same way a data center might be tens or hundreds of megawatts, you might see something similar with, like, creating hydrogen.

Is that the idea behind some of that?

Olivier Corradi: Absolutely, and if if the way that I'm thinking about this if you really want to simplify it, if we take a step back, the opportunity we have ahead of us is large, abundant renewables that are going to be the cheapest and the fastest way for us to expand the system. I mean, nuclear is great as well, and hydro and so on, but it's just, it takes more time, and it's a little bit more expensive when you put it in directly.

And so, if we put all this renewable in the system, we better well make sure that every flexible appliance out there is aligning their consumption to the time at which the renewables are creating that electricity, because else we're just hindering their deployment. You know, it's less batteries that we need.

And I think that's generally also the thinking behind the hydrogen. We want to make sure we're electrolyzing the hydrogen at the times where all these abundant renewables are producing electricity. And so that's the simplest heuristic and the fastest way for me to explain the systemic change we're undergoing and also why we're focusing on these renewable power forecasts.

There's a lot of value there. And by the way, why they also typically align with the price of electricity.

Chris Adams: Ah, okay, thank you. I do hope we still have some people with us, because that was a bit of a deep dive. But as sometimes when we're coming to new fields, working in software engineering, you do need to kind of engage with the details sometimes. Folks, I think we're coming up to the time that we had allocated for this.

And this has been loads and loads of fun. I've really enjoyed this. If people do want to know more about what either of you are doing, can I just give you a bit of space to talk about, like, Follow me on either Twitter, LinkedIn, Mastodon, whatever, like yourselves personally. And then if there's anything you would direct people's attention to, yeah please do, and then I think we'll wrap up actually, so maybe Íngrid if I hand over to you and then you Olivier, we'll wrap up with you okay?

Íngrid Munné Collado: Absolutely, so if people want to know what I do, they can check my LinkedIn, and it's Íngrid Munne, M U N N E, and I'm also on X or Twitter with the same name. So looking forward to that.

Chris Adams: Thank you, Íngrid. And Olivier, for you.

Olivier Corradi: Likewise, like Íngrid, you can reach me on LinkedIn and Twitter. If you search for my name, you'll find me. And I think in terms of resources to watch, we try to publish blog posts that are going deep into the topic, that are thoughtful. We don't publish a lot, but when we do, it's like We, we at least try to have serious research there, so check out our website, check out our blog, and we have a couple of guides on carbon accounting as well if you want to go deeper on the topic, and a few videos on YouTube if you want to nerd out more on some of the things we just discussed.

Chris Adams: Okay, we'll add a few links for all of those and the thing I'll also share is that in the Green Software Foundation, there are ongoing discussions in the standards working groups to discuss all this stuff. So you can see all the kind of back and forth around this, so if you do want to engage with this and possibly join to actually take part in that conversation, there is that available.

So you can see what discussions have come before and how people arrive at deciding which carbon signal to be following. Alright, I think that takes us to our time. Folks, I really enjoyed this trip, and we'll make sure that everything, or as many things as we remember, are in the show notes for people who want to continue this quest to learn more about building more sustainable software. Thanks a lot, folks, and have a lovely time in Denmark. Ta ra!

Íngrid Munné Collado: Thank you. Bye!

Olivier Corradi: Bye-bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode! 



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]]> <![CDATA[The Week in Green Software: Obscuring AI's Real Carbon Footprint]]> Thu, 19 Sep 2024 08:00:00 GMT 43:39 41pmmxx0 false https://podcasts.castplus.fm/e/08j00qq8-the-week-in-green-software-obscuring-ai-s-real-carbon-footprint 68dc7d0f7be17a7f0135693c 68dc7cef46a2532cdd8d1c23 82 Host Chris Adams is joined by Asim Hussain to dive into The Week in Green Software, exploring the environmental impacts of artificial intelligence and how the growing adoption of AI technology affects carbon emissions, as well as the growing complexities in the measurement and reduction of these. They discuss a primer on AI's direct environmental footprint, regulatory trends in Europe and the US, and the complexities surrounding the renewable energy credits tech companies use to offset emissions. The conversation touches on real-time cloud data initiatives, carbon accounting in AI, and the future challenges of balancing sustainability with technological innovation.

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TRANSCRIPT BELOW:
Asim Hussain: Three, four years ago, everybody treated all carbon offsets the same. They didn't realize there was nuance between them. Now that's changed. Everybody needs to now pay attention to the same thing in terms of renewable energy. If you do not pay attention to the fact that there is a lot of variability in a lot of this stuff, it's all going to get tarnished with the same brush in the future and any renewable energy claim is not going to be trusted.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to the Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. This is our news roundup show in the Green Software Foundation podcast. So we aren't doing a domain expert news roundup. Deep dive where we go into a deep narrow subject, but rather we're taking a more broader view.

So we'll try to add some context and commentary to the stories that have been shared with us that we discuss with our guests. With me today is my friend, colleague and mushroom enthusiast Asim Hussain of the Green Software Foundation. Asim, it's really good to see you again. How are your holidays?

Asim Hussain: Yeah, it's been quite a long time off and just gently dipping my toes back into the swing of things. Glad to be on the show. It's a nice gentle introduction back into the world of green software. So, glad to be here again, Chris. Asim Hussain, I'm the executive director of the Green Software Foundation.

And yeah, I spend my day probably similar to you thinking about how to advocate for green software. What do we need? What do we want? What are the questions that need to be answered and what are the levers that we need to pull to get action taken in this space?

Chris Adams: Reduce the environmental impact of digital services. Yeah. Okay, cool. Thanks. I should introduce myself as well. Hello, folks. My name is Chris Adams. I am the executive director of the Green Web Foundation. That's a smaller, a different organization. We are a Dutch non profit focused around reaching a fossil free internet by 2030.

And we do that using open as a lever. So we do loads and loads of stuff with open source, open culture, and things like that. As a quick reminder, we're going to share all the stories that we have and any projects or things that come up, we'll add to the show notes. So if you want to continue your quest to learn more about green software and how to reduce the environmental impact of digital services yourself, just look at podcast.greensoftware.foundation to see this. If you're looking in Spotify or some of the other podcast tools, you might not be able to see the links. So please do go to the website to see the show notes and you will be rewarded with diligently prepared links and helpful notes, and a transcript as well.

Alright then, so, I guess, Asim, I've introduced our News Roundup format, we kind of know what we're going to do, we've done this a few times, I assume you're sitting comfortably, shall we begin?

Asim Hussain: Yeah. Yes. Yeah. Let's go for it.

Chris Adams: Alright, okay, so the first story I see here is actually a post from, from Sasha Luccioni, Bruna Trevelin, and Margaret Mitchell, Hugging Face the Environmental Impacts of AI - A Primer.

So, this is one thing we had shared, and, Asim, I'm going to ask you, you've had a chance to look at this, what made you think this was actually worth discussing, and what would you draw people's attention to if they'd heard about this? And how would you, like, persuade people this is worth a read, for example?

Asim Hussain: Well, I suppose if you're new to the space and I think there's a lot of people out there who, for whom are kind of surprised to find out that AI has an environmental impact. So this is, I mean, A lot of this stuff, obviously we've been talking about different components of it over the last couple of years, but I think it's a really good, it's actually a great summary of the different components of what makes environmental impacts of software.

It's also got information there about what are some of the legislation coming down the pipeline? What are the, some of the actions that you can do? And some some things there. So I think it's a really good kind of primer for people. I think it's the title, is the title Primer? It

Chris Adams: Yes,

Asim Hussain: Yeah. So it does what it says on the tin. And I think it's probably could do as a really great introductory piece of information. It's got some great links there as well.

Chris Adams: Yeah, the thing that it might be worth just focusing on briefly, or one thing that leapt out at me when I looked at this is that it talks about the direct environmental impacts of AI specifically. So rather than talking about AI for good, or like "isn't it great that you could use AI to, say, make it easier to deploy renewable energy, or do this, or do that?" Right?

They're talking about, "no, there's an environmental impact that you still need to address regardless of whether you use something for good or for bad," and it seems to be focusing primarily on that kind of stuff. So as a responsible engineer, these are the things to think about. These are the kind of, what you might call an impact criteria, like there's carbon emissions, there's water, there's other things like that.

And generally this is from one of the kind of most trusted hands-on group who are like at the coalface for all this stuff. I mean, maybe, is coalface the correct term?

Asim Hussain: Yeah. I always use that term as well. And then I'm like, actually, that's a

Chris Adams: Maybe if you're in, maybe if you're in US AWS East, it's coalface and then if it's on AWS west, it's hydro face.

I dunno. 

Asim Hussain: I use coalface and then if I don't, then I sometimes use front lines and I'm like, "actually, that's not a good term as well." We need a term which doesn't have a war Metaphor

Chris Adams: or industrial revolution connotations

Asim Hussain: Metaphor.

But anyway, it's at the cutting edge!

Chris Adams: Yes.

Asim Hussain: Cutting edge. There you go.

Chris Adams: Okay. That, that, that will do for me. Alright then. Okay. That's the first thing. I think on a following from this, when I was reading this. I quite like that it actually touched on some of the regulatory drivers that you have, because it's very common for people to talk about the AI Act, because that's probably the first piece of legislation, but it also calls out stuff taking place in Spain, and taking place in the US, and it shows that there's a kind of growing, I guess, regulatory trend to basically say, "well, If you're going to have this piece of technology in society, then we need to have a data informed discussion about what impacts it might actually have."

So we can talk about, okay, where is it responsible to deploy this? But also just like, okay, how do we actually mitigate this? Because, I don't know, it seemed like cars are useful. And the fact that cars are useful doesn't mean that we don't talk about seatbelts, right? You still have to talk about them being safer, regardless of how useful they are, basically.

So it's really nice to have a trusted organization sharing some information like this in a relatively roundabout in a, in my view, quite concise fashion. But if you look at the set of footnotes, wow, there's so much stuff that you can dive into if you wanted to kind of go down that rabbit hole, basically.

Asim Hussain: Yeah it's a very well researched, almost a state of the art paper. And also say like, I think it's also good to know, because people don't, yes, there's regulation coming down the pipeline, and some of that stuff is more mature than other regulation. But I think when you're working inside large enterprise organizations, this is the kind of stuff that gets people to pay attention.

You can be talking for ages about, "hey, look, there's the carbon impact, it's having a... we should be looking at our AI usage" and sometimes that can land and sometimes that can't, but a regulation or the threat of a future regulation is something that I've seen personally open a lot of doors. It doesn't kind of complete the internal sale of, "we need to invest in mitigating the impacts of our AI use." But it's certainly, I've seen it kind of open up a lot more doors because regulation is something that a lot of organizations pay significant attention to, and it's also something that they will, Invest time before the regulation comes out to look at it and put effort into it.

So I, I look at that, right. And it's kind of, it's great to see those regulations that I wasn't aware of. So I'll be obviously using this as it's a great primer for me as well, but this is a really good way of capturing people's attention. So then you can have that more refined conversation about, well, how are you using your AI?

Let's have a conversation about it. How is it, how is future regulation going to affect you? So it's a really good way of opening that door. And if you're inside an organization and you are a little bit concerned about the consumption of AI that you're having, I think for me personally, like pointing out the regulation that's coming down the pipeline does open a lot of doors, enables some conversations with leaders.

Chris Adams: This actually might be a kind of somewhat appropriate time to mention some of the kind of policy stuff we're doing, because I, so in addition to doing the podcast here, I help co-chair the policy working group. And we've, I think we've, we, we're likely to be getting quite a I'm going to put a policy radar out to see precisely this kind of stuff coming up.

Because, yeah, if you didn't know, I mean, okay, today is the 6th of September, and, you've seen this whole energy efficiency directive thing in Europe, right? So, in nine days time, every datacenter that uses more than 500 kilowatts of power draw has to start reporting and posting in public all of their absolute energy use, absolute water use, the amount of energy coming from renewable energy, how much of it comes from the kind of credits that you might buy which are unbundled, how much is coming from a power purchase agreement, so the kind of green energy that you've purchased directly.

There's all this new stuff. There are some caveats around this. So not every single organization will have to, we'll probably publish, but we basically have a regulation that's saying, "look, this has to happen now in nine days at the time of this recording. So when this goes out, it probably will have already happened.

And like this gives you an idea that if you didn't know this is happening, then you probably will, we do need to know this because this is written into the law in lots and lots of countries now. Well, all across Europe, for example, and I suspect this is the kind of thing we might see in other parts of the world because when you look at the figures and look at the data that people are currently basing policy on, it's really hard to figure out what the environmental impact of, say, data centers might be or what the growth is going to be.

And if you want to plan for a grid or plan for hitting some climate targets, this is the kind of stuff you need to actually be knowing about.

Asim Hussain: But it's also useful because we've been having these conversations for a while about, I think we spoke on the podcast a couple of times in the past and when we were developing the SCI specification, it came up a lot like do you include the data? How much of the data center do you include? But the biggest problem was, is that you don't know.

If you decided to put into the specification, you've got to include certain, the concrete or whatever it is that goes into data center. If that data is not public, then what's the point of putting into specification? That's why these regulations, that specific regulation is so interesting. I'm interested to see what actually happens in nine days time and the quality of data that comes out.

The conversations I've had in the past, because when this first started being discussed, I was chatting to a lot of, not data center operators, but people that worked with data center operators. And obviously it kicked up a storm and everybody's like, well, I need this data. How do I get this data?

What is the minimum level of information I can provide to like meet? And that's where it gets really interesting for me. What is the minimum level of information I need to provide to meet the regulation? And I think in nine days time, we're going to find out what is the minimum level of information that people have figured out that they

Chris Adams: Yeah, they can get away with,

Asim Hussain: that they can get away with? Because when I had that conversations, I don't know where it landed now, to be honest with you, but they were like, is it at the building level? Is it at the rack level? You know, it's, and it was like, it's at the building level is where I was left at. So I think the more and more this regulation comes along and it kind of surfaces this data to us, then we can then use that data to make more informed choices, hopefully not from a consumer level.

I think it should be from a, not from an end user level, but from the people who use data centers and make make different choices.

Chris Adams: I think you're right. Okay, what we'll do, we'll share some, a couple of links to this, because this is something we've discussed in a few places, there's one or two working groups where this has come up, in particular, because there's also, on the, just, just before we move on from this, there's a whole, there's a current kind of, in my view, an interesting discussion going on about, okay, with this, in Europe at least, with this AI act, yes, it says that you need to talk, you need to disclose the training data, the energy used for training a model, right?

But it's not totally clear if you need to also track the inference, right? So if you think about the training part, and I've shared a link to a blog post where I've basically highlighted the bits of the law that make me think that you might need to track inference, or at least disclose some information about likely inference because you can think of like the training part as like the energy going into making a car and then the inference figures as a bit like the car's mile per gallon for that model, for example. And well yeah, well it's not totally clear yet and we've seen the law passed and we'll figure out yeah.

Asim Hussain: Cause I assume the only, I'll be honest with you. I assumed included inference. The only time I did it was when I just like read your article and I was like, oh, hang on. That's and that's where these things get very interesting to me. I mean when I was in working in enterprise like organizations and I've one of the things that was always interesting to me was whenever I asked questions or got meetings together, like "let's talk about, we've got some questions about the, how do we calculate this figure to meet with this specification?" And there's almost always legal got involved and lawyers got involved. And I was always kind of, I'm like, "I don't need to speak to a lawyer. I need to speak to an engineer."

Why am I speaking to a lawyer? Because it's all about, "let me read this text. What can we interpret from this text? What do we need to give?" So I assume just because everybody just, we know inference is where most of the emissions are these days, I just assumed it was that, but you've now actually read the text and gone, the text has, is interpretable.

Chris Adams: Well, yeah, because you think about like when this initiative was written ages ago, like a few years ago, it's gone through this massive kind of gestation process, right? And a couple of years ago, when we hadn't really got to this point where AI is being deployed in the same way, like, was it November 2023?

That was like 100 million users. OpenAI had gone from zero to 100 million users in five months. And maybe last week? We should share a link to Simon Willison's blog post because he wrote about this quite eloquently. He's like, well, OpenAI have just mentioned that they're now at 200 million weekly users. So that's like doubled in a single year. So we've gone from, so inference is now a significant part of the story in a way that it wasn't previously, basically. So it may be that the law, when it was written two years ago and began that process through it, it might not have been such a concern. And this is the thing that we're, this is why it seems a bit unclear, and I think we'll probably end up with a test case that will set a precedent for people to figure out what they should be sharing or what you might need to share if you're building new foundational models in future.

Asim Hussain: I mean, I can tell you that, that preChatGPT kind of announcing, it was very well known that inference was significant, as in way more than training for any, like, I can't reveal too much, but you know, it was known very much that that was the case and a lot of effort had been put into mitigating, not from a carbon emissions perspective, just from a cost and energy, but just all of that stuff.

So it's kind of known, it might not have been in the zeitgeist, it might not have been in the kind of the public discourse because it's so much easier to talk about this big training runner. Maybe there's just more public data about that because inference in a way, if you think about it, is going to be pretty private.

Because that's inference is basically telling everybody the business end of your where you're making money from, and they'll probably keep that pretty private. So yeah, maybe it just wasn't well known, but it was true and well known, I think, to anybody in the kind of the AI space that inference was a pretty big deal prior to this.

But yeah, it makes sense. Yeah, these acts take a long time. So yeah, a couple of years ago, all we were talking about was training. It was a good, it was a good headline to discuss. Yeah.

Chris Adams: Useful insight from the inside tracker team, alright. So there's one thing you mentioned actually, we're just on the subject of inference. There is this, in my view, really interesting project right now, EnergyStarAI, which is a project which is, you see a few names associated with, so, Sasha Luccioni, Sara Hooker, Régis Pierrard, Emma Strubel, Yacine Jernite, I think, Carole-Jean Wu, and one of our own at the Green Software Foundation, Boris Gamazaychikov.

He's at Salesforce and he's been one of the people who's writing publicly about a bunch of this stuff and also about like, quite, the small models as well as large models. And it's, I was really pleased to see his name actually. So. Hi Boris, if you're listening to this. This is a really good story to look at, because this essentially is talking about inference and saying, "well, let's find ways to make this visible for people" two years later now, basically, and say, "well, let's see if we can find ways to introduce some of the incentives to go for more efficient inference," the same way we've done with Energy Star in other kind of industries, for example.

Asim Hussain: There's really interesting, I'll try and get her on the podcast, actually, she's from IBM, her name's slipping me, so I'm not as good as names with you, you can just rattle them off, but I'll make sure to put it in. Because yeah, there's Energy Star for AI, but we're also, there's conversations inside the foundation as well now, kind of looking at SCI and how do you apply SCI to AI and kind of, there's a lot of overlap with a lot of this work as well.

But what's interesting is there's this real question about what to do when it comes to inference and training. Like if you were to report that. How do you report that for a model? And the point that was raised, and I thought it was so, because I never thought of it before, which is, if you've got a foundational model, you've done the training, you've done a big training run for a foundational model, and you're now then running inference on that, when you report, let's say, I don't know how any, I think Energy Star is just going to be like a good bad kind of label, where SCI is more like a score, like a carbon per prompt or something like that.

How do you apportion the training into the, do you include the training every time you call it? Because if you do, there's a really interesting thing that happens, which is these foundational models, like if you're using an open source model and you just, that costs 10 000 to run. Do you include that in your AI solution and then just say like, "Oh, I'm three, three grammars per prompt?"

Then somebody else uses that foundational model. Do you then divide that by two and say, well, now you're, you

Chris Adams: talking about double counting kind of question, right?

Asim Hussain: And then like, if that is how the measurement eventually lands, then if you're an unscrupulous organization, all that you would do is try and get as many people to use this foundational model as possible to then dilute your numbers.

And so I think one of the, one of my little bugbears, and it comes up quite often, is the assumption that something that works in the physical realm will work in the digital realm. And one of the things I try and educate people as much as possible is that stop trying to take something that has worked in the physical realm and apply it to digital because there's so many ways it just doesn't work.

If you're thinking about training like a scope 3 embodied carbon physical device thing. You can't divide a chip in two and I give you half and you, it's like, that chip's yours, but you can do that in the digital realm. So there's this whole supply chain accounting aspects of digital emissions, which, it just needs to be thought of differently in the world that we're in. And if you don't think about it differently, you can then have, I call them unintended, we used to call them gaming, like when people were developing the SCI, like one of the conversations we'd have on the calls is, how is somebody going to game?

How is somebody going to take SCI,

Chris Adams: Carbon Intensity here, right? Yeah, okay.

Asim Hussain: how is somebody going to game the side? That's kind of a lot of the conversations we had at the start. Cause obviously everybody was like, we want to make sure if we develop a standard, people aren't going to then

Chris Adams: Abuse it,

Asim Hussain: it. And therefore the standard has no respect in the world.

And so like a lot of how, like a lot of how I kind of work with the standards projects here is I'm a bit of an annoying devil's advocate. I love it actually because I kind of walk in and go "here are 10 ways I can hack this standard to present a better score without actually doing anything." And so I think that's some of the things we need to think of as we think of SCI for AI, as you think of Energy Star, as you think of these other things is yes, there's this happy path that everybody's a good actor,

it will work and it will give you the right signal, but we need to think about the non-happy part, where people might not even necessarily be bad actors. It's just death by a thousand cuts. You're working in an organization, you've got a deadline, you've got a bonus you have to meet, there's a customer that you're going to have to get or you lose your business.

And so you're just death by a thousand cuts. So yeah, we have to be very careful as we explore like SCI for AI and Energy Star and anything really in this space, which is talking about measuring emissions. Because if you don't think through those unintended consequences, that's a problem. And that's one of the ones I have is like, is if you're including training, how do you apportion that?

You might not actually want to include training. You might actually want a separate. You actually, you might actually only want to measure the inference, because that gives a truer figure.

Chris Adams: So to cap this off, I'm going to, as we move to the next story, I'm going to link, share two links which might be useful for this. So the first is the link to the Meta Llama 3.1 8B, their model card. They literally say "the methodology used to determine energy use and greenhouse gas emissions can be found here."

They've linked to it and they said "since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others." So they're basically saying you don't need to count that part. That gives an example. We'll share a link to that. The other thing we'll share a link to for the show notes is actually the Asim, for this software, carbon, yeah, this is

Asim Hussain: Yeah, this is my unintended.

Chris Adams: This is a good example because this is where you've basically said, this is me, like, red teaming this approach, and these are the ways I can basically, in bad faith, try and engage with this example.

And this will probably be useful for people who are looking at this, to get an idea of, like, how some of these standards or some of these conventions are being developed. Alright, shall we move to the next story? Because it kind of does relate a little bit to, basically, tweaking numbers to present a view of the world, basically.

So this story is now, how tech companies are obscuring AI's real carbon footprint. This is a story from Bloomberg, I believe. Asim, do you want to introduce this one?

Asim Hussain: So yeah, so I thought, well, the reason this kind of popped up on my radar was, I forgot what I posted on LinkedIn, but I was posting, I started posting a bunch of information on LinkedIn about the use of, the use of RECs and the effectively like kind of renewable claims that organization makes and how it's in a really frustrating way, it actually puts us in what we're doing in kind of competition with this very important energy transition, because the argument I'm making is, look, you can either do two things and then we'll talk about AI.

Let's keep it to AI. You can either make your AI model more efficient, so it consumes less energy. Or, you can do absolutely nothing, and just buy offsets, energy offsets, RECs, whatever you want to call them, to mitigate, theoretically, your energy offsets, your energy consumption. And that's kind of like being this "Are we friends?

Are we not friends?" How do we like, we want to support the energy transition, but at the same time, like we really want to advocate for more energy efficiency. So, and I think one of the things we've spoken about is that there's, when you do make these renewable energy claims, like one of the things that you do with all types of offsets to kind of avoid a greenwashing claim, you have to have that additionality component to your offset, which for the audience means that how do you, if I'm saying this thing is offsetting your emissions.

What is it a litmus test to say that is a true statement and is basically, are you actually adding? So for a renewable energy credit, it's like, if you weren't about this renewable energy, would that thing have happened?

Chris Adams: So you're talking about the counterfactual here, right? So you're trying to compare something against. This is, you see a load of this in the hydrogen circle, in the hydrogen, in the world of hydrogen, because Just like datacenters, hydrogen electrolysis, like the electrolyzers use loads and loads of energy, right, and one way that you can do that is just by plugging them into the grid, right, and there's various people doing various things to say, well, I'm just going to buy a bunch of, say, renewable energy credits, right, and that's going to make that count as green, and there's, that's, in some ways, that's kind of somewhat problematic because, essentially,

Asim Hussain: coal to make hydrogen.

Chris Adams: Yeah, that's not exactly what all, you're, in many cases, you're a, you're burning coal to make hydrogen, so the actual net, it's a net loss in climate terms. But also, the, there's been a big fight in the kind of hydrogen world of, to have like this notion of three pillars. Where you basically, if you're going to have something, if you're going to count something as green hydrogen, then you need to be talking about new infrastructure being added to the grid to provide that new supply.

You can't just use, you can't just take from the existing stock of green supply and then count that as green. And this is one of the things that we've seen, like, I don't, Amazon made the news, I think a few weeks, a while, because they basically acquired a data center from a company called Talon, I believe, where they're right next to a nuclear power station, right?

So this, that you, there are some people saying, "oh, this is great, isn't it good that Amazon's using a bunch of clean power," but then you've got to think about, well, okay, who was that clean power going to before? Was it going to the grid? Like, there's a whole discussion there about this. Yeah, so there's a whole set of things to be talking about and this is why this is such a kind of fraught area, basically.

Asim Hussain: I mean, but I think the way to bring it back to something that people understand is when we talk about carbon offsets, I think now it's more understood that it's kind of like you, you have carbon removal offset. So you can plant a tree. Right? And then you planted the tree, that tree will grow, and there's issues there.

That tree will grow and also suck carbon from the atmosphere. And you can say that's a carbon credit of planting a tree. Or, there's carbon avoidance offsets. And there's many various, and there's actually very good variants of carbon avoidance offsets. But there is a variant of a carbon avoidance offset where I've got a tree.

And you pay me not to cut it down. And so where's the additionality? If I'm actually planting a tree, it's happening. I'm planting a tree. I'm adding additional kind of capacity in carbon removal. And in the renewable energy markets it's exactly the same. You can have renewable energy. Which if you buy means a renewable power plant is going to get built and you can have renewable energy which is just kind of sold and if you buy it or you don't buy it there's no change nothing's going to happen there's no more new renewable plants going to get built. Only one of them has that additionality component and so, therefore, only one of them should really be used in any kind of renewable energy claims.

But both of them are allowed in terms of renewable energy claims. So in terms of what this article is talking about, when they're saying "tech companies obscuring AI's real carbon footprint," they're actually talking about companies using what's called those unbundled RECs, which is those RECs which do not have that additionality component.

And then use buying them and then saying, "well, that's mitigating my environmental impact." And what the article is talking about is really, you should be looking at higher quality, Renewable Energy Credits, ones with more additionality components to it. And I think that's really interesting. There's actually also really, Olivier Corradi from, don't know if I'm pronouncing the second name correctly.

Electricity maps. Yeah, he, when I was talking about, he shared a really interesting article he'd written a year ago, which I thought was interesting as well. I'll share that here if you've got it. Yeah. He's actually advocating for like a more nuanced approach to looking at renewable energy in that there's additionality, then there's additionality, and then there's additionality.

There's like different levels of additionality. There's like, "this definitely 100 percent would never have been built unless you bought this renewable energy credit." And there's other ones like, "we may not have been able to build it, but we had some funding from here and there." So there's kind of different levels of additionality here as well, which I thought was really interesting also.

I'd never thought of additionality more than just a binary yes/no. And he was saying it's actually more of a score for a renewable energy credit. But

Chris Adams: Yeah, there's totally a continuum there. So the thing I might share for people who are looking for something actionable to work with here is basically the numbers that you often see reported by technology firms. There's all this, there's all this nuance hidden behind it. And there's one project called the Real-Time Cloud Project inside the Green Software Foundation, which essentially is a data set of the largest three providers.

So that's Amazon, Google, and Microsoft. And they've got the figures shown in both the kind of location-based figure, which is the closest thing you might think to, like, the physical location, the physical impact on the ground. They also talk about some of the market-based figures, which is what lots of firms like to use, like market-based on an annual basis.

But they also provide a few other details and a few other ways of talking about it, because some firms are now talking about hourly, basically hourly green energy versus annual green energy, with the idea being that you, it's a way to try and avoid making claims about saying, "I'm running a data center at night with certificates coming from a solar farm." This is inherently a little bit silly. So they address that stuff. So there's a, there's now, I think it's in the final stage of what's referred to as consistency review, where every member in the GSF is able to just say, "Hey, I object to this, or I'm not sure about this." And then, yeah, there'll be an open data data set for every single region from the three largest providers, which make up more than two thirds of the entire cloud market.

So you have some meaningful numbers that have come from the actual big providers themselves that you can actually, that we can work with.

Asim Hussain: And I think, like, I think basically my, I think the point I'm generally raising out, out there with another kind of, one of the reasons this article was very interesting to me, and especially the work that Realtime Cloud is actually interesting. Three, four years ago, everybody treated all carbon offsets the same.

They didn't realize there was nuance between them. Now that's changed. Everybody needs to now pay attention to the same thing in terms of renewable energy. If you do not pay attention to the fact that there is a lot of variability in a lot of this stuff, it's all going to get tarnished with the same brush in the future and any renewable energy claim is not going to be trusted.

So I was, I'm kind of a guiding and advising organizations to pay very close attention to kind of the type of renewable energy that you're buying. And be aware that because of podcasts like ourselves, there's generally, it's a Bloomberg article talking about this right now. It is now becoming very aware in the minds of a lot of people who care about this space, who listen to our podcasts, who are paying attention, that there is nuance here.

They're paying attention. And so as an organization, you need to pay attention to this as well.

Chris Adams: Cool. Asim, I'm just going to add this one thing because I realised I should have mentioned this. So I work in an organisation where we do track some of this stuff. We track the transition of the internet away from fossil fuels to greener energy. And, I've shared a link for the show notes. Because we're basically reviewing our own evidence that we accept for green hosting.

And we've linked to a couple of papers. And specific reports, which dive into this a bit more, which have kind of also prompted us to start looking at this. So, organizations like the Science Based Targets Initiative, we mentioned there. We talk about some of the other things that we, some of the nuances around RECs.

And yeah, this is, this will be something we'll be doing. So we're going to be essentially figuring out how to do this ourselves in the open over the next few months. So, Yeah, I guess it applies to small firms as well as large firms.

Asim Hussain: Yeah, yes, absolutely.

Chris Adams: All right, should we move to the next story? So, this is a story.

Researchers analysed 1 500 climate policies to find what works. And these are the lessons for Australia. I think this is the link you shared with me, Asim. There's a very kind of Australian centric kind of point of view, which, as someone born in a small mining town in Australia called Prospect, because what else would you name a mining town other than Prospect, because it's full... you? Yeah,

Asim Hussain: I didn't know you were born in Australia.

Chris Adams: Ah yeah, born in Australia, small mining town.

Yeah, I was literally born in a mining town called Prospect, and it's near One Tree Hill. Can you guess how many trees are on that hill? And it's next to Dry Creek. Can you guess the conditions of that river? Yeah, it's descriptive rather than creative, is the term I've heard people in Germany who do similar things talk about, actually.

Okay, so you shared this story, maybe you can introduce this one here, because I think it's quite relevant in this discussion, what we were just talking about in the previous two stories, actually.

Asim Hussain: Hmm. So I think it was just really interesting. It was an analysis of like 1500 climate policies and just really looking at what worked and what didn't work. And I thought it was interesting because we spoke a lot about, we've spoken a lot about things like carbon levies and things like that on this podcast.

But what I found interesting about this article was they, again, brought nuance to the discussion and saying, "actually there's different, different policies seem to work for different types of organizations and also combinations of policies seem to work better than individual policies." So a couple of interesting ones.

So one of the highlights I got, so some of the, in developed economies, some of the most successful cases were the results of two or more policies working together. So that could be like a ban or something, as well as like a carbon tax, kind of pulling those things together. Oh, for instance, like a great example they had here was like, for instance, example, a fuel efficiency mandate for vehicles combined with subsidies for developing like charging stations and things like that. So then you've kind of got the pressure on both sides. And another thing that was really interesting was cause we spoke about kind of carbon levies and pricing was particularly effective policy in sectors dominated by profit orientated companies, such as electricity and industry.

So I just think it was really interesting to kind of think through it from that perspective.

Chris Adams: So there's a really nice example, there's a few really good examples. Good concrete examples of this to make this, like, something you can, like, get your hands around. So in America, right, we've seen the Inflation Reduction Act. So that, in many ways, are kind of it's all carrot, no stick. So the idea is that there's massive amounts of subsidies for building out, like, for, like, EVs or building out new, kind of, battery gigafactories, all this stuff like that, or things which are essentially make deployment of renewables much, much more profitable than they otherwise would be, because they're gonna, because you have all these kind of subsidies saying, well, we're going to give you a production tax levy.

So for every unit of energy you produce, you'll be able to get, you'll be, you'll get a credit that you can actually apply. So your project over the entire length of it will end up being slightly more profitable. That, there's stuff like that, that you see, which is coming from one end. But we've also seen, In America, the EPA, the Environmental Protection Agency, they've got, they've now come in with a stick, or they're coming in with a stick now, to basically say, well, we're going to have to regulate carbon emissions.

And this now means that it's going to be all new kind of coal fired power stations or gas fired power stations, there'll be all these restrictions on how you should, how you can use them. And this is particular, the reason I raise this in America specifically is we were talking a little bit about AI before, right?

Now these regulations, I don't think that many technologists are aware of right now. They basically say if you're going to run a gas-fired power station, you need to fit loads of carbon capture and storage onto it, which is, broadly speaking, if it does work, it's not something that's really used in large amounts right now.

And what you currently see right now is you see lots of utility companies basically saying, "Oh, the only way we can possibly meet demand for AI is to build all this gas right now." And the problem with that is that ends up locking in all kinds of emissions. Because once you've built something, you have this incentive to kind of try and get your return back on building this in the first place.

And this feels like," I don't think people have realized just how much of a stick this is going to be, because as far as I can tell, all the laws from the EPA basically say, look, you can't build gas like this, and you can't actually do this." So we're going to have, we've got like this case of massive build out of AI coming up against all these regulatory forces as well.

And it's going to be quite a significant fight in the next 6 to 12 months, I think, because yeah, this is, we've now had the honeymoon period of all carrot. Like you said in this piece, and now we're coming up to the stick, which is the other part, to kind of make sure that you can, make sure the significant part of the US grid is going to be decarbonized by, I think it's the mid 2030s, basically, is what they're doing, that they're aiming for with this.

But we have the same thing in the UK as well, like, UK right now, we've got a target for, the UK has agreed to try and decarbonize the grid entirely by 2030, which is great for us as an organization because we, we want a fossil free internet by 2030. So we're like, "oh, thank God the UK is doing this." The UK government, one of the big kind of manifesto policies from Labour coming in, who've just won the election is "we're going to have a clean grid, entirely clean grid by 2030."

So five years, basically five years time, they're going to get rid of all the fossil, almost all the fossil gas generation, right?

Asim Hussain: How are they going to do that? 

Chris Adams: That's what we'll find out. But the thing I found out when I spoke to some people who, basically, this is actually all based on some modeling using a piece of software that we interviewed a chap called Iegor Riepin, he was talking about this in one of the episodes, we'll share a link.

That software was, basically, these kind of things were put together by some analysts on our laptop saying, well, this is what you can do. There's a report from Ember Climate where they, the report is called Escape from Gas, I think, or A Path Out of Gas. And this was one of the things that was written in 2022, when gas was super expensive, to say, "well, this is one thing you could plausibly do for this."

And yeah, when, the thing about policy, people reach for what's there. This turned into one of the things that one of the parties led on, and now we're going to see if we do see a fossil free grid and fossil free internet in the UK by 2030. Because, yeah, it's fascinating. I'm so, this is the most exciting, most excited I've been about UK politics in a very long time.

Asim Hussain: I don't know. I might dampen it for you. I'm just not, I'm just not very, I'm just, there's a lot of manifestos that come out from governance when they join and there's a lot of disappointment in the years later when they, when it doesn't manifest, when their manifesto doesn't manifest.

Chris Adams: So this is the final thing that might come in, might be relevant. So the modeling that was used for this as the basis to say, "yeah, we can do this." This one thing that ends up being, so I'll share a post to it, which I end up doing a bit of research and speaking to some of the people about this. It's actually very conservative, more conservative than the National Grid's own

estimates about, specifically in our industry, demand size and batteries. So, these are the two big things that we're likely to see a massive increase in.

Asim Hussain: That's what gas is used for more like this is specifically to get rid of gas.

Right. 

Chris Adams: yeah, so the,

Asim Hussain: peaker plants and then therefore you can do a little demand, demand responsible.

Chris Adams: Partly that, the thing that they said is like, the, their plans basically are relatively conservative about the ability for demand side of reduction, making your, you know, Carbon Aware in stuff like that, right? And there is another thing that we've seen is that the UK government is actually being quite gung ho about deployment or deploying all these new data centers.

So I'm kind of thinking, is there a chance to actually say, "well, okay, if you're going to have this deployment of all these data centers, and you know that one thing you're going to need to have is a much more responsive grid, is there a path for all this kind of carbon aware infrastructure to actually serve some of the roles that you wouldn't have to typically rely on peaker gas plants to actually fit, to like kind of fit?"

There's a bunch of stuff there and I think we'll learn basically because, yeah, this has been a really ambitious goal and you've also got this other idea to like bring in something which, can be quite flexible, but only if you incentivize infrastructure to be flexible, because for the most part, we don't see an economic incentive passed down to the consumers of infrastructure to be using this right now.

So, yeah, maybe this is a help of one piece. Yeah.

Asim Hussain: Yeah, I mean, I think there's been, there was some really good work done kind of several years ago, and this will be really good because one of the things I've seen is that the, all the positive moves I saw kind of three years ago regarding new data center rollouts, hydrogen fuel cells, kind of building kind of a much more advanced data center seems to have gone back a little bit.

And yeah, You're right, I think data centers could lead the way in terms of demand response. I'm not even talking about compute demand response. You can just take batteries, you can fill data centers with batteries and then they can store and then they can do their own sucking from the grid when it's clean and powering their own infrastructure when it's dirty.

You know, there's, there's other solutions, which doesn't even necessarily need kind of a software,

Chris Adams: Yeah, exactly. I mean, this is one thing that we've seen in Ireland. There's precedent in Ireland where people have said, "if you're going to be connected to the grid, you need to be prepared to be kind of complementary or sensitive to the needs of the grid for this." So, I think there's actually room for this, and it will be really nice.

I think that this feels like, given such an ambitious target, it does feel like a role where you could actually tell a good story about green Software, and be part of the solution as opposed to part of the problem, because a lot of the discussions around like rolling out of digital infrastructure is basically saying we can't possibly meet this demand.

But if we accept that demand is dynamic, then there is a chance to actually fit this in, and that feels like definitely worth going for, particularly to kind of maintain this kind of social license for operation, particularly for technology firms.

Asim Hussain: And I think a lot of what you've just said over the last couple of minutes runs very counter to what we were saying before about, I mean, everything you just described, this is all related to that whole idea of additionality. It's all about how do we transition, truly transition the grid to be fossil free?

And you need solutions like this. Not necessarily buying unbundled RECs, but you need to actually, like, think through, "well, how do I how do I be a better citizen in the grid infrastructure, do demand response, be sensitive, not demand energy when everybody needs it and therefore we have to spin up a gas power plant or something like that."

So these are the kind of things you need to actually transition the grid.

Chris Adams: Asim, I think we might have fallen down a bit of a grid rabbit hole,

Asim Hussain: Yeah, we've done it again, haven't we?

Chris Adams: Yeah, so, we're gonna have to move on, I think we've come up to time, but Asim, it's really nice to see you again, I'm glad you had a nice holiday, and I guess we've got a bunch of new things to do this quarter, right? With various projects we have inside the Green Software Foundation, and in the other member organizations related to it.

Alright dude, it's Friday, so have a lovely weekend, and for those listening, we'll put all the links to everything we've discussed in there, and if there's something you didn't see, Please do let us know, and we'll make a point of adding it. Alright, thanks a lot folks.

Asim Hussain: Thanks, Chris. Bye.

Chris Adams: See you around soon. Bye! Hey everyone, thanks for listening!

Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> Host Chris Adams is joined by Asim Hussain to dive into The Week in Green Software, exploring the environmental impacts of artificial intelligence and how the growing adoption of AI technology affects carbon emissions, as well as the growing complexities in the measurement and reduction of these. They discuss a primer on AI's direct environmental footprint, regulatory trends in Europe and the US, and the complexities surrounding the renewable energy credits tech companies use to offset emissions. The conversation touches on real-time cloud data initiatives, carbon accounting in AI, and the future challenges of balancing sustainability with technological innovation.

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TRANSCRIPT BELOW:
Asim Hussain: Three, four years ago, everybody treated all carbon offsets the same. They didn't realize there was nuance between them. Now that's changed. Everybody needs to now pay attention to the same thing in terms of renewable energy. If you do not pay attention to the fact that there is a lot of variability in a lot of this stuff, it's all going to get tarnished with the same brush in the future and any renewable energy claim is not going to be trusted.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to the Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. This is our news roundup show in the Green Software Foundation podcast. So we aren't doing a domain expert news roundup. Deep dive where we go into a deep narrow subject, but rather we're taking a more broader view.

So we'll try to add some context and commentary to the stories that have been shared with us that we discuss with our guests. With me today is my friend, colleague and mushroom enthusiast Asim Hussain of the Green Software Foundation. Asim, it's really good to see you again. How are your holidays?

Asim Hussain: Yeah, it's been quite a long time off and just gently dipping my toes back into the swing of things. Glad to be on the show. It's a nice gentle introduction back into the world of green software. So, glad to be here again, Chris. Asim Hussain, I'm the executive director of the Green Software Foundation.

And yeah, I spend my day probably similar to you thinking about how to advocate for green software. What do we need? What do we want? What are the questions that need to be answered and what are the levers that we need to pull to get action taken in this space?

Chris Adams: Reduce the environmental impact of digital services. Yeah. Okay, cool. Thanks. I should introduce myself as well. Hello, folks. My name is Chris Adams. I am the executive director of the Green Web Foundation. That's a smaller, a different organization. We are a Dutch non profit focused around reaching a fossil free internet by 2030.

And we do that using open as a lever. So we do loads and loads of stuff with open source, open culture, and things like that. As a quick reminder, we're going to share all the stories that we have and any projects or things that come up, we'll add to the show notes. So if you want to continue your quest to learn more about green software and how to reduce the environmental impact of digital services yourself, just look at podcast.greensoftware.foundation to see this. If you're looking in Spotify or some of the other podcast tools, you might not be able to see the links. So please do go to the website to see the show notes and you will be rewarded with diligently prepared links and helpful notes, and a transcript as well.

Alright then, so, I guess, Asim, I've introduced our News Roundup format, we kind of know what we're going to do, we've done this a few times, I assume you're sitting comfortably, shall we begin?

Asim Hussain: Yeah. Yes. Yeah. Let's go for it.

Chris Adams: Alright, okay, so the first story I see here is actually a post from, from Sasha Luccioni, Bruna Trevelin, and Margaret Mitchell, Hugging Face the Environmental Impacts of AI - A Primer.

So, this is one thing we had shared, and, Asim, I'm going to ask you, you've had a chance to look at this, what made you think this was actually worth discussing, and what would you draw people's attention to if they'd heard about this? And how would you, like, persuade people this is worth a read, for example?

Asim Hussain: Well, I suppose if you're new to the space and I think there's a lot of people out there who, for whom are kind of surprised to find out that AI has an environmental impact. So this is, I mean, A lot of this stuff, obviously we've been talking about different components of it over the last couple of years, but I think it's a really good, it's actually a great summary of the different components of what makes environmental impacts of software.

It's also got information there about what are some of the legislation coming down the pipeline? What are the, some of the actions that you can do? And some some things there. So I think it's a really good kind of primer for people. I think it's the title, is the title Primer? It

Chris Adams: Yes,

Asim Hussain: Yeah. So it does what it says on the tin. And I think it's probably could do as a really great introductory piece of information. It's got some great links there as well.

Chris Adams: Yeah, the thing that it might be worth just focusing on briefly, or one thing that leapt out at me when I looked at this is that it talks about the direct environmental impacts of AI specifically. So rather than talking about AI for good, or like "isn't it great that you could use AI to, say, make it easier to deploy renewable energy, or do this, or do that?" Right?

They're talking about, "no, there's an environmental impact that you still need to address regardless of whether you use something for good or for bad," and it seems to be focusing primarily on that kind of stuff. So as a responsible engineer, these are the things to think about. These are the kind of, what you might call an impact criteria, like there's carbon emissions, there's water, there's other things like that.

And generally this is from one of the kind of most trusted hands-on group who are like at the coalface for all this stuff. I mean, maybe, is coalface the correct term?

Asim Hussain: Yeah. I always use that term as well. And then I'm like, actually, that's a

Chris Adams: Maybe if you're in, maybe if you're in US AWS East, it's coalface and then if it's on AWS west, it's hydro face.

I dunno. 

Asim Hussain: I use coalface and then if I don't, then I sometimes use front lines and I'm like, "actually, that's not a good term as well." We need a term which doesn't have a war Metaphor

Chris Adams: or industrial revolution connotations

Asim Hussain: Metaphor.

But anyway, it's at the cutting edge!

Chris Adams: Yes.

Asim Hussain: Cutting edge. There you go.

Chris Adams: Okay. That, that, that will do for me. Alright then. Okay. That's the first thing. I think on a following from this, when I was reading this. I quite like that it actually touched on some of the regulatory drivers that you have, because it's very common for people to talk about the AI Act, because that's probably the first piece of legislation, but it also calls out stuff taking place in Spain, and taking place in the US, and it shows that there's a kind of growing, I guess, regulatory trend to basically say, "well, If you're going to have this piece of technology in society, then we need to have a data informed discussion about what impacts it might actually have."

So we can talk about, okay, where is it responsible to deploy this? But also just like, okay, how do we actually mitigate this? Because, I don't know, it seemed like cars are useful. And the fact that cars are useful doesn't mean that we don't talk about seatbelts, right? You still have to talk about them being safer, regardless of how useful they are, basically.

So it's really nice to have a trusted organization sharing some information like this in a relatively roundabout in a, in my view, quite concise fashion. But if you look at the set of footnotes, wow, there's so much stuff that you can dive into if you wanted to kind of go down that rabbit hole, basically.

Asim Hussain: Yeah it's a very well researched, almost a state of the art paper. And also say like, I think it's also good to know, because people don't, yes, there's regulation coming down the pipeline, and some of that stuff is more mature than other regulation. But I think when you're working inside large enterprise organizations, this is the kind of stuff that gets people to pay attention.

You can be talking for ages about, "hey, look, there's the carbon impact, it's having a... we should be looking at our AI usage" and sometimes that can land and sometimes that can't, but a regulation or the threat of a future regulation is something that I've seen personally open a lot of doors. It doesn't kind of complete the internal sale of, "we need to invest in mitigating the impacts of our AI use." But it's certainly, I've seen it kind of open up a lot more doors because regulation is something that a lot of organizations pay significant attention to, and it's also something that they will, Invest time before the regulation comes out to look at it and put effort into it.

So I, I look at that, right. And it's kind of, it's great to see those regulations that I wasn't aware of. So I'll be obviously using this as it's a great primer for me as well, but this is a really good way of capturing people's attention. So then you can have that more refined conversation about, well, how are you using your AI?

Let's have a conversation about it. How is it, how is future regulation going to affect you? So it's a really good way of opening that door. And if you're inside an organization and you are a little bit concerned about the consumption of AI that you're having, I think for me personally, like pointing out the regulation that's coming down the pipeline does open a lot of doors, enables some conversations with leaders.

Chris Adams: This actually might be a kind of somewhat appropriate time to mention some of the kind of policy stuff we're doing, because I, so in addition to doing the podcast here, I help co-chair the policy working group. And we've, I think we've, we, we're likely to be getting quite a I'm going to put a policy radar out to see precisely this kind of stuff coming up.

Because, yeah, if you didn't know, I mean, okay, today is the 6th of September, and, you've seen this whole energy efficiency directive thing in Europe, right? So, in nine days time, every datacenter that uses more than 500 kilowatts of power draw has to start reporting and posting in public all of their absolute energy use, absolute water use, the amount of energy coming from renewable energy, how much of it comes from the kind of credits that you might buy which are unbundled, how much is coming from a power purchase agreement, so the kind of green energy that you've purchased directly.

There's all this new stuff. There are some caveats around this. So not every single organization will have to, we'll probably publish, but we basically have a regulation that's saying, "look, this has to happen now in nine days at the time of this recording. So when this goes out, it probably will have already happened.

And like this gives you an idea that if you didn't know this is happening, then you probably will, we do need to know this because this is written into the law in lots and lots of countries now. Well, all across Europe, for example, and I suspect this is the kind of thing we might see in other parts of the world because when you look at the figures and look at the data that people are currently basing policy on, it's really hard to figure out what the environmental impact of, say, data centers might be or what the growth is going to be.

And if you want to plan for a grid or plan for hitting some climate targets, this is the kind of stuff you need to actually be knowing about.

Asim Hussain: But it's also useful because we've been having these conversations for a while about, I think we spoke on the podcast a couple of times in the past and when we were developing the SCI specification, it came up a lot like do you include the data? How much of the data center do you include? But the biggest problem was, is that you don't know.

If you decided to put into the specification, you've got to include certain, the concrete or whatever it is that goes into data center. If that data is not public, then what's the point of putting into specification? That's why these regulations, that specific regulation is so interesting. I'm interested to see what actually happens in nine days time and the quality of data that comes out.

The conversations I've had in the past, because when this first started being discussed, I was chatting to a lot of, not data center operators, but people that worked with data center operators. And obviously it kicked up a storm and everybody's like, well, I need this data. How do I get this data?

What is the minimum level of information I can provide to like meet? And that's where it gets really interesting for me. What is the minimum level of information I need to provide to meet the regulation? And I think in nine days time, we're going to find out what is the minimum level of information that people have figured out that they

Chris Adams: Yeah, they can get away with,

Asim Hussain: that they can get away with? Because when I had that conversations, I don't know where it landed now, to be honest with you, but they were like, is it at the building level? Is it at the rack level? You know, it's, and it was like, it's at the building level is where I was left at. So I think the more and more this regulation comes along and it kind of surfaces this data to us, then we can then use that data to make more informed choices, hopefully not from a consumer level.

I think it should be from a, not from an end user level, but from the people who use data centers and make make different choices.

Chris Adams: I think you're right. Okay, what we'll do, we'll share some, a couple of links to this, because this is something we've discussed in a few places, there's one or two working groups where this has come up, in particular, because there's also, on the, just, just before we move on from this, there's a whole, there's a current kind of, in my view, an interesting discussion going on about, okay, with this, in Europe at least, with this AI act, yes, it says that you need to talk, you need to disclose the training data, the energy used for training a model, right?

But it's not totally clear if you need to also track the inference, right? So if you think about the training part, and I've shared a link to a blog post where I've basically highlighted the bits of the law that make me think that you might need to track inference, or at least disclose some information about likely inference because you can think of like the training part as like the energy going into making a car and then the inference figures as a bit like the car's mile per gallon for that model, for example. And well yeah, well it's not totally clear yet and we've seen the law passed and we'll figure out yeah.

Asim Hussain: Cause I assume the only, I'll be honest with you. I assumed included inference. The only time I did it was when I just like read your article and I was like, oh, hang on. That's and that's where these things get very interesting to me. I mean when I was in working in enterprise like organizations and I've one of the things that was always interesting to me was whenever I asked questions or got meetings together, like "let's talk about, we've got some questions about the, how do we calculate this figure to meet with this specification?" And there's almost always legal got involved and lawyers got involved. And I was always kind of, I'm like, "I don't need to speak to a lawyer. I need to speak to an engineer."

Why am I speaking to a lawyer? Because it's all about, "let me read this text. What can we interpret from this text? What do we need to give?" So I assume just because everybody just, we know inference is where most of the emissions are these days, I just assumed it was that, but you've now actually read the text and gone, the text has, is interpretable.

Chris Adams: Well, yeah, because you think about like when this initiative was written ages ago, like a few years ago, it's gone through this massive kind of gestation process, right? And a couple of years ago, when we hadn't really got to this point where AI is being deployed in the same way, like, was it November 2023?

That was like 100 million users. OpenAI had gone from zero to 100 million users in five months. And maybe last week? We should share a link to Simon Willison's blog post because he wrote about this quite eloquently. He's like, well, OpenAI have just mentioned that they're now at 200 million weekly users. So that's like doubled in a single year. So we've gone from, so inference is now a significant part of the story in a way that it wasn't previously, basically. So it may be that the law, when it was written two years ago and began that process through it, it might not have been such a concern. And this is the thing that we're, this is why it seems a bit unclear, and I think we'll probably end up with a test case that will set a precedent for people to figure out what they should be sharing or what you might need to share if you're building new foundational models in future.

Asim Hussain: I mean, I can tell you that, that preChatGPT kind of announcing, it was very well known that inference was significant, as in way more than training for any, like, I can't reveal too much, but you know, it was known very much that that was the case and a lot of effort had been put into mitigating, not from a carbon emissions perspective, just from a cost and energy, but just all of that stuff.

So it's kind of known, it might not have been in the zeitgeist, it might not have been in the kind of the public discourse because it's so much easier to talk about this big training runner. Maybe there's just more public data about that because inference in a way, if you think about it, is going to be pretty private.

Because that's inference is basically telling everybody the business end of your where you're making money from, and they'll probably keep that pretty private. So yeah, maybe it just wasn't well known, but it was true and well known, I think, to anybody in the kind of the AI space that inference was a pretty big deal prior to this.

But yeah, it makes sense. Yeah, these acts take a long time. So yeah, a couple of years ago, all we were talking about was training. It was a good, it was a good headline to discuss. Yeah.

Chris Adams: Useful insight from the inside tracker team, alright. So there's one thing you mentioned actually, we're just on the subject of inference. There is this, in my view, really interesting project right now, EnergyStarAI, which is a project which is, you see a few names associated with, so, Sasha Luccioni, Sara Hooker, Régis Pierrard, Emma Strubel, Yacine Jernite, I think, Carole-Jean Wu, and one of our own at the Green Software Foundation, Boris Gamazaychikov.

He's at Salesforce and he's been one of the people who's writing publicly about a bunch of this stuff and also about like, quite, the small models as well as large models. And it's, I was really pleased to see his name actually. So. Hi Boris, if you're listening to this. This is a really good story to look at, because this essentially is talking about inference and saying, "well, let's find ways to make this visible for people" two years later now, basically, and say, "well, let's see if we can find ways to introduce some of the incentives to go for more efficient inference," the same way we've done with Energy Star in other kind of industries, for example.

Asim Hussain: There's really interesting, I'll try and get her on the podcast, actually, she's from IBM, her name's slipping me, so I'm not as good as names with you, you can just rattle them off, but I'll make sure to put it in. Because yeah, there's Energy Star for AI, but we're also, there's conversations inside the foundation as well now, kind of looking at SCI and how do you apply SCI to AI and kind of, there's a lot of overlap with a lot of this work as well.

But what's interesting is there's this real question about what to do when it comes to inference and training. Like if you were to report that. How do you report that for a model? And the point that was raised, and I thought it was so, because I never thought of it before, which is, if you've got a foundational model, you've done the training, you've done a big training run for a foundational model, and you're now then running inference on that, when you report, let's say, I don't know how any, I think Energy Star is just going to be like a good bad kind of label, where SCI is more like a score, like a carbon per prompt or something like that.

How do you apportion the training into the, do you include the training every time you call it? Because if you do, there's a really interesting thing that happens, which is these foundational models, like if you're using an open source model and you just, that costs 10 000 to run. Do you include that in your AI solution and then just say like, "Oh, I'm three, three grammars per prompt?"

Then somebody else uses that foundational model. Do you then divide that by two and say, well, now you're, you

Chris Adams: talking about double counting kind of question, right?

Asim Hussain: And then like, if that is how the measurement eventually lands, then if you're an unscrupulous organization, all that you would do is try and get as many people to use this foundational model as possible to then dilute your numbers.

And so I think one of the, one of my little bugbears, and it comes up quite often, is the assumption that something that works in the physical realm will work in the digital realm. And one of the things I try and educate people as much as possible is that stop trying to take something that has worked in the physical realm and apply it to digital because there's so many ways it just doesn't work.

If you're thinking about training like a scope 3 embodied carbon physical device thing. You can't divide a chip in two and I give you half and you, it's like, that chip's yours, but you can do that in the digital realm. So there's this whole supply chain accounting aspects of digital emissions, which, it just needs to be thought of differently in the world that we're in. And if you don't think about it differently, you can then have, I call them unintended, we used to call them gaming, like when people were developing the SCI, like one of the conversations we'd have on the calls is, how is somebody going to game?

How is somebody going to take SCI,

Chris Adams: Carbon Intensity here, right? Yeah, okay.

Asim Hussain: how is somebody going to game the side? That's kind of a lot of the conversations we had at the start. Cause obviously everybody was like, we want to make sure if we develop a standard, people aren't going to then

Chris Adams: Abuse it,

Asim Hussain: it. And therefore the standard has no respect in the world.

And so like a lot of how, like a lot of how I kind of work with the standards projects here is I'm a bit of an annoying devil's advocate. I love it actually because I kind of walk in and go "here are 10 ways I can hack this standard to present a better score without actually doing anything." And so I think that's some of the things we need to think of as we think of SCI for AI, as you think of Energy Star, as you think of these other things is yes, there's this happy path that everybody's a good actor,

it will work and it will give you the right signal, but we need to think about the non-happy part, where people might not even necessarily be bad actors. It's just death by a thousand cuts. You're working in an organization, you've got a deadline, you've got a bonus you have to meet, there's a customer that you're going to have to get or you lose your business.

And so you're just death by a thousand cuts. So yeah, we have to be very careful as we explore like SCI for AI and Energy Star and anything really in this space, which is talking about measuring emissions. Because if you don't think through those unintended consequences, that's a problem. And that's one of the ones I have is like, is if you're including training, how do you apportion that?

You might not actually want to include training. You might actually want a separate. You actually, you might actually only want to measure the inference, because that gives a truer figure.

Chris Adams: So to cap this off, I'm going to, as we move to the next story, I'm going to link, share two links which might be useful for this. So the first is the link to the Meta Llama 3.1 8B, their model card. They literally say "the methodology used to determine energy use and greenhouse gas emissions can be found here."

They've linked to it and they said "since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others." So they're basically saying you don't need to count that part. That gives an example. We'll share a link to that. The other thing we'll share a link to for the show notes is actually the Asim, for this software, carbon, yeah, this is

Asim Hussain: Yeah, this is my unintended.

Chris Adams: This is a good example because this is where you've basically said, this is me, like, red teaming this approach, and these are the ways I can basically, in bad faith, try and engage with this example.

And this will probably be useful for people who are looking at this, to get an idea of, like, how some of these standards or some of these conventions are being developed. Alright, shall we move to the next story? Because it kind of does relate a little bit to, basically, tweaking numbers to present a view of the world, basically.

So this story is now, how tech companies are obscuring AI's real carbon footprint. This is a story from Bloomberg, I believe. Asim, do you want to introduce this one?

Asim Hussain: So yeah, so I thought, well, the reason this kind of popped up on my radar was, I forgot what I posted on LinkedIn, but I was posting, I started posting a bunch of information on LinkedIn about the use of, the use of RECs and the effectively like kind of renewable claims that organization makes and how it's in a really frustrating way, it actually puts us in what we're doing in kind of competition with this very important energy transition, because the argument I'm making is, look, you can either do two things and then we'll talk about AI.

Let's keep it to AI. You can either make your AI model more efficient, so it consumes less energy. Or, you can do absolutely nothing, and just buy offsets, energy offsets, RECs, whatever you want to call them, to mitigate, theoretically, your energy offsets, your energy consumption. And that's kind of like being this "Are we friends?

Are we not friends?" How do we like, we want to support the energy transition, but at the same time, like we really want to advocate for more energy efficiency. So, and I think one of the things we've spoken about is that there's, when you do make these renewable energy claims, like one of the things that you do with all types of offsets to kind of avoid a greenwashing claim, you have to have that additionality component to your offset, which for the audience means that how do you, if I'm saying this thing is offsetting your emissions.

What is it a litmus test to say that is a true statement and is basically, are you actually adding? So for a renewable energy credit, it's like, if you weren't about this renewable energy, would that thing have happened?

Chris Adams: So you're talking about the counterfactual here, right? So you're trying to compare something against. This is, you see a load of this in the hydrogen circle, in the hydrogen, in the world of hydrogen, because Just like datacenters, hydrogen electrolysis, like the electrolyzers use loads and loads of energy, right, and one way that you can do that is just by plugging them into the grid, right, and there's various people doing various things to say, well, I'm just going to buy a bunch of, say, renewable energy credits, right, and that's going to make that count as green, and there's, that's, in some ways, that's kind of somewhat problematic because, essentially,

Asim Hussain: coal to make hydrogen.

Chris Adams: Yeah, that's not exactly what all, you're, in many cases, you're a, you're burning coal to make hydrogen, so the actual net, it's a net loss in climate terms. But also, the, there's been a big fight in the kind of hydrogen world of, to have like this notion of three pillars. Where you basically, if you're going to have something, if you're going to count something as green hydrogen, then you need to be talking about new infrastructure being added to the grid to provide that new supply.

You can't just use, you can't just take from the existing stock of green supply and then count that as green. And this is one of the things that we've seen, like, I don't, Amazon made the news, I think a few weeks, a while, because they basically acquired a data center from a company called Talon, I believe, where they're right next to a nuclear power station, right?

So this, that you, there are some people saying, "oh, this is great, isn't it good that Amazon's using a bunch of clean power," but then you've got to think about, well, okay, who was that clean power going to before? Was it going to the grid? Like, there's a whole discussion there about this. Yeah, so there's a whole set of things to be talking about and this is why this is such a kind of fraught area, basically.

Asim Hussain: I mean, but I think the way to bring it back to something that people understand is when we talk about carbon offsets, I think now it's more understood that it's kind of like you, you have carbon removal offset. So you can plant a tree. Right? And then you planted the tree, that tree will grow, and there's issues there.

That tree will grow and also suck carbon from the atmosphere. And you can say that's a carbon credit of planting a tree. Or, there's carbon avoidance offsets. And there's many various, and there's actually very good variants of carbon avoidance offsets. But there is a variant of a carbon avoidance offset where I've got a tree.

And you pay me not to cut it down. And so where's the additionality? If I'm actually planting a tree, it's happening. I'm planting a tree. I'm adding additional kind of capacity in carbon removal. And in the renewable energy markets it's exactly the same. You can have renewable energy. Which if you buy means a renewable power plant is going to get built and you can have renewable energy which is just kind of sold and if you buy it or you don't buy it there's no change nothing's going to happen there's no more new renewable plants going to get built. Only one of them has that additionality component and so, therefore, only one of them should really be used in any kind of renewable energy claims.

But both of them are allowed in terms of renewable energy claims. So in terms of what this article is talking about, when they're saying "tech companies obscuring AI's real carbon footprint," they're actually talking about companies using what's called those unbundled RECs, which is those RECs which do not have that additionality component.

And then use buying them and then saying, "well, that's mitigating my environmental impact." And what the article is talking about is really, you should be looking at higher quality, Renewable Energy Credits, ones with more additionality components to it. And I think that's really interesting. There's actually also really, Olivier Corradi from, don't know if I'm pronouncing the second name correctly.

Electricity maps. Yeah, he, when I was talking about, he shared a really interesting article he'd written a year ago, which I thought was interesting as well. I'll share that here if you've got it. Yeah. He's actually advocating for like a more nuanced approach to looking at renewable energy in that there's additionality, then there's additionality, and then there's additionality.

There's like different levels of additionality. There's like, "this definitely 100 percent would never have been built unless you bought this renewable energy credit." And there's other ones like, "we may not have been able to build it, but we had some funding from here and there." So there's kind of different levels of additionality here as well, which I thought was really interesting also.

I'd never thought of additionality more than just a binary yes/no. And he was saying it's actually more of a score for a renewable energy credit. But

Chris Adams: Yeah, there's totally a continuum there. So the thing I might share for people who are looking for something actionable to work with here is basically the numbers that you often see reported by technology firms. There's all this, there's all this nuance hidden behind it. And there's one project called the Real-Time Cloud Project inside the Green Software Foundation, which essentially is a data set of the largest three providers.

So that's Amazon, Google, and Microsoft. And they've got the figures shown in both the kind of location-based figure, which is the closest thing you might think to, like, the physical location, the physical impact on the ground. They also talk about some of the market-based figures, which is what lots of firms like to use, like market-based on an annual basis.

But they also provide a few other details and a few other ways of talking about it, because some firms are now talking about hourly, basically hourly green energy versus annual green energy, with the idea being that you, it's a way to try and avoid making claims about saying, "I'm running a data center at night with certificates coming from a solar farm." This is inherently a little bit silly. So they address that stuff. So there's a, there's now, I think it's in the final stage of what's referred to as consistency review, where every member in the GSF is able to just say, "Hey, I object to this, or I'm not sure about this." And then, yeah, there'll be an open data data set for every single region from the three largest providers, which make up more than two thirds of the entire cloud market.

So you have some meaningful numbers that have come from the actual big providers themselves that you can actually, that we can work with.

Asim Hussain: And I think, like, I think basically my, I think the point I'm generally raising out, out there with another kind of, one of the reasons this article was very interesting to me, and especially the work that Realtime Cloud is actually interesting. Three, four years ago, everybody treated all carbon offsets the same.

They didn't realize there was nuance between them. Now that's changed. Everybody needs to now pay attention to the same thing in terms of renewable energy. If you do not pay attention to the fact that there is a lot of variability in a lot of this stuff, it's all going to get tarnished with the same brush in the future and any renewable energy claim is not going to be trusted.

So I was, I'm kind of a guiding and advising organizations to pay very close attention to kind of the type of renewable energy that you're buying. And be aware that because of podcasts like ourselves, there's generally, it's a Bloomberg article talking about this right now. It is now becoming very aware in the minds of a lot of people who care about this space, who listen to our podcasts, who are paying attention, that there is nuance here.

They're paying attention. And so as an organization, you need to pay attention to this as well.

Chris Adams: Cool. Asim, I'm just going to add this one thing because I realised I should have mentioned this. So I work in an organisation where we do track some of this stuff. We track the transition of the internet away from fossil fuels to greener energy. And, I've shared a link for the show notes. Because we're basically reviewing our own evidence that we accept for green hosting.

And we've linked to a couple of papers. And specific reports, which dive into this a bit more, which have kind of also prompted us to start looking at this. So, organizations like the Science Based Targets Initiative, we mentioned there. We talk about some of the other things that we, some of the nuances around RECs.

And yeah, this is, this will be something we'll be doing. So we're going to be essentially figuring out how to do this ourselves in the open over the next few months. So, Yeah, I guess it applies to small firms as well as large firms.

Asim Hussain: Yeah, yes, absolutely.

Chris Adams: All right, should we move to the next story? So, this is a story.

Researchers analysed 1 500 climate policies to find what works. And these are the lessons for Australia. I think this is the link you shared with me, Asim. There's a very kind of Australian centric kind of point of view, which, as someone born in a small mining town in Australia called Prospect, because what else would you name a mining town other than Prospect, because it's full... you? Yeah,

Asim Hussain: I didn't know you were born in Australia.

Chris Adams: Ah yeah, born in Australia, small mining town.

Yeah, I was literally born in a mining town called Prospect, and it's near One Tree Hill. Can you guess how many trees are on that hill? And it's next to Dry Creek. Can you guess the conditions of that river? Yeah, it's descriptive rather than creative, is the term I've heard people in Germany who do similar things talk about, actually.

Okay, so you shared this story, maybe you can introduce this one here, because I think it's quite relevant in this discussion, what we were just talking about in the previous two stories, actually.

Asim Hussain: Hmm. So I think it was just really interesting. It was an analysis of like 1500 climate policies and just really looking at what worked and what didn't work. And I thought it was interesting because we spoke a lot about, we've spoken a lot about things like carbon levies and things like that on this podcast.

But what I found interesting about this article was they, again, brought nuance to the discussion and saying, "actually there's different, different policies seem to work for different types of organizations and also combinations of policies seem to work better than individual policies." So a couple of interesting ones.

So one of the highlights I got, so some of the, in developed economies, some of the most successful cases were the results of two or more policies working together. So that could be like a ban or something, as well as like a carbon tax, kind of pulling those things together. Oh, for instance, like a great example they had here was like, for instance, example, a fuel efficiency mandate for vehicles combined with subsidies for developing like charging stations and things like that. So then you've kind of got the pressure on both sides. And another thing that was really interesting was cause we spoke about kind of carbon levies and pricing was particularly effective policy in sectors dominated by profit orientated companies, such as electricity and industry.

So I just think it was really interesting to kind of think through it from that perspective.

Chris Adams: So there's a really nice example, there's a few really good examples. Good concrete examples of this to make this, like, something you can, like, get your hands around. So in America, right, we've seen the Inflation Reduction Act. So that, in many ways, are kind of it's all carrot, no stick. So the idea is that there's massive amounts of subsidies for building out, like, for, like, EVs or building out new, kind of, battery gigafactories, all this stuff like that, or things which are essentially make deployment of renewables much, much more profitable than they otherwise would be, because they're gonna, because you have all these kind of subsidies saying, well, we're going to give you a production tax levy.

So for every unit of energy you produce, you'll be able to get, you'll be, you'll get a credit that you can actually apply. So your project over the entire length of it will end up being slightly more profitable. That, there's stuff like that, that you see, which is coming from one end. But we've also seen, In America, the EPA, the Environmental Protection Agency, they've got, they've now come in with a stick, or they're coming in with a stick now, to basically say, well, we're going to have to regulate carbon emissions.

And this now means that it's going to be all new kind of coal fired power stations or gas fired power stations, there'll be all these restrictions on how you should, how you can use them. And this is particular, the reason I raise this in America specifically is we were talking a little bit about AI before, right?

Now these regulations, I don't think that many technologists are aware of right now. They basically say if you're going to run a gas-fired power station, you need to fit loads of carbon capture and storage onto it, which is, broadly speaking, if it does work, it's not something that's really used in large amounts right now.

And what you currently see right now is you see lots of utility companies basically saying, "Oh, the only way we can possibly meet demand for AI is to build all this gas right now." And the problem with that is that ends up locking in all kinds of emissions. Because once you've built something, you have this incentive to kind of try and get your return back on building this in the first place.

And this feels like," I don't think people have realized just how much of a stick this is going to be, because as far as I can tell, all the laws from the EPA basically say, look, you can't build gas like this, and you can't actually do this." So we're going to have, we've got like this case of massive build out of AI coming up against all these regulatory forces as well.

And it's going to be quite a significant fight in the next 6 to 12 months, I think, because yeah, this is, we've now had the honeymoon period of all carrot. Like you said in this piece, and now we're coming up to the stick, which is the other part, to kind of make sure that you can, make sure the significant part of the US grid is going to be decarbonized by, I think it's the mid 2030s, basically, is what they're doing, that they're aiming for with this.

But we have the same thing in the UK as well, like, UK right now, we've got a target for, the UK has agreed to try and decarbonize the grid entirely by 2030, which is great for us as an organization because we, we want a fossil free internet by 2030. So we're like, "oh, thank God the UK is doing this." The UK government, one of the big kind of manifesto policies from Labour coming in, who've just won the election is "we're going to have a clean grid, entirely clean grid by 2030."

So five years, basically five years time, they're going to get rid of all the fossil, almost all the fossil gas generation, right?

Asim Hussain: How are they going to do that? 

Chris Adams: That's what we'll find out. But the thing I found out when I spoke to some people who, basically, this is actually all based on some modeling using a piece of software that we interviewed a chap called Iegor Riepin, he was talking about this in one of the episodes, we'll share a link.

That software was, basically, these kind of things were put together by some analysts on our laptop saying, well, this is what you can do. There's a report from Ember Climate where they, the report is called Escape from Gas, I think, or A Path Out of Gas. And this was one of the things that was written in 2022, when gas was super expensive, to say, "well, this is one thing you could plausibly do for this."

And yeah, when, the thing about policy, people reach for what's there. This turned into one of the things that one of the parties led on, and now we're going to see if we do see a fossil free grid and fossil free internet in the UK by 2030. Because, yeah, it's fascinating. I'm so, this is the most exciting, most excited I've been about UK politics in a very long time.

Asim Hussain: I don't know. I might dampen it for you. I'm just not, I'm just not very, I'm just, there's a lot of manifestos that come out from governance when they join and there's a lot of disappointment in the years later when they, when it doesn't manifest, when their manifesto doesn't manifest.

Chris Adams: So this is the final thing that might come in, might be relevant. So the modeling that was used for this as the basis to say, "yeah, we can do this." This one thing that ends up being, so I'll share a post to it, which I end up doing a bit of research and speaking to some of the people about this. It's actually very conservative, more conservative than the National Grid's own

estimates about, specifically in our industry, demand size and batteries. So, these are the two big things that we're likely to see a massive increase in.

Asim Hussain: That's what gas is used for more like this is specifically to get rid of gas.

Right. 

Chris Adams: yeah, so the,

Asim Hussain: peaker plants and then therefore you can do a little demand, demand responsible.

Chris Adams: Partly that, the thing that they said is like, the, their plans basically are relatively conservative about the ability for demand side of reduction, making your, you know, Carbon Aware in stuff like that, right? And there is another thing that we've seen is that the UK government is actually being quite gung ho about deployment or deploying all these new data centers.

So I'm kind of thinking, is there a chance to actually say, "well, okay, if you're going to have this deployment of all these data centers, and you know that one thing you're going to need to have is a much more responsive grid, is there a path for all this kind of carbon aware infrastructure to actually serve some of the roles that you wouldn't have to typically rely on peaker gas plants to actually fit, to like kind of fit?"

There's a bunch of stuff there and I think we'll learn basically because, yeah, this has been a really ambitious goal and you've also got this other idea to like bring in something which, can be quite flexible, but only if you incentivize infrastructure to be flexible, because for the most part, we don't see an economic incentive passed down to the consumers of infrastructure to be using this right now.

So, yeah, maybe this is a help of one piece. Yeah.

Asim Hussain: Yeah, I mean, I think there's been, there was some really good work done kind of several years ago, and this will be really good because one of the things I've seen is that the, all the positive moves I saw kind of three years ago regarding new data center rollouts, hydrogen fuel cells, kind of building kind of a much more advanced data center seems to have gone back a little bit.

And yeah, You're right, I think data centers could lead the way in terms of demand response. I'm not even talking about compute demand response. You can just take batteries, you can fill data centers with batteries and then they can store and then they can do their own sucking from the grid when it's clean and powering their own infrastructure when it's dirty.

You know, there's, there's other solutions, which doesn't even necessarily need kind of a software,

Chris Adams: Yeah, exactly. I mean, this is one thing that we've seen in Ireland. There's precedent in Ireland where people have said, "if you're going to be connected to the grid, you need to be prepared to be kind of complementary or sensitive to the needs of the grid for this." So, I think there's actually room for this, and it will be really nice.

I think that this feels like, given such an ambitious target, it does feel like a role where you could actually tell a good story about green Software, and be part of the solution as opposed to part of the problem, because a lot of the discussions around like rolling out of digital infrastructure is basically saying we can't possibly meet this demand.

But if we accept that demand is dynamic, then there is a chance to actually fit this in, and that feels like definitely worth going for, particularly to kind of maintain this kind of social license for operation, particularly for technology firms.

Asim Hussain: And I think a lot of what you've just said over the last couple of minutes runs very counter to what we were saying before about, I mean, everything you just described, this is all related to that whole idea of additionality. It's all about how do we transition, truly transition the grid to be fossil free?

And you need solutions like this. Not necessarily buying unbundled RECs, but you need to actually, like, think through, "well, how do I how do I be a better citizen in the grid infrastructure, do demand response, be sensitive, not demand energy when everybody needs it and therefore we have to spin up a gas power plant or something like that."

So these are the kind of things you need to actually transition the grid.

Chris Adams: Asim, I think we might have fallen down a bit of a grid rabbit hole,

Asim Hussain: Yeah, we've done it again, haven't we?

Chris Adams: Yeah, so, we're gonna have to move on, I think we've come up to time, but Asim, it's really nice to see you again, I'm glad you had a nice holiday, and I guess we've got a bunch of new things to do this quarter, right? With various projects we have inside the Green Software Foundation, and in the other member organizations related to it.

Alright dude, it's Friday, so have a lovely weekend, and for those listening, we'll put all the links to everything we've discussed in there, and if there's something you didn't see, Please do let us know, and we'll make a point of adding it. Alright, thanks a lot folks.

Asim Hussain: Thanks, Chris. Bye.

Chris Adams: See you around soon. Bye! Hey everyone, thanks for listening!

Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> Making Testbeds for Carbon Aware Computing Making Testbeds for Carbon Aware Computing Thu, 12 Sep 2024 07:00:00 GMT 48:21 805667k1 false https://podcasts.castplus.fm/e/68rrr218-making-testbeds-for-carbon-aware-computing 68dc7d18597bc7d53fb83a89 68dc7cef46a2532cdd8d1c23 Host Chris Adams is joined by special guest Philipp Wiesner, a research associate and PhD student at TU Berlin, to discuss how computing systems can better align energy consumption with clean energy availability. Contributing to Project Vessim, Philipp explains how researchers are now able to model different energy consumption scenarios, from solar and wind power integration to the complexities of modern grids despite the scarcity of available testing environments. They discuss federated learning and its role in carbon-aware designs, along with challenges in tracking real energy savings. Tune in to learn about the future of carbon-aware computing and the tools being developed to help software become more sustainable. 81 Host Chris Adams is joined by special guest Philipp Wiesner, a research associate and PhD student at TU Berlin, to discuss how computing systems can better align energy consumption with clean energy availability. Contributing to Project Vessim, Philipp explains how researchers are now able to model different energy consumption scenarios, from solar and wind power integration to the complexities of modern grids despite the scarcity of available testing environments. They discuss federated learning and its role in carbon-aware designs, along with challenges in tracking real energy savings. Tune in to learn about the future of carbon-aware computing and the tools being developed to help software become more sustainable.

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TRANSCRIPT BELOW:

Philipp Wiesner: Optimizing energy usage for when the sun is shining and the wind is blowing, of course it makes sense. But the big problem is that these green energy certificates, these guarantees of origin certificates, are traded independently of the physical flow of energy.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and insights from the world of sustainable software development. I'm your host, Chris Adams. Today, we're diving into the world of carbon-aware computing, how computing systems can align the energy consumption with the availability of clean energy.

Natural cycles govern all kinds of things we do. We mostly go to bed at night, and the food we eat is often influenced by what season we're in, and so on. This also shows up in how energy is available to us. You can see how day follows night in the output from a solar panel. And broadly speaking, it tends to be windier around winter than it is in the summer.

Even when we burn fossil fuels, we're basically digging up energy stored millions of years ago. As we move away from fossil fuels though, where we get to use the sunlight that was conveniently stored for us, we move towards a system based around harvesting energy, like we harvest food. The way we consume energy needs to adapt as well.

And that's the idea behind carbon-aware computing. But how do we know if our carbon-aware designs are actually better for the environment? With us today is Philipp Wiesner, a research associate and fourth year PhD student at TU Berlin, Technical University Berlin, whose work focuses on carbon-aware computing.

Philipp is deeply involved in research that connects the dots between computing power and renewable energy, and today will unpack the work he's been doing, including his work on Project Vessim, a testbed for carbon-aware computing. So let's dive in. Philipp, thanks so much for coming back, and for those who haven't heard the last episode when you joined us in 2022, can I give you a bit of space to introduce yourself?

Philipp Wiesner: Yeah, so hey Chris, and thanks a lot for having me back. I'm really excited. Yeah, so I'm Philipp, I'm a PhD student, as you just said, and a research associate at TU Berlin. I'm in the research group for distributed and operating systems. And I've been doing research on carbon-aware or in general, sustainable computing since the start of my PhD or my master's thesis in 2020.

And yeah, at the start of my PhD, I was mainly looking at the basics. So that's the first podcast we had two years ago. The field was still very young and we were looking at the potential of delaying the execution of workloads to make use of cleaner energy. Later, we also looked at migrating workloads from this to different data centers and so on.

And it was all based on carbon intensity. And then from then on later, we moved on from only optimizing for carbon intensity to really better understanding how electric grids work and rather optimizing for curtailed energy, excess energy. So, for green energy that would otherwise go to waste.

Chris Adams: Cool. Thank you. Okay. So you mentioned we spoke in 2022, two years ago, and the field has moved. And while we'll share a link to that podcast, where we go into a lot of detail, can I just give you a bit of time to just provide a brief summary of the paper that you spoke about then? I think it was called Let's Wait Awhile.

That was one of the projects that you're working on, and that was one thing related to the cloud. If we can talk about that'll be a nice scene setter. Then we can talk about the new work that we, honestly, I'm really excited about in all kind of nerdy glee. So yeah, Wait Awhile. What's the deal with that one?

Philipp Wiesner: Yeah, sure. So the idea in Let's Wait a While was just that in different regions of the world and at different times we have different carbon intensities. So carbon intensity is a metric that describes how dirty or how clean is the energy that you draw from the public grid in terms of emissions per kilowatt hour.

And at the time we were looking just, if you have a single data center and you might have some flexible workloads. So workloads where you do not really care if they happen now or tonight or tomorrow morning, how much potential do we have in shifting them, delaying them, computing them a bit earlier, maybe dissecting them in different parts and computing these times in different parts and so on?

And it was like a bit of a, yeah, it was a basic work at the time.

Chris Adams: And there were some, I think, when I first read about it, there were some quite, the savings there were around the low tens of percent for, compared to what the other ones would be. It was something like that, I think, from there. So that was one thing that caught my attention while we ended up speaking about it last time.

This new work that you presented at Hot Carbon, Vessim, as I understand it, this is something which is more general and applicable to all kinds of grids rather than being focused on one or two. Can you tell me a little bit about why you worked on this and what the general idea for creating a testbed for hardware computing was in the first place?

Philipp Wiesner: Yeah, sure. So the thing is that in carbon-aware computing, it's really common to simulate. The thing is that we're often talking about highly distributed systems, often globally distributed systems. And also these approaches often only take effect if you like simulate them or like run them for weeks or months because we need different seasons.

We need different, yeah. So the thing is that in Let's Wait a While and also in basically pretty much all the papers I wrote so far, we have to simulate systems. And in pretty much all the literature so far, people simulate systems because it's just extremely expensive to actually build and operate such systems.

Only big players can do it. And also results are not reproducible. So no one can really check if the stuff that you're reporting is correct.

Chris Adams: So let me just check. So basically, there is no way to... essentially there's no counterfactual, there's no way to see if my, like, I might say "yeah I've made the savings" but without showing your workings it's quite difficult to stand by them or even challenge them actually, that's one of the things that seems to be a bit complicated with that, right?

Philipp Wiesner: Exactly, a general problem in research, to make stuff reproducible. But here we have the big problem that everyone is implementing their own simulation. So everyone is doing it by themselves and just iterating over a big time series for carbon intensity is maybe not that hard. But the moment you have more complex systems and you're working with forecasts and so on, there's a lot of things you can do wrong.

And it's quite a heavy development effort often to do things right. So, just over the years we collected a lot of experience and eventually came up with the simulator. So, Vessim is basically a big co simulation suite where you can plug and play your own simulators for renewable power generation, carbon intensity of the grid, energy storages, and so on.

And you can connect them, you can connect existing simulators, and then test your real system, real applications, as if they were interacting with this simulated energy system.

Chris Adams: Ah, I see, so let me just check if I'm So one of the things you mentioned before is that we tend to look at the grid as like, just one thing that might be changing. And even our interactions are influencing the grid as well, so there's, that's one thing you need to take into account. But you also mentioned just then, that while most of us think of electricity as just power coming out of the wall, there's actually, it's more helpful to think about it in terms of, well, there's maybe on site clean energy, which is maybe very, clean, but isn't always available, and there's maybe on site batteries where the carbon intensities might be influenced by what has gone into it.

Like if it's clean energy that's gone in, it's going to be very clean. If it's dirty grid energy, it's going to be quite dirty. And then there's the grid. So there's multiple different, kind of, ways energy might go into the system that you use for your computing, for example, and you need to account for some of those things.

And then in addition to that, different parts of the world might have totally different grids. So Poland, for example, right now, loads and loads of coal, but comparing that to Iceland, which is, or France, which is like full of nukes, it's going to be very different carbon intensity. So you can't make the same assumptions, and you'll need to essentially do something to recreate the conditions that you might be in so that you can make somewhat more robust and intelligible statements about the savings you might achieve by making any kind of carbon-aware designs.

Philipp Wiesner: Yes, exactly. So yeah, as you said, like most of the existing research is looking at carbon intensity is looking at the global grid. And the problem with this metric is in general, that it's oversimplifying how complex grids actually are. So energy markets are really complex, grids are really complex. So if we only optimize for carbon intensity, which is a relevant metric in the future, we will run into several problems.

Because it's just hiding a lot of underlying complexities. And, as you said, we can maybe avoid these complexities if we're looking more local at microgrids. So if we assume, I don't know, a data center provider is putting solar panels on their roof or like having a direct power purchase agreement with some close by wind park, then we have a lot more direct connection of who's producing and who's consuming, and Vessim is exactly designed for this, to simulate such systems so that eventually you can optimize really for the energy that you're producing or storing.

Chris Adams: I see, okay, so you're still looking at this and we're looking at more than just a grid, basically. We're saying, we're focusing on the carbon intensity because the power can be coming from different places, like the one I'm immediately thinking of, actually. So in the news, Elon Musk's talking about this new data center where he's, where they bought 100 000 Nvidia H100 GPUs using 100 megawatts of power.

And that's so much power that they can't get that from the grid. So what they've done is, it's not clean energy they've used. They've used, say, like, a bunch of mobile gas generators, plugged into the data center that's come in. So we spoke about on site clean energy, but there's actually all kinds of energy that might be going in, and we're now seeing something like this in this kind of world of centralized AI, where you do see organizations looking to things other than just the grid to power things.

So we might need to take into account a whole basically the system seems to be getting more dynamic and being able to simulate this seems to be one of the ideas that Vessim is intended to kind of support so that we can essentially show our working and understand what kind of changes we might be making, or under these conditions, this is what a carbon-aware design might achieve, for example.

Philipp Wiesner: Yeah, exactly. Yeah. As you're saying, it's unfortunately becoming more and more popular to again, build gas turbines or even small nuclear power plants right into data centers to like come up with this insane amounts of energy that they need by now. We of course, like rather focusing on renewable sources because they're also a lot more difficult to schedule.

But yeah, this is one of the use cases of Wessim. So you can like explore how does it affect my data center if I cover the roof with solar panels, build a wind park nearby. How does it affect my data center if I invest into a lot of energy storage? How can I optimize my data center to make best use of these local resources?

And then there's also a lot of like systems questions, like how do I optimize? How do I make applications aware of locally produced energy? How do I make them aware of the state of charge of local batteries? What are they allowed to know? Like there's also security questions involved. And all of this we want to make explorable, so to say,

by providing this very general purpose, extendable simulation framework.

Chris Adams: Okay, cool. That sounds, okay, this sounds lots and lots of fun, and I remember getting quite excited when I read the paper from Hot Carbon. So you've shared this, but as I understand it, some of this has actually been used for some production work, or this isn't just like a wacky paper, there's actually people, there are examples of this being used.

I know that your paper spoke about the use of "Okay, here's how we've been running this on a kind of test system in a Raspberry Pi. Here's how it works with AWS cloud instances." So there are various examples of it being used. But there is a new project that you told me about beforehand called FedZero.

Can you tell me a little bit more about that? Because that seems to be applying some of these ideas in the real world. So yeah, maybe I'll just give you the chance to plug that one, because yeah, it was a fun read.

Philipp Wiesner: No, sure. Thanks a lot. Yeah, exactly. So we're trying to, deploy Vessim in a lot of different projects now. One of the main ideas behind Vessim is also to be have like hardware and software in-the-loop integration. So you can also plug in real devices, real energy generators, and so on.

In FedZero, we didn't do that. FedZero is also fully simulated. But it's a new, it's a scheduler basically we proposed for federated learning systems. This is,

federated learning is some type of distributed machine learning training. So you have a bunch of different clients and they locally train a machine learning model.

So they locally consume energy and the scheduler basically decides when, who is training. So it's like distributed batch processing in the end. And the entire evaluation of the system was done with Vessim. We have, for example, a lot of data sets that are coming with Vessim. They've been provided, for example, by Solcast.

It's a big solar data provider. And the entire idea behind the system is to only use excess energy. So only use curtailed energy and spare computational resources to perform a distributed machine learning training to make it effectively (an exclamation mark) zero carbon! Because you don't use any energy that would have been used by anyone else, basically.

Chris Adams: Oh, I see. Okay, so there's two things that I'd like to unpack there. So could you maybe just talk a little bit about the relationship to, I think I was confusing this with Flower, perhaps. So Flower is one of the wider projects which has something to do with federated machine learning. you maybe just talk on that because I think, for people who have listened to this before, they might not be aware of Flower or the even idea what federated machine learning might be. Because I just spoke about a centralized massive data center, but that's not the only way you can do machine learning and AI training by the sounds of things.

Philipp Wiesner: No, sure. We can also go a bit more into detail. So in federated learning, we assume that our data is distributed. So it's a different clients, a very common example are hospitals that, for example, want to train a common model for skin cancer detection whatsoever, but they cannot share their private patient data.

So we need to somehow train on their data without them releasing this data. And what's happening and Flower is one of the frameworks that lets you do this, that lets you perform federated learning as we distribute the model to the different hospitals, they locally train on the data and send it back.

And we try to schedule this local training in a way that it's only using green energy. Yeah, that's the main idea.

Chris Adams: Ah, I see. So, these would be devices which might already exist, for example, you don't have to have the same kind of emphasis on having a bunch of new stuff that you're using all the time to kind of sweat the asset and make the most of it, so you could be using maybe older technology or a more kind of mixed set of computing, rather than 100 000 H100s, for example, in one straight place, for example.

Philipp Wiesner: Yes, that's true. That's true in one sense. On the other hand, we always have to see what we're optimizing for. And if you're optimizing for something like curtailed energy, like for example, in the north of Germany, we have a lot of wind parks. But the German grid is not always strong enough to get all this energy to the south where like the big consumers are, the big industries.

So often times we have an excess of energy in the north. Now if you have hundreds of kilowatt hours of excess energy in the north, it doesn't get you anywhere to turn on a bunch of iPhones or old computers to make use of this energy. So usually with our approaches we target big consumers. We target the A100s and H100s to, because they actually make a difference.

Like turning them on or off actually makes a difference to the grid. A few thousand iPhones, not so much.

Chris Adams: Okay, so you still would be using some of this kind of relatively powerful hardware, if it represents a significant power draw on the grid, for example. And, is there anything special about where these might be placed? Because I've, seen examples and I've seen people talking about the idea of like literally putting this stuff directly into, say, the towers of wind turbines.

I know this is one thing that Windnode in Germany were doing, for example, because that's basically, where the power's not being used, it's one of the most fastest ways to actually

Philipp Wiesner: yeah, clearly, yeah, that's a super important topic, like placement of data centers. Just what I said, of course, you like place these data centers again, and if we talk about Germany in like Frankfurt region or in the south of Germany, it doesn't get you anywhere because you still need to transfer the energy there.

So you would actually need to build data centers very close to where energy is produced. However, like the big problem that we're facing right now with all this LLM and GPU stuff. is that there's actually not a lot of flexibility. So, FedZero for example assumes that we have some spare resources and some excess energy, and maybe the excess energy is fair, we do have that but we do not have spare resources.

Like, whenever I keep telling people since the last six months about this project they're like, "no, there are no spare resources." Like, GPUs are used all the time, we have a GPU shortage so, carbon-aware computing on GPUs is not really working at the moment.

Chris Adams: Yeah, I'm glad you mentioned this because this was one thing I've always been struggling to make sense of because when you've got a graphics card which is like 40k and you've got only a certain amount of years to use it, for example, the cost of having it off is actually quite high. So therefore, being able to simulate what the savings might be from some carbon air computing might, would actually be meaningful.

Or, if you're not going to switch off the power, then you might need to find another way to, like, not draw power from the grid. So that kind of implies either an increased amount of storage that you might need, so that rather than switching things off, you just reduce the power you're drawing from the grid by pulling it from somewhere else, for example.

So that's another kind of way you might flex your system to... still reduce the emissions, because it may be the case that you're able to load up on clean energy when there is an excess in other places, whilst keeping that thing still running, for example.

Philipp Wiesner: Yeah, exactly. No, you're completely right. And that was different, a few years ago when you, like, were mostly talking about cloud computing on CPUs, when we were talking about carbon-aware computing, because there you usually have a big buffer, because cloud providers want to have this image of infinite scalability, so they usually have spare resources at hand.

So, yeah, like this entire load shifting stuff was somewhat reasonable. Now with GPUs, I mean, you're right, you would need to plug them off, but no one's doing that. Like, if you're buying a GPU, you're using it.

Chris Adams: Using as much as you possibly can to make it, to make the per hour cost as low as possible, because you're, I see, okay, so, this is one thing I'm really glad you allowed us to dive into this because one of the things that I would initially think about, like there's some trade offs you might be making, and I might, before I spoke to you, one of my assumptions was that by using a federated model, you might have a lower embodied carbon because you're able to use a wider set of computing, which might already exist, for example, but it sounds like, because some of these chips are, they use more power, but per instruction, they're much, much more efficient per instruction. So, you're probably not going to save that much from the total embodied carbon of your fleet, for example, by reusing existing stuff in some cases. Is that what it would be like?

Philipp Wiesner: Yeah, there's different studies on whether highly distributed training or computation in general is actually more or less efficient. It often really depends, like usually the centralized, like you won't get anything more efficient than an H100. Energy per instructions. It's super, super efficient, but then of course, if you put them all in one place, you need to cool more and so on.

So often it can be more efficient to distribute it. there's no clear answer to that. I would say it really depends on the system, whether federated distributed system is more energy efficient or a local one, but of course in distributed system, you might have more flexibility for scheduling and waiting and yeah.

Chris Adams: Ah, I see. And I'm just going to move on to another question in a second, but while we talk about that, I just wanted to speak about maybe this idea of more distributed federated learning. A lot of the time you see people talking about saying, "I need a cluster of machines in one place because they need to talk to each other."

And the closer they are, the lower the latency, the faster the responses might be. And. When you're using a more distributed version, a distributed model, if you're not doing, like, even if you're not thinking about machine learning, you still need to think about latency, how long your response comes back.

How, like, what are the trade offs that you have there when you're looking at a kind of federated model, like with Flower, or providing some of these kinds of more distributed, carbon-aware style approaches, for example? Like, how do you address that?

Philipp Wiesner: So in general, in federated learning, we differentiate between cross device and cross silo federated learning. And cross device is what you were mostly referring to as like you have a lot of iPhones, a very distributed small system. They're often connected via mobile network. So latencies are usually quite high.

And there you really need to consider this latency trade off. In cross silo federated learning, it's a bit less important because you assume that each of these clients is, as I said, like a small data center or a big data center, they are connected via fiber. So, latencies are usually very low and not super relevant compared to how long a training takes.

So like sending models back and forth, model weights is not really relevant compared to how much time you need to compute actually. 

Chris Adams: Ah, thanks for explaining that, because there are various companies who are talking about having a distributed model, or saying, you need to do, you need to have, maybe, infrastructure that is complementary to the grid. And I was never able how to, how you get around the issue of speed of light, right?

Because they're going to be very, different if, even if it's just opposite ends of the country, that's going to, depending on the size of your country, that's going to be somewhat meaningful there. Okay. All right then, so we spoke a little bit about the use of Vessim, we spoke about why it exists, and like kind of this idea that you can simulate various kind of configurations of grids or configurations of power coming into the system.

I imagine there'll be some people who are listening thinking, "this sounds cool, how do I actually use this on a project when I'm designing something in a kind of carbon-aware way, or how do I actually, what am I looking for?" Could you maybe talk me through? The actual process of using Vessim specifically.

We can start with something simple like a machine learning job first and then we might talk about some other configurations like perhaps a CMS because that's what lots of people might be working on for example.

Philipp Wiesner: I mean, the entire idea of Vessim is that you do not really notice that it's a simulator. It should behave as like a proper energy system. So you can, on the one hand, you have your normal applications. It doesn't matter if it's a machine learning job or if it's a CMS or if it's anything else. You just have your applications that are running.

And then all you need to do is you somehow need to get their power usage into the simulation. You can either do that by modeling. You can, I don't know, look at the CPU usage of your application and somehow estimate how much power it would use. Or you actually let it run on a machine, plug a proper power meter, and then periodically feed this information into the simulation.

And the simulation itself you configure as you wish. So we have a lot of READMEs. You can plug and play custom simulators. We have some implemented, we're right now implementing the ones from the National Renewable Energy Laboratory. They have lots of simulators on everything from solar to wind to geothermal and whatsoever.

So you can plug your microgrid together. Also provided with energy storage. We are releasing Vessim 0.9 this week, I think, which then comes with a few more energy storage solutions. So you can really plug this together as you wish. And then you can write your own API basically to access this data center. The new applications, as in normal carbon-aware computing, can just query a REST API or something and really ask "what's the state of charge of my battery, how much energy is produced right now, what's the forecast for carbon intensity," all of this.

Chris Adams: Okay so I'm going to break that down a bit because there's a couple of quite interesting concepts that you spoke about. So first of all I feel a bit embarrassed by not mentioning this, we just assumed it, of course, was open source, but yes, this is entirely open source, anyone can access it, if they search for Vessim, V E S S I M, they'll likely find the project, because there aren't that many other things called Vessim right now, is that the case?

Philipp Wiesner: I hope so, because we were searching for a searchable name. 

Chris Adams: We'll also share links to the actual project on GitHub as well. It's written primarily in Python as I understand it, so if you know Python or know someone who works in Python, and given that Python's basically the most popular programming language on Earth, or by various metrics, is one of them, then the chances are fairly high that you can start doing this.

Are there, like, presets for different grids? So in the same way that you might have a preset for, I'm viewing a website on a mobile phone, or I'm viewing a website on a desktop, you said a little bit about NREL, the National Renewable Energy Laboratory. They've said, "well, this is what the, this is how a geothermal plant might work, or this is how a wind turbine might work and this is solar panel might work."

And that gives you an idea of saying, "well, this is somewhat similar to my grid." And then there's kind of, are there some presets or things so people can come up with some approximation of what it might be in their part of the world?

Philipp Wiesner: Not yet publicly. We're working on this and we will publish it soon. But right now there's no presets, but Vessim comes with some data sets that were provided. One was provided by Watttime on carbon intensity. One was provided by Solcast on solar data. And you're right, Vessim itself is in Python, but we're like building on a library called Mosaik.

Been developed in Oldenburg, also in Germany, and this is basically the entire co-simulation system behind Vessim. And they allow you to basically plug and play any type of simulator that can be written in Java, or it can also be a real system. You can also just take a real battery and measure that and just feed this information.

So it's really very flexible in that sense.

Chris Adams: Ah, cool, I'm glad you mentioned about the power use thing, because that's the thing I was going to speak to you about, because you said that you can draw power from a physical system, so you might time series of power usage, but one thing that we've spoken about previously with other guests on this podcast is that if you know what kind of compute usage, how much of your compute of a server is, you can make some I'm a bit worried about using the word inference when we've just spoken about AI.

But like, you can, there'll be some idea of what the likely power might be, just because machines tend to use more power at 100 percent utilization versus maybe 50 percent utilization. There's various curves and things that give you some numbers. And I believe there's an organization, Green Coding Solutions, who share some of this.

But also, I think this is under the hood what, other projects like Kepler also have some reference to. So, there are different ways that exist right now to give you some numbers. Even if you don't have something plugged into a system, for example. Maybe, you don't have access to, a power meter like you're deploying in a cloud, for example.

Philipp Wiesner: Exactly. Yeah. And this is cool that you mentioned that because this is one of the, it's a master thesis that we are running right now with one of the students who has also been developing Vessim for the last two years. And we are also using Kepler and trying to really calibrate Vessim for real systems.

And of course, these calibrations can be published. So we can really see, because there's this big difference between software-based power metering and like actual hardware power metering. In software-based power metering that's like included in Intel processors and also NVIDIA graphics cards, they just estimate how much power do I draw right now.

But it comes with a certain error. And what we're doing is like we're benchmarking these machines, using the integrated software-based power metering that actually everyone has access to but also like plug a real power meter and see how big is the difference under different conditions and so on and try to, yeah, make Vessim a bit easier to configure for bigger systems. Because this is the entire idea, maybe you have access to one GPU that you can benchmark but then you want to explore, "okay, how does my system behave if I have 200 GPUs?" And you can scale up in the simulation.

Chris Adams: Okay, so when we, so when someone is starting with this, like, we're going to talk about actually running the software, but we're just setting the scene to figure out, okay, what does the setup look like? Because I think most developers might be familiar with the idea of running a test to see if it works as intended.

There's this whole new world outside of your computer system that we need to talk about that I think you might need to kind of, that people need to understand before they start thinking about what, how the system is supposed to be designed. So we spoke a little bit about the fact that Vessim is using a existing tool called Mosaik, which designed for simulating grids.

So someone's done all that work already to figure out how to simulate a grid and you're using, you're relying on some of that. So there's a bunch of work that's gone into that already that you can draw upon. And we spoke a little bit about, there are ways that you can get readings on power, so even if you don't have something plugged in, you can still get some maybe low precision but still useful numbers to help inform some of your, like, design choices.

And then, now you've got that part there. You said a little bit about maybe things being in the, software being in-the-loop here, and I think while I've spoken excitedly about the notion of ecovisors and things on previous podcasts, I think that's what you're referring to here, like if the software itself knows how much, how full a battery is, or how green the power is, it might change in certain ways or it might respond to certain things.

Could you talk a little bit about that part? Because I think the software in-the-loop part is the thing that most people who've been taking an existing project and maybe trying to extend it to be carbon-aware. That's where they might be spending their most times either thinking about how to test something or figuring out how to make and adapt their existing system so they can start coming up with some metrics.

Philipp Wiesner: Yeah, It's interesting that you mentioned Ecovisor because it was a big inspiration for this project. Because I think they spent tens of thousands of euros on this one solar array simulator and a testbed. And we wanted to, like, explore similar ideas, but we didn't have the funding at the time to just buy such a machine.

So I had to write a simulator, more or less. So this is how it all started. Sorry, what was the question?

Chris Adams: in-the-loop, software in-the-loop,

Philipp Wiesner: Ah, software-in-the-loop, sure. Yeah, 

Chris Adams: We've simulated all this stuff, we know roughly how much power we're using, so the system, the software now has the data going into it. So what happens next? That's the thing.

Philipp Wiesner: Yes. So the idea is we need to run the simulation in real time, right? In wall clock time. So one second of simulated time needs to be the same as one second of real time. And if we manage this, then we can simply from the simulation, host any kind of API. We can just spin up a REST API that gives us access to the system.

And in Vessim, users can define these APIs themselves. So in Ecovisor, for example, they already came up with an API. I don't know, "/battery" and then you can query the battery state of charge, but something like this. Vessim lets you explore this. How do we abstract the complicated energy system and how do we like expose relevant information for the computing system?

And then you can just run a real normal application running on your computer, running in a docker container whatsoever. And it can send requests to this energy system API. And best case, if we did everything correctly, it behaves exactly as if you were doing a request to an actual energy system. So your application doesn't know that it's working with the simulation.

Chris Adams: Okay, so I'll try and make this a bit concrete. So when I'm working on a project, in an existing kind of carbon-aware system, I might make an API call to like, I might ask electricity maps, "Hey, what's the local grid intensity where I am right now?" For example, and like, it's a little bit like that kind of request I'd be making, but I might be making it to Vessim to say, "what's the grid intensity?

What's the state of my charge or stuff like that?"

Philipp Wiesner: Exactly. You just write a normal application that is running on a system, just that the system is power metered, so Vessim can simulate how it would be behaving if it was plugged to batteries, for example, and then it can talk to this simulated energy system.

Chris Adams: Gotcha. Okay. And we're using Vessim as just the kind of, simulator to share this stuff and it may be that in the real world, like I might be, speaking to the electricity maps API or something like that.

Philipp Wiesner: Absolutely, would be, yeah, absolutely.

Chris Adams: Okay.

Philipp Wiesner: A plug and play thingy, yeah.

Chris Adams: All right, that part, I think I understand. So then at that point, I'm then able to basically say, well, let's imagine electricity maps has given me this figure for carbon intensity, or let's imagine my, the state of my battery is high rather than low, I then might make some changes and say, well, "When the energy is low, don't do this stuff, or just delay it for maybe half an hour to check it again."

So that's the kind of stuff. So maybe you could talk a little bit about, now that we're at that point, how does that manifest into me seeing if that has actually reduced carbon emissions over a period of time, for example. Maybe we could talk about that part, because I think that's the final thing, now that we've set up our system, we've connected to, we've figured out how to get data coming into my system to tell me what the carbon intensity might be, or what batteries might be, or what the power from a wind farm might be. So yeah, maybe you can talk about that. So over a given time, has this actually helped, for example?

Philipp Wiesner: Yeah, And this is actually what makes simulations so powerful, because you can just execute a bunch of different scenarios. You can execute your scenario one time without any carbon-aware strategies. You can execute it a second time with the carbon-aware strategies. You can simulate as if you were executing it in summer or winter, or you can simulate power outages and whatsoever.

Because it's all really fast and really cheap to do. And then of course Vessim is recording all these metrics. Like for every second it's recording how much power has been drawn, produced, how much emissions were there. So afterwards you can analyze these results and compare the different runs.

Chris Adams: Ah, okay. I think I'm understanding this now and I

kind of figure out how I might build some of this now if I was to be building maybe a batch job, for example. I can kind of maybe imagine how this might work for maybe a content management system or like a CMS or WordPress or something like that. So let's say I would do, would I be looking at like, this would be my traffic the last month, run it through and then compare it to what the carbon intensity might have been.

Is that how I might do this?

for 

Philipp Wiesner: Yeah, absolutely. Absolutely. And you can also use it in testing pipelines, for example, in continuous integration pipelines. You can just simulate a bunch of different scenarios for your carbon-aware application. Some of them might be power outages or something like really edge cases that you do not want to happen, but you can really test cheaply that your behavior, that your system or your application would behave as expected

Chris Adams: Ah, 

Philipp Wiesner: In a reproducible manner.

Chris Adams: So you've raised something quite interesting for me here because you, when you spoke about the idea of like things, power outages or stuff like that, this kind of implies that if I've got a system here, I could see to what degree I might make a change to the software, or I might just say, well, given I have control over my infrastructure, I'm running a data center, what if I was to just put a battery inside that data center?

How would that impact the carbon footprint of using this system every month, for example? Would that, could that be something that I could simulate with that?

Philipp Wiesner: Yes, absolutely. Absolutely. And if you have a model that is able to, like a battery model, a simulator that is able to simulate battery aging, for example, you can also simulate what's happening over the course of the next three years with my batteries and so on.

Chris Adams: Ah, okay, so it's actually two way So therefore, I can look at, I can, look at, say, making interventions at my code level, and I can interventions at the kind of deployment scenario level as well, actually. Okay, that is something I hadn't actually realised before then. Okay, so we've dived into this quite a bit and I hope there are at least some people following us now.

Can we just take a step back to say because we have just been assuming that carbon emissions or carbon intensity is absolutely the figure we should be looking for and that is our North Star and you mentioned before that maybe that isn't or there's a bit more to it than that because we can't just look at this number, see if it's going down, and say, "Oh yeah, we're making progress."

Maybe you could outline on some of that, because it's a lot of nuance here that people often miss, I think. 

Philipp Wiesner: Ah Yeah, sure sure sure. So carbon intensity is a metric that defines how clean or dirty the energy in the public grid is. That means if it's low, you can use it if it's high, you should maybe rather not. And the problem here is that it's hiding a lot of the underlying complexities of how grids work and how energy markets work.

So for example, one big problem that we have in energy markets is, like in carbon accounting in general, is this double spending of guarantees of origin certificates. So for example, you can easily buy certificates produced in Iceland or something. They produce almost all the energy through renewable sources, hydropower, geothermal power.

You can buy their energy and then tell everyone you're 100 percent renewable energy. But at the same time, of course, any data center that is located in Iceland will tell you that they're 100 percent renewable, which is also true. and in fact, there's not even a power line between Iceland and mainland Europe, and still you can buy their certificates.

so we have a lot of double accounting and of course, everyone is coming up with new methodologies of what's good and what's the right thing to do. And of course, everyone's also just doing what's in their favor. And this is the big problem with carbon intensity. So of course you can say now "carbon intensity in Germany is really low.

I should consume energy." But unless you have a green energy contract, you're for sure only getting the dirty fraction, because all the green energy is already sold.

Chris Adams: So let me just check if I can stop you there for a second. So what you spoke about just then was there are kind of two approaches or like there's one one approach of looking at how kind of green power is might be the existing kind of system that you see in most developed energy markets right now where there's like a market-based system where you essentially can purchase renewable energy certificates and apply that to kind of brown energy to then say it's green.

And there may be reasons why we have ended up with this scenario. As you said, there are two problems there when we talk about, say, the idea of buying green power certificates from Iceland and then applying them to dirty power in, say, Poland or Germany to say it's green. There's like a, there's one issue which is that there's literally no way to connect, there's no connection, like we definitely are not using geothermal that's generated in Germany to get there. There's no physical connection so it's not deliverable. And the other issue is that if Iceland was to do that, they would then have to stop saying their power is green because they've already sold on their greenness. And in many cases they're not doing that. They're saying, "oh, we're still green even though we've solved the greenness."

So, there is a kind of problem about the, yes, let's assume that the market, the concept was conceptually sound. There is a whole issue about people kind of cheating a bit and saying no, it's still green when they've sold the greenness on it already, basically.

Yes, 

Philipp Wiesner: and exactly in this sense, optimizing for carbon intensity is cheating, because you're doing exactly this. You're like saying, "I'm using low carbon energy," but all the green, as you said, has already been sold. At the same time, it makes sense. The metric also makes sense. Optimizing energy usage for when the sun is shining and the wind is blowing, of course it makes sense.

But the big problem is that these green energy certificates, these guarantees of origin certificates, are traded independently of the physical flow of energy.

Chris Adams: Ah, okay. Okay, so there is, there's a whole discussion about this, like, where we work at the Green Web Foundation, we're kind of borrowing some of the ideas we've seen in the hydrogen sector, where they talk about if you want to say energy is green, it needs to be, I believe it is, timely, deliverable and additional.

So you need to be new power, like the, you mentioned this example of nuclear being used in other data centers, where essentially you're reallocating power that would have been going into the grid. So everyone has slightly greener power, but you say, no, that all has to go over the data center. Then that has the impact of essentially making the power that everyone else is using be seen as slightly less clean because of this accounting system, and that's one of the unfortunate artifacts of that. So that's one thing you mentioned about that. And I think the system that you might be talking about was the idea of like a location-based system, which is much more about like the physical environmental footprint of the power you're using rather than, say, the market-based approach, right?

Philipp Wiesner: Exactly. So we need a system that is somehow time based and location-based, a certificate system, then this all would make a lot more sense. But even then, grids are very complex and it's too easy to boil it all down to a single number and say at Germany, for an EPM we have this number of carbon intensity.

It would maybe make more sense if we, like, break down everything into smaller pieces, again, go more local. I have, for sure, Northern Germany separated from the South and stuff like this. But still, it makes sense, and this is what, for example, Vessim is good for, to really think about and understand how is energy actually flowing, how is the energy system actually working, because it's a bit more complex.

Chris Adams: Ah, I see. Okay, so for people who want to go down this weird accounting rabbit hole that we just kind of skirted around, I will share a link to a journalist called Hanno Böck who's dived into the world of Iceland, and in particular about this whole idea of selling green energy twice like this, because if you are looking into this, it's, as we end up with a much more decarbonised grid, this is going to come up again and again.

And right now, we do see the systems being used, like the GHG protocol, the people who actually define these standards. They're going through a process of reform and basically saying, "well, these are the changes we need to make and this is why this is possibly not fit for the purposes that it was 10 years ago," for example.

So there is a chance to make interventions and talk about this, if you want to look into this as well as looking into the other things we spoke about with Vessim. Okay, so we've spoken about this idea of setting up a kind of testbed that we've established that this is kind of open source, that anyone can pick up and start playing with right now, like the code is open source on GitHub, and as I can see, documented well with like, videos and stuff like that.

We're just coming to time, so I just want to give a bit of space for some of the future events that are coming up, because I understand that, well, part of your job, as you told me, is like, I'm the publicity chair for this all about low carbon computing, so I should probably give you a bit of space to talk about that as well.

Philipp Wiesner: Yeah, that's super nice. Thank you to just take two minutes. So we're organizing this workshop. It's the first workshop for low carbon computing, which will take place on the 3rd of December in Glasgow, and It's mainly an initiative, a local initiative from some folks at the University of Glasgow, but it's an international conference, it's an international workshop I mean.

And we expect submissions from all over the world, but mainly probably the European community, I guess. And yeah, welcome everyone to submit your work. So we have a very broad scope on really anything related to sustainable computing, from energy efficiency, carbon efficiency, embodied carbon and so on, but also topics that are often a bit underrepresented in computer science like circular economy, sufficiency, demand reduction, and all of this. exactly. So this is all very welcome. And for regular talks, all you need to submit is like a two page extended abstract by the 24th of September. Although we're like currently discussing maybe a tiny deadline extension and all accepted authors, all accepted papers will afterwards be invited to submit like a full workshop paper of up to eight pages if you're interested, which will be published in our post proceeding.

So yeah, feel free to have a look. I think we will also put a link in the

Chris Adams: a link in the show notes, for sure, absolutely. So if I understand this correctly, is this a little bit like, possibly, a kind of like a sister conference to Hot Carbon, but where it's a little bit colder, and you're more likely to kind of find a burrito served at Legal Jack's.

Is the name of a restaurant in Glasgow that eaten at before.

Philipp Wiesner: Yes, I guess like it's the first version. Let's see where it's going. But maybe we can, if it's going well, we can maybe establish it as like a European alternative to Hot Carbon or something, which will, I guess, take place in the US in the upcoming years. 

Chris Adams: I see. And there's one thing I wanted to just check because when I looked at the kind of call for papers, the set of topics, it's a little bit more wide than just like let's focus on efficiency. There seems to be a kind of systemic angle that I hadn't seen quite in the same way, for example. So there are discussions about like you said,

okay, looking at the demand side as well as just the supply side, for example. And like, I think I've even seen the word frugal computing used inside here. Like, okay, this is almost like somewhat possibly like the limits conference or maybe some of those ideas somewhat comparable to inside this. 

Philipp Wiesner: Yeah,

guess, yeah, I guess it's somewhere in between that, in between Hot Carbon and limits, maybe.

Chris Adams: cool. All right. Well, I am now thinking it might be nice to figure out a way to get a series of trains to get up to Glasgow, because, I really like that city.

Philipp Wiesner: Yeah, 18 the last times.

Chris Adams: Okay, cool. So that's one thing that's taking place. So the deadline was the 24th of September. Easy to apply to, there is, and that'll be taking place in December.

Philipp Wiesner: Just one last thing, it's a hybrid event. It's a hybrid event, so anyone is welcome to participate, and also authors are of course welcome to present remotely.

Chris Adams: Okay, so I don't need to get on that if I really don't want to, because, well, I might want to see people, but it takes quite a long time

Philipp Wiesner: only a one day event, so you need to consider if it's worth two days of travel.

Chris Adams: Alright, thanks for that Philipp, I appreciate that and we'll share a link to that so people know when the CFP is open how, well actually when it closes more specifically because I think it is open right now. Okay, cool. So I'll just touch on some other events that we have inside here and then I think we'll wrap up if that's okay with you.

So these are some of the other events that our producer Chris Skipper has shared. So there's an event called CodeGreen, the intersection of software engineering and sustainability. This is on September the 12th at 11am AEST. This has Sara Bergman from Microsoft Norway, and one of the, one of the authors of the book, Building Green Software, and also a regular on our podcast.

She's presenting, speaking with Michelle Sandford. And there's also an event in Vienna, in Austria. This has Asim Hussain, our Executive Director of the Green Software Foundation. He's talking about the Impact Framework, this radically transparent tool for sharing the environmental impact of your tooling, and that's on September the 16th, and I think that is in person, but then maybe we'll share a link for people to see about if they can get involved remotely.

There's also another event in the UK, Engineering a Greener Future for IT, that's on September the 18th at 5pm British Standard Time in London. And there is also another event in Brighton as well, so the UK is quite heavily represented right now, Collaborating on Digital Sustainability, that's September the 18th.

I'm going to use this platform while I still can. I'll share a link to Green IO London as well, which is the conference that I'll be keynoting at with a number of luminaries. We'll share a link to that because I, it's very front of my mind right now and I'll be there for the day. So if you listen to this and you fancy coming along, we'll share a link where you can get a free ticket to join at the conference.

And yeah, that's us. I think we've come to the time. We'll just actually make sure that all the things we've discussed, like I mentioned before, we always try to get as many of these show notes full of links as possible, because we cover a lot of ground on these free wheeling conversations, so that will be visible.

The thing you might need to do though, if you are listening via Spotify or the Apple platform, you might need to look up podcast.greensoftware.foundation, then follow the link to this podcast to see all the links, because I don't think they're made available if you just are listening to it in Spotify, for example.

And we do put quite a lot of time into doing that. And Philipp, I just want to say thank you so much for helping unpack what can be an extremely complicated topic and putting the work in to make a open source project that makes it easy for people to figure out what to do with carbon-aware computing and hopefully guide themselves to slightly more sustainable software.

Philipp Wiesner: Yeah, no, thanks a lot for having me. Yeah, had a lot of fun. It was a pleasure. Thank you.

Chris Adams: Okay, final question I should ask, if people do want to find out about you, where should people look? Are you on LinkedIn? Are you still using X slash Twitter slash what's the best way to follow your work these days?

Philipp Wiesner: Probably just Google me, I guess there's maybe one or 2, 3, 4 Philipp Wiesners that are more important than me, but if you just look at TU Berlin or Philipp Wiesner carbon-aware where I should pop up and then you can see.

Chris Adams: Cool, well your name will be in the show notes and the show title, so if anyone is curious about how to spell Philipp Wiesner and not sure how many P's they should be using, then that'll be inside there. All right, Philipp, really lovely chatting to you, mate, and thank you so much for coming along, and yeah, have a lovely week.

Philipp Wiesner: Thank you. You too. Thanks a lot.

Chris Adams: Cheers, bye!

Philipp Wiesner: Bye.

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To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!

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]]> Host Chris Adams is joined by special guest Philipp Wiesner, a research associate and PhD student at TU Berlin, to discuss how computing systems can better align energy consumption with clean energy availability. Contributing to Project Vessim, Philipp explains how researchers are now able to model different energy consumption scenarios, from solar and wind power integration to the complexities of modern grids despite the scarcity of available testing environments. They discuss federated learning and its role in carbon-aware designs, along with challenges in tracking real energy savings. Tune in to learn about the future of carbon-aware computing and the tools being developed to help software become more sustainable.

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TRANSCRIPT BELOW:

Philipp Wiesner: Optimizing energy usage for when the sun is shining and the wind is blowing, of course it makes sense. But the big problem is that these green energy certificates, these guarantees of origin certificates, are traded independently of the physical flow of energy.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and insights from the world of sustainable software development. I'm your host, Chris Adams. Today, we're diving into the world of carbon-aware computing, how computing systems can align the energy consumption with the availability of clean energy.

Natural cycles govern all kinds of things we do. We mostly go to bed at night, and the food we eat is often influenced by what season we're in, and so on. This also shows up in how energy is available to us. You can see how day follows night in the output from a solar panel. And broadly speaking, it tends to be windier around winter than it is in the summer.

Even when we burn fossil fuels, we're basically digging up energy stored millions of years ago. As we move away from fossil fuels though, where we get to use the sunlight that was conveniently stored for us, we move towards a system based around harvesting energy, like we harvest food. The way we consume energy needs to adapt as well.

And that's the idea behind carbon-aware computing. But how do we know if our carbon-aware designs are actually better for the environment? With us today is Philipp Wiesner, a research associate and fourth year PhD student at TU Berlin, Technical University Berlin, whose work focuses on carbon-aware computing.

Philipp is deeply involved in research that connects the dots between computing power and renewable energy, and today will unpack the work he's been doing, including his work on Project Vessim, a testbed for carbon-aware computing. So let's dive in. Philipp, thanks so much for coming back, and for those who haven't heard the last episode when you joined us in 2022, can I give you a bit of space to introduce yourself?

Philipp Wiesner: Yeah, so hey Chris, and thanks a lot for having me back. I'm really excited. Yeah, so I'm Philipp, I'm a PhD student, as you just said, and a research associate at TU Berlin. I'm in the research group for distributed and operating systems. And I've been doing research on carbon-aware or in general, sustainable computing since the start of my PhD or my master's thesis in 2020.

And yeah, at the start of my PhD, I was mainly looking at the basics. So that's the first podcast we had two years ago. The field was still very young and we were looking at the potential of delaying the execution of workloads to make use of cleaner energy. Later, we also looked at migrating workloads from this to different data centers and so on.

And it was all based on carbon intensity. And then from then on later, we moved on from only optimizing for carbon intensity to really better understanding how electric grids work and rather optimizing for curtailed energy, excess energy. So, for green energy that would otherwise go to waste.

Chris Adams: Cool. Thank you. Okay. So you mentioned we spoke in 2022, two years ago, and the field has moved. And while we'll share a link to that podcast, where we go into a lot of detail, can I just give you a bit of time to just provide a brief summary of the paper that you spoke about then? I think it was called Let's Wait Awhile.

That was one of the projects that you're working on, and that was one thing related to the cloud. If we can talk about that'll be a nice scene setter. Then we can talk about the new work that we, honestly, I'm really excited about in all kind of nerdy glee. So yeah, Wait Awhile. What's the deal with that one?

Philipp Wiesner: Yeah, sure. So the idea in Let's Wait a While was just that in different regions of the world and at different times we have different carbon intensities. So carbon intensity is a metric that describes how dirty or how clean is the energy that you draw from the public grid in terms of emissions per kilowatt hour.

And at the time we were looking just, if you have a single data center and you might have some flexible workloads. So workloads where you do not really care if they happen now or tonight or tomorrow morning, how much potential do we have in shifting them, delaying them, computing them a bit earlier, maybe dissecting them in different parts and computing these times in different parts and so on?

And it was like a bit of a, yeah, it was a basic work at the time.

Chris Adams: And there were some, I think, when I first read about it, there were some quite, the savings there were around the low tens of percent for, compared to what the other ones would be. It was something like that, I think, from there. So that was one thing that caught my attention while we ended up speaking about it last time.

This new work that you presented at Hot Carbon, Vessim, as I understand it, this is something which is more general and applicable to all kinds of grids rather than being focused on one or two. Can you tell me a little bit about why you worked on this and what the general idea for creating a testbed for hardware computing was in the first place?

Philipp Wiesner: Yeah, sure. So the thing is that in carbon-aware computing, it's really common to simulate. The thing is that we're often talking about highly distributed systems, often globally distributed systems. And also these approaches often only take effect if you like simulate them or like run them for weeks or months because we need different seasons.

We need different, yeah. So the thing is that in Let's Wait a While and also in basically pretty much all the papers I wrote so far, we have to simulate systems. And in pretty much all the literature so far, people simulate systems because it's just extremely expensive to actually build and operate such systems.

Only big players can do it. And also results are not reproducible. So no one can really check if the stuff that you're reporting is correct.

Chris Adams: So let me just check. So basically, there is no way to... essentially there's no counterfactual, there's no way to see if my, like, I might say "yeah I've made the savings" but without showing your workings it's quite difficult to stand by them or even challenge them actually, that's one of the things that seems to be a bit complicated with that, right?

Philipp Wiesner: Exactly, a general problem in research, to make stuff reproducible. But here we have the big problem that everyone is implementing their own simulation. So everyone is doing it by themselves and just iterating over a big time series for carbon intensity is maybe not that hard. But the moment you have more complex systems and you're working with forecasts and so on, there's a lot of things you can do wrong.

And it's quite a heavy development effort often to do things right. So, just over the years we collected a lot of experience and eventually came up with the simulator. So, Vessim is basically a big co simulation suite where you can plug and play your own simulators for renewable power generation, carbon intensity of the grid, energy storages, and so on.

And you can connect them, you can connect existing simulators, and then test your real system, real applications, as if they were interacting with this simulated energy system.

Chris Adams: Ah, I see, so let me just check if I'm So one of the things you mentioned before is that we tend to look at the grid as like, just one thing that might be changing. And even our interactions are influencing the grid as well, so there's, that's one thing you need to take into account. But you also mentioned just then, that while most of us think of electricity as just power coming out of the wall, there's actually, it's more helpful to think about it in terms of, well, there's maybe on site clean energy, which is maybe very, clean, but isn't always available, and there's maybe on site batteries where the carbon intensities might be influenced by what has gone into it.

Like if it's clean energy that's gone in, it's going to be very clean. If it's dirty grid energy, it's going to be quite dirty. And then there's the grid. So there's multiple different, kind of, ways energy might go into the system that you use for your computing, for example, and you need to account for some of those things.

And then in addition to that, different parts of the world might have totally different grids. So Poland, for example, right now, loads and loads of coal, but comparing that to Iceland, which is, or France, which is like full of nukes, it's going to be very different carbon intensity. So you can't make the same assumptions, and you'll need to essentially do something to recreate the conditions that you might be in so that you can make somewhat more robust and intelligible statements about the savings you might achieve by making any kind of carbon-aware designs.

Philipp Wiesner: Yes, exactly. So yeah, as you said, like most of the existing research is looking at carbon intensity is looking at the global grid. And the problem with this metric is in general, that it's oversimplifying how complex grids actually are. So energy markets are really complex, grids are really complex. So if we only optimize for carbon intensity, which is a relevant metric in the future, we will run into several problems.

Because it's just hiding a lot of underlying complexities. And, as you said, we can maybe avoid these complexities if we're looking more local at microgrids. So if we assume, I don't know, a data center provider is putting solar panels on their roof or like having a direct power purchase agreement with some close by wind park, then we have a lot more direct connection of who's producing and who's consuming, and Vessim is exactly designed for this, to simulate such systems so that eventually you can optimize really for the energy that you're producing or storing.

Chris Adams: I see, okay, so you're still looking at this and we're looking at more than just a grid, basically. We're saying, we're focusing on the carbon intensity because the power can be coming from different places, like the one I'm immediately thinking of, actually. So in the news, Elon Musk's talking about this new data center where he's, where they bought 100 000 Nvidia H100 GPUs using 100 megawatts of power.

And that's so much power that they can't get that from the grid. So what they've done is, it's not clean energy they've used. They've used, say, like, a bunch of mobile gas generators, plugged into the data center that's come in. So we spoke about on site clean energy, but there's actually all kinds of energy that might be going in, and we're now seeing something like this in this kind of world of centralized AI, where you do see organizations looking to things other than just the grid to power things.

So we might need to take into account a whole basically the system seems to be getting more dynamic and being able to simulate this seems to be one of the ideas that Vessim is intended to kind of support so that we can essentially show our working and understand what kind of changes we might be making, or under these conditions, this is what a carbon-aware design might achieve, for example.

Philipp Wiesner: Yeah, exactly. Yeah. As you're saying, it's unfortunately becoming more and more popular to again, build gas turbines or even small nuclear power plants right into data centers to like come up with this insane amounts of energy that they need by now. We of course, like rather focusing on renewable sources because they're also a lot more difficult to schedule.

But yeah, this is one of the use cases of Wessim. So you can like explore how does it affect my data center if I cover the roof with solar panels, build a wind park nearby. How does it affect my data center if I invest into a lot of energy storage? How can I optimize my data center to make best use of these local resources?

And then there's also a lot of like systems questions, like how do I optimize? How do I make applications aware of locally produced energy? How do I make them aware of the state of charge of local batteries? What are they allowed to know? Like there's also security questions involved. And all of this we want to make explorable, so to say,

by providing this very general purpose, extendable simulation framework.

Chris Adams: Okay, cool. That sounds, okay, this sounds lots and lots of fun, and I remember getting quite excited when I read the paper from Hot Carbon. So you've shared this, but as I understand it, some of this has actually been used for some production work, or this isn't just like a wacky paper, there's actually people, there are examples of this being used.

I know that your paper spoke about the use of "Okay, here's how we've been running this on a kind of test system in a Raspberry Pi. Here's how it works with AWS cloud instances." So there are various examples of it being used. But there is a new project that you told me about beforehand called FedZero.

Can you tell me a little bit more about that? Because that seems to be applying some of these ideas in the real world. So yeah, maybe I'll just give you the chance to plug that one, because yeah, it was a fun read.

Philipp Wiesner: No, sure. Thanks a lot. Yeah, exactly. So we're trying to, deploy Vessim in a lot of different projects now. One of the main ideas behind Vessim is also to be have like hardware and software in-the-loop integration. So you can also plug in real devices, real energy generators, and so on.

In FedZero, we didn't do that. FedZero is also fully simulated. But it's a new, it's a scheduler basically we proposed for federated learning systems. This is,

federated learning is some type of distributed machine learning training. So you have a bunch of different clients and they locally train a machine learning model.

So they locally consume energy and the scheduler basically decides when, who is training. So it's like distributed batch processing in the end. And the entire evaluation of the system was done with Vessim. We have, for example, a lot of data sets that are coming with Vessim. They've been provided, for example, by Solcast.

It's a big solar data provider. And the entire idea behind the system is to only use excess energy. So only use curtailed energy and spare computational resources to perform a distributed machine learning training to make it effectively (an exclamation mark) zero carbon! Because you don't use any energy that would have been used by anyone else, basically.

Chris Adams: Oh, I see. Okay, so there's two things that I'd like to unpack there. So could you maybe just talk a little bit about the relationship to, I think I was confusing this with Flower, perhaps. So Flower is one of the wider projects which has something to do with federated machine learning. you maybe just talk on that because I think, for people who have listened to this before, they might not be aware of Flower or the even idea what federated machine learning might be. Because I just spoke about a centralized massive data center, but that's not the only way you can do machine learning and AI training by the sounds of things.

Philipp Wiesner: No, sure. We can also go a bit more into detail. So in federated learning, we assume that our data is distributed. So it's a different clients, a very common example are hospitals that, for example, want to train a common model for skin cancer detection whatsoever, but they cannot share their private patient data.

So we need to somehow train on their data without them releasing this data. And what's happening and Flower is one of the frameworks that lets you do this, that lets you perform federated learning as we distribute the model to the different hospitals, they locally train on the data and send it back.

And we try to schedule this local training in a way that it's only using green energy. Yeah, that's the main idea.

Chris Adams: Ah, I see. So, these would be devices which might already exist, for example, you don't have to have the same kind of emphasis on having a bunch of new stuff that you're using all the time to kind of sweat the asset and make the most of it, so you could be using maybe older technology or a more kind of mixed set of computing, rather than 100 000 H100s, for example, in one straight place, for example.

Philipp Wiesner: Yes, that's true. That's true in one sense. On the other hand, we always have to see what we're optimizing for. And if you're optimizing for something like curtailed energy, like for example, in the north of Germany, we have a lot of wind parks. But the German grid is not always strong enough to get all this energy to the south where like the big consumers are, the big industries.

So often times we have an excess of energy in the north. Now if you have hundreds of kilowatt hours of excess energy in the north, it doesn't get you anywhere to turn on a bunch of iPhones or old computers to make use of this energy. So usually with our approaches we target big consumers. We target the A100s and H100s to, because they actually make a difference.

Like turning them on or off actually makes a difference to the grid. A few thousand iPhones, not so much.

Chris Adams: Okay, so you still would be using some of this kind of relatively powerful hardware, if it represents a significant power draw on the grid, for example. And, is there anything special about where these might be placed? Because I've, seen examples and I've seen people talking about the idea of like literally putting this stuff directly into, say, the towers of wind turbines.

I know this is one thing that Windnode in Germany were doing, for example, because that's basically, where the power's not being used, it's one of the most fastest ways to actually

Philipp Wiesner: yeah, clearly, yeah, that's a super important topic, like placement of data centers. Just what I said, of course, you like place these data centers again, and if we talk about Germany in like Frankfurt region or in the south of Germany, it doesn't get you anywhere because you still need to transfer the energy there.

So you would actually need to build data centers very close to where energy is produced. However, like the big problem that we're facing right now with all this LLM and GPU stuff. is that there's actually not a lot of flexibility. So, FedZero for example assumes that we have some spare resources and some excess energy, and maybe the excess energy is fair, we do have that but we do not have spare resources.

Like, whenever I keep telling people since the last six months about this project they're like, "no, there are no spare resources." Like, GPUs are used all the time, we have a GPU shortage so, carbon-aware computing on GPUs is not really working at the moment.

Chris Adams: Yeah, I'm glad you mentioned this because this was one thing I've always been struggling to make sense of because when you've got a graphics card which is like 40k and you've got only a certain amount of years to use it, for example, the cost of having it off is actually quite high. So therefore, being able to simulate what the savings might be from some carbon air computing might, would actually be meaningful.

Or, if you're not going to switch off the power, then you might need to find another way to, like, not draw power from the grid. So that kind of implies either an increased amount of storage that you might need, so that rather than switching things off, you just reduce the power you're drawing from the grid by pulling it from somewhere else, for example.

So that's another kind of way you might flex your system to... still reduce the emissions, because it may be the case that you're able to load up on clean energy when there is an excess in other places, whilst keeping that thing still running, for example.

Philipp Wiesner: Yeah, exactly. No, you're completely right. And that was different, a few years ago when you, like, were mostly talking about cloud computing on CPUs, when we were talking about carbon-aware computing, because there you usually have a big buffer, because cloud providers want to have this image of infinite scalability, so they usually have spare resources at hand.

So, yeah, like this entire load shifting stuff was somewhat reasonable. Now with GPUs, I mean, you're right, you would need to plug them off, but no one's doing that. Like, if you're buying a GPU, you're using it.

Chris Adams: Using as much as you possibly can to make it, to make the per hour cost as low as possible, because you're, I see, okay, so, this is one thing I'm really glad you allowed us to dive into this because one of the things that I would initially think about, like there's some trade offs you might be making, and I might, before I spoke to you, one of my assumptions was that by using a federated model, you might have a lower embodied carbon because you're able to use a wider set of computing, which might already exist, for example, but it sounds like, because some of these chips are, they use more power, but per instruction, they're much, much more efficient per instruction. So, you're probably not going to save that much from the total embodied carbon of your fleet, for example, by reusing existing stuff in some cases. Is that what it would be like?

Philipp Wiesner: Yeah, there's different studies on whether highly distributed training or computation in general is actually more or less efficient. It often really depends, like usually the centralized, like you won't get anything more efficient than an H100. Energy per instructions. It's super, super efficient, but then of course, if you put them all in one place, you need to cool more and so on.

So often it can be more efficient to distribute it. there's no clear answer to that. I would say it really depends on the system, whether federated distributed system is more energy efficient or a local one, but of course in distributed system, you might have more flexibility for scheduling and waiting and yeah.

Chris Adams: Ah, I see. And I'm just going to move on to another question in a second, but while we talk about that, I just wanted to speak about maybe this idea of more distributed federated learning. A lot of the time you see people talking about saying, "I need a cluster of machines in one place because they need to talk to each other."

And the closer they are, the lower the latency, the faster the responses might be. And. When you're using a more distributed version, a distributed model, if you're not doing, like, even if you're not thinking about machine learning, you still need to think about latency, how long your response comes back.

How, like, what are the trade offs that you have there when you're looking at a kind of federated model, like with Flower, or providing some of these kinds of more distributed, carbon-aware style approaches, for example? Like, how do you address that?

Philipp Wiesner: So in general, in federated learning, we differentiate between cross device and cross silo federated learning. And cross device is what you were mostly referring to as like you have a lot of iPhones, a very distributed small system. They're often connected via mobile network. So latencies are usually quite high.

And there you really need to consider this latency trade off. In cross silo federated learning, it's a bit less important because you assume that each of these clients is, as I said, like a small data center or a big data center, they are connected via fiber. So, latencies are usually very low and not super relevant compared to how long a training takes.

So like sending models back and forth, model weights is not really relevant compared to how much time you need to compute actually. 

Chris Adams: Ah, thanks for explaining that, because there are various companies who are talking about having a distributed model, or saying, you need to do, you need to have, maybe, infrastructure that is complementary to the grid. And I was never able how to, how you get around the issue of speed of light, right?

Because they're going to be very, different if, even if it's just opposite ends of the country, that's going to, depending on the size of your country, that's going to be somewhat meaningful there. Okay. All right then, so we spoke a little bit about the use of Vessim, we spoke about why it exists, and like kind of this idea that you can simulate various kind of configurations of grids or configurations of power coming into the system.

I imagine there'll be some people who are listening thinking, "this sounds cool, how do I actually use this on a project when I'm designing something in a kind of carbon-aware way, or how do I actually, what am I looking for?" Could you maybe talk me through? The actual process of using Vessim specifically.

We can start with something simple like a machine learning job first and then we might talk about some other configurations like perhaps a CMS because that's what lots of people might be working on for example.

Philipp Wiesner: I mean, the entire idea of Vessim is that you do not really notice that it's a simulator. It should behave as like a proper energy system. So you can, on the one hand, you have your normal applications. It doesn't matter if it's a machine learning job or if it's a CMS or if it's anything else. You just have your applications that are running.

And then all you need to do is you somehow need to get their power usage into the simulation. You can either do that by modeling. You can, I don't know, look at the CPU usage of your application and somehow estimate how much power it would use. Or you actually let it run on a machine, plug a proper power meter, and then periodically feed this information into the simulation.

And the simulation itself you configure as you wish. So we have a lot of READMEs. You can plug and play custom simulators. We have some implemented, we're right now implementing the ones from the National Renewable Energy Laboratory. They have lots of simulators on everything from solar to wind to geothermal and whatsoever.

So you can plug your microgrid together. Also provided with energy storage. We are releasing Vessim 0.9 this week, I think, which then comes with a few more energy storage solutions. So you can really plug this together as you wish. And then you can write your own API basically to access this data center. The new applications, as in normal carbon-aware computing, can just query a REST API or something and really ask "what's the state of charge of my battery, how much energy is produced right now, what's the forecast for carbon intensity," all of this.

Chris Adams: Okay so I'm going to break that down a bit because there's a couple of quite interesting concepts that you spoke about. So first of all I feel a bit embarrassed by not mentioning this, we just assumed it, of course, was open source, but yes, this is entirely open source, anyone can access it, if they search for Vessim, V E S S I M, they'll likely find the project, because there aren't that many other things called Vessim right now, is that the case?

Philipp Wiesner: I hope so, because we were searching for a searchable name. 

Chris Adams: We'll also share links to the actual project on GitHub as well. It's written primarily in Python as I understand it, so if you know Python or know someone who works in Python, and given that Python's basically the most popular programming language on Earth, or by various metrics, is one of them, then the chances are fairly high that you can start doing this.

Are there, like, presets for different grids? So in the same way that you might have a preset for, I'm viewing a website on a mobile phone, or I'm viewing a website on a desktop, you said a little bit about NREL, the National Renewable Energy Laboratory. They've said, "well, this is what the, this is how a geothermal plant might work, or this is how a wind turbine might work and this is solar panel might work."

And that gives you an idea of saying, "well, this is somewhat similar to my grid." And then there's kind of, are there some presets or things so people can come up with some approximation of what it might be in their part of the world?

Philipp Wiesner: Not yet publicly. We're working on this and we will publish it soon. But right now there's no presets, but Vessim comes with some data sets that were provided. One was provided by Watttime on carbon intensity. One was provided by Solcast on solar data. And you're right, Vessim itself is in Python, but we're like building on a library called Mosaik.

Been developed in Oldenburg, also in Germany, and this is basically the entire co-simulation system behind Vessim. And they allow you to basically plug and play any type of simulator that can be written in Java, or it can also be a real system. You can also just take a real battery and measure that and just feed this information.

So it's really very flexible in that sense.

Chris Adams: Ah, cool, I'm glad you mentioned about the power use thing, because that's the thing I was going to speak to you about, because you said that you can draw power from a physical system, so you might time series of power usage, but one thing that we've spoken about previously with other guests on this podcast is that if you know what kind of compute usage, how much of your compute of a server is, you can make some I'm a bit worried about using the word inference when we've just spoken about AI.

But like, you can, there'll be some idea of what the likely power might be, just because machines tend to use more power at 100 percent utilization versus maybe 50 percent utilization. There's various curves and things that give you some numbers. And I believe there's an organization, Green Coding Solutions, who share some of this.

But also, I think this is under the hood what, other projects like Kepler also have some reference to. So, there are different ways that exist right now to give you some numbers. Even if you don't have something plugged into a system, for example. Maybe, you don't have access to, a power meter like you're deploying in a cloud, for example.

Philipp Wiesner: Exactly. Yeah. And this is cool that you mentioned that because this is one of the, it's a master thesis that we are running right now with one of the students who has also been developing Vessim for the last two years. And we are also using Kepler and trying to really calibrate Vessim for real systems.

And of course, these calibrations can be published. So we can really see, because there's this big difference between software-based power metering and like actual hardware power metering. In software-based power metering that's like included in Intel processors and also NVIDIA graphics cards, they just estimate how much power do I draw right now.

But it comes with a certain error. And what we're doing is like we're benchmarking these machines, using the integrated software-based power metering that actually everyone has access to but also like plug a real power meter and see how big is the difference under different conditions and so on and try to, yeah, make Vessim a bit easier to configure for bigger systems. Because this is the entire idea, maybe you have access to one GPU that you can benchmark but then you want to explore, "okay, how does my system behave if I have 200 GPUs?" And you can scale up in the simulation.

Chris Adams: Okay, so when we, so when someone is starting with this, like, we're going to talk about actually running the software, but we're just setting the scene to figure out, okay, what does the setup look like? Because I think most developers might be familiar with the idea of running a test to see if it works as intended.

There's this whole new world outside of your computer system that we need to talk about that I think you might need to kind of, that people need to understand before they start thinking about what, how the system is supposed to be designed. So we spoke a little bit about the fact that Vessim is using a existing tool called Mosaik, which designed for simulating grids.

So someone's done all that work already to figure out how to simulate a grid and you're using, you're relying on some of that. So there's a bunch of work that's gone into that already that you can draw upon. And we spoke a little bit about, there are ways that you can get readings on power, so even if you don't have something plugged in, you can still get some maybe low precision but still useful numbers to help inform some of your, like, design choices.

And then, now you've got that part there. You said a little bit about maybe things being in the, software being in-the-loop here, and I think while I've spoken excitedly about the notion of ecovisors and things on previous podcasts, I think that's what you're referring to here, like if the software itself knows how much, how full a battery is, or how green the power is, it might change in certain ways or it might respond to certain things.

Could you talk a little bit about that part? Because I think the software in-the-loop part is the thing that most people who've been taking an existing project and maybe trying to extend it to be carbon-aware. That's where they might be spending their most times either thinking about how to test something or figuring out how to make and adapt their existing system so they can start coming up with some metrics.

Philipp Wiesner: Yeah, It's interesting that you mentioned Ecovisor because it was a big inspiration for this project. Because I think they spent tens of thousands of euros on this one solar array simulator and a testbed. And we wanted to, like, explore similar ideas, but we didn't have the funding at the time to just buy such a machine.

So I had to write a simulator, more or less. So this is how it all started. Sorry, what was the question?

Chris Adams: in-the-loop, software in-the-loop,

Philipp Wiesner: Ah, software-in-the-loop, sure. Yeah, 

Chris Adams: We've simulated all this stuff, we know roughly how much power we're using, so the system, the software now has the data going into it. So what happens next? That's the thing.

Philipp Wiesner: Yes. So the idea is we need to run the simulation in real time, right? In wall clock time. So one second of simulated time needs to be the same as one second of real time. And if we manage this, then we can simply from the simulation, host any kind of API. We can just spin up a REST API that gives us access to the system.

And in Vessim, users can define these APIs themselves. So in Ecovisor, for example, they already came up with an API. I don't know, "/battery" and then you can query the battery state of charge, but something like this. Vessim lets you explore this. How do we abstract the complicated energy system and how do we like expose relevant information for the computing system?

And then you can just run a real normal application running on your computer, running in a docker container whatsoever. And it can send requests to this energy system API. And best case, if we did everything correctly, it behaves exactly as if you were doing a request to an actual energy system. So your application doesn't know that it's working with the simulation.

Chris Adams: Okay, so I'll try and make this a bit concrete. So when I'm working on a project, in an existing kind of carbon-aware system, I might make an API call to like, I might ask electricity maps, "Hey, what's the local grid intensity where I am right now?" For example, and like, it's a little bit like that kind of request I'd be making, but I might be making it to Vessim to say, "what's the grid intensity?

What's the state of my charge or stuff like that?"

Philipp Wiesner: Exactly. You just write a normal application that is running on a system, just that the system is power metered, so Vessim can simulate how it would be behaving if it was plugged to batteries, for example, and then it can talk to this simulated energy system.

Chris Adams: Gotcha. Okay. And we're using Vessim as just the kind of, simulator to share this stuff and it may be that in the real world, like I might be, speaking to the electricity maps API or something like that.

Philipp Wiesner: Absolutely, would be, yeah, absolutely.

Chris Adams: Okay.

Philipp Wiesner: A plug and play thingy, yeah.

Chris Adams: All right, that part, I think I understand. So then at that point, I'm then able to basically say, well, let's imagine electricity maps has given me this figure for carbon intensity, or let's imagine my, the state of my battery is high rather than low, I then might make some changes and say, well, "When the energy is low, don't do this stuff, or just delay it for maybe half an hour to check it again."

So that's the kind of stuff. So maybe you could talk a little bit about, now that we're at that point, how does that manifest into me seeing if that has actually reduced carbon emissions over a period of time, for example. Maybe we could talk about that part, because I think that's the final thing, now that we've set up our system, we've connected to, we've figured out how to get data coming into my system to tell me what the carbon intensity might be, or what batteries might be, or what the power from a wind farm might be. So yeah, maybe you can talk about that. So over a given time, has this actually helped, for example?

Philipp Wiesner: Yeah, And this is actually what makes simulations so powerful, because you can just execute a bunch of different scenarios. You can execute your scenario one time without any carbon-aware strategies. You can execute it a second time with the carbon-aware strategies. You can simulate as if you were executing it in summer or winter, or you can simulate power outages and whatsoever.

Because it's all really fast and really cheap to do. And then of course Vessim is recording all these metrics. Like for every second it's recording how much power has been drawn, produced, how much emissions were there. So afterwards you can analyze these results and compare the different runs.

Chris Adams: Ah, okay. I think I'm understanding this now and I

kind of figure out how I might build some of this now if I was to be building maybe a batch job, for example. I can kind of maybe imagine how this might work for maybe a content management system or like a CMS or WordPress or something like that. So let's say I would do, would I be looking at like, this would be my traffic the last month, run it through and then compare it to what the carbon intensity might have been.

Is that how I might do this?

for 

Philipp Wiesner: Yeah, absolutely. Absolutely. And you can also use it in testing pipelines, for example, in continuous integration pipelines. You can just simulate a bunch of different scenarios for your carbon-aware application. Some of them might be power outages or something like really edge cases that you do not want to happen, but you can really test cheaply that your behavior, that your system or your application would behave as expected

Chris Adams: Ah, 

Philipp Wiesner: In a reproducible manner.

Chris Adams: So you've raised something quite interesting for me here because you, when you spoke about the idea of like things, power outages or stuff like that, this kind of implies that if I've got a system here, I could see to what degree I might make a change to the software, or I might just say, well, given I have control over my infrastructure, I'm running a data center, what if I was to just put a battery inside that data center?

How would that impact the carbon footprint of using this system every month, for example? Would that, could that be something that I could simulate with that?

Philipp Wiesner: Yes, absolutely. Absolutely. And if you have a model that is able to, like a battery model, a simulator that is able to simulate battery aging, for example, you can also simulate what's happening over the course of the next three years with my batteries and so on.

Chris Adams: Ah, okay, so it's actually two way So therefore, I can look at, I can, look at, say, making interventions at my code level, and I can interventions at the kind of deployment scenario level as well, actually. Okay, that is something I hadn't actually realised before then. Okay, so we've dived into this quite a bit and I hope there are at least some people following us now.

Can we just take a step back to say because we have just been assuming that carbon emissions or carbon intensity is absolutely the figure we should be looking for and that is our North Star and you mentioned before that maybe that isn't or there's a bit more to it than that because we can't just look at this number, see if it's going down, and say, "Oh yeah, we're making progress."

Maybe you could outline on some of that, because it's a lot of nuance here that people often miss, I think. 

Philipp Wiesner: Ah Yeah, sure sure sure. So carbon intensity is a metric that defines how clean or dirty the energy in the public grid is. That means if it's low, you can use it if it's high, you should maybe rather not. And the problem here is that it's hiding a lot of the underlying complexities of how grids work and how energy markets work.

So for example, one big problem that we have in energy markets is, like in carbon accounting in general, is this double spending of guarantees of origin certificates. So for example, you can easily buy certificates produced in Iceland or something. They produce almost all the energy through renewable sources, hydropower, geothermal power.

You can buy their energy and then tell everyone you're 100 percent renewable energy. But at the same time, of course, any data center that is located in Iceland will tell you that they're 100 percent renewable, which is also true. and in fact, there's not even a power line between Iceland and mainland Europe, and still you can buy their certificates.

so we have a lot of double accounting and of course, everyone is coming up with new methodologies of what's good and what's the right thing to do. And of course, everyone's also just doing what's in their favor. And this is the big problem with carbon intensity. So of course you can say now "carbon intensity in Germany is really low.

I should consume energy." But unless you have a green energy contract, you're for sure only getting the dirty fraction, because all the green energy is already sold.

Chris Adams: So let me just check if I can stop you there for a second. So what you spoke about just then was there are kind of two approaches or like there's one one approach of looking at how kind of green power is might be the existing kind of system that you see in most developed energy markets right now where there's like a market-based system where you essentially can purchase renewable energy certificates and apply that to kind of brown energy to then say it's green.

And there may be reasons why we have ended up with this scenario. As you said, there are two problems there when we talk about, say, the idea of buying green power certificates from Iceland and then applying them to dirty power in, say, Poland or Germany to say it's green. There's like a, there's one issue which is that there's literally no way to connect, there's no connection, like we definitely are not using geothermal that's generated in Germany to get there. There's no physical connection so it's not deliverable. And the other issue is that if Iceland was to do that, they would then have to stop saying their power is green because they've already sold on their greenness. And in many cases they're not doing that. They're saying, "oh, we're still green even though we've solved the greenness."

So, there is a kind of problem about the, yes, let's assume that the market, the concept was conceptually sound. There is a whole issue about people kind of cheating a bit and saying no, it's still green when they've sold the greenness on it already, basically.

Yes, 

Philipp Wiesner: and exactly in this sense, optimizing for carbon intensity is cheating, because you're doing exactly this. You're like saying, "I'm using low carbon energy," but all the green, as you said, has already been sold. At the same time, it makes sense. The metric also makes sense. Optimizing energy usage for when the sun is shining and the wind is blowing, of course it makes sense.

But the big problem is that these green energy certificates, these guarantees of origin certificates, are traded independently of the physical flow of energy.

Chris Adams: Ah, okay. Okay, so there is, there's a whole discussion about this, like, where we work at the Green Web Foundation, we're kind of borrowing some of the ideas we've seen in the hydrogen sector, where they talk about if you want to say energy is green, it needs to be, I believe it is, timely, deliverable and additional.

So you need to be new power, like the, you mentioned this example of nuclear being used in other data centers, where essentially you're reallocating power that would have been going into the grid. So everyone has slightly greener power, but you say, no, that all has to go over the data center. Then that has the impact of essentially making the power that everyone else is using be seen as slightly less clean because of this accounting system, and that's one of the unfortunate artifacts of that. So that's one thing you mentioned about that. And I think the system that you might be talking about was the idea of like a location-based system, which is much more about like the physical environmental footprint of the power you're using rather than, say, the market-based approach, right?

Philipp Wiesner: Exactly. So we need a system that is somehow time based and location-based, a certificate system, then this all would make a lot more sense. But even then, grids are very complex and it's too easy to boil it all down to a single number and say at Germany, for an EPM we have this number of carbon intensity.

It would maybe make more sense if we, like, break down everything into smaller pieces, again, go more local. I have, for sure, Northern Germany separated from the South and stuff like this. But still, it makes sense, and this is what, for example, Vessim is good for, to really think about and understand how is energy actually flowing, how is the energy system actually working, because it's a bit more complex.

Chris Adams: Ah, I see. Okay, so for people who want to go down this weird accounting rabbit hole that we just kind of skirted around, I will share a link to a journalist called Hanno Böck who's dived into the world of Iceland, and in particular about this whole idea of selling green energy twice like this, because if you are looking into this, it's, as we end up with a much more decarbonised grid, this is going to come up again and again.

And right now, we do see the systems being used, like the GHG protocol, the people who actually define these standards. They're going through a process of reform and basically saying, "well, these are the changes we need to make and this is why this is possibly not fit for the purposes that it was 10 years ago," for example.

So there is a chance to make interventions and talk about this, if you want to look into this as well as looking into the other things we spoke about with Vessim. Okay, so we've spoken about this idea of setting up a kind of testbed that we've established that this is kind of open source, that anyone can pick up and start playing with right now, like the code is open source on GitHub, and as I can see, documented well with like, videos and stuff like that.

We're just coming to time, so I just want to give a bit of space for some of the future events that are coming up, because I understand that, well, part of your job, as you told me, is like, I'm the publicity chair for this all about low carbon computing, so I should probably give you a bit of space to talk about that as well.

Philipp Wiesner: Yeah, that's super nice. Thank you to just take two minutes. So we're organizing this workshop. It's the first workshop for low carbon computing, which will take place on the 3rd of December in Glasgow, and It's mainly an initiative, a local initiative from some folks at the University of Glasgow, but it's an international conference, it's an international workshop I mean.

And we expect submissions from all over the world, but mainly probably the European community, I guess. And yeah, welcome everyone to submit your work. So we have a very broad scope on really anything related to sustainable computing, from energy efficiency, carbon efficiency, embodied carbon and so on, but also topics that are often a bit underrepresented in computer science like circular economy, sufficiency, demand reduction, and all of this. exactly. So this is all very welcome. And for regular talks, all you need to submit is like a two page extended abstract by the 24th of September. Although we're like currently discussing maybe a tiny deadline extension and all accepted authors, all accepted papers will afterwards be invited to submit like a full workshop paper of up to eight pages if you're interested, which will be published in our post proceeding.

So yeah, feel free to have a look. I think we will also put a link in the

Chris Adams: a link in the show notes, for sure, absolutely. So if I understand this correctly, is this a little bit like, possibly, a kind of like a sister conference to Hot Carbon, but where it's a little bit colder, and you're more likely to kind of find a burrito served at Legal Jack's.

Is the name of a restaurant in Glasgow that eaten at before.

Philipp Wiesner: Yes, I guess like it's the first version. Let's see where it's going. But maybe we can, if it's going well, we can maybe establish it as like a European alternative to Hot Carbon or something, which will, I guess, take place in the US in the upcoming years. 

Chris Adams: I see. And there's one thing I wanted to just check because when I looked at the kind of call for papers, the set of topics, it's a little bit more wide than just like let's focus on efficiency. There seems to be a kind of systemic angle that I hadn't seen quite in the same way, for example. So there are discussions about like you said,

okay, looking at the demand side as well as just the supply side, for example. And like, I think I've even seen the word frugal computing used inside here. Like, okay, this is almost like somewhat possibly like the limits conference or maybe some of those ideas somewhat comparable to inside this. 

Philipp Wiesner: Yeah,

guess, yeah, I guess it's somewhere in between that, in between Hot Carbon and limits, maybe.

Chris Adams: cool. All right. Well, I am now thinking it might be nice to figure out a way to get a series of trains to get up to Glasgow, because, I really like that city.

Philipp Wiesner: Yeah, 18 the last times.

Chris Adams: Okay, cool. So that's one thing that's taking place. So the deadline was the 24th of September. Easy to apply to, there is, and that'll be taking place in December.

Philipp Wiesner: Just one last thing, it's a hybrid event. It's a hybrid event, so anyone is welcome to participate, and also authors are of course welcome to present remotely.

Chris Adams: Okay, so I don't need to get on that if I really don't want to, because, well, I might want to see people, but it takes quite a long time

Philipp Wiesner: only a one day event, so you need to consider if it's worth two days of travel.

Chris Adams: Alright, thanks for that Philipp, I appreciate that and we'll share a link to that so people know when the CFP is open how, well actually when it closes more specifically because I think it is open right now. Okay, cool. So I'll just touch on some other events that we have inside here and then I think we'll wrap up if that's okay with you.

So these are some of the other events that our producer Chris Skipper has shared. So there's an event called CodeGreen, the intersection of software engineering and sustainability. This is on September the 12th at 11am AEST. This has Sara Bergman from Microsoft Norway, and one of the, one of the authors of the book, Building Green Software, and also a regular on our podcast.

She's presenting, speaking with Michelle Sandford. And there's also an event in Vienna, in Austria. This has Asim Hussain, our Executive Director of the Green Software Foundation. He's talking about the Impact Framework, this radically transparent tool for sharing the environmental impact of your tooling, and that's on September the 16th, and I think that is in person, but then maybe we'll share a link for people to see about if they can get involved remotely.

There's also another event in the UK, Engineering a Greener Future for IT, that's on September the 18th at 5pm British Standard Time in London. And there is also another event in Brighton as well, so the UK is quite heavily represented right now, Collaborating on Digital Sustainability, that's September the 18th.

I'm going to use this platform while I still can. I'll share a link to Green IO London as well, which is the conference that I'll be keynoting at with a number of luminaries. We'll share a link to that because I, it's very front of my mind right now and I'll be there for the day. So if you listen to this and you fancy coming along, we'll share a link where you can get a free ticket to join at the conference.

And yeah, that's us. I think we've come to the time. We'll just actually make sure that all the things we've discussed, like I mentioned before, we always try to get as many of these show notes full of links as possible, because we cover a lot of ground on these free wheeling conversations, so that will be visible.

The thing you might need to do though, if you are listening via Spotify or the Apple platform, you might need to look up podcast.greensoftware.foundation, then follow the link to this podcast to see all the links, because I don't think they're made available if you just are listening to it in Spotify, for example.

And we do put quite a lot of time into doing that. And Philipp, I just want to say thank you so much for helping unpack what can be an extremely complicated topic and putting the work in to make a open source project that makes it easy for people to figure out what to do with carbon-aware computing and hopefully guide themselves to slightly more sustainable software.

Philipp Wiesner: Yeah, no, thanks a lot for having me. Yeah, had a lot of fun. It was a pleasure. Thank you.

Chris Adams: Okay, final question I should ask, if people do want to find out about you, where should people look? Are you on LinkedIn? Are you still using X slash Twitter slash what's the best way to follow your work these days?

Philipp Wiesner: Probably just Google me, I guess there's maybe one or 2, 3, 4 Philipp Wiesners that are more important than me, but if you just look at TU Berlin or Philipp Wiesner carbon-aware where I should pop up and then you can see.

Chris Adams: Cool, well your name will be in the show notes and the show title, so if anyone is curious about how to spell Philipp Wiesner and not sure how many P's they should be using, then that'll be inside there. All right, Philipp, really lovely chatting to you, mate, and thank you so much for coming along, and yeah, have a lovely week.

Philipp Wiesner: Thank you. You too. Thanks a lot.

Chris Adams: Cheers, bye!

Philipp Wiesner: Bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!

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]]> Academic Forefronts Academic Forefronts Thu, 05 Sep 2024 07:00:00 GMT 51:13 x0lnnj70 false https://podcasts.castplus.fm/e/pnlxx2mn-academic-forefronts 68dc7d15597bc7d53fb83970 68dc7cef46a2532cdd8d1c23 This week we are joined by two PhD researchers, Silke Kaiser and Chiara Fusar Bassini, from the Hertie School in Berlin. With host Chris Adams they discuss their use of data science and machine learning and how they are using them to tackle some of today’s most pressing environmental challenges. Silke shares insights into her research on predicting cycling traffic in cities to better inform urban planning and promote sustainable transport, while Chiara discusses her work on analyzing European energy data to support the renewable energy transition. Together, they explore the intersection of technology, data, and policy, highlighting the importance of data-driven decision-making in achieving sustainability goals. 80 This week we are joined by two PhD researchers, Silke Kaiser and Chiara Fusar Bassini, from the Hertie School in Berlin. With host Chris Adams they discuss their use of data science and machine learning and how they are using them to tackle some of today’s most pressing environmental challenges. Silke shares insights into her research on predicting cycling traffic in cities to better inform urban planning and promote sustainable transport, while Chiara discusses her work on analyzing European energy data to support the renewable energy transition. Together, they explore the intersection of technology, data, and policy, highlighting the importance of data-driven decision-making in achieving sustainability goals.

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TRANSCRIPT BELOW:

Silke Kaiser: I like the term of fighting fire with fire. You know, you're trying to make it better, but you're making it maybe even worse. But I think if we make some smart choices along the way, I rather like to compare it to the idea of fighting a forest fire with a controlled burn. What I'm trying to say is that there are different approaches that we can actually also reduce the emissions caused by AI.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host Chris Adams. Hello and welcome to Environment Variables, where we bring you the latest updates and news in the world of sustainable software development. I'm your host Chris Adams. It's important to understand that when we talk about sustainability and technology, it's easy to mix up sustainability of software with sustainability through software. Sustainability of software development is about understanding the direct impacts of technology and doing as much as we can to reduce it without delivering a worse experience for people using the software. Here, we care about the impact of code, like making it more efficient or making sure the energy we use is cleaner and coming from the cleanest possible sources. So when we talk about green software or green IT, this is what we're talking about. Sustainability through software development, this occurs through the application of software to solve a specific sustainability problem or provide us with insights that we didn't have previously to help us meet some of our sustainability goals. To make this really concrete, you can use green software and talk all day long about the sustainability of software whilst helping people drill for oil and gas. Now you can do that, but it's really not a good idea if you wanna hit any kind of societal climate goals. And if you're listening to this podcast, I think you probably don't wanna do that either. So while we usually cover the sustainability of software. It can also be helpful to look up from our keyboards sometimes to talk about the effects of software, the effects it can have for helping us reach our climate goals. So in this episode, we'll be diving deep into the work of PhD candidates who are pushing the boundaries of what's possible in sustainability ad software. In this episode, we're joined by two researchers from Berlin Institutions, the Berlin School of Economics and the Berlin Hertie School of Governance. So what insights can we gain from their research? How are they using technology to address some of the challenges around sustainability today? Let's find out. So, first of all, Silke, can I just give you folks a bit of time to introduce yourselves? I'll hand over to you, Silke, and then hand over to you, Chiara, to introduce and give you the floor. So, yeah, Silke, thank you very much for joining. The floor is yours.

Silke Kaiser: Thank you very much, Chris. I'm Silke Kaiser. I'm a PhD researcher at the Berlin School of Economics and at the Hertie School Berlin. I'm excited to be here today. My research focuses on the analysis of sustainable transport data, with particular emphasis on cycling data. I utilize various tools from machine learning, data science, and spatial statistics to explore this field.

Maybe more on a personal note, outside of academia, I would say I'm a bit of an auto enthusiast. Just earlier this week, I came back from a vacation in France, but I'm happy to be back and to join you for this episode today.

Chris Adams: I'll be Back just in time for the weather to be nice in Berlin, right?

Silke Kaiser: Exactly.

Chris Adams: Cool. Thank you for that, Silke. And, Chiara, can I do the same to give you some space to introduce yourself as well?

Chiara Fusar Bassini: Yes, thank you for that. My name is Chiara Fusar Bassini. I'm a PhD researcher at Hertie school in Berlin. I'm very excited to be here for my very first podcast. My own research focuses on the analysis of European energy markets. And I use data science and machine learning to analyze time series of dispatch of single power plants and try to look at how power plant dispatch has changed in the context of an evolving energy system. Beside being an academic, I'm also a rather lousy actor in an amateur theater group here in Berlin.

Chris Adams: Cool, thank you. So you said you're working in a theatre. Are there any particular roles you play or anything like that? Because I think you may be the first actor who's come onto the podcast actually, Kiara. Chiara.

Chiara Fusar Bassini: Well, I have a tendency to take on the roles of either mad people or police men or like with gender changes. So anything in between is business, usually like mad people it has been, like the latest role I've had was a police officer. And before that I was a mentally ill person.

Chris Adams: Wow. I did not, I was not expecting that. Okay. All right. Well, welcome onto the show. And I guess that maybe we'll see some, productions in future. Folks, if you're new to the podcast, my name is Chris Adams. I am the executive director of the Green Web Foundation. That's not the same as the Green Software Foundation.

It's one of the members of the Green Software Foundation. I'm also one of the chairs of the Policy Working Group and also one of the hosts of this podcast. Okay, so before we talk in depth about your research, I just want to check for people who are also listening to this, you've been doing this research under the supervision of Professor Lynn Kaack, I believe, is that the case?

Chiara Fusar Bassini: Precisely. We've been working with her for a few years now, each of us.

Chris Adams: Brilliant. Okay, so what we've, the reason I'm sharing this for listeners is that we did an episode 5, where we spoke to Lynn and another person, Will, oh his name has changed, I think it's Will Alpine now, talking all about climate change and AI, two years ago. So if you enjoyed this, I would suggest looking at that to learn a little bit more.

And that might provide some extra context for this discussion. All right, then. Are you two folks sitting comfortably? Happy to go ahead with this?

Silke Kaiser: I think it's good to go and share insights on our research.

Chris Adams: Good stuff. Thank you. For anyone who is listening as well, the thing I'll just share is that we will share show notes with links to all the projects and papers that come up to this. So if any of this is interesting to you, then yes, we, you can continue your quest for more knowledge and insight outside of this podcast. All right. Silke. Let's start with you. Your research focuses on predicting cycling traffic in cities using data from bike sharing systems. And this is something I believe you worked with Lynn and another researcher, Nadja Klein, on from this. Could you maybe just explain a little bit about how this actually helps people when they're trying to design how people move around in cities like, say, Berlin or Paris or things like that?

Silke Kaiser: Yes, I'd love to. So what we generally see when we think about transport in cities is that public space in cities is limited. Whether it be in Europe, the USA, or any other place. Generally, then when we think about how we want to redistribute the space among different mode shares in city, we see that there often tends to be a heated debate.

And especially as we work towards promoting more sustainable modes of transport and therefore reducing the CO2 footprint of our cities, conflicts often arise. And the question then remains, how do we actually want to prioritize these different modes of transport and allocate the space and also financial resources among them?

So, for example, take Paris as an example. I've lived in the city for several years during my studies. And what you can see in the city that in the past few years, they made a lot of changes to prioritize cyclists, which has improved the uptake of cycling, but which has also led to quite heated debates.

The same we can see here in Berlin, the city that we're currently both located in, is that we had a re-election last year here in February, and a lot about the debate actually hinged in part on the choice between prioritizing cycling and individualized motorized transport. So what I do try to do in my research is actually to provide more data to this debate, because what I see as the main challenge in these kinds of debates is that we don't have accurate data on how much cycling traffic we actually have in cities.

Chris Adams: Ah, okay. So maybe just kind of dive into there. So it's basically, we don't have the data to really have a data informed discussion, basically. That's one of the things that is the challenge here. And for context, so the three of us live in berlin. We saw, basically, the new government and the new mayor come into power on a very kind of pro car platform, basically.

So this is what you're referring to, right?

Silke Kaiser: Precisely. So, answering maybe the first part of your question, so for example, in Berlin, we have only 40 locations where we count cyclists. I think in Paris, it's around 53, in New York, it's 41. And then in Berlin, for example, we have around eight times more locations where we count motorized traffic. So we just have much more data and much more information on motorized traffic than we do have on cycling.

And then yes, in Berlin, actually, there was quite a bit of a heated debate pretty much between let's say the inner city, which was more pro cycling and in the suburbs, which were more likely pro cars. The government switched from a green to a more conservative government, which actually decided to suspend actually just this month quite some, projects, long distance commuter paths, and both bicycle parking houses in cities.

But that's just more really on the political side of the debate. And then what I really see as the main challenge is that just this data and the information is missing on where we actually would need the infrastructure most, given how much we want to prioritize cyclists.

Chris Adams: Okay, so maybe I should ask, where is the data coming from then, for this?

Silke Kaiser: So, we do have these 40 counting stations in Berlin, that's the case study that I'm looking at, and what we then figured is, well, we don't have that much precise data on cycling, but we do have an abundance of other data on cycling. So, for example, we do have, as you mentioned, the bike sharing data. We do have as well data from Strava.

That's an app popular to record yourself while doing sports. We do have data on infrastructure, we do have data on weather, we do have data on socio economic factors. And we figured, well, why don't we use all this available data to actually extrapolate from these few isolated locations to actually obtain city wide estimates.

And that's what I did in this research with Lynn Kaack and Nadja Klein. So what, precisely we do is that we train various machine learning algorithms to use all this kind of data in combination with the cycling counting station data that we have to obtain citywide estimates. And what we actually found is that only using this data is a bit tricky.

It provides us estimates for completely new locations 32%, which is you know, rather good in comparison to having no data at all, but still 32 percent is an error I take seriously. And what we then simulated, continuing with this research was, what if we make some sample counts for new locations? So for example, if I want to estimate the traffic in front of your house, Chris, the cycling volume in front of your house, we figured that if we would maybe put you, someone else, on an automated machine there to count the cyclists, and we were to count the traffic for 10 days.

And then combine it with our models, we're able to get estimates with an error of only 17%, so really rather low for complete new locations. And this gives us a good estimate of how much cycling traffic we actually have in every single street of a city.

Chris Adams: Ah, I see. Okay, so you're using the machine learning model, basically, to make the extrapolations when there's, there might not be so much data, give you more accurate so you can say, "well, I'm more confident that this many people are trying to get around using an active, non gas burning form of transportation," for example, right?

Silke Kaiser: Precisely,

Chris Adams: Ah, okay. All right. Thank you for clearing that up. And I understand. And just, we'll come back to this a little bit later, but you said you're using ML. not the same as generative AI or something like that. That's a different, there's, a whole flavor of different things you might be using there, right?

Silke Kaiser: precisely. I mean, there's many different models out there. In this paper, we used rather simple machine learning algorithms, nothing comparable to maybe what most people think of when they think about chat GPT or whichever generative AI you might think about. Those are really rather simple models making usage of the data we have.

And those models So, I just tend to have, I sometimes, you know, sometimes I think about, I mean, you can target these problems with very complicated algorithms, but sometimes using rather simple algorithms might just be sufficient. And that's what we actually found in this research.

Chris Adams: Okay, alright, thanks. When I was doing a bit of research, I realized that you also wrote a piece around, I think in a publication called Catalyse, where you're talking about active transport and why it's important for greenhouse gas emissions, because you've spoken about like mode shift, which I assume means basically moving from a Being in a car to moving and maybe a active transport, which people use like scooters, bikes, stuff like that.

That's what I think you're referring to there. Could you maybe talk a little bit about how this research actually contributes to like the adoption of cycling as one of the potential modes, because you spoke about it in Paris and I went to Paris and was being pretty transformational when I was there compared to being a few years back.

And I also bike around Berlin too. So I have a vested interest in learning this. Maybe you could like lay out why that's actually benefiting and how active transport helps. Basically, us meet our climate goals.

Silke Kaiser: Absolutely. So, when I, Referring to other research that I've read, research that I haven't done myself, but it is out there and it's been cited a lot, is that we do see that cycling has numerous benefits. It benefits your individual health. If you cycle, it's good for your physical health and then all the sicknesses or illnesses related to insufficient physical health.

We can also see that if you cycle, it's also good for me because then generally we see a reduction in noise and air pollution in cities. So it really benefits the public health, the broader public. And then yes, absolutely. I mean, I did read the IPCC report, which is a report on climate change and it comes out, the last one came out in 2023.

And what they found is that actually 15 percent of net global greenhouse gas emissions are related to transport. This of course includes all kinds of transport, but also, one of them is urban transport. And then switching, there are many levers how to tackle this, right? And within general, as in climate change, there's no one solution fits all, but switching from motorized traffic to cycling is one of those means to actually reduce those greenhouse gas emissions.

And coming a bit back to my research as well, what we find also in research and science is that all you can think about, you know, talk to your friends and family and gather some anecdotal evidence, you'll probably find that one of the biggest deterrents that keeps people from cycling is that they actually are afraid because there's not enough cycling infrastructure.

They're afraid of accidents. And that's a relevant fear. We do see many accidents in cities right now, mainly between motorized traffic and cycling, but also all kinds of other accidents. And what we do can do in cities is, to actually promote cycling, is to build more attractive infrastructure for cyclists.

This can include bicycle lanes, a better design of roundabouts. And all this attracts people to cycle, but, it actually also reduces the risk of serious and fatal accidents. So what I really try to do with my research is that again, if we have these heated debates in cities, how we want to distribute space among cyclists, cars, delivery trucks, etc.

I'm trying to provide data on where we actually have how many cyclists in which street, so that when policy makers or transport planners come around, they can use my data and actually make fact driven decisions when and where infrastructure benefits the most, the greatest number of people. And that's how I hope my research can contribute 

Chris Adams: Ah, I see. Okay. So that's really helpful. And I think there are some kind of comparisons I can make, which make to maybe help me understand if, and some of the listeners. So you know how a couple of years ago, in the middle of the pandemic and COVID, one way to reduce the number of COVID cases was just to reduce the number of people taking tests, right? You know, that's not necessarily the best way to solve that, know, and it feels like

we've got a similar situation. We've got a data asymmetry problem here it looks like you're doing some work to address for that. I mean, Also, you've spoken about, as I understand it, there are various parts of, like, our economy which are easier to decarbonize than other ones.

Like if, for example, in Germany and in America, transport's the biggest, one of the places where we've seen not so much progress on reduction, on carbon emission reductions compared to things like the energy sector and stuff, which is decarbonizing relatively quickly. So this is what some of this is a reference to. Okay, so what we'll do is we'll share some links to Catalyst, I'm sorry, Catalyse, the paper there, and also some of the papers that you have. So we spoke about Paris, and we've spoke about Berlin, where we both live. Are there any other places you would point people to as examples of, okay, this is what good might look like, and this is one place which actually has quite good data to show where you've actually seen quite effective policymaking to kind of change the environment to make it easier to cycle? Because, yeah, not everyone wants to become a MAMIL, like middle aged man in Lycra wearing the helmets and everything like that.

Silke Kaiser: So, I mean, there are definitely some cities that you know that are popular for cycling, for example, just earlier this spring, I did a research day for some months in Copenhagen. And obviously Copenhagen is a bit of a dream for cyclists, right? I'm not the first one to mention this. And then there are other cities, Amsterdam, you name it, but generally I do have to admit that in my research, I haven't really come across cities that do have much better data.

I would say it's a grasping problem across different cities that data is missing. Copenhagen and Amsterdam have taken political decisions to prioritize cycling, but I do have to admit that I didn't, I haven't come so much across that they've made this as a data-driven decision, but this was more of a political decision.

Chris Adams: Ah, okay. And there's one thing I'll just ask before, Chiara, I'm, okay, I am a closet energy, well, not very closet energy nerd. I'm totally gonna, looking forward to talk about that. But Silke, I was just going to ask you, so you mentioned use of Strava and you mentioned the use of, Okay. It's useful to have these new sources of data, but there's also a question about the provenance of that data and like the circumstances under which it's collected.

So for example, we've seen Strava used in lots of other places and if you're using Strava, you tend to be a bit richer, a bit younger, a bit healthier than most people. Maybe you could talk a little bit about some of that, because there are various sources available to inform these policy sessions, and like, Strava is one.

But like, where else, like, assuming you had, you were suddenly queen of the world, where would you wish you could get some of the data from to kind of inform this in future?

Silke Kaiser: So you're absolutely right, Strava definitely is quite biased. It's the data, for example, for Berlin, I definitely know that they're all male, they're mainly male, young, and they do tend to do very sporty biking in comparison, for example, what I probably do to commute work. So it is true that some of the data we use is biased and we're trying to balance this off with the other data sources that we're having.

We're also taking socioeconomic factors into account because obviously we do not want to have, infrastructure is meant to be there for everyone and not for privileged or less privileged people. It's, meant to be equal for everyone. But then obviously I thought about a lot, well, how could we actually improve the data availability in cities?

And I definitely see two levers that we have. Well, first of all, we can place more cycling counting stations. That is a bit challenging because, for example, we have so many kilometers of roads and it's hard to track them all. There are cheaper options than the ones that we're currently employing. So this might be our one option.

Some of them are then using cameras, for example, that are just much cheaper to put them out there. And then the other question, and that's something I'm also looking forward to, to answer this question is because I'll be looking at this in my future research, is that actually how can we place the sensors that we have better across a city? Because currently look it up again for your city.

You'll probably find a similar image is that we do tend to place these censuses as very busy and scenic roads. And the question is actually, can we maybe place them at more diverse spots within a city? And if yes, how can we choose those streets to place those sensors at to actually get a more comprehensive image of the cycling traffic and then also of all kinds of socioeconomic areas and a more equal data image.

Chris Adams: Cool. Thank you for that, Silke. All right. We'll come back to you a little bit later about some of the specific techniques that we were using, because we spoke a little bit about ML and there's a lot more we might dive into there. Chiara, if it's okay, can I ask you a little bit about your research analyzing European energy data?

Because you didn't hint a little bit about how this can affect renewable energy transition, and one of the, one of the things that Germany has in particular is a target to have 80 percent of the grid running on renewables by in, wow, in five years. So that's not much time, and we've also spoken about on the grid, we've spoken about things like time shifting and location shifting as a kind of carbon-aware software, particularly in changing how data centers, like, fit into the grid, I suppose, or the energy they use. Can you maybe talk a little bit about some of the challenges you've found actually working with this data hands on? Because we At best, most of us developers, we might use it in a really nice, pretty fashion from electricity map or Watttime or in an SDK, but it sounds like you're pretty much at the front end having to figure out how the sausage gets made. So yeah, if I can ask you, maybe you could tell us a little bit more about how this data comes about and what are some of the challenges.

Chiara Fusar Bassini: I mean, if you've been using Electricity Maps, you probably have been using an application that in the back uses NSOE data. So you, in Europe, we are rather lucky because. There have been two regulation that have been released in 2011 and 2013, which forced in a way transmission operators to publish a variety of a time series of energy data from the grid and from the markets in an effort to increase transparency. And we have a number of data in a central, on a central repository, which is called the Transparency Platform. We have load data, we have generation data, we have transmission data about the grids, we have balancing data, balancing markets, but also a lot of information on individual power plants. This data is overall extremely useful, but unfortunately it's not Always accurate and it's not always complete and not all the data is not always published in a timely manner.

That very much depends on the type of data, the country itself. We are still very much better off than other markets where there's no data at all. But it's still an issue of like how good and like what the data quality actually is. Because you mentioned time and location shifting. To do time and location shifting, most likely you will be working with aggregated data.

For example, load data, load data, load forecast data. And. One could analyze, for example, a load to decide whether to shift more energy consuming activities at night or at moment where there are off peak time windows. And on the other hand, one could look at aggregated renewable generation data to try to relocate some more of energy consuming activity to time of the day where the grid is actually greener and there are a lot, there's a lot of interest in academia, but also in, in the industry sector to provide us information, to have an estimate of carbon intensity. there are a number of startups out there. You mentioned Electricity Maps, but also academics have come up with top, bottom up, top down and bottom up approaches to compute really at hourly or quarter hourly level, these carbon intensity estimates. The trick here is that you are working with, the aggregated data and aggregate the quality of aggregated data and the timeliness of aggregated data is rather high, the situation is a little bit different when you move to a more geographically granular, like a higher geographical granularity.

Chris Adams: Okay, so from Germany going to like Berlin or Germany going to another part like Frankfurt, for example, something like that. Yeah? 

Chiara Fusar Bassini: Rather, when you're looking from, the aggregated generation to the generation of individual power plants, because in that sense, you might be interested to know which power plants are actually generating right now. And you might be interested to know which areas are generating more solar, for example, which areas are generating more wind. Unfortunately, we don't have data on all power plants. Which would be rather impossible in terms of like amount and extent of the data, but we have only data for power plants that are at least 100 megawatt. It's mostly conventional power plants. So, for example, we have no individual information or very little information on wind farms, for example, because Some of them are not big enough to qualify for this criteria. And also this data get published with a significant delay of four days so that you can't really use it to

do anything operational. It's also not conceived for that. And we can or we cannot use the data to properly, like we cannot do it for, we use it more for analysis than for forecasting, but nonetheless, we can use this data to understand a lot on individual power plant data, why they decide to dispatch on how they are dispatched, and especially in the context of conventional power plants, how their dispatch has changed over time because of political reasons, but also because of the increase in cycling of fossil power plants, because they have to adapt to the renewable energy generation, to more renewable energy generation.

Chris Adams: Can I just quickly stop? I just want to check I understand some of the terms you've used for listeners who might not be familiar with load, cycling, some of these things here. So when you talk about loads, you talk about energy, like basically that's power draw, what people are trying to draw from the grid.

You mentioned that. And then you also mentioned, I think, like cycling. So that's like basically scaling down a power station in response to there being loads of wind on the grid or stuff like that. Maybe is that about right?

Chiara Fusar Bassini: Yes. Thanks for asking. So to clarify, so load means the demand and in the past, I mean, demand, especially from, industry, but also from household, it has been rather predictable. And the way we faced demand, or we satisfied demand, because well, in an energy system, demand and supply need to be equalized at any time. In the past, the most of the baseload, so the main bulk of consumption, have been satisfied using traditional fossil fuel technologies, so called dispatchable, because you can, decide when and how to switch them on and off.

Chris Adams: Ah, okay.

Chiara Fusar Bassini: But the thing is, as more and more renewables enter the grid, they cannot be dispatched whenever, they can only be dispatched, or they can't choose when to dispatch a

Chris Adams: Yeah, control the sun and the wind. We can just respond. Yeah.

Chiara Fusar Bassini: They just respond to external weather factors, right? But that also implies that we still have these conventional power plants that are

dispatchable, but we now have to operate them with increased flexibility. So they have to be able to ramp up and ramp down as the load is more and more satisfied by renewable energy sources.

Sources when they are there. And we always make the assumption that say conventional power plants are a hundred percent flexible, but that's not actually the case. For example, some power plants, when they are turned on, they have to generate a minimal capacity. And if the demand for that capacity is not there, that might be an issue, or they might have some minimal times to be switched on and switch off.

So there, there is a plenty of interesting question that arise from the increase of renewables. Like how will conventional power plants cope with more renewables in the grid?

Chris Adams: Ah, I see. Okay. So one thing you're saying, she's like, yes, it's not like a computer. You can't turn it on straight away, like in milliseconds. And so that's one thing you mentioned and the fact they need to do that more is another issue. And if I understand it, what you described was quite a physical process.

It's like, we're not using bits, we're using atoms, like burning coal, things are expanding and contracting. Like, is there a risk that, you know, the, a big power plant could be damaged some more, or does that introduce any wear and tear when people need to scale something back? Because I can imagine someone saying, "hey, you're making me change how I do things, and therefore you're introducing some risk into this.

That's not what this was designed for in the first place."

Chiara Fusar Bassini: Yes, that's actually a very interesting question. What I mentioned, cycling, meaning that you operate conventional power plants more flexibly, has some consequences on the lifetime of power plants, especially if you keep on turning it on and turning it off. There are some wear and tear indeed for thermal power plants, wear and tear consequences, some of some power plants may not even be able to do so because they have some agreements with O&M managers that tell them, you know, "you can do that, but then you'll have to pay more because you will have, we'll have to do more main maintenance." And also, there are a number of obstacles that arise, especially for older power plants that have not been conceived with this flexibility option in mind, but rather to satisfy baseload.

Chris Adams: Ah, okay, thanks for clarifying that. And you're essentially doing some of the research to see how you might predict some of this better to either reduce or basically accommodate some of these changes that we might have when we've got a much more dynamic grid that is influenced by the sun shining and the wind and all the things like that, right? So maybe if we can talk a little bit about some of the techniques being used to track this and reduce the amount of, maybe, reserved capacity that needs to be done, or reduce the amount of wear and tear that might be imposed on the kind of entire system full of all different power generation. You said you spoke a little bit about using machine learning, and We spoke to Silke.

Silke mentioned that she's using some ML models, which are not like generative AI. That's a very, it's a different kind of AI. Could you maybe talk a little bit about like how that gets used, because you hinted at it, and like what some of the barriers are for using some of that, because it, that sounded quite enticing and interesting.

Chiara Fusar Bassini: Yes, before, before that, I might, I want to add on this. So there is some parallel research being done, especially like at engineering department of a lot of engineers trying to use machine learning to efficiently operate conventional power plants to reduce this wear and tear of wear and tear problems.

And in general, like damages from cycling while still satisfying a change in demand. What I'm doing is and Rather different analysis of historical usage of power plants. So to see how power plants are, have actually been operated so far in the markets, how they're, how they, how flexible they actually are.

Because sometimes we assume that they're 100%, again, we assume how they're 100 percent flexible, but how flexible are power plants that we already have in the grid? And also how available are there in cases of outages, for example, how, like. What's the percentage of time in the year that they actually could provide electricity, for example? And in terms of techniques, well, it's a lot of time series data, so most time series apt methods can be used here. It very much depends on the ultimate task, but one of the major obstacles I encounter is that this high granularity data is by far not as good as the aggregated data, especially, for example, an availability of power plants that has to be reported in a rather accurate way, but then is not one to one translatable to time series format because it's published as market messages, meaning that the data that we have is not in a format that makes it directly usable for a researcher. So there there are a number of obstacles that are really determined by the data quality rather than by the task itself.

Chris Adams: Ah, okay, so it comes down to the data a lot of the time then, basically, yeah?

Chiara Fusar Bassini: Again, like as Silke said, sometimes it's really just a matter of the data that you have, like the research that you can do is going to be determined by the quality of the data that you have.

Chris Adams: Okay, we'll touch on that a little bit later, but I guess the, that does make me think about, particularly in Germany and countries where we've seen very rapid changes. Like, Germany, there's, you know, there's a massive craze of balcony solar, for example, or we've seen loads of battery coming onto the grid, or even Pakistan. We've seen, like, a third of the power, the new power introduced this year, was come from rooftop solar, and each one of those is individually less than 100 megawatts. That's an enormous chunk of power. So there's all this new stuff that we're not, don't necessarily have access to the data for to actually figure out, okay, how will the grid work and how can we make sensible predictions on this? That's useful to know. Brilliant. 

Okay. So we're speaking a little bit about the upsides and how, where some of the potential might be. We do speak about Green Software, about reducing the environmental impact of some of this, and obviously when we're doing some of this work, I've asked a little bit about the kind of models you might be using, partly because there's a question, whenever we start using technology to help us meet climate goals, it's when some of that energy is still coming from burning fossil fuels, for example, there's trade offs to be made. Does anyone want to go first, talking about how we think about these trade offs? Because as practitioners, I imagine you're at the coalface, but you're also working with some of the people who think about this every single day. And like, if you're working with Lynn, and like, Lynn was one of the founders of Climate Change AI, I reckon she probably has some reckons and you've probably had some conversations about this, right?

Silke Kaiser: Absolutely. Actually, just, I think just the last group meeting we had, we just discussed about precisely this topic, because it obviously, it is a question that keeps coming, a question that we do want to answer. And it is also like, it's in our minds, right? Because if we want to do something positive for the climate, and then actually, the net result might be negative, because our models consume that much energy.

This definitely is a topic that we think about a lot, I would say. I see Chiara nodding. I think she's agreeing with me, but, and I can see that maybe to the outside world, often this can seem a bit like, I like the term of fighting fire with fire, you know, you, you're trying to make it better, but you're making it maybe even worse, but I think if we make some smart choices along the way, I rather like to compare it to the idea of fighting a forest fire with a controlled burn. So right, that we do try, for example, in, in the models that I was employing, I did partially check how much, how big the energy usage was.

I was using simpler models, as I mentioned earlier. So the energy consumption wasn't that high, but I think it's good for us and for everyone out there using, similar models. To track your energy consumption and there are very nice packages and libraries out there, tools, all kinds of things, open source, freely available that are very good in, in managing or in measuring the energy consumption you have.

And then of course there are a whole bunch of other approaches that you can take. Right? I mean, you mentioned it's an issue if it comes from fossil fueled energy, but obviously you know, you can think about, I know that there are a lot of like. Service and data science centers, for example, out there in Iceland, where you tend to have more natural cooling, where a lot of the energy being produced is renewable.

I'm not saying that at all perfect, but what I'm trying to say is that there are different approaches that we can actually also reduce the emissions caused by AI. 

Chris Adams: Ok, so there was one thing about the actual technique, like, AI is not a monolith. There's all different approaches within this, in some ways, not particularly helpful term, like the use of relatively small machine learning models, which are relatively simple, that's going to have a totally different footprint to the model used to generate SOAR, like a video or something like that.

And that's something that we probably would benefit from having a better kind of intuition off as practitioners, for example, and you spoke a little about the carbon intensity. So there's two, two things that you have there. And you mentioned some software that you have. And you said that, Chiara, if I can kind of give the floor to you, because I think you mentioned you, you've spoken about some of this before about, yeah, there are some tools and I use them as well. Can I ask you a little bit about when you've been thinking, I mean, how do you think about these trade offs? Or is it a trade off? Or is there another set of dimensions you might be thinking on rather than like forest fires and controlled burns, for example?

Chiara Fusar Bassini: Yeah, I think, there are two things that need to be thought through when using AI. Number one is like, how do you develop your model, and then what do you use your model for? So how do you develop your model? That's similar to what Silke said, for example, doing emission emission tracking while developing the code and while training the code. And at the moment, I think AI is missing some embedded indicator of the social environmental cost of the training. So. We kind of think of performance metrics such as accuracy, such as like classic cross entropy losses and so on, and we think only about precision. But sometimes we need to be a little bit more critical of whether an increase of accuracy of 0,1 percent is worth an increase in the training time of two hours or an increase in the size of the model of 25%. These are like actual numbers and scientists have coined for that the term green AI, meaning Okay, can you know, can we, in a way, embed this measure of the size of the model within the loss that we are trying to minimize in the training of our model? There is another, a good example, for example, is the Bloom model that is an alternative large language model to GPT. It is similar in size, but it required Like the CO2 emissions of the model are 20 times lower than GPT 3. And this has been made possible by, first of all, in smart usage of the training and also tracking of the carbon intensity of the grid. It system was trained, the model was trained mainly in France, which is, which has runs predominantly on nuclear power. So in like carb, much more carbon neutral system. So there are a number of things that one can consider while training their model. But also another thing that is very important, and I think that we sometimes don't really think through, is what are we using AI for? And in that sense, there is currently no standard assessment in place. Like, is this application really worth using AI? AI is, by its nature, ethically neutral. It can be used for anything from targeted advertisement that will have probably a negative impact on environment to detecting wildfires. So very positive impact. I think policymakers in that sense can make a great deal to really make a difference and start, for example, by providing a classification of which user cases are positive for environment and which are negative. It sounds, it may sound like science fiction, but it has already been done in the European AI Act in looking at the perspective of risks, like which application have a high risk and hence should be more controlled and which other have lower risk. And I think a similar classification would be also very useful for environmental purposes.

Chris Adams: I'm really glad you mentioned that because I ended up reading through the AI act for research recently. And the idea of the risk that is, you're right, there doesn't seem to that much be that much reference to the use of AI for, let's say, you know, increasing the extraction of fossil fuels, right, versus that.

That's, there doesn't seem to be much to mention about that, but there is some information about the transparency around training. And now that we've looked at it a bit closer, so within the Green Software Foundation, there is a group called the Real Time Carbon Group. We've been looking into some of the specific implications of this, and it looks like the AI Act, it also, it looks like it's probably going to suggest not just understanding the training, but also the cost of inference, like the use of the model rather than just the training of the model. If I can just quickly, you've, you, mentioned there are tools out there, and Silke also mentioned there are tools out there. If I did want to measure some of this, and if I did think there was some legislation coming for this, what tools are there available for me to measure the direct impact? So at least I know what the trade off might be.

So we understand that the carbon footprint of decarbonizing transport, like Silke mentioned, that's going to be, you know, positive, but quite, but there's ways of calculating that, but for us as practitioners, are there any software or any tools you might recommend that are kind of common in the field now? Either goes. I'm happy to, whoever's more comfortable talking about this.

Chiara Fusar Bassini: I'm thinking CodeCarbon is more probably a standard used by many scientists. I know there are more applications that might have a higher granularity, but I guess that's a

Chris Adams: That's the one that you folks have used, right? Okay, I hear CodeCarbon used a lot, and I, as I understand it, that's the one that's been used for the Bloom model when they wrote a paper about that. That's what I'm not sure Facebook have actually explained this because when I was looking at LLlama's model, so AI models have model cards, which basically, which I think, various responsible practitioners now say, "this model took this much carbon, or they had this much energy gone in to kind of create it," for example, if you go to the existing Llama 3.1 model card on Hugging Face, and you try to follow a link to the actual methodology, It's not actually explaining how it works. there's now a bug. I filed a bug to ask out, ask, well, how did you work these figures out? Because these feel like it's quite important, especially because when you look at the numbers, it's significantly larger than Bloom, basically. That's, so, so what you're referring to is CodeCarbon. That's one tool that people can use that will give you some idea that is in use in a few places already that's relatively safe to start off with. Great. Okay. And we spoke a little bit about some tools. So if someone is, wants to take their first steps, they might look at this.

And there are various projects I'm aware of to make it a bit easier to understand the impact of one versus another. I believe there's one Energy Star AI or Energy Star, AI Energy Star or something like that. There's one person who I've spoken to who's involved with it. Boris. I'm so sorry I can't pronounce your surname, but I do know you're the AI lead, the AI sustainability lead at Salesforce. Boris G is one of the people who's been writing about this. He's not the only author, but he's the person I know, and we'll share a link to that as well, because that's the first thing I've seen of a useful, like, A kind of nice idea to give you an idea of what the inference, the usage as well as the training might actually be. If you were to look at this, we've still got this issue of data or having access to data like, and Silke, I ask you, if you were like queen of everywhere for a moment, how would I change it for here, right? Let's say that you want to be responsible AI practitioners, like what are the things that we need to see in the next, in the coming years to make it possible to be like responsible practitioners so that when we do use AI, we're using it in the kind of greenest possible fashion. Silke, I asked you first about Queen of Everywhere, so maybe you go first and I'll hand over to Chiara.

Silke Kaiser: Well, that's a very good question. I definitely say, as in general, with all kinds of, you see, in more technical approaches, we do need reproducibility and traceability of what we do in our research. I mean, just as you mentioned with the Llama, I think it's important that other people are also able to understand what we did, what was the energy consumption of what did, how can they, how can we check the things that we've done and, see if, we did it properly, if it took a right approach?

And then obviously, I think this is a bit less related to, the topic that I'm or that Chiara was working on, but also in the longterm, we do need to think about ethical concerns coming down to this. And then again, I think just, really, transparency. So I really think that transparency is a good way to address this.

What take do you have on this Chiara? I think one of my major takes also from what Silke mentioned, and I'm really glad you mentioned, is the fact that when we were talking about policy making is that very often policy making is not data-driven. One problem is that we don't have the data and it can be addressed partly by regulators asking for those data, right? But another issue is also that we don't really do data-driven assessment of the policy that we implement then. And I came across very recently a paper that tries to systematically evaluate policies.

And having been implemented in the last 25 years, this very recent paper has been published like a few days ago.

And I thought it was very interesting to well, once again, the results of the paper is there's no one size fits all and some countries depending on their level of development might need different policies. And we have to keep that in mind that we can't use the same policies for a developed country whose energy consumption, for example, is no longer linearly dependent on its GDP from a different, from a developed country or a developing country that has very different issues. But I think in general, this approach of doing data-driven policymaking and science-driven policymaking is something that would really, we would really need in this space.

Chiara Fusar Bassini: I think that's something I can really agree on. I often feel that as a scientist, we feel like we're trying to really produce clear results, objective results. And then often we feel there's maybe a bit of a lack between the two. The research that we do and how much this is sometimes uptaken, by policy.

And obviously we hope that because we do really put so much effort into this and always try to be objective. We hope that this will eventually be more used more and more in the policy sector.

Chris Adams: You've touched on a really interesting point, and I can think of some examples that just occurred to me. So, we had an interview with, oh, Vlad Kor, his first name is Vlad, I'm gonna mispronounce his surname, but we spoke a little about, all about the rebound effect, and Vlad Coraoma, that's it, Vlad Coraoma had this lovely post actually on LinkedIn talking about the curse of potentialities, potential itis, which is basically talking about, we have all these kinds of really exciting projects, but whether people follow through to check whether the actual gains materialised, or the benefits materialised, there's much less effort put into that.

And we've been seeing, like you said, Chiara, from the last 25, we've been seeing predictions for things that would happen in 2020 or 2030. And 2020 is in the past now, we can check if this is, if these actually delivered, but a lot of the time we do not see that. And in our field, specifically as kind of cloud providers, or people who might be consuming services, there's some, there's a really, I'm thinking of a really good example. Microsoft has a whole thing about pushing for AI and everything like that. And we know that, as you mentioned, AI can be used for good, and can be used for bad, or used for Climate aware things, which are really helpful and things are not so good. And we've even seen like people who are workers really pushing for this. I'll share a link to an article in Grist where, written by Maddie Stone, where she talks about some of the sustainable connected community inside Microsoft, speaking to some of the management there. There's a guy called Darrell Willis. He's the vice president of energy. And they spoke and said, "hi, we are pushing for," you know, "can we please have a conversation about what we're using AI for inside our company, because we're one of the largest companies in the world and we're one of the leaders in various industries," right?

And there was a commitment to say, we're going to produce, as the management said, "we're going to start releasing information about, okay, how much of our use of AI is coming from the fossil part of the industry versus the renewable part of the industry?" And this feels like a really important data point if we're going to be looking at tens of billions of dollars used on AI.

I mean We know that it's an accelerant. If it's an accelerant of fossil fuel extraction and burning, that's a very different story to using tens of billions of dollars for renewable energy, for example. And if we've seen commitments at a management level, then it would be nice to see these. As we understand, these commitments were made, these were shared inside the team, but we don't have this, and we'll share a link to the specific terms, because actually, I'll just share the quote with you, because I think it's one thing that, if you're an employee of a cloud firm, or a customer of a cloud firm, it's the kind of thing you might want to know about, so on the call, "Darrell Willis, committed to providing employees with updates on net zero requirements as Microsoft continued to implement these energy principles. Committed to providing a breakdown of energy divisions revenue across six different sectors from oil, gas extraction, to all zero, low to zero carbon energy. So sharing this information internally." Now this feels like a thing that employees probably should be aware of or asking for. Also feels like something that if you're an investor of Microsoft or a customer, you might want to know.

Because there's an impact inside your supply chain thinking about this. And if you're choosing one provider because they have really strong GSG credentials, this may make you view it somewhat differently. We'll share the links because it seems to be the best concrete example I can think of at significant scale that we might be talking about. And I'll get down on my soapbox because that's just the thing that really leapt out when I, when you spoke about that. So we coming up to time, and we've spoken about the different uses of AI, sustainability of software, as well as some of the Things you might want to use or be aware of as a practitioner. If people do want to find out about the work that the two of you are doing, where should people be looking? So Silke, if people are interested in your work, is there a LinkedIn page or is there a website that you direct people's attention to?

Silke Kaiser: I normally try to direct people to my personal webpage, which is silkekeiser.github.io. Or you also, you can also find me on X or on LinkedIn. And I'm always happy to share news on my research as well as the articles that are out there. And I'd be happy to, if people were to look at those pieces of information.

Chris Adams: Cool, thank you. Alright, and Chiara, if I just hand over for you?

Chiara Fusar Bassini: I've seen Silke's website and you guys should really see it.

It's a very nice animation. I don't have myself a website, but I'm very active on LinkedIn. You can find me under Chiara Fosar Fusar Bassini.

Chris Adams: Chiara F U S A R, we'll put it in the link, we'll add it in show notes. So, Chiara Fusar Bassini. Brilliant. Thank you, folks. This has been lots and lots of fun. I've learned a lot from this, and this has been a really nice chat. Hopefully, we'll cross paths sometime in Berlin, but otherwise, thanks again for coming on, and have a lovely week.

Silke Kaiser: Thank you very much for having us.

Chris Adams: Ta ra! Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> This week we are joined by two PhD researchers, Silke Kaiser and Chiara Fusar Bassini, from the Hertie School in Berlin. With host Chris Adams they discuss their use of data science and machine learning and how they are using them to tackle some of today’s most pressing environmental challenges. Silke shares insights into her research on predicting cycling traffic in cities to better inform urban planning and promote sustainable transport, while Chiara discusses her work on analyzing European energy data to support the renewable energy transition. Together, they explore the intersection of technology, data, and policy, highlighting the importance of data-driven decision-making in achieving sustainability goals.

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TRANSCRIPT BELOW:

Silke Kaiser: I like the term of fighting fire with fire. You know, you're trying to make it better, but you're making it maybe even worse. But I think if we make some smart choices along the way, I rather like to compare it to the idea of fighting a forest fire with a controlled burn. What I'm trying to say is that there are different approaches that we can actually also reduce the emissions caused by AI.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host Chris Adams. Hello and welcome to Environment Variables, where we bring you the latest updates and news in the world of sustainable software development. I'm your host Chris Adams. It's important to understand that when we talk about sustainability and technology, it's easy to mix up sustainability of software with sustainability through software. Sustainability of software development is about understanding the direct impacts of technology and doing as much as we can to reduce it without delivering a worse experience for people using the software. Here, we care about the impact of code, like making it more efficient or making sure the energy we use is cleaner and coming from the cleanest possible sources. So when we talk about green software or green IT, this is what we're talking about. Sustainability through software development, this occurs through the application of software to solve a specific sustainability problem or provide us with insights that we didn't have previously to help us meet some of our sustainability goals. To make this really concrete, you can use green software and talk all day long about the sustainability of software whilst helping people drill for oil and gas. Now you can do that, but it's really not a good idea if you wanna hit any kind of societal climate goals. And if you're listening to this podcast, I think you probably don't wanna do that either. So while we usually cover the sustainability of software. It can also be helpful to look up from our keyboards sometimes to talk about the effects of software, the effects it can have for helping us reach our climate goals. So in this episode, we'll be diving deep into the work of PhD candidates who are pushing the boundaries of what's possible in sustainability ad software. In this episode, we're joined by two researchers from Berlin Institutions, the Berlin School of Economics and the Berlin Hertie School of Governance. So what insights can we gain from their research? How are they using technology to address some of the challenges around sustainability today? Let's find out. So, first of all, Silke, can I just give you folks a bit of time to introduce yourselves? I'll hand over to you, Silke, and then hand over to you, Chiara, to introduce and give you the floor. So, yeah, Silke, thank you very much for joining. The floor is yours.

Silke Kaiser: Thank you very much, Chris. I'm Silke Kaiser. I'm a PhD researcher at the Berlin School of Economics and at the Hertie School Berlin. I'm excited to be here today. My research focuses on the analysis of sustainable transport data, with particular emphasis on cycling data. I utilize various tools from machine learning, data science, and spatial statistics to explore this field.

Maybe more on a personal note, outside of academia, I would say I'm a bit of an auto enthusiast. Just earlier this week, I came back from a vacation in France, but I'm happy to be back and to join you for this episode today.

Chris Adams: I'll be Back just in time for the weather to be nice in Berlin, right?

Silke Kaiser: Exactly.

Chris Adams: Cool. Thank you for that, Silke. And, Chiara, can I do the same to give you some space to introduce yourself as well?

Chiara Fusar Bassini: Yes, thank you for that. My name is Chiara Fusar Bassini. I'm a PhD researcher at Hertie school in Berlin. I'm very excited to be here for my very first podcast. My own research focuses on the analysis of European energy markets. And I use data science and machine learning to analyze time series of dispatch of single power plants and try to look at how power plant dispatch has changed in the context of an evolving energy system. Beside being an academic, I'm also a rather lousy actor in an amateur theater group here in Berlin.

Chris Adams: Cool, thank you. So you said you're working in a theatre. Are there any particular roles you play or anything like that? Because I think you may be the first actor who's come onto the podcast actually, Kiara. Chiara.

Chiara Fusar Bassini: Well, I have a tendency to take on the roles of either mad people or police men or like with gender changes. So anything in between is business, usually like mad people it has been, like the latest role I've had was a police officer. And before that I was a mentally ill person.

Chris Adams: Wow. I did not, I was not expecting that. Okay. All right. Well, welcome onto the show. And I guess that maybe we'll see some, productions in future. Folks, if you're new to the podcast, my name is Chris Adams. I am the executive director of the Green Web Foundation. That's not the same as the Green Software Foundation.

It's one of the members of the Green Software Foundation. I'm also one of the chairs of the Policy Working Group and also one of the hosts of this podcast. Okay, so before we talk in depth about your research, I just want to check for people who are also listening to this, you've been doing this research under the supervision of Professor Lynn Kaack, I believe, is that the case?

Chiara Fusar Bassini: Precisely. We've been working with her for a few years now, each of us.

Chris Adams: Brilliant. Okay, so what we've, the reason I'm sharing this for listeners is that we did an episode 5, where we spoke to Lynn and another person, Will, oh his name has changed, I think it's Will Alpine now, talking all about climate change and AI, two years ago. So if you enjoyed this, I would suggest looking at that to learn a little bit more.

And that might provide some extra context for this discussion. All right, then. Are you two folks sitting comfortably? Happy to go ahead with this?

Silke Kaiser: I think it's good to go and share insights on our research.

Chris Adams: Good stuff. Thank you. For anyone who is listening as well, the thing I'll just share is that we will share show notes with links to all the projects and papers that come up to this. So if any of this is interesting to you, then yes, we, you can continue your quest for more knowledge and insight outside of this podcast. All right. Silke. Let's start with you. Your research focuses on predicting cycling traffic in cities using data from bike sharing systems. And this is something I believe you worked with Lynn and another researcher, Nadja Klein, on from this. Could you maybe just explain a little bit about how this actually helps people when they're trying to design how people move around in cities like, say, Berlin or Paris or things like that?

Silke Kaiser: Yes, I'd love to. So what we generally see when we think about transport in cities is that public space in cities is limited. Whether it be in Europe, the USA, or any other place. Generally, then when we think about how we want to redistribute the space among different mode shares in city, we see that there often tends to be a heated debate.

And especially as we work towards promoting more sustainable modes of transport and therefore reducing the CO2 footprint of our cities, conflicts often arise. And the question then remains, how do we actually want to prioritize these different modes of transport and allocate the space and also financial resources among them?

So, for example, take Paris as an example. I've lived in the city for several years during my studies. And what you can see in the city that in the past few years, they made a lot of changes to prioritize cyclists, which has improved the uptake of cycling, but which has also led to quite heated debates.

The same we can see here in Berlin, the city that we're currently both located in, is that we had a re-election last year here in February, and a lot about the debate actually hinged in part on the choice between prioritizing cycling and individualized motorized transport. So what I do try to do in my research is actually to provide more data to this debate, because what I see as the main challenge in these kinds of debates is that we don't have accurate data on how much cycling traffic we actually have in cities.

Chris Adams: Ah, okay. So maybe just kind of dive into there. So it's basically, we don't have the data to really have a data informed discussion, basically. That's one of the things that is the challenge here. And for context, so the three of us live in berlin. We saw, basically, the new government and the new mayor come into power on a very kind of pro car platform, basically.

So this is what you're referring to, right?

Silke Kaiser: Precisely. So, answering maybe the first part of your question, so for example, in Berlin, we have only 40 locations where we count cyclists. I think in Paris, it's around 53, in New York, it's 41. And then in Berlin, for example, we have around eight times more locations where we count motorized traffic. So we just have much more data and much more information on motorized traffic than we do have on cycling.

And then yes, in Berlin, actually, there was quite a bit of a heated debate pretty much between let's say the inner city, which was more pro cycling and in the suburbs, which were more likely pro cars. The government switched from a green to a more conservative government, which actually decided to suspend actually just this month quite some, projects, long distance commuter paths, and both bicycle parking houses in cities.

But that's just more really on the political side of the debate. And then what I really see as the main challenge is that just this data and the information is missing on where we actually would need the infrastructure most, given how much we want to prioritize cyclists.

Chris Adams: Okay, so maybe I should ask, where is the data coming from then, for this?

Silke Kaiser: So, we do have these 40 counting stations in Berlin, that's the case study that I'm looking at, and what we then figured is, well, we don't have that much precise data on cycling, but we do have an abundance of other data on cycling. So, for example, we do have, as you mentioned, the bike sharing data. We do have as well data from Strava.

That's an app popular to record yourself while doing sports. We do have data on infrastructure, we do have data on weather, we do have data on socio economic factors. And we figured, well, why don't we use all this available data to actually extrapolate from these few isolated locations to actually obtain city wide estimates.

And that's what I did in this research with Lynn Kaack and Nadja Klein. So what, precisely we do is that we train various machine learning algorithms to use all this kind of data in combination with the cycling counting station data that we have to obtain citywide estimates. And what we actually found is that only using this data is a bit tricky.

It provides us estimates for completely new locations 32%, which is you know, rather good in comparison to having no data at all, but still 32 percent is an error I take seriously. And what we then simulated, continuing with this research was, what if we make some sample counts for new locations? So for example, if I want to estimate the traffic in front of your house, Chris, the cycling volume in front of your house, we figured that if we would maybe put you, someone else, on an automated machine there to count the cyclists, and we were to count the traffic for 10 days.

And then combine it with our models, we're able to get estimates with an error of only 17%, so really rather low for complete new locations. And this gives us a good estimate of how much cycling traffic we actually have in every single street of a city.

Chris Adams: Ah, I see. Okay, so you're using the machine learning model, basically, to make the extrapolations when there's, there might not be so much data, give you more accurate so you can say, "well, I'm more confident that this many people are trying to get around using an active, non gas burning form of transportation," for example, right?

Silke Kaiser: Precisely,

Chris Adams: Ah, okay. All right. Thank you for clearing that up. And I understand. And just, we'll come back to this a little bit later, but you said you're using ML. not the same as generative AI or something like that. That's a different, there's, a whole flavor of different things you might be using there, right?

Silke Kaiser: precisely. I mean, there's many different models out there. In this paper, we used rather simple machine learning algorithms, nothing comparable to maybe what most people think of when they think about chat GPT or whichever generative AI you might think about. Those are really rather simple models making usage of the data we have.

And those models So, I just tend to have, I sometimes, you know, sometimes I think about, I mean, you can target these problems with very complicated algorithms, but sometimes using rather simple algorithms might just be sufficient. And that's what we actually found in this research.

Chris Adams: Okay, alright, thanks. When I was doing a bit of research, I realized that you also wrote a piece around, I think in a publication called Catalyse, where you're talking about active transport and why it's important for greenhouse gas emissions, because you've spoken about like mode shift, which I assume means basically moving from a Being in a car to moving and maybe a active transport, which people use like scooters, bikes, stuff like that.

That's what I think you're referring to there. Could you maybe talk a little bit about how this research actually contributes to like the adoption of cycling as one of the potential modes, because you spoke about it in Paris and I went to Paris and was being pretty transformational when I was there compared to being a few years back.

And I also bike around Berlin too. So I have a vested interest in learning this. Maybe you could like lay out why that's actually benefiting and how active transport helps. Basically, us meet our climate goals.

Silke Kaiser: Absolutely. So, when I, Referring to other research that I've read, research that I haven't done myself, but it is out there and it's been cited a lot, is that we do see that cycling has numerous benefits. It benefits your individual health. If you cycle, it's good for your physical health and then all the sicknesses or illnesses related to insufficient physical health.

We can also see that if you cycle, it's also good for me because then generally we see a reduction in noise and air pollution in cities. So it really benefits the public health, the broader public. And then yes, absolutely. I mean, I did read the IPCC report, which is a report on climate change and it comes out, the last one came out in 2023.

And what they found is that actually 15 percent of net global greenhouse gas emissions are related to transport. This of course includes all kinds of transport, but also, one of them is urban transport. And then switching, there are many levers how to tackle this, right? And within general, as in climate change, there's no one solution fits all, but switching from motorized traffic to cycling is one of those means to actually reduce those greenhouse gas emissions.

And coming a bit back to my research as well, what we find also in research and science is that all you can think about, you know, talk to your friends and family and gather some anecdotal evidence, you'll probably find that one of the biggest deterrents that keeps people from cycling is that they actually are afraid because there's not enough cycling infrastructure.

They're afraid of accidents. And that's a relevant fear. We do see many accidents in cities right now, mainly between motorized traffic and cycling, but also all kinds of other accidents. And what we do can do in cities is, to actually promote cycling, is to build more attractive infrastructure for cyclists.

This can include bicycle lanes, a better design of roundabouts. And all this attracts people to cycle, but, it actually also reduces the risk of serious and fatal accidents. So what I really try to do with my research is that again, if we have these heated debates in cities, how we want to distribute space among cyclists, cars, delivery trucks, etc.

I'm trying to provide data on where we actually have how many cyclists in which street, so that when policy makers or transport planners come around, they can use my data and actually make fact driven decisions when and where infrastructure benefits the most, the greatest number of people. And that's how I hope my research can contribute 

Chris Adams: Ah, I see. Okay. So that's really helpful. And I think there are some kind of comparisons I can make, which make to maybe help me understand if, and some of the listeners. So you know how a couple of years ago, in the middle of the pandemic and COVID, one way to reduce the number of COVID cases was just to reduce the number of people taking tests, right? You know, that's not necessarily the best way to solve that, know, and it feels like

we've got a similar situation. We've got a data asymmetry problem here it looks like you're doing some work to address for that. I mean, Also, you've spoken about, as I understand it, there are various parts of, like, our economy which are easier to decarbonize than other ones.

Like if, for example, in Germany and in America, transport's the biggest, one of the places where we've seen not so much progress on reduction, on carbon emission reductions compared to things like the energy sector and stuff, which is decarbonizing relatively quickly. So this is what some of this is a reference to. Okay, so what we'll do is we'll share some links to Catalyst, I'm sorry, Catalyse, the paper there, and also some of the papers that you have. So we spoke about Paris, and we've spoke about Berlin, where we both live. Are there any other places you would point people to as examples of, okay, this is what good might look like, and this is one place which actually has quite good data to show where you've actually seen quite effective policymaking to kind of change the environment to make it easier to cycle? Because, yeah, not everyone wants to become a MAMIL, like middle aged man in Lycra wearing the helmets and everything like that.

Silke Kaiser: So, I mean, there are definitely some cities that you know that are popular for cycling, for example, just earlier this spring, I did a research day for some months in Copenhagen. And obviously Copenhagen is a bit of a dream for cyclists, right? I'm not the first one to mention this. And then there are other cities, Amsterdam, you name it, but generally I do have to admit that in my research, I haven't really come across cities that do have much better data.

I would say it's a grasping problem across different cities that data is missing. Copenhagen and Amsterdam have taken political decisions to prioritize cycling, but I do have to admit that I didn't, I haven't come so much across that they've made this as a data-driven decision, but this was more of a political decision.

Chris Adams: Ah, okay. And there's one thing I'll just ask before, Chiara, I'm, okay, I am a closet energy, well, not very closet energy nerd. I'm totally gonna, looking forward to talk about that. But Silke, I was just going to ask you, so you mentioned use of Strava and you mentioned the use of, Okay. It's useful to have these new sources of data, but there's also a question about the provenance of that data and like the circumstances under which it's collected.

So for example, we've seen Strava used in lots of other places and if you're using Strava, you tend to be a bit richer, a bit younger, a bit healthier than most people. Maybe you could talk a little bit about some of that, because there are various sources available to inform these policy sessions, and like, Strava is one.

But like, where else, like, assuming you had, you were suddenly queen of the world, where would you wish you could get some of the data from to kind of inform this in future?

Silke Kaiser: So you're absolutely right, Strava definitely is quite biased. It's the data, for example, for Berlin, I definitely know that they're all male, they're mainly male, young, and they do tend to do very sporty biking in comparison, for example, what I probably do to commute work. So it is true that some of the data we use is biased and we're trying to balance this off with the other data sources that we're having.

We're also taking socioeconomic factors into account because obviously we do not want to have, infrastructure is meant to be there for everyone and not for privileged or less privileged people. It's, meant to be equal for everyone. But then obviously I thought about a lot, well, how could we actually improve the data availability in cities?

And I definitely see two levers that we have. Well, first of all, we can place more cycling counting stations. That is a bit challenging because, for example, we have so many kilometers of roads and it's hard to track them all. There are cheaper options than the ones that we're currently employing. So this might be our one option.

Some of them are then using cameras, for example, that are just much cheaper to put them out there. And then the other question, and that's something I'm also looking forward to, to answer this question is because I'll be looking at this in my future research, is that actually how can we place the sensors that we have better across a city? Because currently look it up again for your city.

You'll probably find a similar image is that we do tend to place these censuses as very busy and scenic roads. And the question is actually, can we maybe place them at more diverse spots within a city? And if yes, how can we choose those streets to place those sensors at to actually get a more comprehensive image of the cycling traffic and then also of all kinds of socioeconomic areas and a more equal data image.

Chris Adams: Cool. Thank you for that, Silke. All right. We'll come back to you a little bit later about some of the specific techniques that we were using, because we spoke a little bit about ML and there's a lot more we might dive into there. Chiara, if it's okay, can I ask you a little bit about your research analyzing European energy data?

Because you didn't hint a little bit about how this can affect renewable energy transition, and one of the, one of the things that Germany has in particular is a target to have 80 percent of the grid running on renewables by in, wow, in five years. So that's not much time, and we've also spoken about on the grid, we've spoken about things like time shifting and location shifting as a kind of carbon-aware software, particularly in changing how data centers, like, fit into the grid, I suppose, or the energy they use. Can you maybe talk a little bit about some of the challenges you've found actually working with this data hands on? Because we At best, most of us developers, we might use it in a really nice, pretty fashion from electricity map or Watttime or in an SDK, but it sounds like you're pretty much at the front end having to figure out how the sausage gets made. So yeah, if I can ask you, maybe you could tell us a little bit more about how this data comes about and what are some of the challenges.

Chiara Fusar Bassini: I mean, if you've been using Electricity Maps, you probably have been using an application that in the back uses NSOE data. So you, in Europe, we are rather lucky because. There have been two regulation that have been released in 2011 and 2013, which forced in a way transmission operators to publish a variety of a time series of energy data from the grid and from the markets in an effort to increase transparency. And we have a number of data in a central, on a central repository, which is called the Transparency Platform. We have load data, we have generation data, we have transmission data about the grids, we have balancing data, balancing markets, but also a lot of information on individual power plants. This data is overall extremely useful, but unfortunately it's not Always accurate and it's not always complete and not all the data is not always published in a timely manner.

That very much depends on the type of data, the country itself. We are still very much better off than other markets where there's no data at all. But it's still an issue of like how good and like what the data quality actually is. Because you mentioned time and location shifting. To do time and location shifting, most likely you will be working with aggregated data.

For example, load data, load data, load forecast data. And. One could analyze, for example, a load to decide whether to shift more energy consuming activities at night or at moment where there are off peak time windows. And on the other hand, one could look at aggregated renewable generation data to try to relocate some more of energy consuming activity to time of the day where the grid is actually greener and there are a lot, there's a lot of interest in academia, but also in, in the industry sector to provide us information, to have an estimate of carbon intensity. there are a number of startups out there. You mentioned Electricity Maps, but also academics have come up with top, bottom up, top down and bottom up approaches to compute really at hourly or quarter hourly level, these carbon intensity estimates. The trick here is that you are working with, the aggregated data and aggregate the quality of aggregated data and the timeliness of aggregated data is rather high, the situation is a little bit different when you move to a more geographically granular, like a higher geographical granularity.

Chris Adams: Okay, so from Germany going to like Berlin or Germany going to another part like Frankfurt, for example, something like that. Yeah? 

Chiara Fusar Bassini: Rather, when you're looking from, the aggregated generation to the generation of individual power plants, because in that sense, you might be interested to know which power plants are actually generating right now. And you might be interested to know which areas are generating more solar, for example, which areas are generating more wind. Unfortunately, we don't have data on all power plants. Which would be rather impossible in terms of like amount and extent of the data, but we have only data for power plants that are at least 100 megawatt. It's mostly conventional power plants. So, for example, we have no individual information or very little information on wind farms, for example, because Some of them are not big enough to qualify for this criteria. And also this data get published with a significant delay of four days so that you can't really use it to

do anything operational. It's also not conceived for that. And we can or we cannot use the data to properly, like we cannot do it for, we use it more for analysis than for forecasting, but nonetheless, we can use this data to understand a lot on individual power plant data, why they decide to dispatch on how they are dispatched, and especially in the context of conventional power plants, how their dispatch has changed over time because of political reasons, but also because of the increase in cycling of fossil power plants, because they have to adapt to the renewable energy generation, to more renewable energy generation.

Chris Adams: Can I just quickly stop? I just want to check I understand some of the terms you've used for listeners who might not be familiar with load, cycling, some of these things here. So when you talk about loads, you talk about energy, like basically that's power draw, what people are trying to draw from the grid.

You mentioned that. And then you also mentioned, I think, like cycling. So that's like basically scaling down a power station in response to there being loads of wind on the grid or stuff like that. Maybe is that about right?

Chiara Fusar Bassini: Yes. Thanks for asking. So to clarify, so load means the demand and in the past, I mean, demand, especially from, industry, but also from household, it has been rather predictable. And the way we faced demand, or we satisfied demand, because well, in an energy system, demand and supply need to be equalized at any time. In the past, the most of the baseload, so the main bulk of consumption, have been satisfied using traditional fossil fuel technologies, so called dispatchable, because you can, decide when and how to switch them on and off.

Chris Adams: Ah, okay.

Chiara Fusar Bassini: But the thing is, as more and more renewables enter the grid, they cannot be dispatched whenever, they can only be dispatched, or they can't choose when to dispatch a

Chris Adams: Yeah, control the sun and the wind. We can just respond. Yeah.

Chiara Fusar Bassini: They just respond to external weather factors, right? But that also implies that we still have these conventional power plants that are

dispatchable, but we now have to operate them with increased flexibility. So they have to be able to ramp up and ramp down as the load is more and more satisfied by renewable energy sources.

Sources when they are there. And we always make the assumption that say conventional power plants are a hundred percent flexible, but that's not actually the case. For example, some power plants, when they are turned on, they have to generate a minimal capacity. And if the demand for that capacity is not there, that might be an issue, or they might have some minimal times to be switched on and switch off.

So there, there is a plenty of interesting question that arise from the increase of renewables. Like how will conventional power plants cope with more renewables in the grid?

Chris Adams: Ah, I see. Okay. So one thing you're saying, she's like, yes, it's not like a computer. You can't turn it on straight away, like in milliseconds. And so that's one thing you mentioned and the fact they need to do that more is another issue. And if I understand it, what you described was quite a physical process.

It's like, we're not using bits, we're using atoms, like burning coal, things are expanding and contracting. Like, is there a risk that, you know, the, a big power plant could be damaged some more, or does that introduce any wear and tear when people need to scale something back? Because I can imagine someone saying, "hey, you're making me change how I do things, and therefore you're introducing some risk into this.

That's not what this was designed for in the first place."

Chiara Fusar Bassini: Yes, that's actually a very interesting question. What I mentioned, cycling, meaning that you operate conventional power plants more flexibly, has some consequences on the lifetime of power plants, especially if you keep on turning it on and turning it off. There are some wear and tear indeed for thermal power plants, wear and tear consequences, some of some power plants may not even be able to do so because they have some agreements with O&M managers that tell them, you know, "you can do that, but then you'll have to pay more because you will have, we'll have to do more main maintenance." And also, there are a number of obstacles that arise, especially for older power plants that have not been conceived with this flexibility option in mind, but rather to satisfy baseload.

Chris Adams: Ah, okay, thanks for clarifying that. And you're essentially doing some of the research to see how you might predict some of this better to either reduce or basically accommodate some of these changes that we might have when we've got a much more dynamic grid that is influenced by the sun shining and the wind and all the things like that, right? So maybe if we can talk a little bit about some of the techniques being used to track this and reduce the amount of, maybe, reserved capacity that needs to be done, or reduce the amount of wear and tear that might be imposed on the kind of entire system full of all different power generation. You said you spoke a little bit about using machine learning, and We spoke to Silke.

Silke mentioned that she's using some ML models, which are not like generative AI. That's a very, it's a different kind of AI. Could you maybe talk a little bit about like how that gets used, because you hinted at it, and like what some of the barriers are for using some of that, because it, that sounded quite enticing and interesting.

Chiara Fusar Bassini: Yes, before, before that, I might, I want to add on this. So there is some parallel research being done, especially like at engineering department of a lot of engineers trying to use machine learning to efficiently operate conventional power plants to reduce this wear and tear of wear and tear problems.

And in general, like damages from cycling while still satisfying a change in demand. What I'm doing is and Rather different analysis of historical usage of power plants. So to see how power plants are, have actually been operated so far in the markets, how they're, how they, how flexible they actually are.

Because sometimes we assume that they're 100%, again, we assume how they're 100 percent flexible, but how flexible are power plants that we already have in the grid? And also how available are there in cases of outages, for example, how, like. What's the percentage of time in the year that they actually could provide electricity, for example? And in terms of techniques, well, it's a lot of time series data, so most time series apt methods can be used here. It very much depends on the ultimate task, but one of the major obstacles I encounter is that this high granularity data is by far not as good as the aggregated data, especially, for example, an availability of power plants that has to be reported in a rather accurate way, but then is not one to one translatable to time series format because it's published as market messages, meaning that the data that we have is not in a format that makes it directly usable for a researcher. So there there are a number of obstacles that are really determined by the data quality rather than by the task itself.

Chris Adams: Ah, okay, so it comes down to the data a lot of the time then, basically, yeah?

Chiara Fusar Bassini: Again, like as Silke said, sometimes it's really just a matter of the data that you have, like the research that you can do is going to be determined by the quality of the data that you have.

Chris Adams: Okay, we'll touch on that a little bit later, but I guess the, that does make me think about, particularly in Germany and countries where we've seen very rapid changes. Like, Germany, there's, you know, there's a massive craze of balcony solar, for example, or we've seen loads of battery coming onto the grid, or even Pakistan. We've seen, like, a third of the power, the new power introduced this year, was come from rooftop solar, and each one of those is individually less than 100 megawatts. That's an enormous chunk of power. So there's all this new stuff that we're not, don't necessarily have access to the data for to actually figure out, okay, how will the grid work and how can we make sensible predictions on this? That's useful to know. Brilliant. 

Okay. So we're speaking a little bit about the upsides and how, where some of the potential might be. We do speak about Green Software, about reducing the environmental impact of some of this, and obviously when we're doing some of this work, I've asked a little bit about the kind of models you might be using, partly because there's a question, whenever we start using technology to help us meet climate goals, it's when some of that energy is still coming from burning fossil fuels, for example, there's trade offs to be made. Does anyone want to go first, talking about how we think about these trade offs? Because as practitioners, I imagine you're at the coalface, but you're also working with some of the people who think about this every single day. And like, if you're working with Lynn, and like, Lynn was one of the founders of Climate Change AI, I reckon she probably has some reckons and you've probably had some conversations about this, right?

Silke Kaiser: Absolutely. Actually, just, I think just the last group meeting we had, we just discussed about precisely this topic, because it obviously, it is a question that keeps coming, a question that we do want to answer. And it is also like, it's in our minds, right? Because if we want to do something positive for the climate, and then actually, the net result might be negative, because our models consume that much energy.

This definitely is a topic that we think about a lot, I would say. I see Chiara nodding. I think she's agreeing with me, but, and I can see that maybe to the outside world, often this can seem a bit like, I like the term of fighting fire with fire, you know, you, you're trying to make it better, but you're making it maybe even worse, but I think if we make some smart choices along the way, I rather like to compare it to the idea of fighting a forest fire with a controlled burn. So right, that we do try, for example, in, in the models that I was employing, I did partially check how much, how big the energy usage was.

I was using simpler models, as I mentioned earlier. So the energy consumption wasn't that high, but I think it's good for us and for everyone out there using, similar models. To track your energy consumption and there are very nice packages and libraries out there, tools, all kinds of things, open source, freely available that are very good in, in managing or in measuring the energy consumption you have.

And then of course there are a whole bunch of other approaches that you can take. Right? I mean, you mentioned it's an issue if it comes from fossil fueled energy, but obviously you know, you can think about, I know that there are a lot of like. Service and data science centers, for example, out there in Iceland, where you tend to have more natural cooling, where a lot of the energy being produced is renewable.

I'm not saying that at all perfect, but what I'm trying to say is that there are different approaches that we can actually also reduce the emissions caused by AI. 

Chris Adams: Ok, so there was one thing about the actual technique, like, AI is not a monolith. There's all different approaches within this, in some ways, not particularly helpful term, like the use of relatively small machine learning models, which are relatively simple, that's going to have a totally different footprint to the model used to generate SOAR, like a video or something like that.

And that's something that we probably would benefit from having a better kind of intuition off as practitioners, for example, and you spoke a little about the carbon intensity. So there's two, two things that you have there. And you mentioned some software that you have. And you said that, Chiara, if I can kind of give the floor to you, because I think you mentioned you, you've spoken about some of this before about, yeah, there are some tools and I use them as well. Can I ask you a little bit about when you've been thinking, I mean, how do you think about these trade offs? Or is it a trade off? Or is there another set of dimensions you might be thinking on rather than like forest fires and controlled burns, for example?

Chiara Fusar Bassini: Yeah, I think, there are two things that need to be thought through when using AI. Number one is like, how do you develop your model, and then what do you use your model for? So how do you develop your model? That's similar to what Silke said, for example, doing emission emission tracking while developing the code and while training the code. And at the moment, I think AI is missing some embedded indicator of the social environmental cost of the training. So. We kind of think of performance metrics such as accuracy, such as like classic cross entropy losses and so on, and we think only about precision. But sometimes we need to be a little bit more critical of whether an increase of accuracy of 0,1 percent is worth an increase in the training time of two hours or an increase in the size of the model of 25%. These are like actual numbers and scientists have coined for that the term green AI, meaning Okay, can you know, can we, in a way, embed this measure of the size of the model within the loss that we are trying to minimize in the training of our model? There is another, a good example, for example, is the Bloom model that is an alternative large language model to GPT. It is similar in size, but it required Like the CO2 emissions of the model are 20 times lower than GPT 3. And this has been made possible by, first of all, in smart usage of the training and also tracking of the carbon intensity of the grid. It system was trained, the model was trained mainly in France, which is, which has runs predominantly on nuclear power. So in like carb, much more carbon neutral system. So there are a number of things that one can consider while training their model. But also another thing that is very important, and I think that we sometimes don't really think through, is what are we using AI for? And in that sense, there is currently no standard assessment in place. Like, is this application really worth using AI? AI is, by its nature, ethically neutral. It can be used for anything from targeted advertisement that will have probably a negative impact on environment to detecting wildfires. So very positive impact. I think policymakers in that sense can make a great deal to really make a difference and start, for example, by providing a classification of which user cases are positive for environment and which are negative. It sounds, it may sound like science fiction, but it has already been done in the European AI Act in looking at the perspective of risks, like which application have a high risk and hence should be more controlled and which other have lower risk. And I think a similar classification would be also very useful for environmental purposes.

Chris Adams: I'm really glad you mentioned that because I ended up reading through the AI act for research recently. And the idea of the risk that is, you're right, there doesn't seem to that much be that much reference to the use of AI for, let's say, you know, increasing the extraction of fossil fuels, right, versus that.

That's, there doesn't seem to be much to mention about that, but there is some information about the transparency around training. And now that we've looked at it a bit closer, so within the Green Software Foundation, there is a group called the Real Time Carbon Group. We've been looking into some of the specific implications of this, and it looks like the AI Act, it also, it looks like it's probably going to suggest not just understanding the training, but also the cost of inference, like the use of the model rather than just the training of the model. If I can just quickly, you've, you, mentioned there are tools out there, and Silke also mentioned there are tools out there. If I did want to measure some of this, and if I did think there was some legislation coming for this, what tools are there available for me to measure the direct impact? So at least I know what the trade off might be.

So we understand that the carbon footprint of decarbonizing transport, like Silke mentioned, that's going to be, you know, positive, but quite, but there's ways of calculating that, but for us as practitioners, are there any software or any tools you might recommend that are kind of common in the field now? Either goes. I'm happy to, whoever's more comfortable talking about this.

Chiara Fusar Bassini: I'm thinking CodeCarbon is more probably a standard used by many scientists. I know there are more applications that might have a higher granularity, but I guess that's a

Chris Adams: That's the one that you folks have used, right? Okay, I hear CodeCarbon used a lot, and I, as I understand it, that's the one that's been used for the Bloom model when they wrote a paper about that. That's what I'm not sure Facebook have actually explained this because when I was looking at LLlama's model, so AI models have model cards, which basically, which I think, various responsible practitioners now say, "this model took this much carbon, or they had this much energy gone in to kind of create it," for example, if you go to the existing Llama 3.1 model card on Hugging Face, and you try to follow a link to the actual methodology, It's not actually explaining how it works. there's now a bug. I filed a bug to ask out, ask, well, how did you work these figures out? Because these feel like it's quite important, especially because when you look at the numbers, it's significantly larger than Bloom, basically. That's, so, so what you're referring to is CodeCarbon. That's one tool that people can use that will give you some idea that is in use in a few places already that's relatively safe to start off with. Great. Okay. And we spoke a little bit about some tools. So if someone is, wants to take their first steps, they might look at this.

And there are various projects I'm aware of to make it a bit easier to understand the impact of one versus another. I believe there's one Energy Star AI or Energy Star, AI Energy Star or something like that. There's one person who I've spoken to who's involved with it. Boris. I'm so sorry I can't pronounce your surname, but I do know you're the AI lead, the AI sustainability lead at Salesforce. Boris G is one of the people who's been writing about this. He's not the only author, but he's the person I know, and we'll share a link to that as well, because that's the first thing I've seen of a useful, like, A kind of nice idea to give you an idea of what the inference, the usage as well as the training might actually be. If you were to look at this, we've still got this issue of data or having access to data like, and Silke, I ask you, if you were like queen of everywhere for a moment, how would I change it for here, right? Let's say that you want to be responsible AI practitioners, like what are the things that we need to see in the next, in the coming years to make it possible to be like responsible practitioners so that when we do use AI, we're using it in the kind of greenest possible fashion. Silke, I asked you first about Queen of Everywhere, so maybe you go first and I'll hand over to Chiara.

Silke Kaiser: Well, that's a very good question. I definitely say, as in general, with all kinds of, you see, in more technical approaches, we do need reproducibility and traceability of what we do in our research. I mean, just as you mentioned with the Llama, I think it's important that other people are also able to understand what we did, what was the energy consumption of what did, how can they, how can we check the things that we've done and, see if, we did it properly, if it took a right approach?

And then obviously, I think this is a bit less related to, the topic that I'm or that Chiara was working on, but also in the longterm, we do need to think about ethical concerns coming down to this. And then again, I think just, really, transparency. So I really think that transparency is a good way to address this.

What take do you have on this Chiara? I think one of my major takes also from what Silke mentioned, and I'm really glad you mentioned, is the fact that when we were talking about policy making is that very often policy making is not data-driven. One problem is that we don't have the data and it can be addressed partly by regulators asking for those data, right? But another issue is also that we don't really do data-driven assessment of the policy that we implement then. And I came across very recently a paper that tries to systematically evaluate policies.

And having been implemented in the last 25 years, this very recent paper has been published like a few days ago.

And I thought it was very interesting to well, once again, the results of the paper is there's no one size fits all and some countries depending on their level of development might need different policies. And we have to keep that in mind that we can't use the same policies for a developed country whose energy consumption, for example, is no longer linearly dependent on its GDP from a different, from a developed country or a developing country that has very different issues. But I think in general, this approach of doing data-driven policymaking and science-driven policymaking is something that would really, we would really need in this space.

Chiara Fusar Bassini: I think that's something I can really agree on. I often feel that as a scientist, we feel like we're trying to really produce clear results, objective results. And then often we feel there's maybe a bit of a lack between the two. The research that we do and how much this is sometimes uptaken, by policy.

And obviously we hope that because we do really put so much effort into this and always try to be objective. We hope that this will eventually be more used more and more in the policy sector.

Chris Adams: You've touched on a really interesting point, and I can think of some examples that just occurred to me. So, we had an interview with, oh, Vlad Kor, his first name is Vlad, I'm gonna mispronounce his surname, but we spoke a little about, all about the rebound effect, and Vlad Coraoma, that's it, Vlad Coraoma had this lovely post actually on LinkedIn talking about the curse of potentialities, potential itis, which is basically talking about, we have all these kinds of really exciting projects, but whether people follow through to check whether the actual gains materialised, or the benefits materialised, there's much less effort put into that.

And we've been seeing, like you said, Chiara, from the last 25, we've been seeing predictions for things that would happen in 2020 or 2030. And 2020 is in the past now, we can check if this is, if these actually delivered, but a lot of the time we do not see that. And in our field, specifically as kind of cloud providers, or people who might be consuming services, there's some, there's a really, I'm thinking of a really good example. Microsoft has a whole thing about pushing for AI and everything like that. And we know that, as you mentioned, AI can be used for good, and can be used for bad, or used for Climate aware things, which are really helpful and things are not so good. And we've even seen like people who are workers really pushing for this. I'll share a link to an article in Grist where, written by Maddie Stone, where she talks about some of the sustainable connected community inside Microsoft, speaking to some of the management there. There's a guy called Darrell Willis. He's the vice president of energy. And they spoke and said, "hi, we are pushing for," you know, "can we please have a conversation about what we're using AI for inside our company, because we're one of the largest companies in the world and we're one of the leaders in various industries," right?

And there was a commitment to say, we're going to produce, as the management said, "we're going to start releasing information about, okay, how much of our use of AI is coming from the fossil part of the industry versus the renewable part of the industry?" And this feels like a really important data point if we're going to be looking at tens of billions of dollars used on AI.

I mean We know that it's an accelerant. If it's an accelerant of fossil fuel extraction and burning, that's a very different story to using tens of billions of dollars for renewable energy, for example. And if we've seen commitments at a management level, then it would be nice to see these. As we understand, these commitments were made, these were shared inside the team, but we don't have this, and we'll share a link to the specific terms, because actually, I'll just share the quote with you, because I think it's one thing that, if you're an employee of a cloud firm, or a customer of a cloud firm, it's the kind of thing you might want to know about, so on the call, "Darrell Willis, committed to providing employees with updates on net zero requirements as Microsoft continued to implement these energy principles. Committed to providing a breakdown of energy divisions revenue across six different sectors from oil, gas extraction, to all zero, low to zero carbon energy. So sharing this information internally." Now this feels like a thing that employees probably should be aware of or asking for. Also feels like something that if you're an investor of Microsoft or a customer, you might want to know.

Because there's an impact inside your supply chain thinking about this. And if you're choosing one provider because they have really strong GSG credentials, this may make you view it somewhat differently. We'll share the links because it seems to be the best concrete example I can think of at significant scale that we might be talking about. And I'll get down on my soapbox because that's just the thing that really leapt out when I, when you spoke about that. So we coming up to time, and we've spoken about the different uses of AI, sustainability of software, as well as some of the Things you might want to use or be aware of as a practitioner. If people do want to find out about the work that the two of you are doing, where should people be looking? So Silke, if people are interested in your work, is there a LinkedIn page or is there a website that you direct people's attention to?

Silke Kaiser: I normally try to direct people to my personal webpage, which is silkekeiser.github.io. Or you also, you can also find me on X or on LinkedIn. And I'm always happy to share news on my research as well as the articles that are out there. And I'd be happy to, if people were to look at those pieces of information.

Chris Adams: Cool, thank you. Alright, and Chiara, if I just hand over for you?

Chiara Fusar Bassini: I've seen Silke's website and you guys should really see it.

It's a very nice animation. I don't have myself a website, but I'm very active on LinkedIn. You can find me under Chiara Fosar Fusar Bassini.

Chris Adams: Chiara F U S A R, we'll put it in the link, we'll add it in show notes. So, Chiara Fusar Bassini. Brilliant. Thank you, folks. This has been lots and lots of fun. I've learned a lot from this, and this has been a really nice chat. Hopefully, we'll cross paths sometime in Berlin, but otherwise, thanks again for coming on, and have a lovely week.

Silke Kaiser: Thank you very much for having us.

Chris Adams: Ta ra! Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> Green Software with Gaël Amongst the Whales Green Software with Gaël Amongst the Whales Thu, 22 Aug 2024 07:00:00 GMT 1:07:00 80qq77q0 false https://podcasts.castplus.fm/e/vn55ll5n-green-software-with-gael-amongst-the-whales 68dc7d20597bc7d53fb84006 68dc7cef46a2532cdd8d1c23 TWiGS host Chris Adams is joined once again by Gaël Duez to discuss the latest news in green software around AI. They discuss insights from recent reports by Google, Meta, and Amazon, as well as looking at the implementation of the GSF’s Software Carbon Intensity metric. Similarly, the conversation touches on the distribution of renewable energies and the use of different means of measuring carbon in reporting, and how this can affect the behavior of consumers and organizations alike. Tune in for an enlightening discussion on the latest in green software. 79 TWiGS host Chris Adams is joined once again by Gaël Duez to discuss the latest news in green software around AI. They discuss insights from recent reports by Google, Meta, and Amazon, as well as looking at the implementation of the GSF’s Software Carbon Intensity metric. Similarly, the conversation touches on the distribution of renewable energies and the use of different means of measuring carbon in reporting, and how this can affect the behavior of consumers and organizations alike. Tune in for an enlightening discussion on the latest in green software.

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TRANSCRIPT BELOW:

Gaël Duez: We cannot fully rely on companies agreeing on how they should measure their own environmental impact, even if they are well meaning, with tons of great people trying to do the right things, etc. It's not a black and white world out there, but there is a question at some point of financial pressure, shareholder pressures in many of these companies.

They're just stronger than the entire stakeholder's pressure.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Round about this time last year, Gaël Duez, the voice of the Green IO podcast, came on to Environment Variables, This Week in Green Software to talk tech, sustainability, and moving from France to live on La Reunion Island.

And the ups and downs of consulting remotely on digital sustainability from a small island off the coast of East Africa. It was a fun chat. And when I asked him if he'd be up for coming on again to review a few of the latest stories around green software, he basically said, "yes, Chris, but I can't do Friday because I promised my daughter we'd pop out to go whale watching." As one does on a tropical island, I guess. So here we are, recording on a Monday morning instead. Gaël, thanks so much for coming back on. Can I give you a bit of space to introduce yourself and what you're up to these days? Also, how are the whales? 

Gaël Duez: Oh Thanks, Chris. It's great to be back again on Environment Variables. And the whales were there, like several made quite a show, including a mother and her little newborn. Well, little meaning four tons. So it's always impressive to see there's a 20 tons mammals jump in the air like this and cherry on top of the cake, actually, we saw a lot of dolphins and two turtles.

So it was a perfect trip. 

Chris Adams: Wow, I'm jealous. Okay. Yeah? 

Gaël Duez: Actually, to connect a bit more on our sustainability topic, I'm also relieved that the best practices for whale watching are more and more enforced, such as, minimum distance to approach them, turning off the engine when they come in, or direction, et cetera.

And, I also say, on top of the pleasure of, watching them and being on the boat, etc. It's a very positive sign that we can enjoy nature without destroying it. And that's pretty cool.

Chris Adams: Cool. So it sounds like there might be, hopefully, when your daughter's taking, maybe some of her kids for whale watching, there's a chance to continue that, by the sounds of things.

Gaël Duez: Yeah, I'd like to focus on whale and dolphins rather than coral reefs, which are something that really is puzzling me, but yeah, we might, hopefully. And to answer your other question, when I'm not whale watching in Reunion, I try to be useful in the tech community, by advocating for more sustainable ways of designing, coding, hosting, and even considering the use of technology itself. And my main tools remain the Green IO podcast, as you mentioned it, as well as the Green IO conferences, which I started to organize in several cities, but I'm sure we'll get back to this point later. And besides, as you already mentioned, besides as volunteering activities, I do public speaking and consulting on systemic strategy for mostly for tech companies, for both paying the bills, but also to have an impact.

Chris Adams: Cool. Thank you for that. I will also share a link to the previous episode if you're for people who are listening, so they can get an idea of some of the other things that Gaël has been working on and been discussing previously. If you're new to this podcast, I should probably introduce myself as well. As I mentioned, my name is Chris Adams. I am the executive director of the Green Web Foundation, which is a Dutch nonprofit based around reaching a fossil free internet by 2030. And I also work as one of the policy chairs in the green Software Foundation, particularly in the policy working group. Okay, and before we dive into this podcast fully, just a quick reminder, everything we refer to, every story, we'll share in the show notes and there will be a transcript as well for you to kind of search through and look into later. Okay, so as per usual with this week in Green Software, We run through some of the latest stories or projects that have caught our eyes over the last few weeks. And Gaël, I should probably ask you, are you sitting comfortably, ready to go?

Gaël Duez: Perfect. Everything is fine.

Chris Adams: Brilliant. Okay. Let's start with the first story then. So this is the first one that kind of came up on my radar. There's a website called Green Coding AI. And we've spoken about the environmental impact of AI on this show multiple times.

We've also spoken about there is different models that you can actually use to maybe ask the same question and get something back. This one is actually a project from, the Berlin based group, Green Coding Solutions. And what they've actually done is put together a service, running on their own hardware, where you can basically try various, models to prompt them to, like, try out, say, Llama 3, one of the big versions, maybe a smaller version of some of these to see, to ask a question and see what kind of responses get back. And one of the things that's particularly interesting in my point, from my perspective is A, the transparency, but also it gives you a software carbon intensity score for every single inference when you ask a question. So you can start comparing the utility of a large model versus a small model. Gaël, I think you might've had a chance to play with some of this already.

When you looked at it, what, were there any things that sprung to mind for you when you first had a bit of a kind of kick the tires and mess around with it at all?

Gaël Duez: Well, what I was pleasantly surprised with is, as you mentioned, the use of the software carbon intensity. I, as far as I know, this is the first time that I've seen it being used for this kind of tools. I know that what is very interesting is that we can see now, not blossoming yet, but several initiatives have popped up with that kind of approach and I need to shout out and at least I need to give a big kudos to Arne and his team because once again it's very thoroughly done for what they've done in Berlin.

What I also like is the work that has been done with another initiative, and I hope that they will start discussing, with each other, which is, it used to be a data for good, project, but now it's a, an, an association on their own called Gen AI Impact. Yeah. So the association is called Gen AI Impact and they created this tool Ecologits, which is kind of just kind of the same approach.

With a very strong focus on transparency, how they calculate everything and all the code is accessible, et cetera. And there is also obviously Code Carbon by Jürgen Fais, which was kind of the trailblazer, if I recall. And I think it says something positive about the trend in the AI industry, that they need to tool up to Assess more and more precisely what is the carbon impact and hopefully soon the water impact.

And that's still, I know that it's not feasible at the moment because of the lack of transparency around water, but we will discuss water later in this episode. I'm pretty sure regarding the other news that, that we have, but it, is very important. I cannot count how many times I've got potential customers or heads of IT telling me what are the tools we can use to measure the footprint of AI.

And Also to use them to push back a little on the AI hype. everything should be AI'ed. I don't know about it. With a pinch of AI everywhere, but it comes with a cost. Now, going back to your question. So I really love the multiplicity of tools that we see at the moment. And now going back to more specifically in Green Coding AI, I think the way they, try to cover all the angles when it comes to energy, having both the CPU joules, the GPU joules, but also the temperature.

It's very interesting because the temperature, it connects a lot with a discussion I had with Professor Lee. One of the big experts on water cooling, especially in data center in tropical island or in tropical climate, sorry, not island. I'm the one on a tropical island, but there are a lot of tropical climate without tropical island, starting with Virginia in US that we tend to forget.

And he was telling me how important it is to understand that there are thresholds when you use a chip. And there's a threshold that impact massively the energy consumption. So it's not the same to have a chip being used around 25 degrees Celsius, for instance, and 30 degrees Celsius. It's just a linear progression in energy consumption versus, temperature.

And I was really delighted to see the temperature being put as a metrics, because that's where we are getting more and more in details and understanding that it's not that easy, it's not that linear, and we need to investigate things in a more systemic view. And the temperature for many people operating data centers, is absolutely pivotal when it comes to anticipating energy consumption.

So I think, yeah, I was very pleased with this CPU temperature metric that they added. I have no idea how they made to calculate it. So that's something I'm going to investigate, but I like the idea of putting this kind of metrics on the table as well. 

Chris Adams: So, thank you for that, Gaël. So, what I can share with you, there's a chap, one of the chaps working at Green Coding Solutions, Didi Hoffman, was one of the people doing a significant amount of the work on this, and I've used some of their tooling, they've made open source before, and they basically pull some of the temperature figures from the, from basically the CPU itself, I mean from the system that they use, because for example, most machines can tell you how hot or cold they are, for example.

And that's for pulling some of those numbers actually from. The thing that I might share with you that I think is interesting here is, normally, Green Coding Solutions, they tend to open source as much of the work as possible. And they have one of the nicer tools out there for looking at the environmental impact of pretty much any kind of service, for example.

And the thing that So the, I did actually ask Diddy about this and said, "Diddy, this looks really cool. Are you planning on open sourcing?" He says, "yes. The only thing that has stopped me open sourcing it so far is tidying up because it is a bit of a mess." But the thing we can point you to in the meantime is a paper that he worked with, I think Professor Verena Majuntke from HTW Berlin. They submitted a paper specifically for, to Hot Carbon talking about, essentially being able to optimise different models for optimize the use of AI services specifically for particular tasks. And that paper is really interesting in my view, because they say, well, given these particular tasks, some tasks are amenable to running on small models.

And some tasks are better for large models, for example. And they were basically demonstrating how you can pretty much use a system, which looks at the prompt and then will kind of route it to the appropriately sized model to reduce the environmental impact of this. But one of the things they said was that they can look at the energy used when making an inference like, asking for a response back, but it's still a massive challenge to get any numbers from the embodied energy in making some software. And this is one of the key things that we might, maybe wasn't so obvious when we first spoke about this, that right now there are kind of three broad life cycle stages in an AI project. There's a training part where you create a model. There's an inference part where you have the use of the model. And then there's the kind of embodied carbon around this. And this is one of the first projects we've seen that really makes the figures for the inference part quite visible and quite easy to work at, and one of the key differences between this and the Ecologits project that you just mentioned before, was that this is actually running on the software itself, so they have access to the hardware, they're running the hardware themselves, and they, actually document how they do this. The, when I spoke to Samuel Rince at GenAI, I asked him, "how does your thing work?" Because that's really cool, I really would love to see some figures for the inference prices. Well, what we do is we have to make some educated guesses and estimates based on how big a model might be, how much memory might be allocated for it, and how long it's run and how big the response is.

So they're essentially annotating responses that come back from Open AI or from Mistral or from Claude or anything like that to give you some numbers for this. And this kind of does beg the question, if we see green AI instrumenting their own physical hardware, and if we know that the best we can see elsewhere is us having to instrument things ourselves. Based on guesses, why is it such a challenge to find services that provide these numbers as part of how they work? This is still a challenge in 2024. And I think these projects here demonstrate that yes, there is demand and there's also ways to do it. So if these aren't being exposed, they really should, because it becomes much easier to be a responsible technologist if you have access to the figures about the environmental impact of what you're using AI, for example. So yeah, those are the things I'd share, and I'll add some links to that to follow on from this.

Gaël Duez: I wasn't aware that they were using their own materials, and that, that's a massive change indeed, because no more educated guess, as you said, and the ability to measure yourself. Of course, it means that they create their own assumption when it comes to the hardware setup, but Yeah, I really love the idea.

And I have a question for you because I wasn't able to unpack yet all the publications from Hot Carbon. So how was this article aligned or not aligned with the last one from Sasha Luchoni when she was actually testing? So once again, on the inference side, because we know that this is where most of the impact comes from, when she was categorizing the use of a different LLM

in front of different use case and, she was, for instance raising the alarm that for speech recognition text or other, I would say, basic AI solution, using large LLM was a complete waste. And that's something that really struck me that how important it is to pick the right model for the right task and not using LLM, especially GPT 3 or GPT 4 for everything.

So I was wondering, do you know how well that was aligned or not? Their own findings.

Chris Adams: Okay, so it's been a couple of months since I read the paper, or about one month, because the actual paper was released, I think, on the 11th of July or

something. The key thing was this paper from Verena and Didi, the people at the University HTW Berlin,

and, Green Coding Solutions. Their one, while they do talk a little bit about classification of tasks, they don't go into the same detail that the paper from Sacha Luchoni, which was, that was the one that we saw in MIT Technology Review, which very much said, generating an image has the same kind of energy demand as charging a phone, for example.

So they don't provide that same kind of breakdown because their primary focus was working with text. However, there is something we should share a link to, which is some recent work in April where the, where Sasha Luchoni and other people, other luminaries at Hugging Face spent some time working on what you might refer to as like Energy Star for AI.

This idea that for different tasks, you do have models and so you can start making some more informed choices about your choice of model when you're doing some of this stuff. So we should share links to some of these things because

there is quite a lot of work taking place that I think not everyone is aware of right now and it's a really useful kind of jump off point for this.

So yeah, useful questions Gaël, thank you.

Gaël Duez: It connects well with what you've mentioned, that there's a rating should be provided by the one making these models or operating these models. And it's great to see all those initiatives, but eventually, and I think this is a nice connection to the next stories, but when you operate something, you need to be transparent about the environmental footprint of what you do. 

Chris Adams: So this is actually, you're right, that's a very good point, Gaël, and maybe just the final thing I'll share with this before we move on to the next story is that for training, we've seen pretty much the industry start to settle on the use of CodeCarbon, which is an open source Python project that you can use to essentially work out the energy use for any piece of Python code, but specifically it's used for quantifying the energy usage for training.

So, this is what's used for some of the papers by, from Hugging Face. I believe this was also the tool that was used for the most recent information from Meta when they shared the carbon footprint of Llama 3, their big model as well. So you do have some existing tools that are out there. One thing I've actually tried looking at recently was I realized that you can use these tools and there are tools like GitHub and GitHub Spaces which allow you to run a virtual machine, leave and run, like some inference locally, for example, to try and like test something out, for example. And, what I found was that I was preparing a workshop for, to deliver at DjangoCon in June earlier on this year to help people, like, figure out, okay, what's the environment impact of maybe using an AI service or figure out what some of the kind of service side environmental footprints might be. I found that, there's a competitor to GitHub spaces called Gitpod. They use a slightly more up-to-date version of Ubuntu, which basically means that if you're running a virtual machine inside this, you can actually use Code Carbon and get numbers back. But when I try to use this with GitHub spaces, because they're using a slightly older version of the underlying operating Linux that's used for this, you can't get the same numbers back. And I think this is important or worth being aware of because there's a recent release from GitHub, I think. I'm not sure if it's totally available for everyone yet, but there are now some tools specifically for using inference in GitHub spaces specifically. So it'll be really lovely, and I know I'm kind of nerd sniping the GitHub team here, if they could expose some of these numbers, because the tooling totally exists now, and the bar is so low that even just having something like CodeCarbon, which is useful, but has some, when you look into the details, has a few kind of compromises and few issues. That would still be massively more useful than what we have available to right now. So yeah, there's there's definitely worth, there's definitely tools out there that organizations can use to make it easier for consumers like yourself or me to use these in a more kind of responsible fashion. All right. Thanks, Gaël. Should we look at the next story now?

Gaël Duez: Let's do it.

Chris Adams: All right. The next one. This is a story from a former VP of Sustainability Architecture, I believe, at Amazon, Adrian Cockcroft. he's written a piece for The New Stack and the title is, How did Amazon, Azure and Google perform in 2023 Sustainability? So this is a piece by Adrian Cockcroft where he's basically read through the three sustainability reports. And as someone who actually does have a significant amount of context working on the platform side as well as, since leaving Amazon looking at the tools out there and trying to collate a kind of like useful data set with the Green Software Foundation Real Time Cloud framework, he's been able to say well these things which are easy to use this is where some of the data is helpful this is where there are real challenges and he's it's really useful to get to have someone who, in my view is very much seen as like a kind of real kind of trusted message of saying look,

these bits are okay, this is where the bar is really low and we probably should be expecting quite a bit more given the amount of resources available to people. And yeah, I wanted to just check, is there anything that caught your eye or that really leapt out at you when you were looking at this? I really like this piece and I'm really glad it's actually out in the public domain.

Gaël Duez: Yeah, I love Adrian's work. I love his optimism. I'm not, I would be a bit more cautious is when he, well, especially regarding Amazon, but I guess we're all biased at some point. And what caught my eyes was we discuss a lot numbers that are not that easily assessed and separated. The very big first issue that I had is when we talk about Amazon, it's not the main, the same thing that when we talk about Google or Meta, because, or Microsoft, because there is this big on premise brick and mortar, as we used to say, chunk of Amazon's carbon footprint.

And what is strictly related to AWS should be extracted. And that's not the case with all the numbers.

Chris Adams: You're referring to AWS and

Amazon the retailer, like there's

been separate business, that's what you're talking about here. fact they're not breaking down makes it harder to understand, right?

Gaël Duez: It's, very hard because it's a bit like if we were discussing the, Fusabi report of Google plus Walmart or Sainsbury's or whatever, or Carrefour, and, I'm always very concerned about how globalized the data are.

And I've, got some cave hats with what Adrian said. I won't challenge the numbers or his analysis because I think it was well done and the trend are there. Like AWS, Amazon is doing a bit better and Google and Microsoft are slipping up as the, as he mentioned, but, focusing a bit more on Amazon, for instance, there are a few things that I'm a bit concerned about.

So the first one is that they gather everything in this big Amazon and what actually as techies, we would like to understand better is AWS on its own. I think it's big enough to have its own sustainability report. The second is that they continuously provide numbers on the market-based approach, especially for energy. And I think that there are now countless examples where it's not really how Sustainably is done. Sustainably is as much a global matter than a local matter.

And I'd like just to take the example of Ireland. So if you run as many European techies, your instance on AWS, there are a great deal of chance that by default, you will be using the Ireland region. And when you log in the dashboard or your Sustainably dashboard, exactly as Adrian mentioned, you will see that everything is fine.

You're zero percent, you're carbon neutral, everything has been offsetted, and ciao, bye bye, well done, you can, do business as usual. Now the reality of the Ireland electricity grid is that one year ago, in 2023, the amount of electricity consumed by data center equals residential urban residentials.

It means that every houses, every buildings in Ireland consumes now less, a bit less electricity than data centers. So it has put a tremendous pressure on the electricity grid and the Irish electricity grid is not the cleanest or sorry, the lowest carbon on Earth at all. So, technically speaking, when we add resources, when we add instances on AWS Irish region, we are adding pressure and pushing the Ireland electricity providers to emit, to produce more energy, which is kind of high intensive energy.

And now you've got this market based approach, which has is it's prone. I'm not like, it's not black and white here, but saying, okay, but we invested energy elsewhere. And we show it either by a, power purchasing agreement or AAC. And so that's all good on the market because everything shall be offset.

But the local realities matters and that's even more true for water. But let's put that aside for the moment. So as long as they don't at least try to localize a bit more the carbon emission and the related energy carbon emission. I think it will be always very hard to say, okay, the trend is okay, the trend is not okay.

So that's my first issue. My second issue is that, and I think it was our dear friend from SDIA, Mike Schultz, who once said, one of the most precious resource on earth today is renewable energy. Because of course it's growing, but we don't have that much. And we should always question how much we allocate to which use.

And by having this 100 percent focused on offset or net zero approach, that is the one from Amazon, Google, etc. We cannot leave the elephant in the room, which is, but what are the absolute numbers? And when the absolute number are getting higher and higher, almost from a logarithmic perspective, it's almost exponential, not fully, but almost, we should question ourselves, but where is the limit?

Because we do know that in systemic and in environmental ecology, there are, there is always a limit to how much a system can grow. So, that's my two big issues. It's not localized enough. And it doesn't talk about absolute values. It only talks about the potential of things being offset or being carbon neutral.

And we need to think more about when we slow down or even we reduce our energy consumption. That's not on the table at all. So yes, of course, there are a lot of progress being made. They buy a lot of renewable energy, but is it the best use what we can do about renewable energy? And what are the trends? I know I could speak for ages about it, but sorry, Chris, and I didn't even mention water.

Chris Adams: So just, so let's just check if I understand the key things you're referring to there. So one of them is, just to be clear, we're talking about the Republic of Ireland, as in the island of Ireland here inside Western Europe.

That's we're talking about here. And if I understand what you're referring to here, there is one of the big things, big parts of this story this year is that Amazon has made a big song and dance about saying "yes, we are now 100 percent renewable powered for all of our infrastructure." And what you're, what it sounds like you're saying is that The physical reality in Ireland doesn't necessarily match this claim because it may be that the kind of the way people are substantiating this green claim is that they're basing this on credits like renewable energy credits and while these may be kind of considered kosher or like considered like legitimate in like maybe a trade electricity trading market kind of sense, the fact that we don't see the actual location based figures for these data centers brings up all kinds of questions. And also, the, there is also questions about, are renewable energy credits the correct way to actually basically back up any claims around the use of green energy, particularly when we know that the underlying grid, there may be more power being used than it actually, than renewable energy is actually generated in Ireland itself for this, right?

Gaël Duez: Absolutely. And then to, give them credits, they use less and less, renewable energy credits, which are highly questionable tools, and they use more and more PPA, which are Purchasing Power Agreement, where actually they commit to add new renewable energy via partnership, long term partnership with electricity producer.

So it actually increases the amount of renewable energy available for everyone on the grid. So I'm not saying that everything is bad or everything is great. My question is if you, for instance, just staying within Europe, invest in northern Germany in a wind farm to produce that amount of gigawatt of renewable energy, that's great.

That's necessarily, that's something that is very useful for the German market and for German users, but it will not offset the fact that there are still gas and even, correct me if I'm wrong, coal based port plant in Ireland, and that the use, the rise of energy use in this part of the world will emit more greenhouse gases.

So, once again, it's the incredible ability of humankind to tell itself stories, which has made us what we are today, has also a dark side, which is it's not because we decide that we create a fancy story called the market or the energy market, et cetera, that it is completely disconnected of the physical reality of thing, as you mentioned.

And the physical reality of thing is, it's great to add more renewable energy to the grid every day. Anywhere on earth, because anywhere on earth, we need more renewable energy, but it cannot really offset or compensate the fact that if we put some stress on electricity grid somewhere, it will add the emission of greenhouse gases and, eventually everywhere around the world, because I think it's any like carbon molecule that take 15 days to do a round trip.

So, it's a global challenge.

Chris Adams: If I understand what you're saying, basically, the instruments being used do not fully capture the physical realities of what's taking place. And while there may be progress, we probably need more progress in order to actually face the challenges that are being kind of set out by the actual, the real science that we're seeing. I'll share a couple of little points around Ireland specifically before we move on to this. So Ireland is actually one of the few countries where Green and IT claims around green energy have actually been challenged by the Advertising Standards Agency, specifically saying if you're a green energy firm and you're saying you're using green energy. We've, there have basically been cases where the Advertising Standards Agency in Ireland has said, you can't make these claims in Ireland if you're using just renewable energy credits as the basis for making this claim. So that's one thing we've seen. And that has interesting implications for technology firms that are using these green energies if they're substantiating their kind of claims around green energy by using these certificates.

If you've already had a ruling saying, "nah you're not allowed to do that." The other thing that surprised me, when I was looking into this, because the Renewable Energy 100 is a ranking of the top of a large number of firms who are significant investors in renewable energy. They actually don't accept the use of these kinds of renewable energy credits if they're not physically deliverable.

And one of the challenges you see in Ireland is that there's a limited amount of capacity to move the kind of like green energy that might be generated elsewhere in the world to there for this. So that's one of the challenges that you see. And we'll share links to both of those two things because for people who are kind of wonkish and want to get down to some of the bottom of this, they're really, I think they provide some interesting background to this.

We'll also share a link to the real time, to the Green Software Foundation Real Time Cloud dataset where there's been a bunch of work into trying to find some location based figures for this stuff so you can come up with some more accurate numbers than what we're seeing here. And I think, okay, I'll leave the last word with you, then we'll move on to the next story.

Go for it.

Gaël Duez: There are two things that I'd like to give credits to Adrian in his article. It's like stressing how much there are two sides of the story. And there's reports that focus a lot on sustainably of the cloud. And that's definitely what Amazon, Google, et cetera, are trying to do. But there is also this question of sustainably in the cloud, which is how as a user I can do or not a better job mitigating, reducing my carbon emissions, my water consumption, et cetera.

And he's right to say that not significant, no significant progress has been made on Amazon side and on AWS side story. And they are still infant phase at Google and even at Microsoft when it comes to transparency. And as a CTO, as an software engineer. And when you look at these dashboards and you see that everything is fine, everything is offsetted, you've reached carbon neutrality, it doesn't empower you to do the right things, which is optimizing, reducing your carbon emissions, your water consumption, etc. So that part, empowering consumers is still lagging of what we should expect from these tech behemoths. And my last comment is that I was very pleased that he mentioned and he reviewed, thoroughly the water consumption because for water and that my message about global versus local, it doesn't really matter.

It doesn't really make any sense to analyze the water consumption in terms of global consumption. It's water is a local matter. And it's really region per region, even data center per data centers. How much water comes in? How much water comes out? And in which state? Is it reusable, not reusable? Is it a closed loop or not?

In most of the data centers, including the one from the hyperscalers, are far from a closed loop. I know that Google has experienced once and they told quite a lot about it and it makes total sense. But we need more. precise and localized information on water. And that's a massive challenge as well. We focus a lot on carbon, but water is the next big issue that we need to pay attention to.

Chris Adams: Alright, water, that's the next horizon. I'm going to park that because we'll come back to it a little bit later. The next story is actually from the Financial Times. This is talking about Big Tech's bid to rewrite the rules on net zero. Now, at the time of this going out, it may be that the really nice looking piece may be hard for people to see, but no, the link does seem to work actually still, thankfully. The Financial Times has a really interesting piece, basically talking about the large technology firms that we often see coming up again and again. And this is a bit of a deep dive into some of the things you just referred to about like location based carbon footprints for electricity, because that's one of the key drivers of emissions for our use of digital services, and the market based approaches. And this pretty much dives deeply into something of a bun fight that's taking place where you have two kind of schools of thought where there's one set of companies like, to an extent, Microsoft and Google are pushing for this notion of 24/7 renewable energy and are having a quite kind of tight accounting process. And then you have another approach being largely put forward by Meta and Amazon talking about their kind of emissions first approach saying, no, what we should be looking at is decarbonizing the entire grid, not so much looking at our carbon footprint. And there's a couple of things that are really interesting inside this.

There's a few nice interactive graphics for you to see how people make green claims around energy usage. But one thing that I think is actually really stark is this set of charts showing the difference when you try looking at these figures. So, if you were to look at, say, the carbon footprints from, say, Microsoft, you can see, like, from 2018 to, like, now, you've got a figure of maybe, you see one chart showing the market based footprint, which is, pretty close to zero for Microsoft and close to zero for Meta and likewise for Apple. And then you see the location based figure for Microsoft. It's something in the region of like 8 million tons or zero tons, for example. And likewise with Meta, you're seeing 4 million tons versus zero tons. And Amazon's got the same issue where you're looking at like 15 million tons of location based carbon footprint from using electricity versus 3 million tons from using this.

So you, this really gives an idea of how these two different perspectives end up changing how you might report on this and how you might think about the environmental impact of using some of these tools. And like, to an extent, there is, there are reasons why you have a market based approach because, these come out of the fact that people who are inside large firms are looking for ways to be recognized for the investments they're making so they can justify this internally.

So there is a role that some of these play, but it often, it obviously gets quite a bit more complicated than that, especially because this is the year that the Greenhouse Gas Protocol, the kind of gold standard for reporting, is currently being overhauled to rethink how you should report this stuff and how you should be allowed to talk about energy being green or not green in this context. So Gaël, is there anything that kind of leapt out at you when you had looked through this? Because I would love more people to see this. I think it's a really fascinating story.

Gaël Duez: I think I've already commented it in advance when I was referring to the struggle between market based and local based approach. And once again, I think we should stress how important it is to understand that the way we build things in our mind and in our society as humans is one thing, and the physical reality of the world is another thing.

And when you add energy on a grid, wherever, et cetera, you have no clue on how it will be used, even if it will be used, because when you create PPA, it's potential energy to be used. You create new capacities, whether those capacities will be used or not remains a challenge. Obviously, they will be used, but not necessarily 100%, etc.

And I think the right approach is clean up your own mess. Everyone should start with this. So, I'm fine with having part of the sustainability report explaining what has been done and what could be the approach of market based, but the truth is local based approach. And when you see these figures, they're actually very consistent.

Yes, they're increasing massively their investments in data centers to fuel the AI boom. Their entire business model is based on infinite growth. The numbers go up, that's pretty, pretty logical. And what I've just kind of, when I read this piece of news, it also connected a lot with the crisis at the SBTI, the Science Based Target Initiative, that happened this year, when there was very strong push to allow more offset techniques to be recognized as science based, 

Chris Adams: Ah, you're referring to the Scope 3 thing. The push people being able to use offsets in their supply not just electricity, as a way to kind of decarbonize that without having to necessarily make some of the changes to like reform the supply chain. Is that what you're referring to here?

Gaël Duez: Absolutely. Thanks for making it much clearer than I was about to say. So, I think the struggle is everywhere because we see that the low hanging fruits, most of them has been already taken care of in this big corporation and they're entering the bumpy road where you've got harder choice to do. And when you face this kind of choices, well, either you do the right things and you go back to the physical reality of our world, or you try to change a bit the narrative or change a bit the rules, and I think this is exactly what we've been seeing at the science based target initiative where, some companies were obviously not able at all to meet the decarbonizing plan that they proposed just a few years ago, and they were trying to change a bit the rules.

And that should really question ourselves when it comes to transparency and acknowledge that even the most well intentioned CEO, the most well intentioned C suite, they cannot really do the right things without a bit of external help, whether it comes from pressure from activists or governments or UN, you name it, but we cannot fully rely on companies agreeing on how they should measure their own environmental impact.

Even if they are well meaning with tons of great people trying to do the right things, et cetera. It's not a black and white world out there. But there is a question at some point of financial pressure, shareholder pressures in many of these companies. They're just stronger than the entire stakeholders pressure.

Chris Adams: I think I know what you're referring to here, and what I'll do is we'll share a link to some of the stories about the SBTI, the Science Based Targets Initiative, the whole kind of bun fight that took place there, that ended up resulting in the then CEO stepping down from the organization, partly because there was so much pushback against this idea that you might be able to use offsets for looking at your supply chain emissions is in this same fashion. So we'll definitely add a link to that, thanks for that Gaël. The other thing that we maybe might maybe you must referring to inside this is there are some really nice quotes. Is that what you're talking about here does actually there's some relevance back to the Financial Times piece here, about when you look at the decarbonizing energy in this particular space. One of the approaches being used, which is described as Emissions First, for example, this is the one that's being largely put forward by Meta and Amazon. And one of the arguments for this is that we should be able to be optimized for absolute impact, not necessarily our own carbon footprint.

So given a hundred euros or a hundred dollars, we should be able to deploy that in the most effective place globally, wherever it might be. So one of the arguments being made is that we, if we want a data center in say, North Virginia, we should be allowed to basically purchase the right to claim the emission reductions in somewhere like India, which has a very coal heavy grid, and then kind of count that against our own emissions. And on one level, yes, you are reducing the environmental impact, you can make the argument, but it also means that some of the cheapest possible emissions in India are now being bought by one of the richest, some of the richest companies in the world. And this is very similar to the dynamic you were referring to with offsets, where, essentially, if you have a kind of, climbing scale in terms of how difficult it will be to reduce emissions over time. If you grab all the low hanging fruit, that raises all kinds of equity issues about what's left over for people who don't necessarily have the same resources available to them as Facebook or Meta or Amazon, for example, when looking at this. So, this is one of the challenges you do, find. But I'd really urge people to look over the FT piece and what we can do is we'll share a link to an archive link for this if the one, if it's behind a payroll, because it's a really fascinating piece and it's really worth looking into. Okay, Asim, sorry, Gaël, should we move on to the next story here?

Gaël Duez: Yes. I'm honored that you thought was... I 

Chris Adams: Bit of a Monday morning moment here. Okay, so this is actually a story speaking about AWS again, by moving an AI workload to AWS, you can reduce the carbon footprint by up to 99%. That's the argument being laid out inside this. And this is a piece from The Stack, which is not the same as The New Stack, who covered our previous story.

It's another organization. This one basically takes apart some of these questions, or at least dissects some of these claims and say, well, what's the basis for this? Gaël, I wanted to kind of give you a bit of space to talk about this because I know that you've read this and I know that this is something that is... and you often advise firms who are actually trying to figure out how do I reduce the environmental impact of the services I'm using. So when you read this, what leaps out at you and what kind of things are the kind of most salient points would you say?

Gaël Duez: Well, I think my main reaction was, "oh no, yet another one."

Okay. So there is the SCI piece, which is interesting, but let's go back to basics about it. And I encourage listeners to have a look at two great resources. The first one is an article from the Boavizta Association investigating the claims made by several hyperscalers that when you migrate from your services, from a traditionally hosted perspective to a hyperscaler perspective, you save 60, 70, 80, 90 percent of carbon emissions.

And the second source is actually a man, and I was very pleased to see him being quoted and interviewed in the article, which is, he's Mark Butcher. He has been very vocal about hyperscaler claims, how they measure things, the scope three not being that well taken into consideration. Mark works at Positive Cloud and he works with a lot of clients across the UK on these topics.

And why I'm mentioning this too is that hyperscalers, when they say, basically "move your instances more, move your computing power to our facilities, because we are so much more efficient than the other data centers or the other hosting solutions," they might be right, but the others are a role, a world on their own.

And this is really the question of the baseline. Yes. If I run two or three servers in my office building, in a small room with a very old air conditioning, yes, I might reach a PUE of 2, 2.2, maybe 3, whatever, et cetera. But most of the clients, they don't start with these baselines. They start with servers and instances being managed in already quite professional and by seasoned providers, okay, and I won't provide any name here, who have already reached a decent PUE and the gain is much smaller.

So that's. On, from which baseline do you start?

Chris Adams: I see, like the example given here is like a really inefficient thing. If you compare really an inefficient setup compared

to this idealized, going to give you a disproportionately large saving. That's

Gaël Duez: And this is when you study the case studies, which have been provided by hyperscalers. it's literally my grandma managing a data center. Worst practices possible that are accounted as a baseline. And this is not the truth. Many data centers already reach pretty decent or, pretty good actually,

power efficiency. So where you start from matters. And that's my, point number one, my point number two, and we go all the way back to this local versus market based approach. But I'm sorry to say I'm, I, feel a bit like I'm, rambling here. But once again, if, even if, okay, I run the worst possible data center on Earth, I've got

maybe a PUE of two, maybe three, et cetera, but I run it in a very low carbon place, say France, if you account, for, the nuclear, a share of energy, say Scotland, for instance, and I recall Mark wrote something about it, Mark Butcher wrote something about comparing the energy intensity of North Scotland and Ireland.

And once again, just by migrating your instances there. You might, let's say, divide by two, your energy consumption, thanks to better energy efficiency by AWS, because they're very active in Ireland, but you can do the same math with Google or Azure, but then you start operating in a country which is, and that's the case between, for instance, Northern Scotland and Ireland, six times more energy carbon intensive when it comes to electricity production.

Chris Adams: So let me check if I understand, well, I think with the point you're getting at, you're basically saying, yes, you may, the infrastructure may be more efficient, but if the local energy is dirtier, it doesn't necessarily matter that it's six times more efficient. If, say, the infrastructure is twice as efficient, but the energy is six times worse, then it's still, you're not coming out ahead. That's what I think you seem to be making the argument there

Gaël Duez: I actually, I was actually, I was not the one making the argument. Mark did it and 

Chris Adams: but that's, that's you're saying. Okay. 

Gaël Duez: but that's exactly what I'm saying. And so this question of where do we start from the baseline is super important. Then there is a specificity of the AI itself. And we should always remember that most of the time by AI people, and I think this article is a bit misleading here as well, they think generative AI, which is part of AI on its own.

We start from so high. It's still very infant. The LLM are not that old. And of course you can decrease very significantly the energy consumption and the emissions of your AI model, because we are, we've just started to do so, but what will be the trend for, I would say, everyone rather than AWS is an open question.

Obviously, they're doing a better job reducing and optimizing everything, but it's fair to assume that other actors as well are doing the same. So I'm always very concerned when we take a very new algorithm, I would say, or a new part of the AI industry and say, "Oh, look, we're going to reduce by that, that much."

But of course, it's like with cryptocurrency. They've started at such an inefficient way that they made a great progress. Still, they consume a lot of energy. So you see that's my point. So I will be always super cautious with this kind of stuff. And then comes the good part of it, which is using the, as the software carbon intensity, but maybe Chris, you want to elaborate a bit more on 

Chris Adams: So, it's nice to see an international standard being used in reports like this, but in order for a standard to be used, you need to follow the standard. And one of the key things that the standard actually does is it, basically, when I've looked through this, I look through the report, I can't see any numbers for any of this.

So, it's like you have a car saying, well, this car is twice as efficient as this other car. If you don't have any numbers about, like, the miles per gallon or the equivalent like that, it's very hard to be able to trust that number, for example, or trust any of these claims here. So you have an issue about, well, there's a lack of underlying numbers. Also, the thing we see is that a significant part of this is based around the environmental impact of the energy itself. Again, we don't have the numbers for this, but in particular, one of the key things, one of the key claims being used in the report was we're going to take into account these market based figures here.

Now, the Software Carbon Intensity specs explicitly says we don't use these inside this. So, you've essentially got people using this term, Software Carbon Intensity, without actually following any particular nuances of this. And this, It makes it very difficult for me to recommend this report for anyone else because it essentially is going against how this is intended to be used. And the firm that was working with Amazon, they are very involved inside the Green Software Foundation. This really needs to be a thing that we can't do if we want to see this to be adopted and respected because this essentially, in my view, undermines a huge amount of work that's gone into developing a standard here because this makes me trust the Software Carbon Intensity less after reading this report and seeing people cite it, because it's being used incorrectly. So that's one of the things I would actually raise and something that does need to be addressed. Like, there are mechanisms that the GSF has to say, please do not use it in this way. It's misleading and it undermines some of the work we have. And I think that's something that will need to happen because, yeah, I cannot recommend anyone looking at this report or even recommend using or referring to this standard like this because it's an incorrect use of the standard. So yeah, that's my take on it. 

I'll move on from this because that was a bit of a downer, but it's really important if you want people to trust this in my view.

Gaël Duez: And I think also that there is a way to protect the SCI and the Green Software Foundation tools, which is using the Impact Framework manifest, 

Chris Adams: Yeah, I'm glad you mentioned this, Yeah.

So there is a mechanism. The whole thing around the Impact Framework was specifically set up to say, "hi, you're going to make a claim? Make this transparent. Show you're working inside this." And that's like, there's a huge amount of work that's gone into providing this, and if you're not going to share any of the numbers or share the basis, and there are now lots and lots of really helpful case studies demonstrating how to do this, like we'll share links to this to show this is, these are the correct ways to use this. When you have it being used in a way which is so unhelpful, it's, really problematic, and you can see why people are going to struggle, and why, you can see why people end up essentially dismissing so much of these, efforts as greenwash, when people aren't sharing the underlying numbers for this stuff.

So yeah, that's like the framing I would take, and I would really like to see this addressed, because it's going to be, it's going to be a real challenge going forward, in my view.

Gaël Duez: Transparency, transparency, that's all that matters with this kind of claims.

Chris Adams: So, this probably takes us to the last story I think we have time for. This is one actually, this isn't so much a story, but more of a kind of discussion about some of this. So we've been talking about how it's real, really difficult to actually get some numbers from this, and how the way people report this is also a real challenge.

The thing I kind of want to share with you, and I realize I won't have time to talk about this while I, this is partly what I'll be talking about at some events in September, is the fact that we have, we Some regulation, which is forcing some of this. So in Europe, all across Europe, there is a law called the energy efficiency directive, and basically any data center that uses more than 500 kilowatts of power, which is, that's not a small data.

It's not a tiny data center, but pretty much every single hyperscale you imagine would have to do this. There is now a law, which basically says, every data center and any organization operating a data center has to make publicly available, Information like the name of the data center, who runs it, how large it is, how much power is used. It talks about the amount of energy used, the water used, all this stuff. So we do actually have laws which are kind of forcing some of this now. The, there is one caveat in that where companies consider this information to be a trade secret, they don't necessarily need to publish this information into the public domain, but where companies are not saying this is a trade secret, we now, as technology professionals, can ask and say, this information should be in the public domain if you're in, if you're in Europe. And the thing that I might share as a follow on from this is that for companies that are not sharing this information, they are now mandated to report to a centralized database with the idea that some of this information will be shared in an aggregated format.

So for the first time, we can actually get some meaningful numbers that come out of this. So, companies that are prepared to be transparent, you can ask for this stuff. Companies that are then saying, "we're not going to make this transparent because it's a trade secret." There is still a mechanism by which they will need to report so that we can finally have some data informed policy around this. Because one thing that's come up again and again in this discussion has been that we don't have access to this information. And there is so much pressure or there's so much, there's so many incentives to construct a message which makes you look good that it becomes very difficult to trust a lot of the statements around green software that come from lots and lots of large firms right now.

So yeah, this is, I'll share a link to the issue where this has been discussed inside the real time cloud working group inside the Green Software Foundation. But Gaël, I wanted to just check, as someone who's not in Europe, what's your take on this? Is there anything that kind of caught your eye when you were looking at this?

Gaël Duez: Well, the first thing is that it is required to disclose numbers in both relative, but also absolute numbers. And that's very important. It's not only PUE. It's also how much energy did you consume overall? And that really connects well with what we've discussed before. I also believe that this is something that is

pivotal for country with weakest electricity grid to consider. It's always claimed that for instance, Kenya, my neighboring country of La Reunion island, has a very strong policy of attracting data centers. They want to become a computing power. 

Chris Adams: Iceland of Africa, because have more geothermal than

Gaël Duez: Absolutely. And I reckon that this kind of disclosure will also have them a lot anticipate what is needed for them to prepare the electricity grid to this kind of increase in electricity consumption caused by data centers.

And also making sure that what has been dealt and agreed when they do this big hyperscale deal are actually what is provided, that the energy is there, that the water is there, and I believe that local populations, which are often caught in between, like, "oh, it will create a lot of jobs, but when you do the mass, not that much," so it's not that an obvious investment to say, okay, we will welcome a lot of data centers in our country.

It might be, but it's not like a big investment. A clear win or the case, they will have the ability to scrutinize, how, what are the impacts and environmental impacts. And I must admit that, if you look a bit at the history, in 1982 in Europe, a European directive created the Seveso listing, and the Seveso made it compulsory for every state in Europe to list what are the facilities, industrial, agricultural, mostly industrial, which can create significant environmental risk.

And we were talking about chemical industry, et cetera, et cetera. And to some extent, data centers, they impact a lot of the environment. It's just that they will not blow away like a chemical industry, but on the long run, they've got a lot of impact on their environment and it makes a lot of sense to, at least for the bigger now, the biggest data centers, to be able to provide

environmental information, in a comprehensive way, a comparable way, and to make sure that we monitor the environmental impacts of these big facilities. we're talking about facilities that are built on hectares of land. There's not like the small, tiny warehouse that we might still think of.

They're like massive industrial facilities. So having open and transparent reporting seems to be quite straightforward.

Chris Adams: So maybe we might see some of these ideas adopted in other parts of the world, especially because on the underlying data for this. We'll share a link to this. We've been doing some research ourselves in the Green Web Foundation. A lot of these data points are based on the EU code of conduct, which is a public document for people. So it may be the case that you might see some of these data points being reported in other parts of the world as well to set a precedent. So they actually have the data to make data informed decisions about how, about the role digitalization plays in society and

how the impacts are actually shared around this.

Thank you for the link about Seveso as well. I didn't know about the Seveso directive. That's totally new to me. What I might share is a link to some of the work that the Green Web Foundation has been doing. We have a fellowship and one of our fellows, Samantha Nidwalana, she's based in South Africa and the Netherlands, and she spent a bunch of time looking through, trying to get some numbers in South Africa for, to basically explore okay, what's the environmental impact of data centers inside this?

And we'll share some links to her challenges in this because she's been trying to find these numbers and it's been a real, it's been a real challenge in many cases actually. And it does give you some idea about like where some of this might go, but also hopefully stories like this and seeing some of these laws being passed might help set a precedent so that we can have more transparency in other parts of the world as well. Okay, I realize we're coming up to time. Let's do a quick just run through. We've got some events coming up. I know that I'll be catching a train in a few weeks time to go to London, and I think I'll see you in person for the first time for quite some time actually. This is an event called Green IO Conference. Maybe you could just briefly touch on that before we move on to the next set of events and then close out for the day.

Gaël Duez: Yeah. Well, absolutely. I will be pleased to see you again for the second time. The first time was in Berlin, if I recall, and I think it was for being interviewed on the GreenEye And what. I launched with my partners RPI Days last year, was a series of global conferences called Green IO, not very original, sorry about this.

And the idea was to have also on site events. We can see a lot of hybrid and a lot of online events when it comes to digital sustainability and they're great. We can give kudos to the Green Web Foundation, the Green Software Foundation, CNCF as well. They're doing a lot of these events. Now, what I also realized is that if you look at other specialties in our IT industry, let's take cybersecurity, for instance, accessibility, design, or you name it.

When you work in a city with a significant enough workforce in cybersecurity, for instance, let's say Barcelona, Berlin, Paris, New York, Singapore, Beijing, you have at least 2, 3, 4, sometimes 5 different on site conferences when you know that you will meet your peers. Today, if you are an IT sustainability specialist or green IT folks or whatever, like the dude who anyone reached out to because, "oh, it's about green, et cetera.

He's a person, she's a person we'd like to talk to." You've got basically nowhere to go except for one conference in Paris and one in Belgium now in Brussels this year, where you know that the topic will be a hundred percent your specialty. So don't get me wrong. We can give kudos to a lot of tech conferences like QCon, like even Reinvent AWS, you've got to sustainably track, that's fine, but having a 100 percent green IT focus or IT sustainably focus conference, where you know that basically this is the place to be to meet all your peers, most of the cities around, they don't have it. So that was really the idea of creating this Green IO.

Even since Singapore, London, and Paris this year, we are like 90, 90 percent sure that we will add New York and Munich next year also. And the idea is really, it's just It's a place that has been created for the local communities to do what they want with it. And so you, this year in London, it's a bit like go back to the basics and let's talk step by step.

What about low carbon infrastructure? What about design and eco design? What about beyond, understanding the organizational challenges and HR challenges that when you want to rule out more sustainable approach, but that's a one day conference. It's two days in Paris this year, but in London, it will be one day.

And we expect to have like several hundreds of people joining. And I know also that we will have a great keynote speaker that some of the listeners might known named Chris Adams. So I'm really delighted that you agreed to join Chris. Thanks a lot for this.

Chris Adams: Cool, thanks, so that's the event. It's the 19th of September, taking

place in Bishopsgate in London. And thank you, I am indeed keynoting in the morning, so I hope my trains are on time. But there's a number of people from the Green Software Foundation, for example. I can see Sara Hsu, who is one of the people leading the Green Software patterns project in the Green Software Foundation. There's some people from the W3C on working on web sustainability guidelines. There's also, I'll, share a link to Therese Gale who is working at Salesforce. She'll be talking a little bit about some of the experience, some of her experiences as well. So there's a number of people who

Gaël Duez: Mark Butcher will 

Chris Adams: and Mark, yes, the, yes, Mark Butcher of Positive Cloud.

He's been one of the people who's been really instrumental in some of the most recent work inside the UK government, put together a kind of Digital Sustainability Alliance. There's a bunch of things there. So I'm actually looking forward to this Gaël, and I want to just give people a heads up that it's taking place. Gaël, thanks so much for coming in for this and telling us the stories about Wales, And we're watching and all that sharing all your insights elsewhere. Gaël, if people do want to follow up and see what you're up to, what's the best way to find you for future work?

Gaël Duez: Well I would say on LinkedIn and I'm pretty easy to find Gaël Duez. There is not that many. And more generally, if you are interested in what we do in Green IO, it's greenio.tech. That's the website. You've got access to the podcast, the conferences you've got link to my own websites. But I think greenio.tech is the best place to start.

Chris Adams: Brilliant. Well, thank you for that. We'll share some links to all of those websites and to this event coming up. And, for people who aren't able to go to Green IO, the Cloud Native Computing Foundation, the TAG ENV essentially the green part of the Cloud Native Computing Foundation.

They have a series of remote events taking place in October. We'll show a link to that so that if you still want some, to get an events fixed, that's where to look. Alright, Gaël, thank you once again for making the time and I hope you have a lovely week. Okay. Take care of yourself, mate.

Gaël Duez: Thank you, that was great being there. Bye, have a nice week as well.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode! 



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]]> TWiGS host Chris Adams is joined once again by Gaël Duez to discuss the latest news in green software around AI. They discuss insights from recent reports by Google, Meta, and Amazon, as well as looking at the implementation of the GSF’s Software Carbon Intensity metric. Similarly, the conversation touches on the distribution of renewable energies and the use of different means of measuring carbon in reporting, and how this can affect the behavior of consumers and organizations alike. Tune in for an enlightening discussion on the latest in green software.

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TRANSCRIPT BELOW:

Gaël Duez: We cannot fully rely on companies agreeing on how they should measure their own environmental impact, even if they are well meaning, with tons of great people trying to do the right things, etc. It's not a black and white world out there, but there is a question at some point of financial pressure, shareholder pressures in many of these companies.

They're just stronger than the entire stakeholder's pressure.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Round about this time last year, Gaël Duez, the voice of the Green IO podcast, came on to Environment Variables, This Week in Green Software to talk tech, sustainability, and moving from France to live on La Reunion Island.

And the ups and downs of consulting remotely on digital sustainability from a small island off the coast of East Africa. It was a fun chat. And when I asked him if he'd be up for coming on again to review a few of the latest stories around green software, he basically said, "yes, Chris, but I can't do Friday because I promised my daughter we'd pop out to go whale watching." As one does on a tropical island, I guess. So here we are, recording on a Monday morning instead. Gaël, thanks so much for coming back on. Can I give you a bit of space to introduce yourself and what you're up to these days? Also, how are the whales? 

Gaël Duez: Oh Thanks, Chris. It's great to be back again on Environment Variables. And the whales were there, like several made quite a show, including a mother and her little newborn. Well, little meaning four tons. So it's always impressive to see there's a 20 tons mammals jump in the air like this and cherry on top of the cake, actually, we saw a lot of dolphins and two turtles.

So it was a perfect trip. 

Chris Adams: Wow, I'm jealous. Okay. Yeah? 

Gaël Duez: Actually, to connect a bit more on our sustainability topic, I'm also relieved that the best practices for whale watching are more and more enforced, such as, minimum distance to approach them, turning off the engine when they come in, or direction, et cetera.

And, I also say, on top of the pleasure of, watching them and being on the boat, etc. It's a very positive sign that we can enjoy nature without destroying it. And that's pretty cool.

Chris Adams: Cool. So it sounds like there might be, hopefully, when your daughter's taking, maybe some of her kids for whale watching, there's a chance to continue that, by the sounds of things.

Gaël Duez: Yeah, I'd like to focus on whale and dolphins rather than coral reefs, which are something that really is puzzling me, but yeah, we might, hopefully. And to answer your other question, when I'm not whale watching in Reunion, I try to be useful in the tech community, by advocating for more sustainable ways of designing, coding, hosting, and even considering the use of technology itself. And my main tools remain the Green IO podcast, as you mentioned it, as well as the Green IO conferences, which I started to organize in several cities, but I'm sure we'll get back to this point later. And besides, as you already mentioned, besides as volunteering activities, I do public speaking and consulting on systemic strategy for mostly for tech companies, for both paying the bills, but also to have an impact.

Chris Adams: Cool. Thank you for that. I will also share a link to the previous episode if you're for people who are listening, so they can get an idea of some of the other things that Gaël has been working on and been discussing previously. If you're new to this podcast, I should probably introduce myself as well. As I mentioned, my name is Chris Adams. I am the executive director of the Green Web Foundation, which is a Dutch nonprofit based around reaching a fossil free internet by 2030. And I also work as one of the policy chairs in the green Software Foundation, particularly in the policy working group. Okay, and before we dive into this podcast fully, just a quick reminder, everything we refer to, every story, we'll share in the show notes and there will be a transcript as well for you to kind of search through and look into later. Okay, so as per usual with this week in Green Software, We run through some of the latest stories or projects that have caught our eyes over the last few weeks. And Gaël, I should probably ask you, are you sitting comfortably, ready to go?

Gaël Duez: Perfect. Everything is fine.

Chris Adams: Brilliant. Okay. Let's start with the first story then. So this is the first one that kind of came up on my radar. There's a website called Green Coding AI. And we've spoken about the environmental impact of AI on this show multiple times.

We've also spoken about there is different models that you can actually use to maybe ask the same question and get something back. This one is actually a project from, the Berlin based group, Green Coding Solutions. And what they've actually done is put together a service, running on their own hardware, where you can basically try various, models to prompt them to, like, try out, say, Llama 3, one of the big versions, maybe a smaller version of some of these to see, to ask a question and see what kind of responses get back. And one of the things that's particularly interesting in my point, from my perspective is A, the transparency, but also it gives you a software carbon intensity score for every single inference when you ask a question. So you can start comparing the utility of a large model versus a small model. Gaël, I think you might've had a chance to play with some of this already.

When you looked at it, what, were there any things that sprung to mind for you when you first had a bit of a kind of kick the tires and mess around with it at all?

Gaël Duez: Well, what I was pleasantly surprised with is, as you mentioned, the use of the software carbon intensity. I, as far as I know, this is the first time that I've seen it being used for this kind of tools. I know that what is very interesting is that we can see now, not blossoming yet, but several initiatives have popped up with that kind of approach and I need to shout out and at least I need to give a big kudos to Arne and his team because once again it's very thoroughly done for what they've done in Berlin.

What I also like is the work that has been done with another initiative, and I hope that they will start discussing, with each other, which is, it used to be a data for good, project, but now it's a, an, an association on their own called Gen AI Impact. Yeah. So the association is called Gen AI Impact and they created this tool Ecologits, which is kind of just kind of the same approach.

With a very strong focus on transparency, how they calculate everything and all the code is accessible, et cetera. And there is also obviously Code Carbon by Jürgen Fais, which was kind of the trailblazer, if I recall. And I think it says something positive about the trend in the AI industry, that they need to tool up to Assess more and more precisely what is the carbon impact and hopefully soon the water impact.

And that's still, I know that it's not feasible at the moment because of the lack of transparency around water, but we will discuss water later in this episode. I'm pretty sure regarding the other news that, that we have, but it, is very important. I cannot count how many times I've got potential customers or heads of IT telling me what are the tools we can use to measure the footprint of AI.

And Also to use them to push back a little on the AI hype. everything should be AI'ed. I don't know about it. With a pinch of AI everywhere, but it comes with a cost. Now, going back to your question. So I really love the multiplicity of tools that we see at the moment. And now going back to more specifically in Green Coding AI, I think the way they, try to cover all the angles when it comes to energy, having both the CPU joules, the GPU joules, but also the temperature.

It's very interesting because the temperature, it connects a lot with a discussion I had with Professor Lee. One of the big experts on water cooling, especially in data center in tropical island or in tropical climate, sorry, not island. I'm the one on a tropical island, but there are a lot of tropical climate without tropical island, starting with Virginia in US that we tend to forget.

And he was telling me how important it is to understand that there are thresholds when you use a chip. And there's a threshold that impact massively the energy consumption. So it's not the same to have a chip being used around 25 degrees Celsius, for instance, and 30 degrees Celsius. It's just a linear progression in energy consumption versus, temperature.

And I was really delighted to see the temperature being put as a metrics, because that's where we are getting more and more in details and understanding that it's not that easy, it's not that linear, and we need to investigate things in a more systemic view. And the temperature for many people operating data centers, is absolutely pivotal when it comes to anticipating energy consumption.

So I think, yeah, I was very pleased with this CPU temperature metric that they added. I have no idea how they made to calculate it. So that's something I'm going to investigate, but I like the idea of putting this kind of metrics on the table as well. 

Chris Adams: So, thank you for that, Gaël. So, what I can share with you, there's a chap, one of the chaps working at Green Coding Solutions, Didi Hoffman, was one of the people doing a significant amount of the work on this, and I've used some of their tooling, they've made open source before, and they basically pull some of the temperature figures from the, from basically the CPU itself, I mean from the system that they use, because for example, most machines can tell you how hot or cold they are, for example.

And that's for pulling some of those numbers actually from. The thing that I might share with you that I think is interesting here is, normally, Green Coding Solutions, they tend to open source as much of the work as possible. And they have one of the nicer tools out there for looking at the environmental impact of pretty much any kind of service, for example.

And the thing that So the, I did actually ask Diddy about this and said, "Diddy, this looks really cool. Are you planning on open sourcing?" He says, "yes. The only thing that has stopped me open sourcing it so far is tidying up because it is a bit of a mess." But the thing we can point you to in the meantime is a paper that he worked with, I think Professor Verena Majuntke from HTW Berlin. They submitted a paper specifically for, to Hot Carbon talking about, essentially being able to optimise different models for optimize the use of AI services specifically for particular tasks. And that paper is really interesting in my view, because they say, well, given these particular tasks, some tasks are amenable to running on small models.

And some tasks are better for large models, for example. And they were basically demonstrating how you can pretty much use a system, which looks at the prompt and then will kind of route it to the appropriately sized model to reduce the environmental impact of this. But one of the things they said was that they can look at the energy used when making an inference like, asking for a response back, but it's still a massive challenge to get any numbers from the embodied energy in making some software. And this is one of the key things that we might, maybe wasn't so obvious when we first spoke about this, that right now there are kind of three broad life cycle stages in an AI project. There's a training part where you create a model. There's an inference part where you have the use of the model. And then there's the kind of embodied carbon around this. And this is one of the first projects we've seen that really makes the figures for the inference part quite visible and quite easy to work at, and one of the key differences between this and the Ecologits project that you just mentioned before, was that this is actually running on the software itself, so they have access to the hardware, they're running the hardware themselves, and they, actually document how they do this. The, when I spoke to Samuel Rince at GenAI, I asked him, "how does your thing work?" Because that's really cool, I really would love to see some figures for the inference prices. Well, what we do is we have to make some educated guesses and estimates based on how big a model might be, how much memory might be allocated for it, and how long it's run and how big the response is.

So they're essentially annotating responses that come back from Open AI or from Mistral or from Claude or anything like that to give you some numbers for this. And this kind of does beg the question, if we see green AI instrumenting their own physical hardware, and if we know that the best we can see elsewhere is us having to instrument things ourselves. Based on guesses, why is it such a challenge to find services that provide these numbers as part of how they work? This is still a challenge in 2024. And I think these projects here demonstrate that yes, there is demand and there's also ways to do it. So if these aren't being exposed, they really should, because it becomes much easier to be a responsible technologist if you have access to the figures about the environmental impact of what you're using AI, for example. So yeah, those are the things I'd share, and I'll add some links to that to follow on from this.

Gaël Duez: I wasn't aware that they were using their own materials, and that, that's a massive change indeed, because no more educated guess, as you said, and the ability to measure yourself. Of course, it means that they create their own assumption when it comes to the hardware setup, but Yeah, I really love the idea.

And I have a question for you because I wasn't able to unpack yet all the publications from Hot Carbon. So how was this article aligned or not aligned with the last one from Sasha Luchoni when she was actually testing? So once again, on the inference side, because we know that this is where most of the impact comes from, when she was categorizing the use of a different LLM

in front of different use case and, she was, for instance raising the alarm that for speech recognition text or other, I would say, basic AI solution, using large LLM was a complete waste. And that's something that really struck me that how important it is to pick the right model for the right task and not using LLM, especially GPT 3 or GPT 4 for everything.

So I was wondering, do you know how well that was aligned or not? Their own findings.

Chris Adams: Okay, so it's been a couple of months since I read the paper, or about one month, because the actual paper was released, I think, on the 11th of July or

something. The key thing was this paper from Verena and Didi, the people at the University HTW Berlin,

and, Green Coding Solutions. Their one, while they do talk a little bit about classification of tasks, they don't go into the same detail that the paper from Sacha Luchoni, which was, that was the one that we saw in MIT Technology Review, which very much said, generating an image has the same kind of energy demand as charging a phone, for example.

So they don't provide that same kind of breakdown because their primary focus was working with text. However, there is something we should share a link to, which is some recent work in April where the, where Sasha Luchoni and other people, other luminaries at Hugging Face spent some time working on what you might refer to as like Energy Star for AI.

This idea that for different tasks, you do have models and so you can start making some more informed choices about your choice of model when you're doing some of this stuff. So we should share links to some of these things because

there is quite a lot of work taking place that I think not everyone is aware of right now and it's a really useful kind of jump off point for this.

So yeah, useful questions Gaël, thank you.

Gaël Duez: It connects well with what you've mentioned, that there's a rating should be provided by the one making these models or operating these models. And it's great to see all those initiatives, but eventually, and I think this is a nice connection to the next stories, but when you operate something, you need to be transparent about the environmental footprint of what you do. 

Chris Adams: So this is actually, you're right, that's a very good point, Gaël, and maybe just the final thing I'll share with this before we move on to the next story is that for training, we've seen pretty much the industry start to settle on the use of CodeCarbon, which is an open source Python project that you can use to essentially work out the energy use for any piece of Python code, but specifically it's used for quantifying the energy usage for training.

So, this is what's used for some of the papers by, from Hugging Face. I believe this was also the tool that was used for the most recent information from Meta when they shared the carbon footprint of Llama 3, their big model as well. So you do have some existing tools that are out there. One thing I've actually tried looking at recently was I realized that you can use these tools and there are tools like GitHub and GitHub Spaces which allow you to run a virtual machine, leave and run, like some inference locally, for example, to try and like test something out, for example. And, what I found was that I was preparing a workshop for, to deliver at DjangoCon in June earlier on this year to help people, like, figure out, okay, what's the environment impact of maybe using an AI service or figure out what some of the kind of service side environmental footprints might be. I found that, there's a competitor to GitHub spaces called Gitpod. They use a slightly more up-to-date version of Ubuntu, which basically means that if you're running a virtual machine inside this, you can actually use Code Carbon and get numbers back. But when I try to use this with GitHub spaces, because they're using a slightly older version of the underlying operating Linux that's used for this, you can't get the same numbers back. And I think this is important or worth being aware of because there's a recent release from GitHub, I think. I'm not sure if it's totally available for everyone yet, but there are now some tools specifically for using inference in GitHub spaces specifically. So it'll be really lovely, and I know I'm kind of nerd sniping the GitHub team here, if they could expose some of these numbers, because the tooling totally exists now, and the bar is so low that even just having something like CodeCarbon, which is useful, but has some, when you look into the details, has a few kind of compromises and few issues. That would still be massively more useful than what we have available to right now. So yeah, there's there's definitely worth, there's definitely tools out there that organizations can use to make it easier for consumers like yourself or me to use these in a more kind of responsible fashion. All right. Thanks, Gaël. Should we look at the next story now?

Gaël Duez: Let's do it.

Chris Adams: All right. The next one. This is a story from a former VP of Sustainability Architecture, I believe, at Amazon, Adrian Cockcroft. he's written a piece for The New Stack and the title is, How did Amazon, Azure and Google perform in 2023 Sustainability? So this is a piece by Adrian Cockcroft where he's basically read through the three sustainability reports. And as someone who actually does have a significant amount of context working on the platform side as well as, since leaving Amazon looking at the tools out there and trying to collate a kind of like useful data set with the Green Software Foundation Real Time Cloud framework, he's been able to say well these things which are easy to use this is where some of the data is helpful this is where there are real challenges and he's it's really useful to get to have someone who, in my view is very much seen as like a kind of real kind of trusted message of saying look,

these bits are okay, this is where the bar is really low and we probably should be expecting quite a bit more given the amount of resources available to people. And yeah, I wanted to just check, is there anything that caught your eye or that really leapt out at you when you were looking at this? I really like this piece and I'm really glad it's actually out in the public domain.

Gaël Duez: Yeah, I love Adrian's work. I love his optimism. I'm not, I would be a bit more cautious is when he, well, especially regarding Amazon, but I guess we're all biased at some point. And what caught my eyes was we discuss a lot numbers that are not that easily assessed and separated. The very big first issue that I had is when we talk about Amazon, it's not the main, the same thing that when we talk about Google or Meta, because, or Microsoft, because there is this big on premise brick and mortar, as we used to say, chunk of Amazon's carbon footprint.

And what is strictly related to AWS should be extracted. And that's not the case with all the numbers.

Chris Adams: You're referring to AWS and

Amazon the retailer, like there's

been separate business, that's what you're talking about here. fact they're not breaking down makes it harder to understand, right?

Gaël Duez: It's, very hard because it's a bit like if we were discussing the, Fusabi report of Google plus Walmart or Sainsbury's or whatever, or Carrefour, and, I'm always very concerned about how globalized the data are.

And I've, got some cave hats with what Adrian said. I won't challenge the numbers or his analysis because I think it was well done and the trend are there. Like AWS, Amazon is doing a bit better and Google and Microsoft are slipping up as the, as he mentioned, but, focusing a bit more on Amazon, for instance, there are a few things that I'm a bit concerned about.

So the first one is that they gather everything in this big Amazon and what actually as techies, we would like to understand better is AWS on its own. I think it's big enough to have its own sustainability report. The second is that they continuously provide numbers on the market-based approach, especially for energy. And I think that there are now countless examples where it's not really how Sustainably is done. Sustainably is as much a global matter than a local matter.

And I'd like just to take the example of Ireland. So if you run as many European techies, your instance on AWS, there are a great deal of chance that by default, you will be using the Ireland region. And when you log in the dashboard or your Sustainably dashboard, exactly as Adrian mentioned, you will see that everything is fine.

You're zero percent, you're carbon neutral, everything has been offsetted, and ciao, bye bye, well done, you can, do business as usual. Now the reality of the Ireland electricity grid is that one year ago, in 2023, the amount of electricity consumed by data center equals residential urban residentials.

It means that every houses, every buildings in Ireland consumes now less, a bit less electricity than data centers. So it has put a tremendous pressure on the electricity grid and the Irish electricity grid is not the cleanest or sorry, the lowest carbon on Earth at all. So, technically speaking, when we add resources, when we add instances on AWS Irish region, we are adding pressure and pushing the Ireland electricity providers to emit, to produce more energy, which is kind of high intensive energy.

And now you've got this market based approach, which has is it's prone. I'm not like, it's not black and white here, but saying, okay, but we invested energy elsewhere. And we show it either by a, power purchasing agreement or AAC. And so that's all good on the market because everything shall be offset.

But the local realities matters and that's even more true for water. But let's put that aside for the moment. So as long as they don't at least try to localize a bit more the carbon emission and the related energy carbon emission. I think it will be always very hard to say, okay, the trend is okay, the trend is not okay.

So that's my first issue. My second issue is that, and I think it was our dear friend from SDIA, Mike Schultz, who once said, one of the most precious resource on earth today is renewable energy. Because of course it's growing, but we don't have that much. And we should always question how much we allocate to which use.

And by having this 100 percent focused on offset or net zero approach, that is the one from Amazon, Google, etc. We cannot leave the elephant in the room, which is, but what are the absolute numbers? And when the absolute number are getting higher and higher, almost from a logarithmic perspective, it's almost exponential, not fully, but almost, we should question ourselves, but where is the limit?

Because we do know that in systemic and in environmental ecology, there are, there is always a limit to how much a system can grow. So, that's my two big issues. It's not localized enough. And it doesn't talk about absolute values. It only talks about the potential of things being offset or being carbon neutral.

And we need to think more about when we slow down or even we reduce our energy consumption. That's not on the table at all. So yes, of course, there are a lot of progress being made. They buy a lot of renewable energy, but is it the best use what we can do about renewable energy? And what are the trends? I know I could speak for ages about it, but sorry, Chris, and I didn't even mention water.

Chris Adams: So just, so let's just check if I understand the key things you're referring to there. So one of them is, just to be clear, we're talking about the Republic of Ireland, as in the island of Ireland here inside Western Europe.

That's we're talking about here. And if I understand what you're referring to here, there is one of the big things, big parts of this story this year is that Amazon has made a big song and dance about saying "yes, we are now 100 percent renewable powered for all of our infrastructure." And what you're, what it sounds like you're saying is that The physical reality in Ireland doesn't necessarily match this claim because it may be that the kind of the way people are substantiating this green claim is that they're basing this on credits like renewable energy credits and while these may be kind of considered kosher or like considered like legitimate in like maybe a trade electricity trading market kind of sense, the fact that we don't see the actual location based figures for these data centers brings up all kinds of questions. And also, the, there is also questions about, are renewable energy credits the correct way to actually basically back up any claims around the use of green energy, particularly when we know that the underlying grid, there may be more power being used than it actually, than renewable energy is actually generated in Ireland itself for this, right?

Gaël Duez: Absolutely. And then to, give them credits, they use less and less, renewable energy credits, which are highly questionable tools, and they use more and more PPA, which are Purchasing Power Agreement, where actually they commit to add new renewable energy via partnership, long term partnership with electricity producer.

So it actually increases the amount of renewable energy available for everyone on the grid. So I'm not saying that everything is bad or everything is great. My question is if you, for instance, just staying within Europe, invest in northern Germany in a wind farm to produce that amount of gigawatt of renewable energy, that's great.

That's necessarily, that's something that is very useful for the German market and for German users, but it will not offset the fact that there are still gas and even, correct me if I'm wrong, coal based port plant in Ireland, and that the use, the rise of energy use in this part of the world will emit more greenhouse gases.

So, once again, it's the incredible ability of humankind to tell itself stories, which has made us what we are today, has also a dark side, which is it's not because we decide that we create a fancy story called the market or the energy market, et cetera, that it is completely disconnected of the physical reality of thing, as you mentioned.

And the physical reality of thing is, it's great to add more renewable energy to the grid every day. Anywhere on earth, because anywhere on earth, we need more renewable energy, but it cannot really offset or compensate the fact that if we put some stress on electricity grid somewhere, it will add the emission of greenhouse gases and, eventually everywhere around the world, because I think it's any like carbon molecule that take 15 days to do a round trip.

So, it's a global challenge.

Chris Adams: If I understand what you're saying, basically, the instruments being used do not fully capture the physical realities of what's taking place. And while there may be progress, we probably need more progress in order to actually face the challenges that are being kind of set out by the actual, the real science that we're seeing. I'll share a couple of little points around Ireland specifically before we move on to this. So Ireland is actually one of the few countries where Green and IT claims around green energy have actually been challenged by the Advertising Standards Agency, specifically saying if you're a green energy firm and you're saying you're using green energy. We've, there have basically been cases where the Advertising Standards Agency in Ireland has said, you can't make these claims in Ireland if you're using just renewable energy credits as the basis for making this claim. So that's one thing we've seen. And that has interesting implications for technology firms that are using these green energies if they're substantiating their kind of claims around green energy by using these certificates.

If you've already had a ruling saying, "nah you're not allowed to do that." The other thing that surprised me, when I was looking into this, because the Renewable Energy 100 is a ranking of the top of a large number of firms who are significant investors in renewable energy. They actually don't accept the use of these kinds of renewable energy credits if they're not physically deliverable.

And one of the challenges you see in Ireland is that there's a limited amount of capacity to move the kind of like green energy that might be generated elsewhere in the world to there for this. So that's one of the challenges that you see. And we'll share links to both of those two things because for people who are kind of wonkish and want to get down to some of the bottom of this, they're really, I think they provide some interesting background to this.

We'll also share a link to the real time, to the Green Software Foundation Real Time Cloud dataset where there's been a bunch of work into trying to find some location based figures for this stuff so you can come up with some more accurate numbers than what we're seeing here. And I think, okay, I'll leave the last word with you, then we'll move on to the next story.

Go for it.

Gaël Duez: There are two things that I'd like to give credits to Adrian in his article. It's like stressing how much there are two sides of the story. And there's reports that focus a lot on sustainably of the cloud. And that's definitely what Amazon, Google, et cetera, are trying to do. But there is also this question of sustainably in the cloud, which is how as a user I can do or not a better job mitigating, reducing my carbon emissions, my water consumption, et cetera.

And he's right to say that not significant, no significant progress has been made on Amazon side and on AWS side story. And they are still infant phase at Google and even at Microsoft when it comes to transparency. And as a CTO, as an software engineer. And when you look at these dashboards and you see that everything is fine, everything is offsetted, you've reached carbon neutrality, it doesn't empower you to do the right things, which is optimizing, reducing your carbon emissions, your water consumption, etc. So that part, empowering consumers is still lagging of what we should expect from these tech behemoths. And my last comment is that I was very pleased that he mentioned and he reviewed, thoroughly the water consumption because for water and that my message about global versus local, it doesn't really matter.

It doesn't really make any sense to analyze the water consumption in terms of global consumption. It's water is a local matter. And it's really region per region, even data center per data centers. How much water comes in? How much water comes out? And in which state? Is it reusable, not reusable? Is it a closed loop or not?

In most of the data centers, including the one from the hyperscalers, are far from a closed loop. I know that Google has experienced once and they told quite a lot about it and it makes total sense. But we need more. precise and localized information on water. And that's a massive challenge as well. We focus a lot on carbon, but water is the next big issue that we need to pay attention to.

Chris Adams: Alright, water, that's the next horizon. I'm going to park that because we'll come back to it a little bit later. The next story is actually from the Financial Times. This is talking about Big Tech's bid to rewrite the rules on net zero. Now, at the time of this going out, it may be that the really nice looking piece may be hard for people to see, but no, the link does seem to work actually still, thankfully. The Financial Times has a really interesting piece, basically talking about the large technology firms that we often see coming up again and again. And this is a bit of a deep dive into some of the things you just referred to about like location based carbon footprints for electricity, because that's one of the key drivers of emissions for our use of digital services, and the market based approaches. And this pretty much dives deeply into something of a bun fight that's taking place where you have two kind of schools of thought where there's one set of companies like, to an extent, Microsoft and Google are pushing for this notion of 24/7 renewable energy and are having a quite kind of tight accounting process. And then you have another approach being largely put forward by Meta and Amazon talking about their kind of emissions first approach saying, no, what we should be looking at is decarbonizing the entire grid, not so much looking at our carbon footprint. And there's a couple of things that are really interesting inside this.

There's a few nice interactive graphics for you to see how people make green claims around energy usage. But one thing that I think is actually really stark is this set of charts showing the difference when you try looking at these figures. So, if you were to look at, say, the carbon footprints from, say, Microsoft, you can see, like, from 2018 to, like, now, you've got a figure of maybe, you see one chart showing the market based footprint, which is, pretty close to zero for Microsoft and close to zero for Meta and likewise for Apple. And then you see the location based figure for Microsoft. It's something in the region of like 8 million tons or zero tons, for example. And likewise with Meta, you're seeing 4 million tons versus zero tons. And Amazon's got the same issue where you're looking at like 15 million tons of location based carbon footprint from using electricity versus 3 million tons from using this.

So you, this really gives an idea of how these two different perspectives end up changing how you might report on this and how you might think about the environmental impact of using some of these tools. And like, to an extent, there is, there are reasons why you have a market based approach because, these come out of the fact that people who are inside large firms are looking for ways to be recognized for the investments they're making so they can justify this internally.

So there is a role that some of these play, but it often, it obviously gets quite a bit more complicated than that, especially because this is the year that the Greenhouse Gas Protocol, the kind of gold standard for reporting, is currently being overhauled to rethink how you should report this stuff and how you should be allowed to talk about energy being green or not green in this context. So Gaël, is there anything that kind of leapt out at you when you had looked through this? Because I would love more people to see this. I think it's a really fascinating story.

Gaël Duez: I think I've already commented it in advance when I was referring to the struggle between market based and local based approach. And once again, I think we should stress how important it is to understand that the way we build things in our mind and in our society as humans is one thing, and the physical reality of the world is another thing.

And when you add energy on a grid, wherever, et cetera, you have no clue on how it will be used, even if it will be used, because when you create PPA, it's potential energy to be used. You create new capacities, whether those capacities will be used or not remains a challenge. Obviously, they will be used, but not necessarily 100%, etc.

And I think the right approach is clean up your own mess. Everyone should start with this. So, I'm fine with having part of the sustainability report explaining what has been done and what could be the approach of market based, but the truth is local based approach. And when you see these figures, they're actually very consistent.

Yes, they're increasing massively their investments in data centers to fuel the AI boom. Their entire business model is based on infinite growth. The numbers go up, that's pretty, pretty logical. And what I've just kind of, when I read this piece of news, it also connected a lot with the crisis at the SBTI, the Science Based Target Initiative, that happened this year, when there was very strong push to allow more offset techniques to be recognized as science based, 

Chris Adams: Ah, you're referring to the Scope 3 thing. The push people being able to use offsets in their supply not just electricity, as a way to kind of decarbonize that without having to necessarily make some of the changes to like reform the supply chain. Is that what you're referring to here?

Gaël Duez: Absolutely. Thanks for making it much clearer than I was about to say. So, I think the struggle is everywhere because we see that the low hanging fruits, most of them has been already taken care of in this big corporation and they're entering the bumpy road where you've got harder choice to do. And when you face this kind of choices, well, either you do the right things and you go back to the physical reality of our world, or you try to change a bit the narrative or change a bit the rules, and I think this is exactly what we've been seeing at the science based target initiative where, some companies were obviously not able at all to meet the decarbonizing plan that they proposed just a few years ago, and they were trying to change a bit the rules.

And that should really question ourselves when it comes to transparency and acknowledge that even the most well intentioned CEO, the most well intentioned C suite, they cannot really do the right things without a bit of external help, whether it comes from pressure from activists or governments or UN, you name it, but we cannot fully rely on companies agreeing on how they should measure their own environmental impact.

Even if they are well meaning with tons of great people trying to do the right things, et cetera. It's not a black and white world out there. But there is a question at some point of financial pressure, shareholder pressures in many of these companies. They're just stronger than the entire stakeholders pressure.

Chris Adams: I think I know what you're referring to here, and what I'll do is we'll share a link to some of the stories about the SBTI, the Science Based Targets Initiative, the whole kind of bun fight that took place there, that ended up resulting in the then CEO stepping down from the organization, partly because there was so much pushback against this idea that you might be able to use offsets for looking at your supply chain emissions is in this same fashion. So we'll definitely add a link to that, thanks for that Gaël. The other thing that we maybe might maybe you must referring to inside this is there are some really nice quotes. Is that what you're talking about here does actually there's some relevance back to the Financial Times piece here, about when you look at the decarbonizing energy in this particular space. One of the approaches being used, which is described as Emissions First, for example, this is the one that's being largely put forward by Meta and Amazon. And one of the arguments for this is that we should be able to be optimized for absolute impact, not necessarily our own carbon footprint.

So given a hundred euros or a hundred dollars, we should be able to deploy that in the most effective place globally, wherever it might be. So one of the arguments being made is that we, if we want a data center in say, North Virginia, we should be allowed to basically purchase the right to claim the emission reductions in somewhere like India, which has a very coal heavy grid, and then kind of count that against our own emissions. And on one level, yes, you are reducing the environmental impact, you can make the argument, but it also means that some of the cheapest possible emissions in India are now being bought by one of the richest, some of the richest companies in the world. And this is very similar to the dynamic you were referring to with offsets, where, essentially, if you have a kind of, climbing scale in terms of how difficult it will be to reduce emissions over time. If you grab all the low hanging fruit, that raises all kinds of equity issues about what's left over for people who don't necessarily have the same resources available to them as Facebook or Meta or Amazon, for example, when looking at this. So, this is one of the challenges you do, find. But I'd really urge people to look over the FT piece and what we can do is we'll share a link to an archive link for this if the one, if it's behind a payroll, because it's a really fascinating piece and it's really worth looking into. Okay, Asim, sorry, Gaël, should we move on to the next story here?

Gaël Duez: Yes. I'm honored that you thought was... I 

Chris Adams: Bit of a Monday morning moment here. Okay, so this is actually a story speaking about AWS again, by moving an AI workload to AWS, you can reduce the carbon footprint by up to 99%. That's the argument being laid out inside this. And this is a piece from The Stack, which is not the same as The New Stack, who covered our previous story.

It's another organization. This one basically takes apart some of these questions, or at least dissects some of these claims and say, well, what's the basis for this? Gaël, I wanted to kind of give you a bit of space to talk about this because I know that you've read this and I know that this is something that is... and you often advise firms who are actually trying to figure out how do I reduce the environmental impact of the services I'm using. So when you read this, what leaps out at you and what kind of things are the kind of most salient points would you say?

Gaël Duez: Well, I think my main reaction was, "oh no, yet another one."

Okay. So there is the SCI piece, which is interesting, but let's go back to basics about it. And I encourage listeners to have a look at two great resources. The first one is an article from the Boavizta Association investigating the claims made by several hyperscalers that when you migrate from your services, from a traditionally hosted perspective to a hyperscaler perspective, you save 60, 70, 80, 90 percent of carbon emissions.

And the second source is actually a man, and I was very pleased to see him being quoted and interviewed in the article, which is, he's Mark Butcher. He has been very vocal about hyperscaler claims, how they measure things, the scope three not being that well taken into consideration. Mark works at Positive Cloud and he works with a lot of clients across the UK on these topics.

And why I'm mentioning this too is that hyperscalers, when they say, basically "move your instances more, move your computing power to our facilities, because we are so much more efficient than the other data centers or the other hosting solutions," they might be right, but the others are a role, a world on their own.

And this is really the question of the baseline. Yes. If I run two or three servers in my office building, in a small room with a very old air conditioning, yes, I might reach a PUE of 2, 2.2, maybe 3, whatever, et cetera. But most of the clients, they don't start with these baselines. They start with servers and instances being managed in already quite professional and by seasoned providers, okay, and I won't provide any name here, who have already reached a decent PUE and the gain is much smaller.

So that's. On, from which baseline do you start?

Chris Adams: I see, like the example given here is like a really inefficient thing. If you compare really an inefficient setup compared

to this idealized, going to give you a disproportionately large saving. That's

Gaël Duez: And this is when you study the case studies, which have been provided by hyperscalers. it's literally my grandma managing a data center. Worst practices possible that are accounted as a baseline. And this is not the truth. Many data centers already reach pretty decent or, pretty good actually,

power efficiency. So where you start from matters. And that's my, point number one, my point number two, and we go all the way back to this local versus market based approach. But I'm sorry to say I'm, I, feel a bit like I'm, rambling here. But once again, if, even if, okay, I run the worst possible data center on Earth, I've got

maybe a PUE of two, maybe three, et cetera, but I run it in a very low carbon place, say France, if you account, for, the nuclear, a share of energy, say Scotland, for instance, and I recall Mark wrote something about it, Mark Butcher wrote something about comparing the energy intensity of North Scotland and Ireland.

And once again, just by migrating your instances there. You might, let's say, divide by two, your energy consumption, thanks to better energy efficiency by AWS, because they're very active in Ireland, but you can do the same math with Google or Azure, but then you start operating in a country which is, and that's the case between, for instance, Northern Scotland and Ireland, six times more energy carbon intensive when it comes to electricity production.

Chris Adams: So let me check if I understand, well, I think with the point you're getting at, you're basically saying, yes, you may, the infrastructure may be more efficient, but if the local energy is dirtier, it doesn't necessarily matter that it's six times more efficient. If, say, the infrastructure is twice as efficient, but the energy is six times worse, then it's still, you're not coming out ahead. That's what I think you seem to be making the argument there

Gaël Duez: I actually, I was actually, I was not the one making the argument. Mark did it and 

Chris Adams: but that's, that's you're saying. Okay. 

Gaël Duez: but that's exactly what I'm saying. And so this question of where do we start from the baseline is super important. Then there is a specificity of the AI itself. And we should always remember that most of the time by AI people, and I think this article is a bit misleading here as well, they think generative AI, which is part of AI on its own.

We start from so high. It's still very infant. The LLM are not that old. And of course you can decrease very significantly the energy consumption and the emissions of your AI model, because we are, we've just started to do so, but what will be the trend for, I would say, everyone rather than AWS is an open question.

Obviously, they're doing a better job reducing and optimizing everything, but it's fair to assume that other actors as well are doing the same. So I'm always very concerned when we take a very new algorithm, I would say, or a new part of the AI industry and say, "Oh, look, we're going to reduce by that, that much."

But of course, it's like with cryptocurrency. They've started at such an inefficient way that they made a great progress. Still, they consume a lot of energy. So you see that's my point. So I will be always super cautious with this kind of stuff. And then comes the good part of it, which is using the, as the software carbon intensity, but maybe Chris, you want to elaborate a bit more on 

Chris Adams: So, it's nice to see an international standard being used in reports like this, but in order for a standard to be used, you need to follow the standard. And one of the key things that the standard actually does is it, basically, when I've looked through this, I look through the report, I can't see any numbers for any of this.

So, it's like you have a car saying, well, this car is twice as efficient as this other car. If you don't have any numbers about, like, the miles per gallon or the equivalent like that, it's very hard to be able to trust that number, for example, or trust any of these claims here. So you have an issue about, well, there's a lack of underlying numbers. Also, the thing we see is that a significant part of this is based around the environmental impact of the energy itself. Again, we don't have the numbers for this, but in particular, one of the key things, one of the key claims being used in the report was we're going to take into account these market based figures here.

Now, the Software Carbon Intensity specs explicitly says we don't use these inside this. So, you've essentially got people using this term, Software Carbon Intensity, without actually following any particular nuances of this. And this, It makes it very difficult for me to recommend this report for anyone else because it essentially is going against how this is intended to be used. And the firm that was working with Amazon, they are very involved inside the Green Software Foundation. This really needs to be a thing that we can't do if we want to see this to be adopted and respected because this essentially, in my view, undermines a huge amount of work that's gone into developing a standard here because this makes me trust the Software Carbon Intensity less after reading this report and seeing people cite it, because it's being used incorrectly. So that's one of the things I would actually raise and something that does need to be addressed. Like, there are mechanisms that the GSF has to say, please do not use it in this way. It's misleading and it undermines some of the work we have. And I think that's something that will need to happen because, yeah, I cannot recommend anyone looking at this report or even recommend using or referring to this standard like this because it's an incorrect use of the standard. So yeah, that's my take on it. 

I'll move on from this because that was a bit of a downer, but it's really important if you want people to trust this in my view.

Gaël Duez: And I think also that there is a way to protect the SCI and the Green Software Foundation tools, which is using the Impact Framework manifest, 

Chris Adams: Yeah, I'm glad you mentioned this, Yeah.

So there is a mechanism. The whole thing around the Impact Framework was specifically set up to say, "hi, you're going to make a claim? Make this transparent. Show you're working inside this." And that's like, there's a huge amount of work that's gone into providing this, and if you're not going to share any of the numbers or share the basis, and there are now lots and lots of really helpful case studies demonstrating how to do this, like we'll share links to this to show this is, these are the correct ways to use this. When you have it being used in a way which is so unhelpful, it's, really problematic, and you can see why people are going to struggle, and why, you can see why people end up essentially dismissing so much of these, efforts as greenwash, when people aren't sharing the underlying numbers for this stuff.

So yeah, that's like the framing I would take, and I would really like to see this addressed, because it's going to be, it's going to be a real challenge going forward, in my view.

Gaël Duez: Transparency, transparency, that's all that matters with this kind of claims.

Chris Adams: So, this probably takes us to the last story I think we have time for. This is one actually, this isn't so much a story, but more of a kind of discussion about some of this. So we've been talking about how it's real, really difficult to actually get some numbers from this, and how the way people report this is also a real challenge.

The thing I kind of want to share with you, and I realize I won't have time to talk about this while I, this is partly what I'll be talking about at some events in September, is the fact that we have, we Some regulation, which is forcing some of this. So in Europe, all across Europe, there is a law called the energy efficiency directive, and basically any data center that uses more than 500 kilowatts of power, which is, that's not a small data.

It's not a tiny data center, but pretty much every single hyperscale you imagine would have to do this. There is now a law, which basically says, every data center and any organization operating a data center has to make publicly available, Information like the name of the data center, who runs it, how large it is, how much power is used. It talks about the amount of energy used, the water used, all this stuff. So we do actually have laws which are kind of forcing some of this now. The, there is one caveat in that where companies consider this information to be a trade secret, they don't necessarily need to publish this information into the public domain, but where companies are not saying this is a trade secret, we now, as technology professionals, can ask and say, this information should be in the public domain if you're in, if you're in Europe. And the thing that I might share as a follow on from this is that for companies that are not sharing this information, they are now mandated to report to a centralized database with the idea that some of this information will be shared in an aggregated format.

So for the first time, we can actually get some meaningful numbers that come out of this. So, companies that are prepared to be transparent, you can ask for this stuff. Companies that are then saying, "we're not going to make this transparent because it's a trade secret." There is still a mechanism by which they will need to report so that we can finally have some data informed policy around this. Because one thing that's come up again and again in this discussion has been that we don't have access to this information. And there is so much pressure or there's so much, there's so many incentives to construct a message which makes you look good that it becomes very difficult to trust a lot of the statements around green software that come from lots and lots of large firms right now.

So yeah, this is, I'll share a link to the issue where this has been discussed inside the real time cloud working group inside the Green Software Foundation. But Gaël, I wanted to just check, as someone who's not in Europe, what's your take on this? Is there anything that kind of caught your eye when you were looking at this?

Gaël Duez: Well, the first thing is that it is required to disclose numbers in both relative, but also absolute numbers. And that's very important. It's not only PUE. It's also how much energy did you consume overall? And that really connects well with what we've discussed before. I also believe that this is something that is

pivotal for country with weakest electricity grid to consider. It's always claimed that for instance, Kenya, my neighboring country of La Reunion island, has a very strong policy of attracting data centers. They want to become a computing power. 

Chris Adams: Iceland of Africa, because have more geothermal than

Gaël Duez: Absolutely. And I reckon that this kind of disclosure will also have them a lot anticipate what is needed for them to prepare the electricity grid to this kind of increase in electricity consumption caused by data centers.

And also making sure that what has been dealt and agreed when they do this big hyperscale deal are actually what is provided, that the energy is there, that the water is there, and I believe that local populations, which are often caught in between, like, "oh, it will create a lot of jobs, but when you do the mass, not that much," so it's not that an obvious investment to say, okay, we will welcome a lot of data centers in our country.

It might be, but it's not like a big investment. A clear win or the case, they will have the ability to scrutinize, how, what are the impacts and environmental impacts. And I must admit that, if you look a bit at the history, in 1982 in Europe, a European directive created the Seveso listing, and the Seveso made it compulsory for every state in Europe to list what are the facilities, industrial, agricultural, mostly industrial, which can create significant environmental risk.

And we were talking about chemical industry, et cetera, et cetera. And to some extent, data centers, they impact a lot of the environment. It's just that they will not blow away like a chemical industry, but on the long run, they've got a lot of impact on their environment and it makes a lot of sense to, at least for the bigger now, the biggest data centers, to be able to provide

environmental information, in a comprehensive way, a comparable way, and to make sure that we monitor the environmental impacts of these big facilities. we're talking about facilities that are built on hectares of land. There's not like the small, tiny warehouse that we might still think of.

They're like massive industrial facilities. So having open and transparent reporting seems to be quite straightforward.

Chris Adams: So maybe we might see some of these ideas adopted in other parts of the world, especially because on the underlying data for this. We'll share a link to this. We've been doing some research ourselves in the Green Web Foundation. A lot of these data points are based on the EU code of conduct, which is a public document for people. So it may be the case that you might see some of these data points being reported in other parts of the world as well to set a precedent. So they actually have the data to make data informed decisions about how, about the role digitalization plays in society and

how the impacts are actually shared around this.

Thank you for the link about Seveso as well. I didn't know about the Seveso directive. That's totally new to me. What I might share is a link to some of the work that the Green Web Foundation has been doing. We have a fellowship and one of our fellows, Samantha Nidwalana, she's based in South Africa and the Netherlands, and she spent a bunch of time looking through, trying to get some numbers in South Africa for, to basically explore okay, what's the environmental impact of data centers inside this?

And we'll share some links to her challenges in this because she's been trying to find these numbers and it's been a real, it's been a real challenge in many cases actually. And it does give you some idea about like where some of this might go, but also hopefully stories like this and seeing some of these laws being passed might help set a precedent so that we can have more transparency in other parts of the world as well. Okay, I realize we're coming up to time. Let's do a quick just run through. We've got some events coming up. I know that I'll be catching a train in a few weeks time to go to London, and I think I'll see you in person for the first time for quite some time actually. This is an event called Green IO Conference. Maybe you could just briefly touch on that before we move on to the next set of events and then close out for the day.

Gaël Duez: Yeah. Well, absolutely. I will be pleased to see you again for the second time. The first time was in Berlin, if I recall, and I think it was for being interviewed on the GreenEye And what. I launched with my partners RPI Days last year, was a series of global conferences called Green IO, not very original, sorry about this.

And the idea was to have also on site events. We can see a lot of hybrid and a lot of online events when it comes to digital sustainability and they're great. We can give kudos to the Green Web Foundation, the Green Software Foundation, CNCF as well. They're doing a lot of these events. Now, what I also realized is that if you look at other specialties in our IT industry, let's take cybersecurity, for instance, accessibility, design, or you name it.

When you work in a city with a significant enough workforce in cybersecurity, for instance, let's say Barcelona, Berlin, Paris, New York, Singapore, Beijing, you have at least 2, 3, 4, sometimes 5 different on site conferences when you know that you will meet your peers. Today, if you are an IT sustainability specialist or green IT folks or whatever, like the dude who anyone reached out to because, "oh, it's about green, et cetera.

He's a person, she's a person we'd like to talk to." You've got basically nowhere to go except for one conference in Paris and one in Belgium now in Brussels this year, where you know that the topic will be a hundred percent your specialty. So don't get me wrong. We can give kudos to a lot of tech conferences like QCon, like even Reinvent AWS, you've got to sustainably track, that's fine, but having a 100 percent green IT focus or IT sustainably focus conference, where you know that basically this is the place to be to meet all your peers, most of the cities around, they don't have it. So that was really the idea of creating this Green IO.

Even since Singapore, London, and Paris this year, we are like 90, 90 percent sure that we will add New York and Munich next year also. And the idea is really, it's just It's a place that has been created for the local communities to do what they want with it. And so you, this year in London, it's a bit like go back to the basics and let's talk step by step.

What about low carbon infrastructure? What about design and eco design? What about beyond, understanding the organizational challenges and HR challenges that when you want to rule out more sustainable approach, but that's a one day conference. It's two days in Paris this year, but in London, it will be one day.

And we expect to have like several hundreds of people joining. And I know also that we will have a great keynote speaker that some of the listeners might known named Chris Adams. So I'm really delighted that you agreed to join Chris. Thanks a lot for this.

Chris Adams: Cool, thanks, so that's the event. It's the 19th of September, taking

place in Bishopsgate in London. And thank you, I am indeed keynoting in the morning, so I hope my trains are on time. But there's a number of people from the Green Software Foundation, for example. I can see Sara Hsu, who is one of the people leading the Green Software patterns project in the Green Software Foundation. There's some people from the W3C on working on web sustainability guidelines. There's also, I'll, share a link to Therese Gale who is working at Salesforce. She'll be talking a little bit about some of the experience, some of her experiences as well. So there's a number of people who

Gaël Duez: Mark Butcher will 

Chris Adams: and Mark, yes, the, yes, Mark Butcher of Positive Cloud.

He's been one of the people who's been really instrumental in some of the most recent work inside the UK government, put together a kind of Digital Sustainability Alliance. There's a bunch of things there. So I'm actually looking forward to this Gaël, and I want to just give people a heads up that it's taking place. Gaël, thanks so much for coming in for this and telling us the stories about Wales, And we're watching and all that sharing all your insights elsewhere. Gaël, if people do want to follow up and see what you're up to, what's the best way to find you for future work?

Gaël Duez: Well I would say on LinkedIn and I'm pretty easy to find Gaël Duez. There is not that many. And more generally, if you are interested in what we do in Green IO, it's greenio.tech. That's the website. You've got access to the podcast, the conferences you've got link to my own websites. But I think greenio.tech is the best place to start.

Chris Adams: Brilliant. Well, thank you for that. We'll share some links to all of those websites and to this event coming up. And, for people who aren't able to go to Green IO, the Cloud Native Computing Foundation, the TAG ENV essentially the green part of the Cloud Native Computing Foundation.

They have a series of remote events taking place in October. We'll show a link to that so that if you still want some, to get an events fixed, that's where to look. Alright, Gaël, thank you once again for making the time and I hope you have a lovely week. Okay. Take care of yourself, mate.

Gaël Duez: Thank you, that was great being there. Bye, have a nice week as well.

Chris Adams: Hey everyone, thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode! 



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]]> A Greener Internet that Sleeps More A Greener Internet that Sleeps More Thu, 25 Jul 2024 07:00:00 GMT 59:42 41pm4yj0 false https://podcasts.castplus.fm/e/08j02v38-a-greener-internet-that-sleeps-more 68dc7d19d5c8a1f9b326f845 68dc7cef46a2532cdd8d1c23 Host Chris Adams and guest Romain Jacob delve into the often-overlooked energy demands of networking infrastructure to discover A Greener Internet that Sleeps More. While AI and data centers usually dominate the conversation, networking still consumes significant power, comparable to the energy usage of entire countries. They discuss innovative practices to make the internet greener, such as putting networks to sleep during low usage periods and extending the life of hardware. Romain talks about his recent Hypnos paper, which won Best Paper at HotCarbon 2024. He shares his team’s award-winning research on how energy demand for networking kit powering the internet can be reduced by simply by powering down links when not in use. 78 Host Chris Adams and guest Romain Jacob delve into the often-overlooked energy demands of networking infrastructure to discover A Greener Internet that Sleeps More. While AI and data centers usually dominate the conversation, networking still consumes significant power, comparable to the energy usage of entire countries. They discuss innovative practices to make the internet greener, such as putting networks to sleep during low usage periods and extending the life of hardware. Romain talks about his recent Hypnos paper, which won Best Paper at HotCarbon 2024. He shares his team’s award-winning research on how energy demand for networking kit powering the internet can be reduced by simply by powering down links when not in use.

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TRANSCRIPT BELOW:
Romain Jacob: We used to consider that energy is cheap. Energy is there. We don't need to worry too much about it. So it's just simpler to plug the thing in, assume energy is there. You can draw power as much as you want, whenever you want, for as much as you want. And it's time to get away from that.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Back in episode 10 of this podcast in September 2022, we did a deep dive into the subject of green networking, because while a lot of the time people talk about the energy demands of AI and data centers, in 2024, in absolute terms, the amount of power consumed by networking was still larger.

Back then in 2022, the best figures, when we looked at this, came from the AI, which put the energy usage of data networks at around 250 terawatt hours per year. So that's about the same as all of Spain's energy usage in 2023, so that's not nothing. Now, it's a few years later, 2024, and the best figures from the same agency, the IEA, now give us a range of between 260 and 360 terawatt hours, which could be anything up to a jump of 50 percent in three years now.

Now because of much of this power is coming from fossil fuels, this is a real problem, climate wise. So what can we do about this? With me to explore this once again, is my friend Romain Jacob, who helped guide us through the subject in 2022, along with Dr. Yves Schouler at Intel at the time.

His team's recent research won the Best Paper Award at HotCarbon, the conference that has fast become a fixture on the green IT and digital sustainability circuit. So he seemed a good person to ask about this. Romain, thank you so much for joining me for this podcast. Can I give you the floor to introduce yourself before we revisit the world of green networks?

Romain Jacob: Chris, welcome. I'm very happy to be back on the podcast to talk a little bit more about this. Hello, I'm Romain, I'm a postdoctoral researcher at ETH Zurich in Switzerland. I've been working in sustainability for two to three years now, more or less full time, as much as full time research happens in academia. And yeah, I was, I had the pleasure to present some of our technical work at HotCarbon and I'm sure we're going to deep dive into a bit more in the podcast.

Chris Adams: Okay. Thank you, Romain. And for people who are new to this podcast, my name is Chris Adams. I am the executive director of the Green Web Foundation. We're a Dutch nonprofit focused on reaching a fossil free internet by 2030. I also work as one of the policy chairs inside the larger Green Software Foundation, which is why I'm on this podcast.

Alright, if you're also new to this podcast, every single project and paper that we mention today we'll be posting a link to in the show notes. So for people who are on a quest to learn more about reducing the environmental impact of software engineering, you can use these for your own practices and your own research.

Okay, Romain, are you sitting comfortably?

Romain Jacob: I am.

Chris Adams: Okay, great. Then I'll begin. Okay, Romain, when we last spoke, we covered a range of approaches that people are using right now to rein in the environmental impact of networking. And but before we spend too much time, I wanted to kind of see if you could help set the scene to help folks develop a mental model for thinking about, say, networking versus data centers, because I touched on this a little bit, but it might not be obvious to most people.

So maybe if you could just provide the high level, then we can touch on some of these Differences between the two and why you might care, or how you might think about these differently. So yeah, let's go from there.

Romain Jacob: Yes. Sounds good. It's true, you mentioned in your introduction that we sometimes get numbers that oppose or compare data center corporate footprint or usage and the ones from networks. But now, what does network really mean? It's not really clear what we mean by that. Because there is networking in data centers and, you know, the rest of networks are not completely detached from it.

But at a high level, you have a set of networks that are meant to provide internet access to individuals and to other networks. What this means is that you have companies that are specialized to just make your laptop, your phone, or other appliances you have, being able to talk through the internet. And typically when we refer to networks, without further details, this is the type of network you're talking about.

And data centers, on the other hand, are something that is in the scale of IT fairly recent, where we have this idea of if we centralize in, in one physical location, a lot of powerful resource machines that have a lot of compute, that have a lot of storage available, then we can use that as a remote computer.

And just offload tasks to those data centers and just only get the results back. In today's ecosystem, data centers are a very core element of the internet. The internet today would not really work without data centers. Or at least a lot of the applications we use over the internet only work thanks to data centers.

So from a networking perspective, in a data center, you also have, you need to exchange information and bits and packets between those different machines that live inside the data center, but the way the network looks like is very different from the cellular network that is providing mobile connectivity to your smartphone.

Chris Adams: Ah, I see.

Romain Jacob: So those are very different type of networks, and they have different, very different type of way they are designed, way they are operated, and how they are used. Typically, in a data center, you tend to have a quite high usage of the data center network because you have a lot of exchange and interaction between the different machines that live inside the data center, whereas in the networks that provide internet access, so the networks that are managed by entities we call ISP, for Internet Service Providers.

Chris Adams: Mm hmm.

Romain Jacob: Tends to be much less utilized. There is a lot more capacity. in those network that what is really demanded by the end user.

Chris Adams: Okay.

Romain Jacob: That's a fundamental difference between, between the two networks and something that we try to leverage in our research.

Chris Adams: Okay, so if I just check if I understand this, so you said that you might have networks which might be like, say the ISPs and things, they are individually not that high themselves, but because there's so many of them and because they're so diffuse, in aggregate, this can work out to be a very large figure, for example, and, and, that also speaks a little bit about, I guess, how you might power some of these.

So, like, when we think about a large hyperscale data center, that's something in the region of maybe, if you're looking at a large one, which is maybe the high tens to maybe low hundreds of megawatts, that's maybe thousands of homes. That's a lot of power in one place, whereas with a network, you don't have quite so much, but it's because it's distributed, you might have to have different approaches to managing that.

So, for example, you might and, you might have to take different strategies to either decarbonize that or deal with some of that, some of that load that you actually have. Okay, and I guess one of the questions I might have to ask you about this is then, when you have this, split between the maybe it's just worth talking a little bit about the different kinds of networks that you have here.

So for example, as I understand it, there's maybe an ISP I connect to, maybe my ISP, but then they need to connect to some other cable. And if I'm going across, maybe if I'm connecting to a server across in the Atlantic, then I'm going through some like backbone or something like that, maybe you could talk a little bit about the different layers there, and what some of the, and what the kind of, how much they might make, or if there's any differences in how those ones need to be powered, for example, how they're used?

Romain Jacob: Yeah, totally. So the, the name internet stands for an interconnection of networks. So the internet by name and in private space is a network of networks. Right? So when you connect to the internet. What it means is that you connect to another machine somewhere on the planet that has also access to the same global network.

But these global networks, the connection between those two endpoints has to go through, most of the time, several different networks. And so, typically, if we look at the internet infrastructure, there are several ways of representing this, but one division that we usually use is You have Core, IAP, so the one that kind of sit more in the middle and they provide transit for many, many different interconnections.

Then you have networks that are qualified to be more belonging to the metro area. So this is where it's getting closer to the user, but it's not yet the network that provides direct connectivity to, let's say, your phone or your laptop. And then you have the edge network. And the edge network is really there to provide what is called the last mile connectivity key to the end user.

And those categories exist and were proposed to Helsinki because this share of the network has different characteristics. The core tends to look a bit more like the data center. Like, it's more dense mesh, so there are more interconnections between the different points in that network. And the utilization tends to be higher and kind of constant because it's a global network.

Whereas, the closer you go to the end user, the more you're going to see filtration, because, for example, while users are awake during certain hours during the day, and this is where they tend to use their machine. You will see peak of usage during, you know, TV show primetimes in the evening, but much less at 5 a.m., where most people are deep and not using their phone or their laptops. And so, those networks look differently in terms of What they are used for and how they are built and designed, because we try to adapt the design for the particular use case.

Chris Adams: Okay, thank you for that. So it's a little bit, if you squint, it's a little bit like how you might have motorways and then A roads and B roads and maybe smaller roads, for example.

Romain Jacob: Very like that. It's very much like that.

Chris Adams: All right. Okay. So that's actually quite helpful. And when I think about other kind of systems, I think a little bit about say, like electricity networks, which have, you know, big fat transmission ones which send lots of things, but then you have like the smaller distribution networks which are, so it's somewhat comparable. Okay.

Romain Jacob: It very similar in principle.

Chris Adams: Okay, so this is helpful for developing a mental model about some of this. Alright, okay, so last episode when we spoke about the different techniques people spoke about, you, we spoke about things like carbon away networking, different protocol designs like I think SCION, which was one of the, projects proposed.

And you kind of coined this phrase that an internet of the future needs to grow old and sleep more. And I really, I found this kind of quite entertaining and it stuck with me. But for people who are new to this, maybe you could just unpack what you meant by that because not everyone has read the paper or seen the talk. And I think it's quite helpful for thinking about this subject in general, actually.

Romain Jacob: Yeah, sure. So, so two years ago in the first edition of the HotCarbon Workshop, we, we outlined this vision of what could be relevant to work on in sustainable networking area. And the two ideas that emerged were essentially captured by this growing old and fleeting more aspect. So what do we mean by that?

Growing old is essentially the idea that we tend to be using the hardware we buy not long enough. So if we take an end user perspective, we tend to change phones every couple years. Numbers are changing about this, but we can debate whether this is a good thing or a bad thing. In the networking area, so for the hardware that operators buy to make up the network, so devices that we call routers and switches, it tends to be a bit of the same thing.

Devices were changed, the standard used to be every three years. So in three years, the entire infrastructure would be renewed. So you would buy new hardware to get higher speed or better energy and so on. And there are various reasons for doing that. We can detail it if you're interested afterwards. But it has a very significant cost, financial cost, but also in terms of carbon cost.

Because one people need to understand is that every time you manufacture a product. Not just for networking, but for any product, there is a carbon footprint associated to it. This is where we typically refer to the embodied carbon footprint. And so this embodied carbon is a one time pay, but if you buy more often, well, you pay this price more often.

Now, there's a bit of a tricky thing, which is that you, you can argue that If I buy a new device, a new phone or a new router that is 10 times more energy efficient, then over time, I will then do more saving that would compensate for the embodied cost. The problem is that you, it's very hard to estimate how much you would save and how much is the embodied footprint.

It's a very firmware, a break even point where you're doing this upgrade, buying this new hardware, start paying off from a carbon perspective, but it's not necessarily clear ahead of time when that happened. Generally speaking though, what was pretty clear to us is that we could and we probably should be using the hardware longer.

And this is what we meant by the grow old idea.

Chris Adams: Ah, okay. Thanks for that.

Romain Jacob: Now, to describe more, this is kind of simpler. This is the idea that is very common in other fields like what we know as embedded systems or the Internet of Things. Think about devices that run on battery, to make things simple, that are more or less small but run on battery. And because they need to run on battery, for decades, engineers have been trying to optimize the energy efficiency of those devices.

And the most efficient way of doing this is essentially turning off everything you don't need when you don't need it. So if you think about your phone, your, the screen of your phone is off, I don't know, maybe 90 percent of the time. And this is to save the power drawn by your screen, which is by far the most expensive or power hungry element in a smartphone.

And we do this in order to save on power, on the average power and so on energy at the end of the day. And we are arguing that in the networking world, this is not done too much. And it should probably be done more. So now I need to be quite precise here when I talk about the networking world, I'm talking about the wired networking domain.

In the mobile domain, so in cellular communication that connect to your phone, or also in Wi Fi and so on, the idea of sleeping is already used quite a lot. But in the wired domain, it did not transfer too much. And so the reason why it did not transfer is because we used to come to the idea that energy is cheap. Energy is there. We don't need to worry too much about it. So, it's just simpler to plug the thing in, assume energy is there. You can draw power as much as you want, whenever you want, for as much as you want. And it's time to get away from that.

Chris Adams: Okay, alright, so if I just play that back to you to make sure I understand it correctly. So, the growing old part is essentially a reference to the embodied energy that goes into making various kinds of hardware. So, like, when we're looking at a laptop, around 80 percent of the carbon footprint, it comes from the manufacturing, compared to the running of this.

And, if I keep that laptop for a short period of time, It's a great proportion of the life cycle, lifetime emissions, for example. And we see the same thing in data centers as well. So, for example, Facebook and company, you know, some companies and hyperscalers, they might have had this three year period that you spoke about before, but in the 2010s, we saw figures anecdotally, but not published ever.

It was like 20 years ago. But sometimes these would go down to as much as as little as 18 months for some service because they wanted to get the maximum usage of kind of compute for the power they're using for example. So they had incentives to change like that. So that's what that part is a reference to.

And I think on the eImpact mailing list, where I've seen a lot of the discussion. I will share a link to this in the show notes of this really cool 3D chart showing how, where the break even points are that you mentioned about that. And the sleeping part seems to be this reference that, in many ways, networks are often designed for kind of maximum amount of usage, not necessarily what the average usage might be, similar to how, say, the electricity grid in America, for example, is designed currently designed for everyone to be using aircon at the same time, when normally it's maybe 40 percent utilization.

So there's all this kind of headroom, which doesn't need to be accounted for. And we, and it's a bit like service, you know, we have, as software engineers, we're taught generally to size for the maximum output because the loss of business is supposed to be worse than the cost of having that extra capacity.

But in 2024, there are new approaches that could be taken. And we do things like serverless and scaling things down. And these ideas are - they've been slower to be adopted in the networking field essentially, right?

Romain Jacob: Yeah, that's kind of the, that's kind of the idea.

Chris Adams: Brilliant! Okay, that is good. We'll share a link to the paper because it's quite a fun read and I really helped, it stuck with me ever since I saw you speaking about that.

Okay, so we've kind of set some of the scene so far. We've got some nice mental models for thinking about this. We referred to the energy and the embodied part and I guess the thing we didn't mention too much was that the growing old thing is going to be, you know, more of an issue over time because while we're getting better at decarbonising the electricity of the internet, we're not doing such a good job of decarbonising the extremely energy intensive process of making electronics right now. So we're only, this is only going to become more acute over time.

So, maybe I can allow you to just talk a little bit about this Hypnos paper, because as I understand it, it was an extension of some of this vision going forward, and I know that it wasn't, you weren't presenting yourself, but I know it was your team who were presenting it at HotCarbon, so maybe you can talk a little bit about that, and maybe say who was presenting, or some of those things there, because, yeah, I enjoyed reading this, it was quite fun, it, similar way, I enjoyed it as well, basically.

Romain Jacob: So Hypnos is a, a recent proposal that we've made to essentially try to quantify this sleeping principle. So in two and a half years ago, we said, okay, we could look at the embedded aspect, we could look at the operational aspect and how to improve them. And we thought back then that the one way was to just try to apply those principles of heaping to wired networks.

And so together with some math students from ETH, we started looking into this and say, okay, in theory, we know how to do this. Let's try. You know, let's try for real, let's take some hardware, let's design a prototype, protocol that would just put some things to sleep and see what happens. What was surprising to us was that the, the theory of how you would do the sleeping in a wired network and how much you would expect to save by doing that was old.

It was the first papers go back 2008 or so, so there's been a while and back then, people were saying, okay, assuming we have hardware that, that allows us to do everything we want, then we could implement seeding in this way and then we would save so much. So they knew that the proposals that were made, they were making back then were not readily applicable.

And so we felt like 15 years later, it's kind of interesting to see where are we today? Like how can we do things? And the key element, key there, was how quickly you can turn on something. You turn it off, you can always turn off something, you take some time, you save some power, okay? But then, eventually, if you need it back on, you want it to react quickly.

You know, I talked about the screen of your phone before. It's always off, and it's fine, because as soon as you press the button, and you touch with your finger, the screen lights up, right? It feels instant, right? So it needs to happen quickly to be usable. Except that in networks, it's not like that, it's, I mean, not, not, at least not today.

So if, if you think about a link that connects two routers, this was the first, the first thing that we started considering. Okay, let's put that to sleep. It's essentially the smallest unit in a network that you could put to sleep.

Right.

Chris Adams: A bit like a lane in a, like a multi-lane in a multi-lane car road.

Romain Jacob: Yeah. I would think about the road network, like, turning off a port or turning off a link in a network would be like. Cutting one road in your network. You know, like here in the city, you have many different ways to go to different end points and you would just say, okay, this street is closed. So you can't use it.

Chris Adams: Ah, I see. Okay.

Romain Jacob: That's kind of like the simplest thing one can do from an networking perspective, except that to turn the thing back on, to reopen the street would take multiple seconds.

Chris Adams: Mm.

Romain Jacob: And it doesn't sound like much, but in the networking area, multiple seconds is a lot of time because a lot of traffic can be sent during this time.

And. If you make things short and not too technical, it's way too long.

Chris Adams: So we're looking, you want milliseconds, which are like thousandth of a second, and if something, it takes two or three, it's two or 3000 times slower than you'd like it to be basically.

Romain Jacob: Exactly. So, without getting too nerdy and too technical, the problem is, we can't do what we're suggesting in the literature because we cannot sleep at short timescales as we were planned. And so we're like, okay, so is it over or can we still do something? And so what we were thinking is maybe we cannot sleep, you know, at millisecond time scales, but we can still leverage the fact that networks, some networks, are a lot more used during the day than during the night.

So we, we have a lot of patterns that are daily or hourly that we can leverage to say, okay, well, we have a predictable variation in the average use of the network. And so when we reach the value to declare night time. Then maybe there are some things we can share. And so we, we try to implement a protocol that we do do.

We say, okay, let's do the simplest thing possible and see how well it works. And Hypnos is essentially the outcome of that. So in essence, it's a very simple tentacle that looks at all of the roads, so all of the links in the network and how much they are used. And then we start turning off the, the unused one.

Until we reach some kind of like stopping condition that we say, okay, now it's enough. Like the rest we really need to keep it. At a high level, this is what we do. So one, one challenge was to get actual data to test it.

Chris Adams: Mm-Hmm.

Romain Jacob: Because if you stimulate a network and you stimulate the utilization of your network, you can make things as pretty or as, as ugly as you want, you know, depending on how you look at things.

And so it was, what was really missing from the literature was precise case study that says, okay, here is the data from a given ISP. Here's what the network looks like, and here's what the utilization looks like. In this network, what can we do? So, there has been a long, very long effort to actually get this data.

And then, the Hypnos paper is essentially say, okay, we have the protocol, we have the theory, now we have the data. Let's match the two things together and see where that takes us. And, we looked at two internet service providers that, that belong to the access part. So, those are, networks that are very close to the end user, where you would expect more of D&I fluctuation.

And we do see that. What we were a bit surprised to confirm is that those networks are effectively underutilized. You want to dare a guess what's the average utilization in those networks?

Chris Adams: I literally couldn't, I have no idea what the number might actually be to be honest.

Romain Jacob: Guess!

Chris Adams: Okay is it like Okay, so I said the national grid was about 40%. Is it like 40, 50%? Like, that's, like, not-

Romain Jacob: Four, four zero?

Chris Adams: Yeah, four zero is like what I, is what national, electricity grid is. So maybe it's like, something like that, maybe?

That's my guess.

Romain Jacob: Now you're an order of magnitude too high. So we are talking a couple of percent.

Chris Adams: Oh, wow! Okay, and the whole point about the internet is that if you don't have one route, you can still route other ways. So you've got all these under, you've got all these things which people are currently on that almost no one is using ever, basically, at like 2%. Okay, alright.

Romain Jacob: I need to modulate this, right? Okay. For the couple of networks that we got, we managed to get access to the data, right? So I'm not claiming this is the general number. I would love to know, if you have data, please let us know.

Chris Adams: Mm

Romain Jacob: But for the networks we could get access to, this is the type of numbers you would see. An average utilization of a couple of percent. And again, going back to what we were saying before, in a data center, things would be different.

Chris Adams: Mm hmm.

Romain Jacob: I actually don't know because I was there a little in data center networking, but I would expect things to be more in the 40 50 percent kind of like what you were mentioning before.

But in an end to end service provider network, the underutilization is extreme. There are various reasons for that, but it tends to be the case.

Chris Adams: That's really interesting, because when you look at data centers, so like, I can tell you about the service that I run, or that our organization runs, the Green Web Foundation, so we run a checking service that gets around between 5 and 10 million, like, checks every day, right? So that's maybe In the order of like 400 million per month, for example, something like that.

It's, a relatively high number, for example, and even when we have that, we've got around 50 percent, we, we did, we started working out the environmental impact of our own systems recently, and that's with us with utilization around 50 percent for our systems and in cloud typically you'll see cloud providers saying oh we're really good we're 30 or 40 percent like the highest I've seen is Facebook's most recent stuff about XFaaS and they say oh yeah we can achieve utilization of as high as 60 odd percent right but for lots of data centers the kind of old Data centers would have been in the low digits.

And you've had this whole wave of people saying, well, let's move to the cloud by making much better use of a smaller number one. So it sounds like the same kind of ideas of massive underutilization and therefore huge amounts of essentially hardware, you know, it seems like it's somewhat similar in the networking field as well.

And there's maybe scope for reductions in that field as well. Okay.

Romain Jacob: Exactly. And so, I want to make it clear, like, it's not happening this way because operators are idiots, right? It's just, there are a number of reasons why you have such underutilization. One what I would say is probably the main one from a decision point of view is that you want to provide high performance for number of connections in your network.

So to reach from point A to point B, you want to make sure that you want to have the lowest delay typically. And that requires to have a direct line. Do you have other concerns that are that things do fail in networks. Link failures happen and they can be quite drastic. You operate like a physical infrastructure in a country where people leave and work, you have incidents, fibers get cut, and those are things that take a long time to fix and so on.

So you want to have some resiliency in your network. So that if some part of the network goes down, you can still reroute the traffic the other way around and still have enough capacity to serve that traffic. So you have some names that are not used by default intentionally. So you get to a 2 percent or a couple of percent average.

But you don't want to be at 50 percent because if you are at 50 percent and something really goes down, then you may run into a situation where you don't have enough capacity left to run your business. So the point is, we have such a high underutilization and something that I don't think we explained so far is in network equipment, so routers and switches, you have very little proportionality.

What I mean by that is that the amount of power that is drawn by a router. It's essentially, from at a height, it's not exactly true, but at a high level.

Chris Adams: Hmm.

Romain Jacob: The amount of power drawn is almost independent or varies very little if you send no traffic at all or if you send at 100%.

Chris Adams: Okay.

Romain Jacob: So, what it means is that if you have a router that you use at 1 percent of its total capacity, you pay almost 100 percent of the power.

Chris Adams: It's almost like one person in that plane going back and forwards, for example. Like, if I'm going to fly, there's, you know, if I'm going to fly somewhere and I'm the only person, it's going to be the same footprint as if that plane was entirely full, for example.

Romain Jacob: Kind of, yes. It was the same kind of idea. And this is why for us, investigating this clipping was kind of interesting because we know we have such method underutilization, although I probably would not have guessed it was that low, and it wastes a lot. Third, we are essentially operating most of those links at the worst efficiency point possible. And so we try to remedy this and it goes one step in that direction.

Chris Adams: I see. And I think one thing that you mentioned before was this idea, you can power these things down and you know there's very, because there are alternative routes through the network at any time, it may be the case that even if you do have these things powered off in response to upticks in demand, just like with, say, national grids, people might. You know, switch on batteries to, or feed power into the grid from a battery or possibly a peak of gas plant.

You have, you still have the option of switching these route, these links back on when there is mass a, a, a big peak in power, for example.

Romain Jacob: Exactly. And it's the same with the kitting protocol we proposed, right? So, it still makes sense to have those redundant links deployed, you know, those fibers laid out. And then you may say, yeah, but we've paid all this effort to actually install this and it's there. Why should I not turn it on? Well, because it consumes energy whether you use it or not. That's for one. And second, It's good to have it in case you need it. But you can turn it off so that you save energy. If you can turn it up quickly, right, then it goes back to what I was saying before. The turning of quickly part is still problematic today.

Chris Adams: Hmm.

Romain Jacob: So orders of magnitude that the time it takes to actually do this would be in 10, 10 seconds, roughly a few seconds, let's say, up to more, a minute or so.

So then what, what do you have to wait? Yeah. The benefit you gain by turning links off in terms of energy versus the time you may have to wait until you go back to a good state in your network in case you have some failures in your network. And of course you need to multiply that risk by the likelihood of getting such link failures.

So if, let's say, if you have a doomsday event that, you know, will just kill the network error, but that happens one every hundred year. Maybe you can be fine having a day to day management policy that says, okay, to manage this doomsday event, we will need an hour long, but in all the rest of the time, it will be fine and we'll save energy every single day.

You know, you, you have weighed the pros and cons of a strategy in terms of performance and in terms of energy usage.

Chris Adams: And presumably, one thing that you've entioned is because you mentioned that you often have these regular, kind of, predictable cycles, like, most people don't, you know, fewer people use the internet when they're asleep than when they're awake, for example. Like, it sounds really silly, but like, yeah, you're going to see these predictable patterns.

Some of the work with the Hypnos paper was basically, essentially taking some of these things into account. So you can say, well, you need to have this buffer, but we don't have to have the buffer massive you don't need to have every single car in the world engine on idling just in case you need to use it you can turn off some of these car engines for it so that was the kind of idea behind this.

Okay neat so I've used this car model a few times but it suggests that actually think about how, the amount of energy usage and how it scales with how we use the Internet. It might not be the correct mental model. And I just want to kind of run this by you, because this is one thing that I've been thinking about recently is that a lot of us tend to instinctively reached to a kind of car and driving and burning fuel model, because that's how a lot of expo experience costs of energy a lot of the time, right?

But it feels almost like if you've got this thing, it may be a different model might be like, I don't know, like bike lanes where there's a matter of time that you need to build something. You might need to light, make sure a bike lane is well lit, for example, if you do use, but the amount of people using the bike lane that isn't the big driver of emissions in this, for example, maybe something like that.

Romain Jacob: Yeah, I was about to say, I think the analogy is not wrong per se, it's just a matter of the trade off between the, the infrastructure cost and the driving cost, let's say. So let's assume you, you're using a, a mean of transport, whatever that may be. That has a cost X per kilometer, but then you need light and you need, I don't know, cooling or if you're using something that works on under like, I don't know, superconductive environment, then you need extreme cooling. And so the cost for the environment gets very high. I think the superconducting thing is actually a, a, a pretty, a, a much, very much closer analogy to how the way network works.

And you need to spend a lot of power, or to draw a lot of power, just to get the infrastructure on. But once the infrastructure is on, once you get your superconductive environment, then traversing this environment is very cheap.

Chris Adams: Ah, okay.

Romain Jacob: And networks are a bit like that today, right? So turning the wires on costs a lot, but when it's on, sending the bits through the wire, it's pretty cheap.

Chris Adams: Ah, I see, and if I understand it, when I've spoke to other people who know more about networking than me, they've basically told me that at some levels, even when you're not sending any data, there is a signal being sent that basically says, I'm not sending data, I'm not sending data, I'm not sending data, just to make sure so that you've got that connection so that when you do send some data, there's a fast response time.

So, just because we aren't perceiving something doesn't mean there isn't energy use taking place for example. So there's maybe some leakiness in the models that we might instinctively just use or intuitively try applying when we're trying to figure out, okay, how do I make something more sustainable for example?

Romain Jacob: Yeah, this is very true. And it also, it gets a bit more detailed than that. It also depends on the type of physical layer you use for sending your information. In networks today, you have, I think, I guess we could differentiate between three main types of physical layer. One is the electrical communication, so you send an electrical signal through a power rail.

You have optical communication, so essentially using light that you modulate in some way. And then there's everything that is kind of wireless and radio wave communication. So I'll leave the wireless part out because I know less about it and it's a very complicated bee. But if you compare electrical to optical, things work kind of differently.

In the electrical environment, you, you can, you have essentially a physical connection between the two points that try to talk to each other. And so, when the physical connection is there, you may send messages as you were saying before, like, I'm not sending, I'm not sending, I'm not sending, but you can do this, for example, once it be, I don't know, 30 seconds or so.

It will be enough to, yeah, keep the connection alive. Whereas if you use optical, it's different. Because if you use optical communication, the line does not exist. The line between the two exists only because you have a laser that is sending some photons from one end to the other. So, where it's different is that a laser is an access component.

You need to, to, to send energy to create this link between the two ends.

Chris Adams: Mm.

Romain Jacob: And so, now, it's not that every 30 seconds you need to say, I'm not sending anything. It's like, all the time, you need to have this laser on so that the two endpoints know they are connected to, to each other.

Chris Adams: Ah, okay.

Romain Jacob: And it's actually one of the reasons why the early ideas about tweaking are not so much in use today is because they don't work nicely with optical communication.

And optical communications are the de facto standard in networks today, in the, in the core of the internet and in data centers as well. For reasons that we don't have time to detail, optical is the primary means of communication. And it is by design the laser needs to be on for the link for the communication to exist.

Whether you send data or not.

Chris Adams: Alright, okay, thank you for elucidating this part here. So it sounds like the models we might use a lot of the time, as lots of, when you're working with digital sustainability, it's very common to look at a kind of figure per gigabyte sent, for example, and like in some cases It's better than having nothing, for example, but there is a lot of extra nuance here.

And, there is, we have seen some new papers, I think there was one paper by David Mytton, who, that we'll share a link to, he's been speaking, he's, he shared one recently about the fact that, there are other approaches you might take, for example, for this. There, if you could, just brief, it'd be really nice to just touch on some of that, if we could, and then just, and then to add some extra nuance, realize that, like, It's not that there is no proportion, because there is something you need to do.

Maybe we could just talk a little bit about some of the things that David Mytton's been proposing as an alternative way to figure out a number here, because I'm mainly sharing this for developers who get access to these numbers, and they want to make a number go up or down, and it's useful to understand what goes into these models so that you are incentivizing the correct interventions essentially.

Romain Jacob: Yeah, of course. So that's actually a very important point, I think. You will often find if you look in the, on the web or anywhere. Figures are in energy per bit, or energy per X, or energy per web search, or energy per email sort of thing. Whatever we can think about whether computing such numbers make sense, what, what is very important to understand is that those numbers were derived in an attributional way.

That means that you take the total power cost of a system. And then you divide by the number of bits that were transmitted. If you take a network, you take the sum of the energy consumption of all the routers and all the links and all the calling and all of everything. And then you look at the total amount of traffic you've sent over your reporting interval, like a year.

And you take one, you divide by the other and ta da, you get energy per bit. That is interesting. That is interesting to get an idea of how much, how much energy you spend for the useful work you've done in that network. But it should not be interpreted as, this is the cost for a single bit, because if you do this, then, and that would be a different type of reasoning that we call consequential reasoning.

If you do this, you would then draw the conclusion, the wrong conclusion, that if I have a network that has, I don't know, a hundred kilowatt hour, if I send a hundred gigabit more, I will use ten kilowatt hour of-

Chris Adams: It increases entire system by that rather than my share of this, for example.

Romain Jacob: Exactly. Except that it's not true. It's not true because the total number in watt, in energy per bit accounts encapsulate all the infrastructure costs. And those infrastructure costs are constant, they are independent of the amount of traffic. And so this summary statistics is useful in order to track the evolution of how, how much is used, how much is your network used over time.

But it's not good to predict the effect of sending more or less traffic. And it's a subtle thing that if you overlook this, you can make the very wrong statement and make bad decisions. And so this is what kind of like these papers you refer to try to highlight and explain. And say that you need to have a finer view on the, the energy per unit that you're interested in.

It's a bit more subtle than that. People should read the paper. It's a great paper. It's very accessible. It's not too technical, I think. And it's great for people that are interested in this area to get a good primer on the challenge of computing the energy efficiency of a network. I think it's really a great piece.

Chris Adams: Okay, cool. So basically the, I think one of the implications of that is that let's say I'm designing a website, for example, if I make the website maybe half the size, it doesn't necessarily mean I have half the carbon footprint of it because some of the models we use and they're popular, they are an improvement on having nothing, but there's extra nuance that we might actually have.

I say this as someone who works in an organization where we have a library, we have one, we have a software library called CO2.js. We have a transfer based model for this because this is one of the ones that's most common that is just like one of the defaults. We also have like an issue open specifically about this paper because there is, when you're starting out, you will often reach for some of these things for this.

And while there's benefit and there's some value in actually having some of these models to help you work out, it's also worth understanding that there is extra nuance to this. And they can end up with slightly different incentives for this. This is something we'll talk about carbon aware as well because again, different ways you measure the carbon intensity of electricity can create different incentives as well. So, like, this is one thing we'll be, I guess, we'll be developing over time, but the thing that I just, if we may, I'm just going to touch on this other thing before we move on to kind of wrap up on this.

This can give the impression that there is no proportionality between using digital tools, and, like roll out of extra infrastructure and if you said there's no link that would be an oversimplification as well and I think we're gonna one of the previous guests Daniel Schien he came on he spoke about some of this and maybe you might paraphrase some of this because I think his this perspective is also very helpful and kind of illustrates why we need to be doing coming up with better models to represent this stuff.

Romain Jacob: Yes, exactly. Thanks for bringing that up. That is also extremely important. So I've said before, power is kind of constant. It doesn't depend so much on how much you send. Two, two things to keep in mind. First, there is some correlation. So if you do send more traffic, there will be an increase in power and so you will consume more energy. That is true. And the work that I'm doing and fuel make that even more so in the future. So what we are trying to do is essentially say. We tried to find ways of reducing the power draw when you're under low utilization. And if we were successful in doing that, by sleeping and by other methods, then it will create a stronger correlation between traffic and power.

Right? So, and this is actually good, right? For energy efficient theories, the closer you are to proportionality, the better. So if we are successful, then the correlation will increase. And then sending more bits or, or having smaller website will have a stronger impact, in energy consumption in carbon footprint.

So that's one aspect.

Chris Adams: Mhmm.

Romain Jacob: The second aspect was also extremely important. You mentioned already the, the work of Daniel Schien that is great about this is to think about the internet in, in, in a different timescale. If you look at one point in time right now, the network is the static element. There's so many nodes in the network.

There's so many networks and therefore today, if I send more traffic; be low impact. However, if you put a longer timescale and you look at a one year, six month, or ten year horizon, what happens is that when people spend more traffic, you see the utilization of the links going up, and that will have a future consequence of incentivizing people to deploy new links, to increase the capacity of the network.

Chris Adams: Hmm.

Romain Jacob: Which means that over a year, so over time, as you send more traffic, you create more demand. As you create more demand, you will create more offer. That means scaling up your network, and every time you scale up, almost away, you will increase the energy consumption of the network, right? So, you will further increase the infrastructure costs.

So, as you send more traffic, as you watch more Netflix today, It does not consume more energy, not so much, but it will incentivize the network to be scaled up, and that will consume more energy. So, there is a good reason to advocate for what is known as digital sobriety, to be, to try to use less of the network or to make a more sensible use of the network, because if we use it more, It will incentivize future increase of the digital network size and therefore future increase of the energy consumption.

Chris Adams: I see. Okay. So basically, if you like set these norms of all this extra use, even though you're not making these changes in the meantime, on a kind of large, on a multiple, multi year timescale that people make investments, like infrastructure investments on, they would then respond to make sure that they've got that kind of headroom available over time.

And I think that's actually some of the work that Daniel has been doing to model, because there is no way that we can do that. We're going to deploy new infrastructure where there being zero carbon footprint, even if everything is green. So there is a, there's an impact there that we need to be mindful of.

That's some of the work that he's referring to there. We'll share a link to that paper as well, because I'm not quite sure how Okay, I know that we can't model that in co2.js, for example, in our library, but he's, this is literally the cutting edge work that I think he's been doing. And he's, last time he was on, he was hiring for some researchers to find out, okay, how do you represent this stuff?

Because when we think about large organizations of the scale of Amazon or Microsoft who are spending literally tens of billions of dollars each year, then you do need to think about these kind of multi year, decade style infrastructure kind of investment scale. Okay. All right. So, we've gone really into the details, then we've spoken at the kind of macroeconomic level now.

I wonder if I can just bring this back to the kind of frame for developers who are like, oh, this sounds really cool. How do I use some of this? Or what would I do? Like, if you wanted to have an internet that was able to kind of sleep more and could grow old, are there any ideas you might use? Like how might it change how you build, for example?

Are there any kind of sensibilities you might take into account? Because as I understand it, some of the things with Hypnos were primarily designed to say, this is how you can do this without forcing people to make too many changes at the end user level. But there may be things that as a practitioner you might make things more conducive to or something like that, for example.

Romain Jacob: Yes, definitely. So I think it goes back to the question we had just before about Daniel's work, about looking at the longer time scale perspective. I think as an end user, as a software designer, I think thinking about sobriety is something that everybody should be doing. It's not just for sustainability.

I think one very recent Environment Variables podcast was about the alignment between the sustainable practices and the financial operations, and in many cases, those two things align. In a similar mindset, if you think about web design, this, if you look at the system, the WebW3C sustainability guideline, they align pretty much almost perfectly with the accessibility guideline and with the performance optimization guideline. Why? Because a smaller website will also load faster and, you know, get people faster to what they want. This is the content they really want to consume. So, there's general value into being as, modest in your demand from the system or from the network as, as possible and for the compute as well.

It's the same thing. Today, it does not yet translate into net benefits. At the network level, but it might in the future. And, you know, somebody has to stop. So you need to, I need the efforts of all sides in order to, you know, make that work. In the networking domain, I know there have been some people studying this from a theoretical point of view where you would say, the end user could be able to say, I want to, I want to place a phone call.

But I'm willing to wait for, I don't know, 20 seconds or 30 seconds before my call is being played. And if you have an ecosystem of users of that network, where the sufficiently large share of users are so called delay tolerant, then you can optimize your network in order to save in resources. So saving energy and ultimately in reducing your carbon footprint.

In the more traditional networking domain, one could envision something like that. There is no work in this area, as far as I know. One way you can think about this would be, the incentive would be pricing. That you, you, you could say, okay, I'm, I'm winning. So not so much to wait the most, most likely to cap the bandwidth I can get out of the network.

But you would, if you were to say, I'm winning to get at most, I don't know, 100 megabits per second in high utilization times, then you would get a discount on your internet deal. I think that's sizable, that's possible. One quick working thing that could happen. But that would be more like a global effect, like it's between the user and the internet service provider.

If you're a software developer, If you think about how your application could be built in such a way, I honestly don't know. I think it's extremely easy today, technically speaking, to have any sort of flagging where your application can say, I'm data intolerant, I can wait, I will not use more than an egg.

One can do that, that's easy. Your network can get that information. The tricky bit is, how would the network then use that information to route traffic in a way that would save energy? That is much trickier.

Chris Adams: Ah, okay, so that sounds like a possible route that people might choose to go. Because I think, I, I know, for example, there is some work in the world of streaming, where there was a notion of a, I think it was the gold button, that was put together by the Greening of Streaming group. They were basically saying, look, most of the time, I, if I'm looking at television from across the room, I can't really tell if it's 8K or 4K.

So, allow me to, you know, have a default which lets me kind of, reduce the resolution or the quality so that when there's lots of people trying to use something, we can see the amount of data reduce somewhat. And that reduces the amount of kind of extra peak capacity people might need, for example. These are some ways to kind of make use of the existing capacity that lives inside the entire network to kind of smooth off that peak as it were, for example.

That's some of the stuff that we might be looking at. So it's very, in some ways, it might be, kind of providing hints to when you send things over the network. And I think there's actually some work that we've seen from some existing tools. I know that Facebook's serverless platform does precisely this.

And there is also some work in Intel, we'll share some links to this, where when you have a computing workload, you can basically say, well, I'm not worried about when this gets delivered, for example, or I have a degree of, as long as it happens before this time, it's okay. And this does provide the information for people running these systems to essentially, like, move things around to avoid having to increase the total capacity, for example.

So you make better use of the existing capacity you have before you have to buy new capacity or deploy new wires or anything like that. That seems to be what you're kind of suggesting.

Romain Jacob: In the cloud computing world, that does exist for real. Yeah, for sure.

Chris Adams: Okay, so there's a possible path for future, future research. And maybe this is the thing I, this is what we can kind of wrap up on. So we've spoke and we've done a dive into sleeping and getting old. Right. But that's not the only tool available to us. Are there any kind of papers or projects or things that you would direct people's attention to that you think is really exciting but may not necessarily be in your field that you think is worth, that you're excited about?

For example, because you spend a lot more time thinking about networks than I do, and I'm pretty sure there's some things you might say that that, that other people listening here might, might enjoy following, for example.

Romain Jacob: Yeah. Yeah. So I think two things come to mind. The first is that today I talked about protocol adaptation, about, you know, we, we would put things to sleep and save energy and so on. But one, one key problem we have, we as practitioners in this field and researchers and operators is the lack of visibility in the power data.

It's actually really hard to get a good understanding about how much power is going to be drawn by a given router, depending on the amount of traffic and depending on how this thing is configured and so on. So, this became very clear at the beginning where we started working on this. And so, a big part of my research has been to try to, to develop tools for building datasets to aggregate such power information in a way that people can contribute to and then use in their own research, do their own analysis.

And try to do some predictions about, okay, now, if I were to buy this device, for example, it will cost me so much in embodied carbon footprint that this is how much I could hope to save. Because I know how this device typically operates and how much it consumes. And so, this led to a dataset on a platform project we call now the Network Power Zoo.

Which is actually a reference to another very well known networking data set that was the Network Topology Zoo. It's kind of like a historical reference to that. But it is really a zoo in a sense that it's very broad. From devices that look the same can consume from, I don't know, two, three times more magnitude power.

Whereas it seems there's the same number of ports, the same part of the number of connections, but it can change drastically. So, it's still a work in progress. I mean, the database is in building and we're starting pouring data in. And very soon we'll do an open call for anyone to contribute their own data sources into this database.

So that people can have access to richer power related data.

Chris Adams: For a data informed discussion. Yeah.

Romain Jacob: So that's one thing that is very active for us. It's still very much related to networking, but that's one thing that is not related to protocols so much. More generally, what I think has become clear and clear to me is that if we want to address the sustainability problems in networking, what we need are not really networking researchers, because a lot has to do with the hardware design, and the hardware architecture, and writing good software for optimizing the hell out of the hardware we get.

And those are just not the typical expertise that you find in networking people. So, networking people are a bit at a protocol, but, you know, they don't know as much how the hardware is built and designed. Maybe I should not make such generalities, but it's definitely true for myself. So, we've been poking more and more people from the computer architecture area, from the hardware design to collaborate with us and say, okay, look, we have those sorts of needs.

This is mainstream in embedded systems for 20 years. We still don't have it yet in routers, it's hightime, we need it now. If anyone is working in this area and is interested, please reach out to me, I'll be happy to chat.

Chris Adams: Brilliant, thank you for that. Okay, and I suspect there's some, I'm just looking through our notes. We actually have been in touch on the E Impact mailing list, which is one project, but I think it's by the IETF. That's one of the things that we can share a link to where there's often quite a lot of, if you want to go into the networking, that's probably one of the deeper ones I've found.

Okay, great. I think, is there anything else? I should, we're just coming to time, so I just want to check. This has been really fascinating, and I've learned a huge amount from this. So, if people are interested in the work that you're doing or they want to learn more about this, where should people look to find more about this?

Like, we'll share a link to the paper that you worked on, for example. But beyond that though, where do we find out what's going on with Romain Jacob and his team of research, researchers?

Romain Jacob: So, nowadays the best place to find me would be on LinkedIn. So, we'll add a link to my profile, but my name is not that common, usually I'm findable on LinkedIn at least. Yeah, that's the best place for you to reach out, I'm quite reactive there.

Chris Adams: Okay, cool. So I'll share the link there. I'll also share the link to HotCarbon, which had the paper that you had. If there's a, and if there's a link with ETH, the research institution you're part of, I'll add a link to that as well. Brilliant! Well, this has been really enlightening for me and hopefully other people who've been listening along with this.

Thanks once again for being so generous with your time, Romain, and, yeah, have a lovely holiday over the summer, okay? Take care of yourself.

Romain Jacob: Thanks, bye bye.

Chris Adams: bye! Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again and see you in the next episode!

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]]> Host Chris Adams and guest Romain Jacob delve into the often-overlooked energy demands of networking infrastructure to discover A Greener Internet that Sleeps More. While AI and data centers usually dominate the conversation, networking still consumes significant power, comparable to the energy usage of entire countries. They discuss innovative practices to make the internet greener, such as putting networks to sleep during low usage periods and extending the life of hardware. Romain talks about his recent Hypnos paper, which won Best Paper at HotCarbon 2024. He shares his team’s award-winning research on how energy demand for networking kit powering the internet can be reduced by simply by powering down links when not in use.

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TRANSCRIPT BELOW:
Romain Jacob: We used to consider that energy is cheap. Energy is there. We don't need to worry too much about it. So it's just simpler to plug the thing in, assume energy is there. You can draw power as much as you want, whenever you want, for as much as you want. And it's time to get away from that.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to another edition of Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Back in episode 10 of this podcast in September 2022, we did a deep dive into the subject of green networking, because while a lot of the time people talk about the energy demands of AI and data centers, in 2024, in absolute terms, the amount of power consumed by networking was still larger.

Back then in 2022, the best figures, when we looked at this, came from the AI, which put the energy usage of data networks at around 250 terawatt hours per year. So that's about the same as all of Spain's energy usage in 2023, so that's not nothing. Now, it's a few years later, 2024, and the best figures from the same agency, the IEA, now give us a range of between 260 and 360 terawatt hours, which could be anything up to a jump of 50 percent in three years now.

Now because of much of this power is coming from fossil fuels, this is a real problem, climate wise. So what can we do about this? With me to explore this once again, is my friend Romain Jacob, who helped guide us through the subject in 2022, along with Dr. Yves Schouler at Intel at the time.

His team's recent research won the Best Paper Award at HotCarbon, the conference that has fast become a fixture on the green IT and digital sustainability circuit. So he seemed a good person to ask about this. Romain, thank you so much for joining me for this podcast. Can I give you the floor to introduce yourself before we revisit the world of green networks?

Romain Jacob: Chris, welcome. I'm very happy to be back on the podcast to talk a little bit more about this. Hello, I'm Romain, I'm a postdoctoral researcher at ETH Zurich in Switzerland. I've been working in sustainability for two to three years now, more or less full time, as much as full time research happens in academia. And yeah, I was, I had the pleasure to present some of our technical work at HotCarbon and I'm sure we're going to deep dive into a bit more in the podcast.

Chris Adams: Okay. Thank you, Romain. And for people who are new to this podcast, my name is Chris Adams. I am the executive director of the Green Web Foundation. We're a Dutch nonprofit focused on reaching a fossil free internet by 2030. I also work as one of the policy chairs inside the larger Green Software Foundation, which is why I'm on this podcast.

Alright, if you're also new to this podcast, every single project and paper that we mention today we'll be posting a link to in the show notes. So for people who are on a quest to learn more about reducing the environmental impact of software engineering, you can use these for your own practices and your own research.

Okay, Romain, are you sitting comfortably?

Romain Jacob: I am.

Chris Adams: Okay, great. Then I'll begin. Okay, Romain, when we last spoke, we covered a range of approaches that people are using right now to rein in the environmental impact of networking. And but before we spend too much time, I wanted to kind of see if you could help set the scene to help folks develop a mental model for thinking about, say, networking versus data centers, because I touched on this a little bit, but it might not be obvious to most people.

So maybe if you could just provide the high level, then we can touch on some of these Differences between the two and why you might care, or how you might think about these differently. So yeah, let's go from there.

Romain Jacob: Yes. Sounds good. It's true, you mentioned in your introduction that we sometimes get numbers that oppose or compare data center corporate footprint or usage and the ones from networks. But now, what does network really mean? It's not really clear what we mean by that. Because there is networking in data centers and, you know, the rest of networks are not completely detached from it.

But at a high level, you have a set of networks that are meant to provide internet access to individuals and to other networks. What this means is that you have companies that are specialized to just make your laptop, your phone, or other appliances you have, being able to talk through the internet. And typically when we refer to networks, without further details, this is the type of network you're talking about.

And data centers, on the other hand, are something that is in the scale of IT fairly recent, where we have this idea of if we centralize in, in one physical location, a lot of powerful resource machines that have a lot of compute, that have a lot of storage available, then we can use that as a remote computer.

And just offload tasks to those data centers and just only get the results back. In today's ecosystem, data centers are a very core element of the internet. The internet today would not really work without data centers. Or at least a lot of the applications we use over the internet only work thanks to data centers.

So from a networking perspective, in a data center, you also have, you need to exchange information and bits and packets between those different machines that live inside the data center, but the way the network looks like is very different from the cellular network that is providing mobile connectivity to your smartphone.

Chris Adams: Ah, I see.

Romain Jacob: So those are very different type of networks, and they have different, very different type of way they are designed, way they are operated, and how they are used. Typically, in a data center, you tend to have a quite high usage of the data center network because you have a lot of exchange and interaction between the different machines that live inside the data center, whereas in the networks that provide internet access, so the networks that are managed by entities we call ISP, for Internet Service Providers.

Chris Adams: Mm hmm.

Romain Jacob: Tends to be much less utilized. There is a lot more capacity. in those network that what is really demanded by the end user.

Chris Adams: Okay.

Romain Jacob: That's a fundamental difference between, between the two networks and something that we try to leverage in our research.

Chris Adams: Okay, so if I just check if I understand this, so you said that you might have networks which might be like, say the ISPs and things, they are individually not that high themselves, but because there's so many of them and because they're so diffuse, in aggregate, this can work out to be a very large figure, for example, and, and, that also speaks a little bit about, I guess, how you might power some of these.

So, like, when we think about a large hyperscale data center, that's something in the region of maybe, if you're looking at a large one, which is maybe the high tens to maybe low hundreds of megawatts, that's maybe thousands of homes. That's a lot of power in one place, whereas with a network, you don't have quite so much, but it's because it's distributed, you might have to have different approaches to managing that.

So, for example, you might and, you might have to take different strategies to either decarbonize that or deal with some of that, some of that load that you actually have. Okay, and I guess one of the questions I might have to ask you about this is then, when you have this, split between the maybe it's just worth talking a little bit about the different kinds of networks that you have here.

So for example, as I understand it, there's maybe an ISP I connect to, maybe my ISP, but then they need to connect to some other cable. And if I'm going across, maybe if I'm connecting to a server across in the Atlantic, then I'm going through some like backbone or something like that, maybe you could talk a little bit about the different layers there, and what some of the, and what the kind of, how much they might make, or if there's any differences in how those ones need to be powered, for example, how they're used?

Romain Jacob: Yeah, totally. So the, the name internet stands for an interconnection of networks. So the internet by name and in private space is a network of networks. Right? So when you connect to the internet. What it means is that you connect to another machine somewhere on the planet that has also access to the same global network.

But these global networks, the connection between those two endpoints has to go through, most of the time, several different networks. And so, typically, if we look at the internet infrastructure, there are several ways of representing this, but one division that we usually use is You have Core, IAP, so the one that kind of sit more in the middle and they provide transit for many, many different interconnections.

Then you have networks that are qualified to be more belonging to the metro area. So this is where it's getting closer to the user, but it's not yet the network that provides direct connectivity to, let's say, your phone or your laptop. And then you have the edge network. And the edge network is really there to provide what is called the last mile connectivity key to the end user.

And those categories exist and were proposed to Helsinki because this share of the network has different characteristics. The core tends to look a bit more like the data center. Like, it's more dense mesh, so there are more interconnections between the different points in that network. And the utilization tends to be higher and kind of constant because it's a global network.

Whereas, the closer you go to the end user, the more you're going to see filtration, because, for example, while users are awake during certain hours during the day, and this is where they tend to use their machine. You will see peak of usage during, you know, TV show primetimes in the evening, but much less at 5 a.m., where most people are deep and not using their phone or their laptops. And so, those networks look differently in terms of What they are used for and how they are built and designed, because we try to adapt the design for the particular use case.

Chris Adams: Okay, thank you for that. So it's a little bit, if you squint, it's a little bit like how you might have motorways and then A roads and B roads and maybe smaller roads, for example.

Romain Jacob: Very like that. It's very much like that.

Chris Adams: All right. Okay. So that's actually quite helpful. And when I think about other kind of systems, I think a little bit about say, like electricity networks, which have, you know, big fat transmission ones which send lots of things, but then you have like the smaller distribution networks which are, so it's somewhat comparable. Okay.

Romain Jacob: It very similar in principle.

Chris Adams: Okay, so this is helpful for developing a mental model about some of this. Alright, okay, so last episode when we spoke about the different techniques people spoke about, you, we spoke about things like carbon away networking, different protocol designs like I think SCION, which was one of the, projects proposed.

And you kind of coined this phrase that an internet of the future needs to grow old and sleep more. And I really, I found this kind of quite entertaining and it stuck with me. But for people who are new to this, maybe you could just unpack what you meant by that because not everyone has read the paper or seen the talk. And I think it's quite helpful for thinking about this subject in general, actually.

Romain Jacob: Yeah, sure. So, so two years ago in the first edition of the HotCarbon Workshop, we, we outlined this vision of what could be relevant to work on in sustainable networking area. And the two ideas that emerged were essentially captured by this growing old and fleeting more aspect. So what do we mean by that?

Growing old is essentially the idea that we tend to be using the hardware we buy not long enough. So if we take an end user perspective, we tend to change phones every couple years. Numbers are changing about this, but we can debate whether this is a good thing or a bad thing. In the networking area, so for the hardware that operators buy to make up the network, so devices that we call routers and switches, it tends to be a bit of the same thing.

Devices were changed, the standard used to be every three years. So in three years, the entire infrastructure would be renewed. So you would buy new hardware to get higher speed or better energy and so on. And there are various reasons for doing that. We can detail it if you're interested afterwards. But it has a very significant cost, financial cost, but also in terms of carbon cost.

Because one people need to understand is that every time you manufacture a product. Not just for networking, but for any product, there is a carbon footprint associated to it. This is where we typically refer to the embodied carbon footprint. And so this embodied carbon is a one time pay, but if you buy more often, well, you pay this price more often.

Now, there's a bit of a tricky thing, which is that you, you can argue that If I buy a new device, a new phone or a new router that is 10 times more energy efficient, then over time, I will then do more saving that would compensate for the embodied cost. The problem is that you, it's very hard to estimate how much you would save and how much is the embodied footprint.

It's a very firmware, a break even point where you're doing this upgrade, buying this new hardware, start paying off from a carbon perspective, but it's not necessarily clear ahead of time when that happened. Generally speaking though, what was pretty clear to us is that we could and we probably should be using the hardware longer.

And this is what we meant by the grow old idea.

Chris Adams: Ah, okay. Thanks for that.

Romain Jacob: Now, to describe more, this is kind of simpler. This is the idea that is very common in other fields like what we know as embedded systems or the Internet of Things. Think about devices that run on battery, to make things simple, that are more or less small but run on battery. And because they need to run on battery, for decades, engineers have been trying to optimize the energy efficiency of those devices.

And the most efficient way of doing this is essentially turning off everything you don't need when you don't need it. So if you think about your phone, your, the screen of your phone is off, I don't know, maybe 90 percent of the time. And this is to save the power drawn by your screen, which is by far the most expensive or power hungry element in a smartphone.

And we do this in order to save on power, on the average power and so on energy at the end of the day. And we are arguing that in the networking world, this is not done too much. And it should probably be done more. So now I need to be quite precise here when I talk about the networking world, I'm talking about the wired networking domain.

In the mobile domain, so in cellular communication that connect to your phone, or also in Wi Fi and so on, the idea of sleeping is already used quite a lot. But in the wired domain, it did not transfer too much. And so the reason why it did not transfer is because we used to come to the idea that energy is cheap. Energy is there. We don't need to worry too much about it. So, it's just simpler to plug the thing in, assume energy is there. You can draw power as much as you want, whenever you want, for as much as you want. And it's time to get away from that.

Chris Adams: Okay, alright, so if I just play that back to you to make sure I understand it correctly. So, the growing old part is essentially a reference to the embodied energy that goes into making various kinds of hardware. So, like, when we're looking at a laptop, around 80 percent of the carbon footprint, it comes from the manufacturing, compared to the running of this.

And, if I keep that laptop for a short period of time, It's a great proportion of the life cycle, lifetime emissions, for example. And we see the same thing in data centers as well. So, for example, Facebook and company, you know, some companies and hyperscalers, they might have had this three year period that you spoke about before, but in the 2010s, we saw figures anecdotally, but not published ever.

It was like 20 years ago. But sometimes these would go down to as much as as little as 18 months for some service because they wanted to get the maximum usage of kind of compute for the power they're using for example. So they had incentives to change like that. So that's what that part is a reference to.

And I think on the eImpact mailing list, where I've seen a lot of the discussion. I will share a link to this in the show notes of this really cool 3D chart showing how, where the break even points are that you mentioned about that. And the sleeping part seems to be this reference that, in many ways, networks are often designed for kind of maximum amount of usage, not necessarily what the average usage might be, similar to how, say, the electricity grid in America, for example, is designed currently designed for everyone to be using aircon at the same time, when normally it's maybe 40 percent utilization.

So there's all this kind of headroom, which doesn't need to be accounted for. And we, and it's a bit like service, you know, we have, as software engineers, we're taught generally to size for the maximum output because the loss of business is supposed to be worse than the cost of having that extra capacity.

But in 2024, there are new approaches that could be taken. And we do things like serverless and scaling things down. And these ideas are - they've been slower to be adopted in the networking field essentially, right?

Romain Jacob: Yeah, that's kind of the, that's kind of the idea.

Chris Adams: Brilliant! Okay, that is good. We'll share a link to the paper because it's quite a fun read and I really helped, it stuck with me ever since I saw you speaking about that.

Okay, so we've kind of set some of the scene so far. We've got some nice mental models for thinking about this. We referred to the energy and the embodied part and I guess the thing we didn't mention too much was that the growing old thing is going to be, you know, more of an issue over time because while we're getting better at decarbonising the electricity of the internet, we're not doing such a good job of decarbonising the extremely energy intensive process of making electronics right now. So we're only, this is only going to become more acute over time.

So, maybe I can allow you to just talk a little bit about this Hypnos paper, because as I understand it, it was an extension of some of this vision going forward, and I know that it wasn't, you weren't presenting yourself, but I know it was your team who were presenting it at HotCarbon, so maybe you can talk a little bit about that, and maybe say who was presenting, or some of those things there, because, yeah, I enjoyed reading this, it was quite fun, it, similar way, I enjoyed it as well, basically.

Romain Jacob: So Hypnos is a, a recent proposal that we've made to essentially try to quantify this sleeping principle. So in two and a half years ago, we said, okay, we could look at the embedded aspect, we could look at the operational aspect and how to improve them. And we thought back then that the one way was to just try to apply those principles of heaping to wired networks.

And so together with some math students from ETH, we started looking into this and say, okay, in theory, we know how to do this. Let's try. You know, let's try for real, let's take some hardware, let's design a prototype, protocol that would just put some things to sleep and see what happens. What was surprising to us was that the, the theory of how you would do the sleeping in a wired network and how much you would expect to save by doing that was old.

It was the first papers go back 2008 or so, so there's been a while and back then, people were saying, okay, assuming we have hardware that, that allows us to do everything we want, then we could implement seeding in this way and then we would save so much. So they knew that the proposals that were made, they were making back then were not readily applicable.

And so we felt like 15 years later, it's kind of interesting to see where are we today? Like how can we do things? And the key element, key there, was how quickly you can turn on something. You turn it off, you can always turn off something, you take some time, you save some power, okay? But then, eventually, if you need it back on, you want it to react quickly.

You know, I talked about the screen of your phone before. It's always off, and it's fine, because as soon as you press the button, and you touch with your finger, the screen lights up, right? It feels instant, right? So it needs to happen quickly to be usable. Except that in networks, it's not like that, it's, I mean, not, not, at least not today.

So if, if you think about a link that connects two routers, this was the first, the first thing that we started considering. Okay, let's put that to sleep. It's essentially the smallest unit in a network that you could put to sleep.

Right.

Chris Adams: A bit like a lane in a, like a multi-lane in a multi-lane car road.

Romain Jacob: Yeah. I would think about the road network, like, turning off a port or turning off a link in a network would be like. Cutting one road in your network. You know, like here in the city, you have many different ways to go to different end points and you would just say, okay, this street is closed. So you can't use it.

Chris Adams: Ah, I see. Okay.

Romain Jacob: That's kind of like the simplest thing one can do from an networking perspective, except that to turn the thing back on, to reopen the street would take multiple seconds.

Chris Adams: Mm.

Romain Jacob: And it doesn't sound like much, but in the networking area, multiple seconds is a lot of time because a lot of traffic can be sent during this time.

And. If you make things short and not too technical, it's way too long.

Chris Adams: So we're looking, you want milliseconds, which are like thousandth of a second, and if something, it takes two or three, it's two or 3000 times slower than you'd like it to be basically.

Romain Jacob: Exactly. So, without getting too nerdy and too technical, the problem is, we can't do what we're suggesting in the literature because we cannot sleep at short timescales as we were planned. And so we're like, okay, so is it over or can we still do something? And so what we were thinking is maybe we cannot sleep, you know, at millisecond time scales, but we can still leverage the fact that networks, some networks, are a lot more used during the day than during the night.

So we, we have a lot of patterns that are daily or hourly that we can leverage to say, okay, well, we have a predictable variation in the average use of the network. And so when we reach the value to declare night time. Then maybe there are some things we can share. And so we, we try to implement a protocol that we do do.

We say, okay, let's do the simplest thing possible and see how well it works. And Hypnos is essentially the outcome of that. So in essence, it's a very simple tentacle that looks at all of the roads, so all of the links in the network and how much they are used. And then we start turning off the, the unused one.

Until we reach some kind of like stopping condition that we say, okay, now it's enough. Like the rest we really need to keep it. At a high level, this is what we do. So one, one challenge was to get actual data to test it.

Chris Adams: Mm-Hmm.

Romain Jacob: Because if you stimulate a network and you stimulate the utilization of your network, you can make things as pretty or as, as ugly as you want, you know, depending on how you look at things.

And so it was, what was really missing from the literature was precise case study that says, okay, here is the data from a given ISP. Here's what the network looks like, and here's what the utilization looks like. In this network, what can we do? So, there has been a long, very long effort to actually get this data.

And then, the Hypnos paper is essentially say, okay, we have the protocol, we have the theory, now we have the data. Let's match the two things together and see where that takes us. And, we looked at two internet service providers that, that belong to the access part. So, those are, networks that are very close to the end user, where you would expect more of D&I fluctuation.

And we do see that. What we were a bit surprised to confirm is that those networks are effectively underutilized. You want to dare a guess what's the average utilization in those networks?

Chris Adams: I literally couldn't, I have no idea what the number might actually be to be honest.

Romain Jacob: Guess!

Chris Adams: Okay is it like Okay, so I said the national grid was about 40%. Is it like 40, 50%? Like, that's, like, not-

Romain Jacob: Four, four zero?

Chris Adams: Yeah, four zero is like what I, is what national, electricity grid is. So maybe it's like, something like that, maybe?

That's my guess.

Romain Jacob: Now you're an order of magnitude too high. So we are talking a couple of percent.

Chris Adams: Oh, wow! Okay, and the whole point about the internet is that if you don't have one route, you can still route other ways. So you've got all these under, you've got all these things which people are currently on that almost no one is using ever, basically, at like 2%. Okay, alright.

Romain Jacob: I need to modulate this, right? Okay. For the couple of networks that we got, we managed to get access to the data, right? So I'm not claiming this is the general number. I would love to know, if you have data, please let us know.

Chris Adams: Mm

Romain Jacob: But for the networks we could get access to, this is the type of numbers you would see. An average utilization of a couple of percent. And again, going back to what we were saying before, in a data center, things would be different.

Chris Adams: Mm hmm.

Romain Jacob: I actually don't know because I was there a little in data center networking, but I would expect things to be more in the 40 50 percent kind of like what you were mentioning before.

But in an end to end service provider network, the underutilization is extreme. There are various reasons for that, but it tends to be the case.

Chris Adams: That's really interesting, because when you look at data centers, so like, I can tell you about the service that I run, or that our organization runs, the Green Web Foundation, so we run a checking service that gets around between 5 and 10 million, like, checks every day, right? So that's maybe In the order of like 400 million per month, for example, something like that.

It's, a relatively high number, for example, and even when we have that, we've got around 50 percent, we, we did, we started working out the environmental impact of our own systems recently, and that's with us with utilization around 50 percent for our systems and in cloud typically you'll see cloud providers saying oh we're really good we're 30 or 40 percent like the highest I've seen is Facebook's most recent stuff about XFaaS and they say oh yeah we can achieve utilization of as high as 60 odd percent right but for lots of data centers the kind of old Data centers would have been in the low digits.

And you've had this whole wave of people saying, well, let's move to the cloud by making much better use of a smaller number one. So it sounds like the same kind of ideas of massive underutilization and therefore huge amounts of essentially hardware, you know, it seems like it's somewhat similar in the networking field as well.

And there's maybe scope for reductions in that field as well. Okay.

Romain Jacob: Exactly. And so, I want to make it clear, like, it's not happening this way because operators are idiots, right? It's just, there are a number of reasons why you have such underutilization. One what I would say is probably the main one from a decision point of view is that you want to provide high performance for number of connections in your network.

So to reach from point A to point B, you want to make sure that you want to have the lowest delay typically. And that requires to have a direct line. Do you have other concerns that are that things do fail in networks. Link failures happen and they can be quite drastic. You operate like a physical infrastructure in a country where people leave and work, you have incidents, fibers get cut, and those are things that take a long time to fix and so on.

So you want to have some resiliency in your network. So that if some part of the network goes down, you can still reroute the traffic the other way around and still have enough capacity to serve that traffic. So you have some names that are not used by default intentionally. So you get to a 2 percent or a couple of percent average.

But you don't want to be at 50 percent because if you are at 50 percent and something really goes down, then you may run into a situation where you don't have enough capacity left to run your business. So the point is, we have such a high underutilization and something that I don't think we explained so far is in network equipment, so routers and switches, you have very little proportionality.

What I mean by that is that the amount of power that is drawn by a router. It's essentially, from at a height, it's not exactly true, but at a high level.

Chris Adams: Hmm.

Romain Jacob: The amount of power drawn is almost independent or varies very little if you send no traffic at all or if you send at 100%.

Chris Adams: Okay.

Romain Jacob: So, what it means is that if you have a router that you use at 1 percent of its total capacity, you pay almost 100 percent of the power.

Chris Adams: It's almost like one person in that plane going back and forwards, for example. Like, if I'm going to fly, there's, you know, if I'm going to fly somewhere and I'm the only person, it's going to be the same footprint as if that plane was entirely full, for example.

Romain Jacob: Kind of, yes. It was the same kind of idea. And this is why for us, investigating this clipping was kind of interesting because we know we have such method underutilization, although I probably would not have guessed it was that low, and it wastes a lot. Third, we are essentially operating most of those links at the worst efficiency point possible. And so we try to remedy this and it goes one step in that direction.

Chris Adams: I see. And I think one thing that you mentioned before was this idea, you can power these things down and you know there's very, because there are alternative routes through the network at any time, it may be the case that even if you do have these things powered off in response to upticks in demand, just like with, say, national grids, people might. You know, switch on batteries to, or feed power into the grid from a battery or possibly a peak of gas plant.

You have, you still have the option of switching these route, these links back on when there is mass a, a, a big peak in power, for example.

Romain Jacob: Exactly. And it's the same with the kitting protocol we proposed, right? So, it still makes sense to have those redundant links deployed, you know, those fibers laid out. And then you may say, yeah, but we've paid all this effort to actually install this and it's there. Why should I not turn it on? Well, because it consumes energy whether you use it or not. That's for one. And second, It's good to have it in case you need it. But you can turn it off so that you save energy. If you can turn it up quickly, right, then it goes back to what I was saying before. The turning of quickly part is still problematic today.

Chris Adams: Hmm.

Romain Jacob: So orders of magnitude that the time it takes to actually do this would be in 10, 10 seconds, roughly a few seconds, let's say, up to more, a minute or so.

So then what, what do you have to wait? Yeah. The benefit you gain by turning links off in terms of energy versus the time you may have to wait until you go back to a good state in your network in case you have some failures in your network. And of course you need to multiply that risk by the likelihood of getting such link failures.

So if, let's say, if you have a doomsday event that, you know, will just kill the network error, but that happens one every hundred year. Maybe you can be fine having a day to day management policy that says, okay, to manage this doomsday event, we will need an hour long, but in all the rest of the time, it will be fine and we'll save energy every single day.

You know, you, you have weighed the pros and cons of a strategy in terms of performance and in terms of energy usage.

Chris Adams: And presumably, one thing that you've entioned is because you mentioned that you often have these regular, kind of, predictable cycles, like, most people don't, you know, fewer people use the internet when they're asleep than when they're awake, for example. Like, it sounds really silly, but like, yeah, you're going to see these predictable patterns.

Some of the work with the Hypnos paper was basically, essentially taking some of these things into account. So you can say, well, you need to have this buffer, but we don't have to have the buffer massive you don't need to have every single car in the world engine on idling just in case you need to use it you can turn off some of these car engines for it so that was the kind of idea behind this.

Okay neat so I've used this car model a few times but it suggests that actually think about how, the amount of energy usage and how it scales with how we use the Internet. It might not be the correct mental model. And I just want to kind of run this by you, because this is one thing that I've been thinking about recently is that a lot of us tend to instinctively reached to a kind of car and driving and burning fuel model, because that's how a lot of expo experience costs of energy a lot of the time, right?

But it feels almost like if you've got this thing, it may be a different model might be like, I don't know, like bike lanes where there's a matter of time that you need to build something. You might need to light, make sure a bike lane is well lit, for example, if you do use, but the amount of people using the bike lane that isn't the big driver of emissions in this, for example, maybe something like that.

Romain Jacob: Yeah, I was about to say, I think the analogy is not wrong per se, it's just a matter of the trade off between the, the infrastructure cost and the driving cost, let's say. So let's assume you, you're using a, a mean of transport, whatever that may be. That has a cost X per kilometer, but then you need light and you need, I don't know, cooling or if you're using something that works on under like, I don't know, superconductive environment, then you need extreme cooling. And so the cost for the environment gets very high. I think the superconducting thing is actually a, a, a pretty, a, a much, very much closer analogy to how the way network works.

And you need to spend a lot of power, or to draw a lot of power, just to get the infrastructure on. But once the infrastructure is on, once you get your superconductive environment, then traversing this environment is very cheap.

Chris Adams: Ah, okay.

Romain Jacob: And networks are a bit like that today, right? So turning the wires on costs a lot, but when it's on, sending the bits through the wire, it's pretty cheap.

Chris Adams: Ah, I see, and if I understand it, when I've spoke to other people who know more about networking than me, they've basically told me that at some levels, even when you're not sending any data, there is a signal being sent that basically says, I'm not sending data, I'm not sending data, I'm not sending data, just to make sure so that you've got that connection so that when you do send some data, there's a fast response time.

So, just because we aren't perceiving something doesn't mean there isn't energy use taking place for example. So there's maybe some leakiness in the models that we might instinctively just use or intuitively try applying when we're trying to figure out, okay, how do I make something more sustainable for example?

Romain Jacob: Yeah, this is very true. And it also, it gets a bit more detailed than that. It also depends on the type of physical layer you use for sending your information. In networks today, you have, I think, I guess we could differentiate between three main types of physical layer. One is the electrical communication, so you send an electrical signal through a power rail.

You have optical communication, so essentially using light that you modulate in some way. And then there's everything that is kind of wireless and radio wave communication. So I'll leave the wireless part out because I know less about it and it's a very complicated bee. But if you compare electrical to optical, things work kind of differently.

In the electrical environment, you, you can, you have essentially a physical connection between the two points that try to talk to each other. And so, when the physical connection is there, you may send messages as you were saying before, like, I'm not sending, I'm not sending, I'm not sending, but you can do this, for example, once it be, I don't know, 30 seconds or so.

It will be enough to, yeah, keep the connection alive. Whereas if you use optical, it's different. Because if you use optical communication, the line does not exist. The line between the two exists only because you have a laser that is sending some photons from one end to the other. So, where it's different is that a laser is an access component.

You need to, to, to send energy to create this link between the two ends.

Chris Adams: Mm.

Romain Jacob: And so, now, it's not that every 30 seconds you need to say, I'm not sending anything. It's like, all the time, you need to have this laser on so that the two endpoints know they are connected to, to each other.

Chris Adams: Ah, okay.

Romain Jacob: And it's actually one of the reasons why the early ideas about tweaking are not so much in use today is because they don't work nicely with optical communication.

And optical communications are the de facto standard in networks today, in the, in the core of the internet and in data centers as well. For reasons that we don't have time to detail, optical is the primary means of communication. And it is by design the laser needs to be on for the link for the communication to exist.

Whether you send data or not.

Chris Adams: Alright, okay, thank you for elucidating this part here. So it sounds like the models we might use a lot of the time, as lots of, when you're working with digital sustainability, it's very common to look at a kind of figure per gigabyte sent, for example, and like in some cases It's better than having nothing, for example, but there is a lot of extra nuance here.

And, there is, we have seen some new papers, I think there was one paper by David Mytton, who, that we'll share a link to, he's been speaking, he's, he shared one recently about the fact that, there are other approaches you might take, for example, for this. There, if you could, just brief, it'd be really nice to just touch on some of that, if we could, and then just, and then to add some extra nuance, realize that, like, It's not that there is no proportion, because there is something you need to do.

Maybe we could just talk a little bit about some of the things that David Mytton's been proposing as an alternative way to figure out a number here, because I'm mainly sharing this for developers who get access to these numbers, and they want to make a number go up or down, and it's useful to understand what goes into these models so that you are incentivizing the correct interventions essentially.

Romain Jacob: Yeah, of course. So that's actually a very important point, I think. You will often find if you look in the, on the web or anywhere. Figures are in energy per bit, or energy per X, or energy per web search, or energy per email sort of thing. Whatever we can think about whether computing such numbers make sense, what, what is very important to understand is that those numbers were derived in an attributional way.

That means that you take the total power cost of a system. And then you divide by the number of bits that were transmitted. If you take a network, you take the sum of the energy consumption of all the routers and all the links and all the calling and all of everything. And then you look at the total amount of traffic you've sent over your reporting interval, like a year.

And you take one, you divide by the other and ta da, you get energy per bit. That is interesting. That is interesting to get an idea of how much, how much energy you spend for the useful work you've done in that network. But it should not be interpreted as, this is the cost for a single bit, because if you do this, then, and that would be a different type of reasoning that we call consequential reasoning.

If you do this, you would then draw the conclusion, the wrong conclusion, that if I have a network that has, I don't know, a hundred kilowatt hour, if I send a hundred gigabit more, I will use ten kilowatt hour of-

Chris Adams: It increases entire system by that rather than my share of this, for example.

Romain Jacob: Exactly. Except that it's not true. It's not true because the total number in watt, in energy per bit accounts encapsulate all the infrastructure costs. And those infrastructure costs are constant, they are independent of the amount of traffic. And so this summary statistics is useful in order to track the evolution of how, how much is used, how much is your network used over time.

But it's not good to predict the effect of sending more or less traffic. And it's a subtle thing that if you overlook this, you can make the very wrong statement and make bad decisions. And so this is what kind of like these papers you refer to try to highlight and explain. And say that you need to have a finer view on the, the energy per unit that you're interested in.

It's a bit more subtle than that. People should read the paper. It's a great paper. It's very accessible. It's not too technical, I think. And it's great for people that are interested in this area to get a good primer on the challenge of computing the energy efficiency of a network. I think it's really a great piece.

Chris Adams: Okay, cool. So basically the, I think one of the implications of that is that let's say I'm designing a website, for example, if I make the website maybe half the size, it doesn't necessarily mean I have half the carbon footprint of it because some of the models we use and they're popular, they are an improvement on having nothing, but there's extra nuance that we might actually have.

I say this as someone who works in an organization where we have a library, we have one, we have a software library called CO2.js. We have a transfer based model for this because this is one of the ones that's most common that is just like one of the defaults. We also have like an issue open specifically about this paper because there is, when you're starting out, you will often reach for some of these things for this.

And while there's benefit and there's some value in actually having some of these models to help you work out, it's also worth understanding that there is extra nuance to this. And they can end up with slightly different incentives for this. This is something we'll talk about carbon aware as well because again, different ways you measure the carbon intensity of electricity can create different incentives as well. So, like, this is one thing we'll be, I guess, we'll be developing over time, but the thing that I just, if we may, I'm just going to touch on this other thing before we move on to kind of wrap up on this.

This can give the impression that there is no proportionality between using digital tools, and, like roll out of extra infrastructure and if you said there's no link that would be an oversimplification as well and I think we're gonna one of the previous guests Daniel Schien he came on he spoke about some of this and maybe you might paraphrase some of this because I think his this perspective is also very helpful and kind of illustrates why we need to be doing coming up with better models to represent this stuff.

Romain Jacob: Yes, exactly. Thanks for bringing that up. That is also extremely important. So I've said before, power is kind of constant. It doesn't depend so much on how much you send. Two, two things to keep in mind. First, there is some correlation. So if you do send more traffic, there will be an increase in power and so you will consume more energy. That is true. And the work that I'm doing and fuel make that even more so in the future. So what we are trying to do is essentially say. We tried to find ways of reducing the power draw when you're under low utilization. And if we were successful in doing that, by sleeping and by other methods, then it will create a stronger correlation between traffic and power.

Right? So, and this is actually good, right? For energy efficient theories, the closer you are to proportionality, the better. So if we are successful, then the correlation will increase. And then sending more bits or, or having smaller website will have a stronger impact, in energy consumption in carbon footprint.

So that's one aspect.

Chris Adams: Mhmm.

Romain Jacob: The second aspect was also extremely important. You mentioned already the, the work of Daniel Schien that is great about this is to think about the internet in, in, in a different timescale. If you look at one point in time right now, the network is the static element. There's so many nodes in the network.

There's so many networks and therefore today, if I send more traffic; be low impact. However, if you put a longer timescale and you look at a one year, six month, or ten year horizon, what happens is that when people spend more traffic, you see the utilization of the links going up, and that will have a future consequence of incentivizing people to deploy new links, to increase the capacity of the network.

Chris Adams: Hmm.

Romain Jacob: Which means that over a year, so over time, as you send more traffic, you create more demand. As you create more demand, you will create more offer. That means scaling up your network, and every time you scale up, almost away, you will increase the energy consumption of the network, right? So, you will further increase the infrastructure costs.

So, as you send more traffic, as you watch more Netflix today, It does not consume more energy, not so much, but it will incentivize the network to be scaled up, and that will consume more energy. So, there is a good reason to advocate for what is known as digital sobriety, to be, to try to use less of the network or to make a more sensible use of the network, because if we use it more, It will incentivize future increase of the digital network size and therefore future increase of the energy consumption.

Chris Adams: I see. Okay. So basically, if you like set these norms of all this extra use, even though you're not making these changes in the meantime, on a kind of large, on a multiple, multi year timescale that people make investments, like infrastructure investments on, they would then respond to make sure that they've got that kind of headroom available over time.

And I think that's actually some of the work that Daniel has been doing to model, because there is no way that we can do that. We're going to deploy new infrastructure where there being zero carbon footprint, even if everything is green. So there is a, there's an impact there that we need to be mindful of.

That's some of the work that he's referring to there. We'll share a link to that paper as well, because I'm not quite sure how Okay, I know that we can't model that in co2.js, for example, in our library, but he's, this is literally the cutting edge work that I think he's been doing. And he's, last time he was on, he was hiring for some researchers to find out, okay, how do you represent this stuff?

Because when we think about large organizations of the scale of Amazon or Microsoft who are spending literally tens of billions of dollars each year, then you do need to think about these kind of multi year, decade style infrastructure kind of investment scale. Okay. All right. So, we've gone really into the details, then we've spoken at the kind of macroeconomic level now.

I wonder if I can just bring this back to the kind of frame for developers who are like, oh, this sounds really cool. How do I use some of this? Or what would I do? Like, if you wanted to have an internet that was able to kind of sleep more and could grow old, are there any ideas you might use? Like how might it change how you build, for example?

Are there any kind of sensibilities you might take into account? Because as I understand it, some of the things with Hypnos were primarily designed to say, this is how you can do this without forcing people to make too many changes at the end user level. But there may be things that as a practitioner you might make things more conducive to or something like that, for example.

Romain Jacob: Yes, definitely. So I think it goes back to the question we had just before about Daniel's work, about looking at the longer time scale perspective. I think as an end user, as a software designer, I think thinking about sobriety is something that everybody should be doing. It's not just for sustainability.

I think one very recent Environment Variables podcast was about the alignment between the sustainable practices and the financial operations, and in many cases, those two things align. In a similar mindset, if you think about web design, this, if you look at the system, the WebW3C sustainability guideline, they align pretty much almost perfectly with the accessibility guideline and with the performance optimization guideline. Why? Because a smaller website will also load faster and, you know, get people faster to what they want. This is the content they really want to consume. So, there's general value into being as, modest in your demand from the system or from the network as, as possible and for the compute as well.

It's the same thing. Today, it does not yet translate into net benefits. At the network level, but it might in the future. And, you know, somebody has to stop. So you need to, I need the efforts of all sides in order to, you know, make that work. In the networking domain, I know there have been some people studying this from a theoretical point of view where you would say, the end user could be able to say, I want to, I want to place a phone call.

But I'm willing to wait for, I don't know, 20 seconds or 30 seconds before my call is being played. And if you have an ecosystem of users of that network, where the sufficiently large share of users are so called delay tolerant, then you can optimize your network in order to save in resources. So saving energy and ultimately in reducing your carbon footprint.

In the more traditional networking domain, one could envision something like that. There is no work in this area, as far as I know. One way you can think about this would be, the incentive would be pricing. That you, you, you could say, okay, I'm, I'm winning. So not so much to wait the most, most likely to cap the bandwidth I can get out of the network.

But you would, if you were to say, I'm winning to get at most, I don't know, 100 megabits per second in high utilization times, then you would get a discount on your internet deal. I think that's sizable, that's possible. One quick working thing that could happen. But that would be more like a global effect, like it's between the user and the internet service provider.

If you're a software developer, If you think about how your application could be built in such a way, I honestly don't know. I think it's extremely easy today, technically speaking, to have any sort of flagging where your application can say, I'm data intolerant, I can wait, I will not use more than an egg.

One can do that, that's easy. Your network can get that information. The tricky bit is, how would the network then use that information to route traffic in a way that would save energy? That is much trickier.

Chris Adams: Ah, okay, so that sounds like a possible route that people might choose to go. Because I think, I, I know, for example, there is some work in the world of streaming, where there was a notion of a, I think it was the gold button, that was put together by the Greening of Streaming group. They were basically saying, look, most of the time, I, if I'm looking at television from across the room, I can't really tell if it's 8K or 4K.

So, allow me to, you know, have a default which lets me kind of, reduce the resolution or the quality so that when there's lots of people trying to use something, we can see the amount of data reduce somewhat. And that reduces the amount of kind of extra peak capacity people might need, for example. These are some ways to kind of make use of the existing capacity that lives inside the entire network to kind of smooth off that peak as it were, for example.

That's some of the stuff that we might be looking at. So it's very, in some ways, it might be, kind of providing hints to when you send things over the network. And I think there's actually some work that we've seen from some existing tools. I know that Facebook's serverless platform does precisely this.

And there is also some work in Intel, we'll share some links to this, where when you have a computing workload, you can basically say, well, I'm not worried about when this gets delivered, for example, or I have a degree of, as long as it happens before this time, it's okay. And this does provide the information for people running these systems to essentially, like, move things around to avoid having to increase the total capacity, for example.

So you make better use of the existing capacity you have before you have to buy new capacity or deploy new wires or anything like that. That seems to be what you're kind of suggesting.

Romain Jacob: In the cloud computing world, that does exist for real. Yeah, for sure.

Chris Adams: Okay, so there's a possible path for future, future research. And maybe this is the thing I, this is what we can kind of wrap up on. So we've spoke and we've done a dive into sleeping and getting old. Right. But that's not the only tool available to us. Are there any kind of papers or projects or things that you would direct people's attention to that you think is really exciting but may not necessarily be in your field that you think is worth, that you're excited about?

For example, because you spend a lot more time thinking about networks than I do, and I'm pretty sure there's some things you might say that that, that other people listening here might, might enjoy following, for example.

Romain Jacob: Yeah. Yeah. So I think two things come to mind. The first is that today I talked about protocol adaptation, about, you know, we, we would put things to sleep and save energy and so on. But one, one key problem we have, we as practitioners in this field and researchers and operators is the lack of visibility in the power data.

It's actually really hard to get a good understanding about how much power is going to be drawn by a given router, depending on the amount of traffic and depending on how this thing is configured and so on. So, this became very clear at the beginning where we started working on this. And so, a big part of my research has been to try to, to develop tools for building datasets to aggregate such power information in a way that people can contribute to and then use in their own research, do their own analysis.

And try to do some predictions about, okay, now, if I were to buy this device, for example, it will cost me so much in embodied carbon footprint that this is how much I could hope to save. Because I know how this device typically operates and how much it consumes. And so, this led to a dataset on a platform project we call now the Network Power Zoo.

Which is actually a reference to another very well known networking data set that was the Network Topology Zoo. It's kind of like a historical reference to that. But it is really a zoo in a sense that it's very broad. From devices that look the same can consume from, I don't know, two, three times more magnitude power.

Whereas it seems there's the same number of ports, the same part of the number of connections, but it can change drastically. So, it's still a work in progress. I mean, the database is in building and we're starting pouring data in. And very soon we'll do an open call for anyone to contribute their own data sources into this database.

So that people can have access to richer power related data.

Chris Adams: For a data informed discussion. Yeah.

Romain Jacob: So that's one thing that is very active for us. It's still very much related to networking, but that's one thing that is not related to protocols so much. More generally, what I think has become clear and clear to me is that if we want to address the sustainability problems in networking, what we need are not really networking researchers, because a lot has to do with the hardware design, and the hardware architecture, and writing good software for optimizing the hell out of the hardware we get.

And those are just not the typical expertise that you find in networking people. So, networking people are a bit at a protocol, but, you know, they don't know as much how the hardware is built and designed. Maybe I should not make such generalities, but it's definitely true for myself. So, we've been poking more and more people from the computer architecture area, from the hardware design to collaborate with us and say, okay, look, we have those sorts of needs.

This is mainstream in embedded systems for 20 years. We still don't have it yet in routers, it's hightime, we need it now. If anyone is working in this area and is interested, please reach out to me, I'll be happy to chat.

Chris Adams: Brilliant, thank you for that. Okay, and I suspect there's some, I'm just looking through our notes. We actually have been in touch on the E Impact mailing list, which is one project, but I think it's by the IETF. That's one of the things that we can share a link to where there's often quite a lot of, if you want to go into the networking, that's probably one of the deeper ones I've found.

Okay, great. I think, is there anything else? I should, we're just coming to time, so I just want to check. This has been really fascinating, and I've learned a huge amount from this. So, if people are interested in the work that you're doing or they want to learn more about this, where should people look to find more about this?

Like, we'll share a link to the paper that you worked on, for example. But beyond that though, where do we find out what's going on with Romain Jacob and his team of research, researchers?

Romain Jacob: So, nowadays the best place to find me would be on LinkedIn. So, we'll add a link to my profile, but my name is not that common, usually I'm findable on LinkedIn at least. Yeah, that's the best place for you to reach out, I'm quite reactive there.

Chris Adams: Okay, cool. So I'll share the link there. I'll also share the link to HotCarbon, which had the paper that you had. If there's a, and if there's a link with ETH, the research institution you're part of, I'll add a link to that as well. Brilliant! Well, this has been really enlightening for me and hopefully other people who've been listening along with this.

Thanks once again for being so generous with your time, Romain, and, yeah, have a lovely holiday over the summer, okay? Take care of yourself.

Romain Jacob: Thanks, bye bye.

Chris Adams: bye! Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again and see you in the next episode!

Hosted on Acast. See acast.com/privacy for more information.

]]> The Week in Green Software: Tackling the Energy Challenges of AI The Week in Green Software: Tackling the Energy Challenges of AI Thu, 18 Jul 2024 08:00:00 GMT 42:48 61m6p280 false https://podcasts.castplus.fm/e/2nx0jpwn-the-week-in-green-software-tackling-the-energy-challenges-of-ai 68dc7d1009b1c365e49ab665 68dc7cef46a2532cdd8d1c23 77 Producer Chris Skipper is joined by guests Marjolein Pordon of ladylowcode.com fame and Andri Johnston from Cambridge University Press & Assessment to discuss the sustainability challenges associated with AI's increasing energy demands and the role of data centers in addressing these challenges. Marjolein emphasizes the need for sustainable infrastructure and the potential synergy between low-code platforms and AI. Andri shares insights from CUP&A's efforts to understand and mitigate digital carbon emissions, highlighting the importance of transparency and accurate reporting from cloud service providers like AWS. 

Learn more about our people:

Find out more about the GSF:

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Hosted on Acast. See acast.com/privacy for more information.

]]> Producer Chris Skipper is joined by guests Marjolein Pordon of ladylowcode.com fame and Andri Johnston from Cambridge University Press & Assessment to discuss the sustainability challenges associated with AI's increasing energy demands and the role of data centers in addressing these challenges. Marjolein emphasizes the need for sustainable infrastructure and the potential synergy between low-code platforms and AI. Andri shares insights from CUP&A's efforts to understand and mitigate digital carbon emissions, highlighting the importance of transparency and accurate reporting from cloud service providers like AWS. 

Learn more about our people:

Find out more about the GSF:

News:

Events:

Resources:

If you enjoyed this episode then please either:

Hosted on Acast. See acast.com/privacy for more information.

]]> Preview: CXO Bytes Preview: CXO Bytes Thu, 11 Jul 2024 07:00:00 GMT 12:10 209q65y0 false https://podcasts.castplus.fm/e/18206qm8-preview-cxo-bytes 68dc7d067be17a7f0135658a 68dc7cef46a2532cdd8d1c23 76 In this episode we preview the first episode of CXO Bytes. Join host Sanjay Podder as he talks to leaders in technology, sustainability, and AI in their pursuit of a sustainable future through green software. Joined by Dr. Ong Chen Hui, Assistant CEO of Singapore's Infocomm Media Development Authority (IMDA), the discussion focuses on Singapore's comprehensive approach to digital sustainability. Dr. Ong highlights IMDA's efforts to drive green software adoption across various sectors, emphasizing the importance of efficiency in data centers and the broader ICT ecosystem. Listen to the full episode via the links below.

Listen to CXO Bytes:

Find out more about the GSF:

If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Chris Skipper: Hello, and welcome to Environment Variables. This is Chris Skipper, the podcast producer, bringing you a very special episode of Environment Variables. Today, I'm excited to share with you a preview from our new podcast series, CXO Bytes. Hosted by Sanjay Poddar, Chairperson of the Green Software Foundation.

CXObytes dives deep into the intersection of innovation and sustainability within the tech industry. In each episode, Sanjay will be joined by industry leaders from the C Suite to explore practical strategies for greening software while driving enterprise growth. So without further ado, let's get started.

Let's listen in on a sneak peek of the first episode. 

Sanjay Podder: Hello and welcome to CXO Bytes, a podcast brought to you by the Green Software Foundation and dedicated to supporting chiefs of information, technology, sustainability, and AI as they aim to shape a sustainable future through green software. We will uncover the strategies and a big green move that's helped drive results for business and for the planet.

I am your host, Sanjay Podder.

Hello everyone. Welcome to CXO Bytes. This is our inaugural podcast on how do you use green software for building a sustainable future. This is a new podcast series and the whole idea behind it is, you know, embracing a culture of green software, it needs to come from the top. And we therefore want to talk with decision makers, with business leaders, with leaders who are running nation states like Singapore, for example, at C level.

You know, how are they driving this culture change when it comes to digital sustainability and green software, for example?

Today I am super excited to invite Dr. Ong. She is the Assistant CEO of IMDA, which is the Infocomm Media Development Authority of Singapore. And we are going to chat on how IMDA is championing digital sustainability as well as green software. Welcome, Dr. Ong.

Dr. Ong Chen Hui: Thank you for having me on your inaugural podcast on green software.

Sanjay Podder: And you know, I had my own selfish reason for inviting you because while the Green Software Foundation has been interacting with many, many large businesses across the world, IMDA and Singapore GovTech, these are two members of Green Software Foundation who represent the government, right?

And we all know the very important role that government will play in sustainability in general. So I wanted to understand from you, you know, how are you looking into this space? So we will talk a lot about that. The other aspect is probably to begin with, for our audience, a perspective on what is IMDA.

You know, what is your specific remit, what you are trying to do in Singapore, if you can give us, you know, a few insights into that.

Dr. Ong Chen Hui: Okay, so here in Singapore, of course, climate change is actually something that is a bit of a existential thing for us, us being a small nation state and we're also an island, to us, climate change and the associated rising sea level is a matter of concern. Right? So, as a result, we have put in a green plan that states our, sustainability goals by the time we reach 2050. And this is actually a whole government effort. So, I don't think it is a case where it's one ministry or one agency that's responsible for the whole world. It is about the whole of government working together in order to make sure that we meet the goals of our Green Plan.

Now, what are some of the things that we are doing? Many things, for example, the National Environment Agency is actually rolling out some of the regulations. We have things like e-waste management, for example. Just now you mentioned GovTech, which is our sister agency. GovTech is also rolling out green procurement when they're actually procuring software solutions. Within IMDA, we are responsible for some of the industry development. We're also what we call a sectoral lead of the ICT sector. So, our own green strategy, comprised broadly of three different strokes. The first is about greening ourselves as an organization.

The second is really about greening the sector that we are responsible for, that we are leading. So, in that case, there will be things like the telecommunications sector, the media sector. And the third thing we want to do is to enable our ICT solution providers to provide green solutions to the broader economy so that we can scale the adoption, we can ease the friction out there in the ecosystem.

So essentially, that's greening ourselves, greening the sector, as the lead. And the third is really to kind of provide solutions through the ecosystem so that the wider community can actually benefit.

Sanjay Podder: Now this is really a full 360 degree kind of approach and it is phenomenal. And, I was, I was wondering, you know, and you mentioned briefly on Singapore being an island state. I was thinking, why digital sustainability? What will happen if Singapore decides not to do it, for example, right? Do you have a point of view, say, because, you know, there are many different levers of, sustainability, you know, I can understand the larger sustainability, but what is the importance of digital sustainability?

Do you think it's an important enough lever or maybe you can look at nature biodiversity or something else, right? So specifically for digital sustainability. What is it that triggers IMDA that this is a important initiative? And I'm, I'm seeing this is my second year in Asia Tech that, you know, this is something you give a lot of importance to.

Bringing in leaders from various organizations. Doing deep deliberation. I also remember last year, you brought out your new data center standards, I think increasing the temperature by one degree that has an implication. If you could throw a little bit more light on digital sustainability in particular,

Dr. Ong Chen Hui: Mm hmm.

Sanjay Podder: why do you feel that's a very important lever for a country like Singapore and maybe for many other countries around the world?

Dr. Ong Chen Hui: Yeah. Well, I think you're actually exactly right that when we are trying to drive sustainability, actually there are many different strokes. Some of it includes looking at energy sources and all that, which actually is also very important for Singapore because we are small. We do, have to look at, different kinds of energy sources and how we can potentially actually import some of them, right?

Now, when it comes to digital sustainability, actually our journey, I would say started many years ago. Maybe more than a decade ago, when we started looking at, some of the research work within the research community about, making sure that our data centers, can operate more efficiently in the tropical climate.

Now, data centers, comprise of almost a fifth, of the ICT carbon emissions. And because they are such a huge component of the carbon emissions, of course, their efficiency has always been top of the mind. Now in the tropical climate like ours, a large part of the energy sometimes is attributed to the cooling systems, right?

The air conditioning that's actually needed to bring the temperatures down. So as you rightly pointed out, what we found out is that actually if you were to increase the temperature by one degree, that can lead to a savings of between two to five percent off. Carbon emissions. So, and that as a result, we have been investing in research within our academia, funding some of the innovation projects with our ITC players, in order to look at what actually works and what doesn't.

Because I think in Singapore, regulations always need to be balanced with innovation. So that have kind of, led to what happened last year, which was that we released the first, standards for tropical data sensors. But we wanted to go a lot more, right, because some of those standards, around cooling and all that, that's kind of like looking at how efficient the radiators are in a car.

But we also need to look at how efficient the engines are. And the reality is that, if you look at the trends of ICT usage of software applications. I mean, so much of our lives, whether it is watching videos, watching TikTok, right, our education, around all that, most of this have moved to become, to be enabled by digital technologies.

And when we look at the consumption of, data centers and the kind of workload in it, it is increasing year by year. Now, with the explosion of AI, we know that the trend is probably that there will be more consumption of digital technologies. And those are the engines that sits withinssb the data centers.

And we need to make them efficient. And as a result of that, we have decided that we need to also get onto this journey of greening the software stack. And greening the software stack means a few things. The first is, of course, I think this is still a fairly nascent area. How do we make software more measurable, so that there's a basis of comparison, so that we can identify hot spots that I think is important.

The second part that I think is important is also, given all the trends today, GPUs, CPUs all needing to work together, how do you make them work efficiently? How do you process data efficiently? How do you make sure that the networks and the interconnects within the data centers are efficient.

I think all of these are worthy problems, to look at. Some of it will rightfully stay, still in the research stage. So we'll be funding, research programs, called the Green Computing Funding Initiative around it. But at the same time, we also think that there are some practices that may be a bit more mature already, and we should encourage companies to actually innovate on top of it.

So we're also conducting green software trials.

Chris Skipper: Hey, everyone. I hope you enjoyed that preview from CXO Bytes. If you want to listen to the rest of the episode, please go over to the CXO Bytes page on wherever you find your podcasts. Just search for CXO Bytes and enjoy the rest of this insightful conversation between Sanjay and Dr. Ong Chen Hui of the IMDA.

And to listen to more episodes of Environment Variables, Please visit podcast. greensoftware. foundation. Bye for now!

Hosted on Acast. See acast.com/privacy for more information.

]]> In this episode we preview the first episode of CXO Bytes. Join host Sanjay Podder as he talks to leaders in technology, sustainability, and AI in their pursuit of a sustainable future through green software. Joined by Dr. Ong Chen Hui, Assistant CEO of Singapore's Infocomm Media Development Authority (IMDA), the discussion focuses on Singapore's comprehensive approach to digital sustainability. Dr. Ong highlights IMDA's efforts to drive green software adoption across various sectors, emphasizing the importance of efficiency in data centers and the broader ICT ecosystem. Listen to the full episode via the links below.

Listen to CXO Bytes:

Find out more about the GSF:

If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Chris Skipper: Hello, and welcome to Environment Variables. This is Chris Skipper, the podcast producer, bringing you a very special episode of Environment Variables. Today, I'm excited to share with you a preview from our new podcast series, CXO Bytes. Hosted by Sanjay Poddar, Chairperson of the Green Software Foundation.

CXObytes dives deep into the intersection of innovation and sustainability within the tech industry. In each episode, Sanjay will be joined by industry leaders from the C Suite to explore practical strategies for greening software while driving enterprise growth. So without further ado, let's get started.

Let's listen in on a sneak peek of the first episode. 

Sanjay Podder: Hello and welcome to CXO Bytes, a podcast brought to you by the Green Software Foundation and dedicated to supporting chiefs of information, technology, sustainability, and AI as they aim to shape a sustainable future through green software. We will uncover the strategies and a big green move that's helped drive results for business and for the planet.

I am your host, Sanjay Podder.

Hello everyone. Welcome to CXO Bytes. This is our inaugural podcast on how do you use green software for building a sustainable future. This is a new podcast series and the whole idea behind it is, you know, embracing a culture of green software, it needs to come from the top. And we therefore want to talk with decision makers, with business leaders, with leaders who are running nation states like Singapore, for example, at C level.

You know, how are they driving this culture change when it comes to digital sustainability and green software, for example?

Today I am super excited to invite Dr. Ong. She is the Assistant CEO of IMDA, which is the Infocomm Media Development Authority of Singapore. And we are going to chat on how IMDA is championing digital sustainability as well as green software. Welcome, Dr. Ong.

Dr. Ong Chen Hui: Thank you for having me on your inaugural podcast on green software.

Sanjay Podder: And you know, I had my own selfish reason for inviting you because while the Green Software Foundation has been interacting with many, many large businesses across the world, IMDA and Singapore GovTech, these are two members of Green Software Foundation who represent the government, right?

And we all know the very important role that government will play in sustainability in general. So I wanted to understand from you, you know, how are you looking into this space? So we will talk a lot about that. The other aspect is probably to begin with, for our audience, a perspective on what is IMDA.

You know, what is your specific remit, what you are trying to do in Singapore, if you can give us, you know, a few insights into that.

Dr. Ong Chen Hui: Okay, so here in Singapore, of course, climate change is actually something that is a bit of a existential thing for us, us being a small nation state and we're also an island, to us, climate change and the associated rising sea level is a matter of concern. Right? So, as a result, we have put in a green plan that states our, sustainability goals by the time we reach 2050. And this is actually a whole government effort. So, I don't think it is a case where it's one ministry or one agency that's responsible for the whole world. It is about the whole of government working together in order to make sure that we meet the goals of our Green Plan.

Now, what are some of the things that we are doing? Many things, for example, the National Environment Agency is actually rolling out some of the regulations. We have things like e-waste management, for example. Just now you mentioned GovTech, which is our sister agency. GovTech is also rolling out green procurement when they're actually procuring software solutions. Within IMDA, we are responsible for some of the industry development. We're also what we call a sectoral lead of the ICT sector. So, our own green strategy, comprised broadly of three different strokes. The first is about greening ourselves as an organization.

The second is really about greening the sector that we are responsible for, that we are leading. So, in that case, there will be things like the telecommunications sector, the media sector. And the third thing we want to do is to enable our ICT solution providers to provide green solutions to the broader economy so that we can scale the adoption, we can ease the friction out there in the ecosystem.

So essentially, that's greening ourselves, greening the sector, as the lead. And the third is really to kind of provide solutions through the ecosystem so that the wider community can actually benefit.

Sanjay Podder: Now this is really a full 360 degree kind of approach and it is phenomenal. And, I was, I was wondering, you know, and you mentioned briefly on Singapore being an island state. I was thinking, why digital sustainability? What will happen if Singapore decides not to do it, for example, right? Do you have a point of view, say, because, you know, there are many different levers of, sustainability, you know, I can understand the larger sustainability, but what is the importance of digital sustainability?

Do you think it's an important enough lever or maybe you can look at nature biodiversity or something else, right? So specifically for digital sustainability. What is it that triggers IMDA that this is a important initiative? And I'm, I'm seeing this is my second year in Asia Tech that, you know, this is something you give a lot of importance to.

Bringing in leaders from various organizations. Doing deep deliberation. I also remember last year, you brought out your new data center standards, I think increasing the temperature by one degree that has an implication. If you could throw a little bit more light on digital sustainability in particular,

Dr. Ong Chen Hui: Mm hmm.

Sanjay Podder: why do you feel that's a very important lever for a country like Singapore and maybe for many other countries around the world?

Dr. Ong Chen Hui: Yeah. Well, I think you're actually exactly right that when we are trying to drive sustainability, actually there are many different strokes. Some of it includes looking at energy sources and all that, which actually is also very important for Singapore because we are small. We do, have to look at, different kinds of energy sources and how we can potentially actually import some of them, right?

Now, when it comes to digital sustainability, actually our journey, I would say started many years ago. Maybe more than a decade ago, when we started looking at, some of the research work within the research community about, making sure that our data centers, can operate more efficiently in the tropical climate.

Now, data centers, comprise of almost a fifth, of the ICT carbon emissions. And because they are such a huge component of the carbon emissions, of course, their efficiency has always been top of the mind. Now in the tropical climate like ours, a large part of the energy sometimes is attributed to the cooling systems, right?

The air conditioning that's actually needed to bring the temperatures down. So as you rightly pointed out, what we found out is that actually if you were to increase the temperature by one degree, that can lead to a savings of between two to five percent off. Carbon emissions. So, and that as a result, we have been investing in research within our academia, funding some of the innovation projects with our ITC players, in order to look at what actually works and what doesn't.

Because I think in Singapore, regulations always need to be balanced with innovation. So that have kind of, led to what happened last year, which was that we released the first, standards for tropical data sensors. But we wanted to go a lot more, right, because some of those standards, around cooling and all that, that's kind of like looking at how efficient the radiators are in a car.

But we also need to look at how efficient the engines are. And the reality is that, if you look at the trends of ICT usage of software applications. I mean, so much of our lives, whether it is watching videos, watching TikTok, right, our education, around all that, most of this have moved to become, to be enabled by digital technologies.

And when we look at the consumption of, data centers and the kind of workload in it, it is increasing year by year. Now, with the explosion of AI, we know that the trend is probably that there will be more consumption of digital technologies. And those are the engines that sits withinssb the data centers.

And we need to make them efficient. And as a result of that, we have decided that we need to also get onto this journey of greening the software stack. And greening the software stack means a few things. The first is, of course, I think this is still a fairly nascent area. How do we make software more measurable, so that there's a basis of comparison, so that we can identify hot spots that I think is important.

The second part that I think is important is also, given all the trends today, GPUs, CPUs all needing to work together, how do you make them work efficiently? How do you process data efficiently? How do you make sure that the networks and the interconnects within the data centers are efficient.

I think all of these are worthy problems, to look at. Some of it will rightfully stay, still in the research stage. So we'll be funding, research programs, called the Green Computing Funding Initiative around it. But at the same time, we also think that there are some practices that may be a bit more mature already, and we should encourage companies to actually innovate on top of it.

So we're also conducting green software trials.

Chris Skipper: Hey, everyone. I hope you enjoyed that preview from CXO Bytes. If you want to listen to the rest of the episode, please go over to the CXO Bytes page on wherever you find your podcasts. Just search for CXO Bytes and enjoy the rest of this insightful conversation between Sanjay and Dr. Ong Chen Hui of the IMDA.

And to listen to more episodes of Environment Variables, Please visit podcast. greensoftware. foundation. Bye for now!

Hosted on Acast. See acast.com/privacy for more information.

]]> The Week in Green Software: AI’s Power Problem The Week in Green Software: AI’s Power Problem Thu, 04 Jul 2024 07:00:00 GMT 53:03 70w73v40 false https://podcasts.castplus.fm/e/rnkm4ywn-the-week-in-green-software-ai-s-power-problem 68dc7d0fd5c8a1f9b326f486 68dc7cef46a2532cdd8d1c23 75
In this episode of Environment Variables, host Chris Adams is joined by Asim Hussain to dive into the complexities of AI's growing energy demands and its environmental impact. They discuss innovative approaches to sustainability, such as using fungi to manage building waste in data centers and the potential for greener materials and practices. The conversation also covers software optimizations to reduce AI's carbon footprint, emphasizing that energy inefficiency cannot be outsourced. They highlight the importance of integrated sustainable practices in tech development, particularly in the face of increasing AI power consumption projections.

Learn more about our people:

Find out more about the GSF:

News:

Events:

Resources:

If you enjoyed this episode then please either:
Connect with us on Twitter, Github and LinkedIn!

TRANSCRIPT BELOW:

Asim Hussain: And now there's a big hoorah in our space because like AI's now gone and

our automation has now gotten the point where even our jobs are now we're all really like nervous and upset and but you know this has been the pressure that we've been applying to the rest of the world with all the industry for decades and decades and it's just now coming to us and affecting us so you know we don't really have a leg to stand on I'd say.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to Environment Variables; This Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Today, in our news roundup, we're diving into some of the pressing issues at the intersection of AI and sustainability.

So, with AI rapidly advancing, the energy demands of training and running these models are also seen to be skyrocketing. Posing significant challenges for the environment. We'll also be touching on some legislation for promoting sustainable business practices amongst AI companies and the potential for a green levy to drive investment for greener eco-friendly technologies.

We'll also be talking about some of the latest papers that have been published for people trying to understand and get grips on cloud carbon emissions. And finally, we'll touch on some of the exciting events in the green software community, including conferences, workshops, and masterclasses aimed at fostering sustainable development practices.

Joining me today for today's news roundup is my longtime friend, Asim Hussain of the Green Software Foundation. Asim, for people who've never listened to the podcast before, can I give you the floor to introduce yourself and some of your background? 

Asim Hussain: Yeah, sure. So my name's Asim Hussain. I am the executive director of the Green Software Foundation. And yeah, I've been at the intersection of sustainability and software. I've been very lucky to be thinking about the same question about the intersection of sustainability and software for quite a few years now. And yeah, I've been well, mate. I've been, I've just come back from a vacation, which has been long time coming. I do have a little bit of a, cold, so that's what would explain the slightly nasally, annoying nasally sound the audience members are going to have to experience for this podcast.

Chris Adams: So this is with your hot beverage and Nurofen chaser 

Asim Hussain: hot beverage. Yeah, that's what I like to do. That's how I like to start every podcast episode is a coffee and a Nurofen, every conversation with Chris Adams has to have a, both a coffee and a Nurofen.

Chris Adams: Wow, that's, one thing to take away with me. All right, folks, I should just briefly introduce myself before we dive in. I am the executive director of the Green Web Foundation. It's a small Dutch nonprofit based, well, in the Netherlands, where we are working towards an entirely fossil-free internet by 2030.

I also am one of the hosts of this podcast here. As well as one of the organizers of ClimateAction.tech, which is an online community, where actually me and Asim first met online before he basically headed off to set up the GSF in its current state. Alright, Asim, are you sitting comfortably?

Asim Hussain: I'm standing uncomfortably.

Chris Adams: uncomfortably at your swanky standing desk.

That's good enough for me. Should we start with news then?

Asim Hussain: Yeah, let's go for

Chris Adams: Alright, okay, so Asim, I was thinking of you when I saw this paper, this story. The first one is a story about mushrooms eating building waste in data centers. So this is a link to the Data Center Dynamics website talking about specifically the use of these, of essentially building-waste-eating mushrooms at the Meta data center and other ones.

And the general spiel of this is that there are now a number of companies which are, essentially deploying fungi, various kinds of fungi, to deal with all the building waste that ends up being created when you might kind of read, when you're essentially demolishing a building or creating a new one.

And, it essentially takes all this waste, and the fungi are able to Essentially, deal with the toxins, and then create something like, kind of, fungi-style bricks that can then be used as a kind of circular building material going forward. And Asim, given that you're our kind of resident mushroom fan, I wanted to just like, see what you thought about this, or if you had any particular immediate like, hot takes or things when you saw this one? 

Asim Hussain: No, that's all. it's a great application. In fact, it's not an uncommon application of, you know, what people are applying, you know, fungi in this technology for. It's actually one of the, one of the, one of the, one of the very exciting kind of broader sustainability solutions in this space that there is.

I mean, there's a couple of different types of fungi. You're going to have to pause me at some point. There's a couple of different types of fungi, but there's one particular type was kind of saprophytic, which is effectively what Fungi, the purpose it has in kind this life that we lead is it basically, it's the thing that destroys things that have died. And if it didn't exist, then we'd be basically living on top of this massive mountain of logs that aren't decomposing. So that's one of the things that they're really, really good at. And it's been, it's, there's been a lot in many different spaces, been a lot of active research, a lot of startups, a lot of organizations exploring how to use Fungi to decompose things that they don't normally decompose. And it's actually quite an interesting technique. Because even people, what's so fascinating about the the fungi space is it's, driven in large part by citizen scientists, which is one of the things I love about it. And there's a lot of citizen scientists out there who are doing things like trying to find a strain of fungi, which can decompose certain types of plastics and you would literally do this. You would literally grab a selection of these plastics. You put them in a, you know, you can go online. It's as simple as this. You put it in a blender, you blend up a plastic in your home blender. And then you just have like, as you can probably see behind me, I have lots of jars out there, which have like different strains of fungi, and you would just put that with other material in the jars.

And then you'd collect lots of different strains. Like. Every single kind of mycelium is like of a different strain of it of the same one. So you can have like millions and millions of different types of strains. You go into the forest and you see a type of mushroom you've never seen before. And you're like, ah, maybe that will absorb this plastic. And so there's a lot of interest in this trying to like find those strains of fungi that can kind of absorb and transform, you know, different materials. Obviously certain fungi only in the forest, they only work on certain types of trees. They have like a relationship with them, but you can actually find strains of fungi, which do different things. And kind of the interesting thing about it is about turning them into, bricks like that as well. There's actually organizations out there trying to replace packing material for boxes. But what you do is you basically, you create like, so you take some time. It's not like a foam that you stick in and like 30 seconds later, there's a thing you basically have to Have the thing you want to pack in a box Inside the box you put like a substrate which can be the thing that mycelium grows on you then like almost like impress inside this substrate the shape of 

Chris Adams: like the

Asim Hussain: book or something the mold and then you inject it with the mycelium and you put it in basically an oven for like a month. And it comes back out and then you basically spray it off and like the actual mycelium has grown into the shape of the thing. And then you've got something which you can put in the packing crate. And then at the end of the day, you just, it's a mycelium. You just break up and put it into your garden and you, and it decomposes.

There's lots of like wonderful stuff. There's a, great guy called Paul Stamets, who's quite a character. But he's done a lot of, he's done some great TED talks in the use of like fungi and mycelium in, kind of waste and getting rid of waste even getting rid of oil and there's a lot of kind of very active stuff in this space.

Chris Adams: Can I stop you one second there, mate? Cause, cause there's, so you just said Stamets, right? So Stamets hit, so in real life, there's someone called Stamets and in the whole, and that's, so the whole, when they had this whole Star Trek discovery thing, where there was this guy called Stamets who was using like the mycelial internet space thing, that's a direct reference to this dude, basically.

Asim Hussain: That's a direct reference to Stamets, which is kind of, which joins two of my biggest nerd bubbles together in the most beautiful way, but yeah, there's the engineer, and in Star Trek Discovery, how they, instantly, there's a new type of drive, and they instantly can, move to one part of the universe to another, and it's called a spore drive, and you need to kind of enter this kind of psychedelic realm to connect transmission, so yeah. 

Chris Adams: My word, Asim, I was not expecting us to dive down that myco rabbit hole for that, but that was a lot of fun. Thank you very much. So you basically said. By doing this, so in this case of packaging, this basically removes the need for like, say, fossil-based expanded polystyrene in packaging, and in the case of materials here for buildings, you would use that instead of having to get a bunch of virgin materials, for example.

This would be like a circular, that's the approach that they'd be using here, right?

Asim Hussain: So what, so the specific approach they're using in this particular article is, I believe it's more about, it looks like they're using, basically trying to get rid of the drywall that they have inside the data centers. So I don't know if it's a particularly from a decommissioning a data perspective or renovating a data center perspective, but they're ending up with a lot of material, which typically you would just dump in a waste fill, but now they've basically got a form of mycelium, which can eat drywall and generate something that's, decomposable, maybe edible.

Yeah 

Chris Adams: All right, cool. And so we, this is, mentioning, referencing the metadata center in, I think, Tennessee. But we've also seen Microsoft, as far as I'm aware, Microsoft has also been a bit of, it's been dipping their toes into this field as well. And one of the reasons why you might care about this is that, well, last year, Microsoft's reported emissions, when they released their sustainability report, it was like, up 30 percent and a significant chunk of that came from buildout of data centers.

So we are now starting to think a lot more about the embodied energy in the facilities that are created so that we can actually have data centers, so we can actually use compute, a lot of the kind of compute power available to us, or even some of the AI power, or the kind of sources of AI and stuff like that, because you need, they need to be in a building somewhere to get this stuff built.

And like, this kind of made me wonder actually, Asim, surely I imagine some of this might show up in an SCI score, a Software Carbon Intensity score, if you're purchasing cloud from a certain place. If you've seen a massive buildup of data centers, surely that might have an impact on the embodied carbon for the compute you might be using, right?

Asim Hussain: It could do, it depends on what your, cause in the SCI, there's kind of two components. One is the software boundary, which is "what are you going to include and not include in that score?" I remember us having quite a few conversations in the early days of the SCIs, which "should you include the, like the concrete that was used to pour the floor of the data center?" And I'm not too sure we really, I don't, think anything particularly made its way into the specification, but it has to be, if you're measuring something, it has to be something which drives a choice or a behavior. So, you know, I suppose what I'm going to with this is, if there was a data center, which was particularly built like a zero carbon, maybe built with mycelium or something.

I don't know, but like, if there was a data center, it was particularly built with that choice, then maybe it is something you want to include in the score, because then that can drive an action of choosing one option over another. But if every single data center is effectively built exactly the same way, the discussions we were having was, well, that's just overhead of adding a sec, effectively a coefficient, which wouldn't really drive a decision-making factor. So, I suppose as what I'm going to this is excitingly, if there are data centers that are being built, which are going to have

vastly different embodied carbon profiles, and then you, and then if that was included in an SCI score... and I think as we move forward with SCI, because one of the things that's happening... SCI on its most, we, launched kind of the version one and now 1,1 of the SCI and it's, and it was, it's very bare, very basic. It's designed to be built upon. And so now what the teams are having conversations around is like, if you were to apply SCI to AI, specifically, what does that include? And one of those questions

they want to answer is what, 

Chris Adams: building, 

Asim Hussain: yeah, like what you include so, so when someone reports an AI score, we can actually start getting to like some apples to apples comparisons. So that's, a conversation that, you know, it's interesting. Maybe we should bring that, bring it up again is do you include the embodied of the data center? But then you also get into the headache of "my God, it's hard enough trying to figure out what the embodied of a chip is."

Chris Adams: Yeah.

Asim Hussain: And now going to ask to figure out like what the embodied, so there's has to be some practicality aspect to this as well.

You know, you know, we have to, there should at least be some models. I don't know.

Chris Adams: So there are models that, as I understand it, there are models for working out the, essentially like the carbon emissions for a kilo of concrete, for example, or stuff like that. Some of these exist. And,

Asim Hussain: exists from a

LCA, 

Chris Adams: and there are companies, and we know there's like, in Europe at least, I know there's a company called LeafCloud.

That are explicitly using, are doing, making reused, or, they're reusing heat, but also a very specific kind of data center, which isn't like a very large outta town thing. They have like essentially shipping containers put into places like say greenhouses, where the heat is being reused, for example, and where they're not having to build a whole bunch new buildings. There's also, I think in Switzerland, there's one company, because we maintain, where I work, we maintain a directory of green data centers. And one of them is a, they basically reused a old factory building with a waterwheel that used to be kind of like a clothing factory, and now it's a data center.

So they've essentially reused the whole building shell. They haven't built a load of stuff as a result. So this is one place where this might show up, but in order to do this, you need to have access to the numbers for this. And that's still a bit of a challenge because, yeah, we don't have the, we don't have easy access to these numbers, and like you do say, it's a challenge just thinking about chips, let alone expanding the boundary to the actual buildings instead.

Asim Hussain: Yeah. I mean, at least you have some information when you're running software, like what, you know, you can, now that we've done, a lot of that workings out so you can figure out, you know, perhaps it's this chip, but I think given the secrecy around data centers, I don't know, I think there's going to be,

I don't know. 

Chris Adams: It's gonna be a challenge, because while we have this practice of, essentially, water usage and electricity use, so many things being under NDA, it'll be very hard to come up with some numbers without using, like, a basic number. Okay, alright. We have totally gone past talking about mushrooms and data centers into all these other things, but I guess this is part of the whole thing about sustainability and technology.

It's all interconnected.

Asim Hussain: It's all interconnected.

Chris Adams: Shall we go to the next story?

Asim Hussain: Yeah. Let's go for it. 

Chris Adams: So this is a piece from Bloomberg, actually, so this is the topic of this is Solutions for AI's Energy Inefficiency Can't Be Outsourced, and this is an opinion column from Bloomberg talking about this projected demand some of the figures which are pretty, pretty impressive, they basically say, in the US at least, it's, there's a projection saying that AI, the growing demands of energy are like, is projected to make up around 8% of the US' power consumption, electricity consumption up from 3% in 2022.

Now these numbers seem a little bit high and they are citing a kind of this arms race of different kinds of organizations, essentially building out these massive data centers but also buying loads and loads of chips, but it does talk about some of the approaches that we're seeing now to kind of rein in some of this growth.

So one of the things was this idea of one-bit architecture, which is essentially, I'm not going to pretend to understand it. And I'm not sure if you are similarly informed in this one, but

Asim Hussain: I'm going to definitely pretend to understand it.

Chris Adams: In that case, I'll hand over for you to confidently bluff it around, just like a ChatGPT would actually, Asim, the floor is yours.

Asim Hussain: I'm going to, I'm asking GPT. No, I'm guessing, and this is, I haven't really, I've, seen it, but because it's one bit and a bit can only be one or zero, I'm guessing what this is, that, you know, instead of like pumping in a number between one and 256 as one of the inputs to a node in a model. Maybe you could just try one or zero. And then output one or zero and then see if that actually still gives you some pretty reasonable results and from what it looks like it might do and you know for those of us I kind of it's like there are extreme inefficiencies you can do when you're working at the bit level in terms of programming and computation and instructions on the chip and things like that because it's so much lower level than the architecture.

I presume that's what it is, which actually to me is. It's really exciting from a, from the level of, this is a software architectural solution, which is effectively, I think what we've been advocating for, a large part of the time, which is, you know, we do need to I hate to use the word code because I don't want people to dive down the, you know, building more. I, well, AI is one of the, one of the few areas I would say where actual code efficiency is extremely important. But yeah, this is kind of, it's interesting. Now that there has been a pressure applied to optimize, the optimization has happened.

Chris Adams: Yeah, that's, so basically, Asim, I think you're about right. Now, when I, remember when I skimmed over this paper before, one of the key ideas was, the one bit approach was, essentially, when you, would be able to, you would use this to encode the difference between different parts of, like a dataset rather than showing absolute numbers.

And one of the things that this allowed you to do was allowed you to just use addition rather than multiplication in some cases. Now, I'm not an AI specialist and I'm not a hardware specialist, but the general idea was by representing things in a more somewhat simple fashion here, you avoided having to make some of the expensive calculations that you would otherwise need to do.

And this basically reduced the energy that you might need to run some of these calculations. So this was like one example. I was quite impressed to see this inside Blumberg because it was a very quite new research, but also really, technical and actually quite promising. So yeah.

Asim Hussain: It's, it, the title is interesting though, isn't it? Cause it's not like, it's not like software making, it's kind of talking about energy inefficiency can't be outsourced.

Chris Adams: Yeah,

Asim Hussain: I just thought it was an interesting, it's like no one really knows or cares or thinks about the software side of the, this whole equation.

To me, this is just like a software optimization. So you would just say like software can be optimized to reduce any AI's energy footprint. It's not, not expressed in that way. It's interesting. Kind of expressed 

Chris Adams: there's a couple of things that I think are also really interesting about this piece, in my view, was that it talks about the kind of economics around some of this, and basically the idea of outsourcing this is essentially how we have a bit of a tendency in the technology industry to say, "well, we're just going to have, like, We realize that data centers use loads and loads of power, so what we're going to have to do is just somehow get loads more power."

And so you basically have people talking about, oh, obviously the solution is to deploy loads and loads of nuclear, for example, right? Never mind that these take between minimum 10 years to get built, right? So, what are we going to do in the meantime? A lot of the time it's likely to be coming from things like gas, if you're going to be using something like that.

So that's an issue there. But it's also worth thinking a little bit about these figures that were mentioned in this story. We've seen numbers like 8 percent of the USA's energy consumption by 2030. It's worth bearing in mind that these numbers are often coming from the utility providers in various states, all right?

So like, say, in, say, Virginia, I think it might be, I forget the name of the actual monopoly provider, but there's only one provider over there. And basically I think that's Pacific General. I think that's actually on the, other coast, basically.

Asim Hussain: We've played this game before where you can name all

Chris Adams: was Caiso last time, which was, which is, that's the Independent Systems Operator, which is not the energy company.

The energy companies are somewhat different because it's a, because in many, because they have very specific, if you're in a state where you've got a single provider, they are allowed The only, the reason you only have a single provider is that you have basically had that state agree to have a, what's referred to as a natural monopoly.

So, they basically, the agreement is, we will give you a guaranteed 10 percent net profit plus for your organization, alright? But you need to basically, yeah, as long as you agree to share your plans, For the new infrastructure you're going to build over the next few years, but also you need to justify this in each of these cases.

And when you think about this, if you're going to get a 10 percent net profit from that for any of the energy you, get. Now, what you, if you want to increase your profits, what you need to do is you need to say, "well, I have loads more demand coming. I need to like double my expected demand to double my profits inside this."

Asim Hussain: Oh, I see.

Chris Adams: is one of the things, because this is a lot of the existing providers, they're used to saying, "well, we've got all this extra demand. What we need to do, we need to build a bunch of new gas, fired power stations. And 'cause we know we're gonna make a 10% guaranteed profit on all the infrastructure we, build.

That's basically, you know, we are incentivized to say it's gonna be really, high" basically. So. You, it's, really worth looking at a paper by one, one gentleman, John Kumi, who's actually, who's spoken a lot about this, because 20 years ago, we had the similar thing when you had people in the coal industry saying, "well, coal was what powers the internet, so you need to have more coal fired power plants if you want more internet."

We have a very similar thing happening. In this case as well actually. So it's worth bearing in mind that yes, we do see these kind of apocalyptic forecasts for energy, but you also see that when you do have constraints on this because it's so difficult to build, then we do end up with a renewed interest in energy efficiency.

And even at the kind of like energy level, right, there are different ways that you can basically meet demand. You can meet demand by adding new supply, but you can also meet demand by investing in energy efficiency. And that's, and this is very much what it looks like, so a lot of the ideas you might see at the energy sector, I think, are at least applicable, or at least relevant in what we talk about with cloud, because essentially you're looking at a kind of commodity that you pay for on an hourly basis, or something like that.

Asim Hussain: Well, that's kind of one of the... All I see is there's, a significant amount. There's not a significant, there's a fixed amount of investment and focus that organizations can put into something. And if you present them with an option, either put all this engineering effort to make something more efficient, which costs 10 or buy renewable energy, which costs five and then, well, I'll choose the five one. So I think that's kind of, that's. That's why kind of investment goes in one way or the other. Whereas I suppose what's happening now is that energy is now, we're reaching the point where energy is, and I'm just throwing out numbers here for energy is now costing 12, but the development still costs 10.

So like, well, maybe we'll put some money into development

Chris Adams: Yeah.

Asim Hussain: So that's kind of, and that's interesting that's why you want things like we were talking about levies and money last time. That's kind of why you want to change that balance of it a little bit. And that's also significantly why in the SCI specification, the decision was made not to include any energy offsets or anything like that.

Just because if you gave somebody the option of spending 1 instead of spending 10, they would spend 1. And we want people to spend the 10 to actually make things more

Chris Adams: Yeah, to address the consumption issue, rather than just think about the intensity. 

Asim Hussain: Exactly, yeah. But this is exa I'm really excited. And the other thing I was thinking about as you were talking was I was just remembering about my time at Intel. I think this maybe links a little bit to the Nvidia's statement as well. So, I think I might have, I always love telling people this story because I just think it's such a cool word and it's such, it tickles my sci-fi bone so much. But there's a statement they used a lot, which was dark silicon. Have I told you this? I

Chris Adams: No, you haven't. 

Asim Hussain: people. There's a dark silicon. And I was like, "Oh, that sounds good. What's the, what's dark silicon?" And what dark silicon is when they're kind of looking at a chip and they put load on it.

And the key thing with a chip is how much can it expel heat and still function at that level. So looking at heat on a chip. And so when they're running a certain software on a chip, they'll put like a, what you call the heat detecting camera on. And you know how they look like this. It's very red, it's very red on the bits that are hot.

And it kind of looks

Chris Adams: Ah, okay, yeah, look at a thermal house, 

Asim Hussain: Thermal imaging. yeah. Yeah. Even though the black might not be like ice

cold, quite hot, but like relatively it's cold. And so the things that they would be really like thinking through is like, how come this software, how come half the chip is black?

Like, why aren't you using the rest of the chip? Like you've maxed out the chip, but half the chip is black. And really what it kind of, you know, what it goes back down to is that, you know, we called it, I think I might call it the silicon gap, which is the gap between what engineers are building and what silicon manufacturers are enabling on their chips. And there's this disconnect between, you know, they're, all building, "why aren't you using this, these more advanced chip sets that are more efficient? Why are you using this stuff on this side of the chip?" And so I think that's something that we need to get down and tighten that gap to use this infrastructure more efficiently, I think over the years, from a developer's perspective, it's always been about time to market.

How do we beat our competition? It's never around, how do we use this chip more efficiently? And so I think that one bit architecture is, it sounds like an example of that. It sounds like an example of, we want to leverage the instruction set on this chip to be as efficient as possible. We need to change. Fundamentally how we're architecting and even thinking through algorithmically this code to take advantage of that. And that's, I think it's also like this, other area, which is completely, we're just ignoring, you know, there is this dark silicon and honestly, the silicon manufacturers are like, why are developers not, I don't understand, we put so much energy and time into like

building 

Chris Adams: we're only using a of it, right? 

Asim Hussain: a The percentage of it, and there's that also, and I'm going to, I'm rambling on for a second, but just one more, one more point. I thought it was really interesting. One of our organization's Entity Data, they did a really great report. It was kind of two years ago now, I think. I don't think we really circulated.

I don't think we circulated that well. And it was, they just, they just looked at Java, you know, Java, like still, there's still a lot of very antiquated Java applications running out there in the world. And they just said, what is the energy difference if we just upgraded, not the code, but the JVM, the underlying JVM.

And that's all they did was I think they upgraded, I cannot remember. I'll find a link to the article and the paper. It was like several steps up. But they were like, "look, most apps are still running on whatever the JVM was they were built with like 10 years ago. And it was a seven, it was a 60 or 70% energy efficiency improvement.

It was unbelievable. The energy efficiency improvement just from grading the JVM. And that was

Chris Adams: Ah, okay. 

Asim Hussain: That, and if you think about what that means, what happens was the chips evolve to have different instruction sets. The JVMs, only the modern JVMs are built to the new one. And so if you're running on the old ones, it's just using the old instructions.

So you're not really leveraging the infrastructure the same way, which is why like recompiling software with like, you know, the latest version of the compiler against the latest version of the chip. It's really important. And it again, that was Intel's, when I was at the time, that was their big push.

They were like, "use the latest bits, use stuff that's compiled now using the latest optimizations." 'Cause they saw a lot of people were still just kind of compiling, leaving that binary, letting it run for like four or five years. And then, that's it. And yeah, I'm going to stop ranting now.

Chris Adams: No, that's actually, I didn't realize, I was somewhat aware of things like the JVM, there's like hotspot or different kinds of flavors of the Java Virtual Machine to run this code, and it's somewhat similar to like in PHP land, like when a new version of PHP came out, because it made much better use of the underlying code, the underlying hardware, you saw a massive increase in performance.

And like, you kind of see something a bit like that with Python as well, with the whole global interpreter lock. Like, I can have a piece of Python that'll be running, and it won't be able to use all the other cores in my machine, in my computer, right? So, rather than lighting up the rest of the silicon, it's got just, it's, most of my computer is dark, basically, in that same kind of approach.

All right, yeah. Cool, alright, so that's like one of the approaches that we have, and this is one thing that you could plausibly do. I've shared a link to a blog post that I've, I was trying to explore this to find a way to explain it, to basically explain the fact that you can reduce the emissions associated with code without actually changing the code, by thinking about what options you might have in terms of, like you said here, like you change the VM or something like that, or change when you run it, or anything like this.

And I'll share a link to that, because I've kind of framed it in terms of If, there are three, three things you can change, basically. You can change the time of running something, which is kind of speaks to carbon awareness. You can change the speed, the amount of compute, computation you might be using, the number of cores you might be using something, or you might change the place, like where in the world you choose to run this for the carbon intensity of the underlying energy.

So I'll add that to the show notes because it might be another nice helpful addition to this. Alright, okay, that was quite, that was fun. Shall we look at the next story?

Asim Hussain: Go on. Yeah.

Chris Adams: Okay, the next story is, this is actually from The Guardian, and this is talking about the balancing some of the incentives of the kind of profits that are projected to come from deploying AI with something like a green levy on these profits, basically.

And this is actually, came from that, Left leaning organization, the International Monetary Fund, 

Asim Hussain: Oh, wonderful. Great. Great to see them in this space.

Chris Adams: And basically what they are, the argument from the IMF is basically saying, well, you've got all these very profitable AI firms, and they, and we know there's both the social and environmental impact that's taking place here. So what you should actually have is some kind of green AI win for tax, essentially, that will be used to fund some of the, sustainability initiatives.

And like, to be honest, I have a lot of sympathy for this because what we've seen from the largest providers in the last like year is that given the choice between investing in efficiency or investing in more capacity and building loads more data centers, we've seen all the big providers go for building more capacity and like emphasize profits rather than the environmental impact here.

So it looks like we're not the only people thinking about this. The IMF is thinking about this as well. And they're saying, about this. You need something a levee to do this. Do you have any kind of particular thoughts on this? First of all, Asim, because I was really surprised to see this come up from the IMF of all organizations.

Asim Hussain: Yes, I thought it was a very important point. I love the fact because the use of the word levy, and I remember us having the conversation last podcast about like use of the term levy rather than

tax instead of, explaining it, but then in the article actually uses the word tax, like all over the place. Yeah, I think it's really important. Like one of the things I'd say is that like, why is everybody so excited about AI in the first place? In my most cynical moments as as a software engineer, I would say, Our purpose in life is to either find solutions that help people waste more of their time or get rid of jobs and automation.

If you think about kind of why we have been like one of the most highly paid sectors for quite a significant amount of time, it's because building automations, yes, you could argue and helps you deliver kind of projects faster, but it also helps you to do more with fewer employees. It decreases the.

You know, the earnings potential, the, a lot of this stuff from that perspective, and now there's a big hoorah in our space. Cause like AI is now gone and

our automation has now gone the point where even our jobs are now we're all really like nervous and upset. And, but, you know, this has been the pressure that we've been applying to the rest of the world with all the industry for decades and decades.

And it's just now coming, to us and affecting us. So, you know, we, don't really have a leg to stand on, I'd say. We also. There's nothing we can do. It's happening is the only other thing I'd say. There's no, you know, we have to just accept it and move forward. But yeah, I really liked the idea of that because like, let me put it another way.

Like there's this huge kind of bro down AI tech bro showdown with like, well, I think Sam Altman posted something a year ago now, which deeply disturbed me. And he said he wouldn't be surprised, I think within the next couple of years, if there's a one person unicorn startup, which is a single, like, billion dollar organization run by one human being. And I was thinking to myself, like, that might actually be true. And I would, you know, I do, there is a chance that would be true. might happen in the future, but how do I feel about that? What is the human impact to that? I mean, what is the green impact to that? So I'm now going beyond green because I think that there's like this AI is going to make a few people and organizations immense amounts of power and wealth. How do we have ways to, redistribute all of that and to kind of add a level of fairness to, the rest of society? Is it okay? And so from a green level, absolutely. But I'd also argue from a societal level as well, like, like, you know, like when we talked, you know, how about this? When we spoke about the green transition, it was impossible for us to have like a proper conversation about the green transition without having a real conversation and talking about how we're going to transition the people who are employed in the fossil fuel industry over to other areas. I don't see having, I don't see us having that conversation here as well.

Like it's just ignored. And so I think that's something that we need to have is like, is if you're, if you want to have. The opportunity to get this much power and money and wealth, I think it should come with a certain amount of social responsibility to, you know, be a green levy in terms of the green ones, but I think it actually should be broader than that. It should be, you know,

Chris Adams: So address some of the. Some of the inevitable costs that might be incurred upon society to provide to, like, ease that transition. Okay,

Asim Hussain: yeah.

Chris Adams: I was not expecting you to go there, Asim, but 

Asim Hussain: Well, I feeling it. 

Chris Adams: Bang yeah, that's usually me kind of jumping up and down, actually. All right, okay, that 

Asim Hussain: How do you feel about it?

Chris Adams: I think, so you said this idea, like, I feel uncomfortable about a billion dollar startup with a single person.

And I, okay, how many, we're not that far from it, I think, because if, actually, no, we've been, so if you think, how many people work for WhatsApp? 

Asim Hussain: WhatsApp! I was WhatsApp, I think it 11 

Chris Adams: was purchased for $12.6 billion by Facebook a few years back, right? So that's not that far away on a kind of per person basis, but that's not a single person.

But you've got to realize that like, you know, if that was probably a lot better for the people who own shares in that than the people who are working for this. And we have seen multiple cases. We've seen cases like when a company has to choose between keeping on staff to work on something and getting rid of them, and then spending multiples of the staff's wages on buying their own shares to kind of in increase the cost, increase the share price. We've seen the decision that people have been taking, and there is the, I think that this is a thing that needs to be addressed. 'Cause the current, if we're going to assume that, if we're gonna accept that digital is gonna be the, this thing which is just as important to our lives as access to water or you to or energy or anything like that then you probably want to have a discussion about okay well how are the dividends shared well how is the upside shared in an equitable fashion so that we don't end up with people rushing outside with guillotines or in the very least right like it's not good for social cohesion basically so that's my the view that i might actually have on some of this

Asim Hussain: No, that's a, really, I think that's something that people, I've been, I don't know, should I say this?

Chris Adams: I'm going to stop you because it's coming up to 40 we've got one story, so we can, talk about societal accounting, all to carbon accounting, this new paper, which I think is really interesting and we both were nerding out about it before this call. So this is a new paper from Google, Carbon Accounting in the Cloud, a methodology for allocating emissions across data center users.

So, We can totally talk about the societal aspects of cloud computing here. But this one here is really interesting because this looks like one of the most interesting papers about how you apportion responsibility for your use of cloud services when, using these things. And for the longest time, we've had a real struggle because we haven't had access to any of these numbers.

And this paper really. Lays out a bunch of really interesting ideas with lots of really helpful diagrams, and it dies into how inside Google, people allocate carbon emissions for both internal use, but also for cloud customers. It's a really, fun paper, but it is also quite a significant piece of work.

Read. Like, me and Asim were quite excited about it, but we realized this is almost like a kind of book club kind of paper to read through, basically. 

Asim Hussain: Yeah. Yeah. Yeah. 

Chris Adams: So, Asim, I'll hand over to you because, there's a couple of things I'd like to draw attention to, but I suspect there's some things that caught your eye as well, or at least maybe you might have some context about why the two of us might be so excited about this.

Asim Hussain: Well, I just think, I think it's really exciting. Well, I get really excited when any organization does such a deep, thorough analysis of their emissions. The thing I'm kind of like going through as I go through this is like, part of me is like, I'm going to try and like represent some of this stuff as an impact framework manifest File because I can read an impact framework manifest file and I can compare it and I can look at it and I can know what's going on. Like one of the first things that the paper outlines, the approach that Google developed to quantify location based emissions of its individual products. And now I'm now like, I now need to dive into this paper to understand, well, yes, I'm understanding the, my definition of location based. However, I'm also seeing references to CFE, which to me doesn't factor as

Chris Adams: CFE being carbon-free energy 

Asim Hussain: carbon-free energy So like, there's like a lot of nuance to this stuff. and yeah, I I would probably, as an experiment for me to try and understand the paper, I might try and represent some of this stuff as a manifest file. 'cause for me that's quite useful as a way of, learning something in, in a way, in such a way where I can compare and contrast it to other methods and methodologies as well.

But it looks very, interesting, very exciting. And when Google first worked on its carbon dashboard as well. They were the only organization to have done it the slightly different way, which is kind of like bottom up, like from products and services up towards the top, and now IBM has actually done actually to give IBM some credit as well. And they've got great white paper as well. If you're not ready, I'll send it over to you, Chris. they've done another approach is kind of bottom up as well. And so they're the only two organizations that have gone bottom up. The other ones have gone top down and I'm always excited when people go bottom up, because then you get the data with the granularity, the level of products and services that people actually use, which they drives.

Which is what you need to drive emissions reductions. So that's it. I haven't read, I haven't read almost any of this So I'm interested to get Chris's, if you've got any time, I don't know. What are your hot

Chris Adams: So, the thing that really caught my eye from this is that it talks about some of the ideas that Google has been doing that other organizations aren't so open about. So, for example, you have like a given amount of capacity that might be available inside this. Now, what Google have done previously is they've kind of said, well, We know that we've got a certain amount of, kind of, energy that's coming from green sources and we've said that we want to have a percentage of our energy that is always running at, say, 100 percent carbon free, for example, right, and they, Google have an approach where they say we count something as carbon free if it's matched at the time of use and if it's I think it's on the same grid.

So it's not literally a solar power, but a set of panels on the data center. It might be a wind farm that's where you could plausibly deliver the power to that place. And they use this to kind of represent the amount of clean capacity as something which they call a virtual capacity curve, because it changes over the time of day, basically.

So they talk about in this paper, there's some production, there's some production loads, like that always have to run and always have to respond very, quickly. And there's things where they've got a Bit of freedom in how they move it around and I think this is actually quite interesting because they talk about where they have some flexibility inside this and they talk about how they account for a bunch of that because it's the first time I've seen a paper, A, talk about this, but also talk about the fact that there's like, a set amount of kind of idle power then there's amount of power that will kind of ramp up and down based on the amount of use you're introducing they do they speak about a bunch of really interesting things inside this and the thing that i think there's like there's a couple of figures which i really find like really quite fascinating actually and the fact that there's like one thing like If you are at all interested in, like, Sankey diagrams, they've got this really cool Sankey diagram of saying, well, this is all the kind of power that goes into running machines, running the overhead.

This is how it gets proportioned across all the different services. And this is how these end up being allocated to both our internal use, but also cloud customers and stuff like that. It's a really, fun read. And I'm probably going to spend like, I think, an afternoon or maybe the weekend making some more notes on this.

'cause there is a bunch of stuff which is beyond my can, like some of the equations are. I, don't have the, I don't have the ability to kind of make sense of those. I am looking forward to reading this nonetheless, because it's really nice to see something like this, not least because by putting this into the public domain, it's now raised the bar for some of the other providers to be more transparent about this.

Because if you're looking at, say, Amazon, you're looking at Amazon's calculator, you don't have scope 3 emissions, so that could be up to 99 percent of your emissions not accounted for in the numbers. So if they look suspiciously good on the Amazon dashboard, maybe they are suspiciously good. But also you look at the resolution.

This is something where they providing information at both the location and a product line value. So let's say I'm using Cloud Run or one particular kind of storage. I can see it at that kind of resolution. And that's that kind of location. In some other providers, you might see Europe and then compute.

So, there is nowhere near that kind of resolution. So, people talking about this is how we do it. This is how it's possible. This is what you should be expecting from other providers. I think it's really, good. And they also do mention the fact that they're using high time resolution. So, they say, "We're using data from electricity maps to help us work out these hourly curves, so that we know at what times of day, what the kind of carbon intensity for the power might be, so that we know that we've got this much kind of green compute that we can plausibly use," and in a defensible and transparent way, say, "yes, this really is running on renewable energy, according to the way that we talk about this."

And like, they do refer to like, they, you don't need to agree with the approach that they use in order to at least understand where they're coming from, because there's plus points and minus points with using the approach here versus having something totally location based like we've spoke about before.

So, yeah, that's kind of my initial thoughts when I see this, actually.

Asim Hussain: We need to get this to the point where it's kind of easily absorbable and understandable by folks in such a way that they can actually, because you know, otherwise it's just, oh, right. There's a great paper we'll use, you know, but I think you need to understand the nuance of a lot of this stuff.

I don't

Chris Adams: Well, there's two things, but what I think, so... you know, there's a, the real time cloud working group that Adrian Corkcroft is 

Asim Hussain: Oh yeah. Yeah. 

Chris Adams: He's been really pushing on a bunch of this stuff. This feels like one of the abs, absolutely worth sharing to that group because they've been doing a really good job of actually collating this data so it can be used.

And like, a scene like that was the thing that kind of fed into the impact framework stuff, right? So there is a path for this. And like, There's a job to decode some of this and make it easier for lay people because, yeah, like, Asim, we've been talking about this for literally five, six, five years to get to, for us to understand why it was exciting.

But yeah, you do need a job to actually make this easier for people new to the field because there's lots of developers who are kind of coming into this kind of sustainable software field.

Asim Hussain: you know, we should also have and this, we're now talking about GSF work. I know I've actually got to drop like very soon, but this is interesting because as I just asked you that question, I think an answer came to my head as well, which is like standardization. So, you know, as we like to, even as a real time cloud project is kind of evolving, like one of the things it's trying to figure out and work through is like, like you mentioned CFE and your definition of CFE.

And I can tell you right now, I've heard different definitions of CFE from other organizations, which don't. Count the, it to be, it doesn't have to be the grid. It could be anywhere. So I think some of this stuff, it might be interesting to have conversation with Google, like other ways to standardize some of the terminology, the methodology, the equations to this, as soon as you can create a standard, maybe something we can push into ISO or something like that. That kind of also in a way also forces the, not only simplifies everything for everybody, cause they're like, "well I don't really understand what the standard is, but I can see that it's got wide adoption and it's a standard and competitors have got together and agreed on this standard." So I don't really, you know, and there seems to be a wide body of people who support it. I don't need to look at this. This equation is so thick. I'm staring at this equ

Woo! 

Chris Adams: it's quite a man, 

Asim Hussain: It's a quite, a yeah, it's quite a lot. Selecting it has like 43 components in the selection.

But... 

Chris Adams: the thing that we can maybe talk about is that there are standards. We don't need to be doing all this work ourselves. Like energy TAG is one standard that is essentially written into European law and American laws around hydrogen now, like hydrogen production. So there's things that, you know, we wouldn't be starting from zero.

We could be using some of that stuff, but you're right. 

Asim Hussain: We how we push 

Chris Adams: Yeah. I need to also. Thank you. Trying to rush us through for the last few bits, because there's a few things inside this that, as hosts, I need to be doing, and I sadly can't talk about the marginal carbon intensity of baked bread, because we had a really lovely follow from Dr Daniel Schien, who responded about the low carbon bread and high carbon bread thing. So maybe we'll, actually, we should commit right now to do an article about the, about how green the energy is, and how you talk about that, because this is what we just spoke about. Right now, Dr schien raised a number of really good points and linked it and shared some really helpful resources with us.

Okay.

Asim Hussain: we should get a proper conversation together with, if we can, like with maybe EM and some

Chris Adams: Yeah, we have some people inside the organization who've been doing that. Alright, let's look at events. So, stuff that's coming up. HotCarbon is a workshop on sustainable computer systems. This is happening on July the 9th. It's free to attend virtually. You can turn up in person if you're in California.

It's really, good. And I, there's, they have videos online and really fascinating papers. It's really worth reading. It's like absolute cutting edge stuff. There's also a, the IEEE. They have a cloud conference on the 7th through to the 13th. This is in Shenzhen, China. And for the first time I've seen, there appears to be something like a sustainable AI track, or sustainable computing. You are? Oh, 

Asim Hussain: to be there. Yeah. I'm going to be there. Yeah. Yeah. I'm going to be there. I was invited by, well, the, anyway. So yeah, I'm going to be, I'm going to be, I'm going to be over there. I'm going to talk about sustainable AI in the cloud. There's going to be a whole track, several panels, discussion topics.

And

Chris Adams: Oh, wow! Cool!

Asim Hussain: You know, I don't think we particularly speak too much to, I'm a big believer in that this is a global challenge and a global issue. And yeah, most of our conversations happen in the Western world. So one of the things I'm personally trying to do is to try and

Chris Adams: Bring the other 1,5 1 point something billion people into that. Okay, great. Okay, so other two things we had. There is a, so outside of programming, this was shared with us. The masterclass on becoming a sustainable UX designer. This is one thing that's been led, I believe, by Thorsten. Oh, Thorsten, I'm afraid I forgot your name.

Thorsten Jonas. He's been helping organizing Sustainable UX as a community, which has the unfortunate short name of SUX, S U X, but it's, this is a virtual masterclass you can join. We'll share a link to that inside the show notes that's taking place, and he has a number of these coming up. And there's also Asim, I think this is yours, right?

The Software Measurement Landscape Workshop on the 9th of July? That's something that you've got, you're looking at? 

Asim Hussain: That's not, well, no, not, it's yeah, it's not something I'm particularly involved with myself, but the green, the, we have a meetup group in Brighton in the UK, organized by an organization called Root & Branch, which do a lot of green software, the kind of green software startup, and they're doing a workshop on software measurement.

Chris Adams: Folks, ah, so Cardamon were the people behind, sorry, Root & Branch were behind the Rust based software tool called Cardamon? I think Cardamon, it's monitor for some of this carbon related things. Okay, that makes sense now, putting two and two together. Okay. And we have one last wrap up before I need to go, actually.

So, this is the, this week in Green Tech, where it's basically you and me, and occasional guests talking about the news. We have a, there's a new podcast, or a new podcast related to the Green Software stuff called CXO Bytes, and this is, I believe, Sanjay Poddar, the chair of the Green Software Foundation.

He'll be talking a little bit about some of this and speaking at the, I guess, CIO level, something like that. That's what we have. So, there's a first episode coming out in July, for this and, yeah, I figure we should tell people about that because it's something that's coming up too. Alright.

Asim Hussain: That will be, that's a separate, just everybody knows that's a separate podcast, so you'll have to find it and put it in the show, no late. We have to find it and subscribe to it separately. And as Chris mentions, like Sanjay, I'll be speaking more to the c-suite level of an organization and like one of his personal beliefs, and I do subscribe to this as well, if you want change in an organization, it needs to come from both directions or it will just stall in the middle. So he's very keen 

Chris Adams: Getting by on it at the management level, at the very top, yeah? The big cheeses, 

Asim Hussain: do is big cheeses and we do the witherbread whatever. That's 

Chris Adams: Or the mushrooms, if you prefer, the 

Asim Hussain: right. There you go. Wither, wither, yeah.

Chris Adams: breaking down barriers to adoption, like you break down dying logs, right? Okay. But Asim, really, enjoyed this, mate, I'd really love to see you again, and get well soon. I hope the Nurofen and coffee thing doesn't have to be a daily thing, and that you get better over the weekend, alright?

Take care of yourself, mate.

Asim Hussain: Wonderful. You too.

Chris Adams: Okay, ta ra!

Asim Hussain: Catch you there.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.



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In this episode of Environment Variables, host Chris Adams is joined by Asim Hussain to dive into the complexities of AI's growing energy demands and its environmental impact. They discuss innovative approaches to sustainability, such as using fungi to manage building waste in data centers and the potential for greener materials and practices. The conversation also covers software optimizations to reduce AI's carbon footprint, emphasizing that energy inefficiency cannot be outsourced. They highlight the importance of integrated sustainable practices in tech development, particularly in the face of increasing AI power consumption projections.

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TRANSCRIPT BELOW:

Asim Hussain: And now there's a big hoorah in our space because like AI's now gone and

our automation has now gotten the point where even our jobs are now we're all really like nervous and upset and but you know this has been the pressure that we've been applying to the rest of the world with all the industry for decades and decades and it's just now coming to us and affecting us so you know we don't really have a leg to stand on I'd say.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Hello, and welcome to Environment Variables; This Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. Today, in our news roundup, we're diving into some of the pressing issues at the intersection of AI and sustainability.

So, with AI rapidly advancing, the energy demands of training and running these models are also seen to be skyrocketing. Posing significant challenges for the environment. We'll also be touching on some legislation for promoting sustainable business practices amongst AI companies and the potential for a green levy to drive investment for greener eco-friendly technologies.

We'll also be talking about some of the latest papers that have been published for people trying to understand and get grips on cloud carbon emissions. And finally, we'll touch on some of the exciting events in the green software community, including conferences, workshops, and masterclasses aimed at fostering sustainable development practices.

Joining me today for today's news roundup is my longtime friend, Asim Hussain of the Green Software Foundation. Asim, for people who've never listened to the podcast before, can I give you the floor to introduce yourself and some of your background? 

Asim Hussain: Yeah, sure. So my name's Asim Hussain. I am the executive director of the Green Software Foundation. And yeah, I've been at the intersection of sustainability and software. I've been very lucky to be thinking about the same question about the intersection of sustainability and software for quite a few years now. And yeah, I've been well, mate. I've been, I've just come back from a vacation, which has been long time coming. I do have a little bit of a, cold, so that's what would explain the slightly nasally, annoying nasally sound the audience members are going to have to experience for this podcast.

Chris Adams: So this is with your hot beverage and Nurofen chaser 

Asim Hussain: hot beverage. Yeah, that's what I like to do. That's how I like to start every podcast episode is a coffee and a Nurofen, every conversation with Chris Adams has to have a, both a coffee and a Nurofen.

Chris Adams: Wow, that's, one thing to take away with me. All right, folks, I should just briefly introduce myself before we dive in. I am the executive director of the Green Web Foundation. It's a small Dutch nonprofit based, well, in the Netherlands, where we are working towards an entirely fossil-free internet by 2030.

I also am one of the hosts of this podcast here. As well as one of the organizers of ClimateAction.tech, which is an online community, where actually me and Asim first met online before he basically headed off to set up the GSF in its current state. Alright, Asim, are you sitting comfortably?

Asim Hussain: I'm standing uncomfortably.

Chris Adams: uncomfortably at your swanky standing desk.

That's good enough for me. Should we start with news then?

Asim Hussain: Yeah, let's go for

Chris Adams: Alright, okay, so Asim, I was thinking of you when I saw this paper, this story. The first one is a story about mushrooms eating building waste in data centers. So this is a link to the Data Center Dynamics website talking about specifically the use of these, of essentially building-waste-eating mushrooms at the Meta data center and other ones.

And the general spiel of this is that there are now a number of companies which are, essentially deploying fungi, various kinds of fungi, to deal with all the building waste that ends up being created when you might kind of read, when you're essentially demolishing a building or creating a new one.

And, it essentially takes all this waste, and the fungi are able to Essentially, deal with the toxins, and then create something like, kind of, fungi-style bricks that can then be used as a kind of circular building material going forward. And Asim, given that you're our kind of resident mushroom fan, I wanted to just like, see what you thought about this, or if you had any particular immediate like, hot takes or things when you saw this one? 

Asim Hussain: No, that's all. it's a great application. In fact, it's not an uncommon application of, you know, what people are applying, you know, fungi in this technology for. It's actually one of the, one of the, one of the, one of the very exciting kind of broader sustainability solutions in this space that there is.

I mean, there's a couple of different types of fungi. You're going to have to pause me at some point. There's a couple of different types of fungi, but there's one particular type was kind of saprophytic, which is effectively what Fungi, the purpose it has in kind this life that we lead is it basically, it's the thing that destroys things that have died. And if it didn't exist, then we'd be basically living on top of this massive mountain of logs that aren't decomposing. So that's one of the things that they're really, really good at. And it's been, it's, there's been a lot in many different spaces, been a lot of active research, a lot of startups, a lot of organizations exploring how to use Fungi to decompose things that they don't normally decompose. And it's actually quite an interesting technique. Because even people, what's so fascinating about the the fungi space is it's, driven in large part by citizen scientists, which is one of the things I love about it. And there's a lot of citizen scientists out there who are doing things like trying to find a strain of fungi, which can decompose certain types of plastics and you would literally do this. You would literally grab a selection of these plastics. You put them in a, you know, you can go online. It's as simple as this. You put it in a blender, you blend up a plastic in your home blender. And then you just have like, as you can probably see behind me, I have lots of jars out there, which have like different strains of fungi, and you would just put that with other material in the jars.

And then you'd collect lots of different strains. Like. Every single kind of mycelium is like of a different strain of it of the same one. So you can have like millions and millions of different types of strains. You go into the forest and you see a type of mushroom you've never seen before. And you're like, ah, maybe that will absorb this plastic. And so there's a lot of interest in this trying to like find those strains of fungi that can kind of absorb and transform, you know, different materials. Obviously certain fungi only in the forest, they only work on certain types of trees. They have like a relationship with them, but you can actually find strains of fungi, which do different things. And kind of the interesting thing about it is about turning them into, bricks like that as well. There's actually organizations out there trying to replace packing material for boxes. But what you do is you basically, you create like, so you take some time. It's not like a foam that you stick in and like 30 seconds later, there's a thing you basically have to Have the thing you want to pack in a box Inside the box you put like a substrate which can be the thing that mycelium grows on you then like almost like impress inside this substrate the shape of 

Chris Adams: like the

Asim Hussain: book or something the mold and then you inject it with the mycelium and you put it in basically an oven for like a month. And it comes back out and then you basically spray it off and like the actual mycelium has grown into the shape of the thing. And then you've got something which you can put in the packing crate. And then at the end of the day, you just, it's a mycelium. You just break up and put it into your garden and you, and it decomposes.

There's lots of like wonderful stuff. There's a, great guy called Paul Stamets, who's quite a character. But he's done a lot of, he's done some great TED talks in the use of like fungi and mycelium in, kind of waste and getting rid of waste even getting rid of oil and there's a lot of kind of very active stuff in this space.

Chris Adams: Can I stop you one second there, mate? Cause, cause there's, so you just said Stamets, right? So Stamets hit, so in real life, there's someone called Stamets and in the whole, and that's, so the whole, when they had this whole Star Trek discovery thing, where there was this guy called Stamets who was using like the mycelial internet space thing, that's a direct reference to this dude, basically.

Asim Hussain: That's a direct reference to Stamets, which is kind of, which joins two of my biggest nerd bubbles together in the most beautiful way, but yeah, there's the engineer, and in Star Trek Discovery, how they, instantly, there's a new type of drive, and they instantly can, move to one part of the universe to another, and it's called a spore drive, and you need to kind of enter this kind of psychedelic realm to connect transmission, so yeah. 

Chris Adams: My word, Asim, I was not expecting us to dive down that myco rabbit hole for that, but that was a lot of fun. Thank you very much. So you basically said. By doing this, so in this case of packaging, this basically removes the need for like, say, fossil-based expanded polystyrene in packaging, and in the case of materials here for buildings, you would use that instead of having to get a bunch of virgin materials, for example.

This would be like a circular, that's the approach that they'd be using here, right?

Asim Hussain: So what, so the specific approach they're using in this particular article is, I believe it's more about, it looks like they're using, basically trying to get rid of the drywall that they have inside the data centers. So I don't know if it's a particularly from a decommissioning a data perspective or renovating a data center perspective, but they're ending up with a lot of material, which typically you would just dump in a waste fill, but now they've basically got a form of mycelium, which can eat drywall and generate something that's, decomposable, maybe edible.

Yeah 

Chris Adams: All right, cool. And so we, this is, mentioning, referencing the metadata center in, I think, Tennessee. But we've also seen Microsoft, as far as I'm aware, Microsoft has also been a bit of, it's been dipping their toes into this field as well. And one of the reasons why you might care about this is that, well, last year, Microsoft's reported emissions, when they released their sustainability report, it was like, up 30 percent and a significant chunk of that came from buildout of data centers.

So we are now starting to think a lot more about the embodied energy in the facilities that are created so that we can actually have data centers, so we can actually use compute, a lot of the kind of compute power available to us, or even some of the AI power, or the kind of sources of AI and stuff like that, because you need, they need to be in a building somewhere to get this stuff built.

And like, this kind of made me wonder actually, Asim, surely I imagine some of this might show up in an SCI score, a Software Carbon Intensity score, if you're purchasing cloud from a certain place. If you've seen a massive buildup of data centers, surely that might have an impact on the embodied carbon for the compute you might be using, right?

Asim Hussain: It could do, it depends on what your, cause in the SCI, there's kind of two components. One is the software boundary, which is "what are you going to include and not include in that score?" I remember us having quite a few conversations in the early days of the SCIs, which "should you include the, like the concrete that was used to pour the floor of the data center?" And I'm not too sure we really, I don't, think anything particularly made its way into the specification, but it has to be, if you're measuring something, it has to be something which drives a choice or a behavior. So, you know, I suppose what I'm going to with this is, if there was a data center, which was particularly built like a zero carbon, maybe built with mycelium or something.

I don't know, but like, if there was a data center, it was particularly built with that choice, then maybe it is something you want to include in the score, because then that can drive an action of choosing one option over another. But if every single data center is effectively built exactly the same way, the discussions we were having was, well, that's just overhead of adding a sec, effectively a coefficient, which wouldn't really drive a decision-making factor. So, I suppose as what I'm going to this is excitingly, if there are data centers that are being built, which are going to have

vastly different embodied carbon profiles, and then you, and then if that was included in an SCI score... and I think as we move forward with SCI, because one of the things that's happening... SCI on its most, we, launched kind of the version one and now 1,1 of the SCI and it's, and it was, it's very bare, very basic. It's designed to be built upon. And so now what the teams are having conversations around is like, if you were to apply SCI to AI, specifically, what does that include? And one of those questions

they want to answer is what, 

Chris Adams: building, 

Asim Hussain: yeah, like what you include so, so when someone reports an AI score, we can actually start getting to like some apples to apples comparisons. So that's, a conversation that, you know, it's interesting. Maybe we should bring that, bring it up again is do you include the embodied of the data center? But then you also get into the headache of "my God, it's hard enough trying to figure out what the embodied of a chip is."

Chris Adams: Yeah.

Asim Hussain: And now going to ask to figure out like what the embodied, so there's has to be some practicality aspect to this as well.

You know, you know, we have to, there should at least be some models. I don't know.

Chris Adams: So there are models that, as I understand it, there are models for working out the, essentially like the carbon emissions for a kilo of concrete, for example, or stuff like that. Some of these exist. And,

Asim Hussain: exists from a

LCA, 

Chris Adams: and there are companies, and we know there's like, in Europe at least, I know there's a company called LeafCloud.

That are explicitly using, are doing, making reused, or, they're reusing heat, but also a very specific kind of data center, which isn't like a very large outta town thing. They have like essentially shipping containers put into places like say greenhouses, where the heat is being reused, for example, and where they're not having to build a whole bunch new buildings. There's also, I think in Switzerland, there's one company, because we maintain, where I work, we maintain a directory of green data centers. And one of them is a, they basically reused a old factory building with a waterwheel that used to be kind of like a clothing factory, and now it's a data center.

So they've essentially reused the whole building shell. They haven't built a load of stuff as a result. So this is one place where this might show up, but in order to do this, you need to have access to the numbers for this. And that's still a bit of a challenge because, yeah, we don't have the, we don't have easy access to these numbers, and like you do say, it's a challenge just thinking about chips, let alone expanding the boundary to the actual buildings instead.

Asim Hussain: Yeah. I mean, at least you have some information when you're running software, like what, you know, you can, now that we've done, a lot of that workings out so you can figure out, you know, perhaps it's this chip, but I think given the secrecy around data centers, I don't know, I think there's going to be,

I don't know. 

Chris Adams: It's gonna be a challenge, because while we have this practice of, essentially, water usage and electricity use, so many things being under NDA, it'll be very hard to come up with some numbers without using, like, a basic number. Okay, alright. We have totally gone past talking about mushrooms and data centers into all these other things, but I guess this is part of the whole thing about sustainability and technology.

It's all interconnected.

Asim Hussain: It's all interconnected.

Chris Adams: Shall we go to the next story?

Asim Hussain: Yeah. Let's go for it. 

Chris Adams: So this is a piece from Bloomberg, actually, so this is the topic of this is Solutions for AI's Energy Inefficiency Can't Be Outsourced, and this is an opinion column from Bloomberg talking about this projected demand some of the figures which are pretty, pretty impressive, they basically say, in the US at least, it's, there's a projection saying that AI, the growing demands of energy are like, is projected to make up around 8% of the US' power consumption, electricity consumption up from 3% in 2022.

Now these numbers seem a little bit high and they are citing a kind of this arms race of different kinds of organizations, essentially building out these massive data centers but also buying loads and loads of chips, but it does talk about some of the approaches that we're seeing now to kind of rein in some of this growth.

So one of the things was this idea of one-bit architecture, which is essentially, I'm not going to pretend to understand it. And I'm not sure if you are similarly informed in this one, but

Asim Hussain: I'm going to definitely pretend to understand it.

Chris Adams: In that case, I'll hand over for you to confidently bluff it around, just like a ChatGPT would actually, Asim, the floor is yours.

Asim Hussain: I'm going to, I'm asking GPT. No, I'm guessing, and this is, I haven't really, I've, seen it, but because it's one bit and a bit can only be one or zero, I'm guessing what this is, that, you know, instead of like pumping in a number between one and 256 as one of the inputs to a node in a model. Maybe you could just try one or zero. And then output one or zero and then see if that actually still gives you some pretty reasonable results and from what it looks like it might do and you know for those of us I kind of it's like there are extreme inefficiencies you can do when you're working at the bit level in terms of programming and computation and instructions on the chip and things like that because it's so much lower level than the architecture.

I presume that's what it is, which actually to me is. It's really exciting from a, from the level of, this is a software architectural solution, which is effectively, I think what we've been advocating for, a large part of the time, which is, you know, we do need to I hate to use the word code because I don't want people to dive down the, you know, building more. I, well, AI is one of the, one of the few areas I would say where actual code efficiency is extremely important. But yeah, this is kind of, it's interesting. Now that there has been a pressure applied to optimize, the optimization has happened.

Chris Adams: Yeah, that's, so basically, Asim, I think you're about right. Now, when I, remember when I skimmed over this paper before, one of the key ideas was, the one bit approach was, essentially, when you, would be able to, you would use this to encode the difference between different parts of, like a dataset rather than showing absolute numbers.

And one of the things that this allowed you to do was allowed you to just use addition rather than multiplication in some cases. Now, I'm not an AI specialist and I'm not a hardware specialist, but the general idea was by representing things in a more somewhat simple fashion here, you avoided having to make some of the expensive calculations that you would otherwise need to do.

And this basically reduced the energy that you might need to run some of these calculations. So this was like one example. I was quite impressed to see this inside Blumberg because it was a very quite new research, but also really, technical and actually quite promising. So yeah.

Asim Hussain: It's, it, the title is interesting though, isn't it? Cause it's not like, it's not like software making, it's kind of talking about energy inefficiency can't be outsourced.

Chris Adams: Yeah,

Asim Hussain: I just thought it was an interesting, it's like no one really knows or cares or thinks about the software side of the, this whole equation.

To me, this is just like a software optimization. So you would just say like software can be optimized to reduce any AI's energy footprint. It's not, not expressed in that way. It's interesting. Kind of expressed 

Chris Adams: there's a couple of things that I think are also really interesting about this piece, in my view, was that it talks about the kind of economics around some of this, and basically the idea of outsourcing this is essentially how we have a bit of a tendency in the technology industry to say, "well, we're just going to have, like, We realize that data centers use loads and loads of power, so what we're going to have to do is just somehow get loads more power."

And so you basically have people talking about, oh, obviously the solution is to deploy loads and loads of nuclear, for example, right? Never mind that these take between minimum 10 years to get built, right? So, what are we going to do in the meantime? A lot of the time it's likely to be coming from things like gas, if you're going to be using something like that.

So that's an issue there. But it's also worth thinking a little bit about these figures that were mentioned in this story. We've seen numbers like 8 percent of the USA's energy consumption by 2030. It's worth bearing in mind that these numbers are often coming from the utility providers in various states, all right?

So like, say, in, say, Virginia, I think it might be, I forget the name of the actual monopoly provider, but there's only one provider over there. And basically I think that's Pacific General. I think that's actually on the, other coast, basically.

Asim Hussain: We've played this game before where you can name all

Chris Adams: was Caiso last time, which was, which is, that's the Independent Systems Operator, which is not the energy company.

The energy companies are somewhat different because it's a, because in many, because they have very specific, if you're in a state where you've got a single provider, they are allowed The only, the reason you only have a single provider is that you have basically had that state agree to have a, what's referred to as a natural monopoly.

So, they basically, the agreement is, we will give you a guaranteed 10 percent net profit plus for your organization, alright? But you need to basically, yeah, as long as you agree to share your plans, For the new infrastructure you're going to build over the next few years, but also you need to justify this in each of these cases.

And when you think about this, if you're going to get a 10 percent net profit from that for any of the energy you, get. Now, what you, if you want to increase your profits, what you need to do is you need to say, "well, I have loads more demand coming. I need to like double my expected demand to double my profits inside this."

Asim Hussain: Oh, I see.

Chris Adams: is one of the things, because this is a lot of the existing providers, they're used to saying, "well, we've got all this extra demand. What we need to do, we need to build a bunch of new gas, fired power stations. And 'cause we know we're gonna make a 10% guaranteed profit on all the infrastructure we, build.

That's basically, you know, we are incentivized to say it's gonna be really, high" basically. So. You, it's, really worth looking at a paper by one, one gentleman, John Kumi, who's actually, who's spoken a lot about this, because 20 years ago, we had the similar thing when you had people in the coal industry saying, "well, coal was what powers the internet, so you need to have more coal fired power plants if you want more internet."

We have a very similar thing happening. In this case as well actually. So it's worth bearing in mind that yes, we do see these kind of apocalyptic forecasts for energy, but you also see that when you do have constraints on this because it's so difficult to build, then we do end up with a renewed interest in energy efficiency.

And even at the kind of like energy level, right, there are different ways that you can basically meet demand. You can meet demand by adding new supply, but you can also meet demand by investing in energy efficiency. And that's, and this is very much what it looks like, so a lot of the ideas you might see at the energy sector, I think, are at least applicable, or at least relevant in what we talk about with cloud, because essentially you're looking at a kind of commodity that you pay for on an hourly basis, or something like that.

Asim Hussain: Well, that's kind of one of the... All I see is there's, a significant amount. There's not a significant, there's a fixed amount of investment and focus that organizations can put into something. And if you present them with an option, either put all this engineering effort to make something more efficient, which costs 10 or buy renewable energy, which costs five and then, well, I'll choose the five one. So I think that's kind of, that's. That's why kind of investment goes in one way or the other. Whereas I suppose what's happening now is that energy is now, we're reaching the point where energy is, and I'm just throwing out numbers here for energy is now costing 12, but the development still costs 10.

So like, well, maybe we'll put some money into development

Chris Adams: Yeah.

Asim Hussain: So that's kind of, and that's interesting that's why you want things like we were talking about levies and money last time. That's kind of why you want to change that balance of it a little bit. And that's also significantly why in the SCI specification, the decision was made not to include any energy offsets or anything like that.

Just because if you gave somebody the option of spending 1 instead of spending 10, they would spend 1. And we want people to spend the 10 to actually make things more

Chris Adams: Yeah, to address the consumption issue, rather than just think about the intensity. 

Asim Hussain: Exactly, yeah. But this is exa I'm really excited. And the other thing I was thinking about as you were talking was I was just remembering about my time at Intel. I think this maybe links a little bit to the Nvidia's statement as well. So, I think I might have, I always love telling people this story because I just think it's such a cool word and it's such, it tickles my sci-fi bone so much. But there's a statement they used a lot, which was dark silicon. Have I told you this? I

Chris Adams: No, you haven't. 

Asim Hussain: people. There's a dark silicon. And I was like, "Oh, that sounds good. What's the, what's dark silicon?" And what dark silicon is when they're kind of looking at a chip and they put load on it.

And the key thing with a chip is how much can it expel heat and still function at that level. So looking at heat on a chip. And so when they're running a certain software on a chip, they'll put like a, what you call the heat detecting camera on. And you know how they look like this. It's very red, it's very red on the bits that are hot.

And it kind of looks

Chris Adams: Ah, okay, yeah, look at a thermal house, 

Asim Hussain: Thermal imaging. yeah. Yeah. Even though the black might not be like ice

cold, quite hot, but like relatively it's cold. And so the things that they would be really like thinking through is like, how come this software, how come half the chip is black?

Like, why aren't you using the rest of the chip? Like you've maxed out the chip, but half the chip is black. And really what it kind of, you know, what it goes back down to is that, you know, we called it, I think I might call it the silicon gap, which is the gap between what engineers are building and what silicon manufacturers are enabling on their chips. And there's this disconnect between, you know, they're, all building, "why aren't you using this, these more advanced chip sets that are more efficient? Why are you using this stuff on this side of the chip?" And so I think that's something that we need to get down and tighten that gap to use this infrastructure more efficiently, I think over the years, from a developer's perspective, it's always been about time to market.

How do we beat our competition? It's never around, how do we use this chip more efficiently? And so I think that one bit architecture is, it sounds like an example of that. It sounds like an example of, we want to leverage the instruction set on this chip to be as efficient as possible. We need to change. Fundamentally how we're architecting and even thinking through algorithmically this code to take advantage of that. And that's, I think it's also like this, other area, which is completely, we're just ignoring, you know, there is this dark silicon and honestly, the silicon manufacturers are like, why are developers not, I don't understand, we put so much energy and time into like

building 

Chris Adams: we're only using a of it, right? 

Asim Hussain: a The percentage of it, and there's that also, and I'm going to, I'm rambling on for a second, but just one more, one more point. I thought it was really interesting. One of our organization's Entity Data, they did a really great report. It was kind of two years ago now, I think. I don't think we really circulated.

I don't think we circulated that well. And it was, they just, they just looked at Java, you know, Java, like still, there's still a lot of very antiquated Java applications running out there in the world. And they just said, what is the energy difference if we just upgraded, not the code, but the JVM, the underlying JVM.

And that's all they did was I think they upgraded, I cannot remember. I'll find a link to the article and the paper. It was like several steps up. But they were like, "look, most apps are still running on whatever the JVM was they were built with like 10 years ago. And it was a seven, it was a 60 or 70% energy efficiency improvement.

It was unbelievable. The energy efficiency improvement just from grading the JVM. And that was

Chris Adams: Ah, okay. 

Asim Hussain: That, and if you think about what that means, what happens was the chips evolve to have different instruction sets. The JVMs, only the modern JVMs are built to the new one. And so if you're running on the old ones, it's just using the old instructions.

So you're not really leveraging the infrastructure the same way, which is why like recompiling software with like, you know, the latest version of the compiler against the latest version of the chip. It's really important. And it again, that was Intel's, when I was at the time, that was their big push.

They were like, "use the latest bits, use stuff that's compiled now using the latest optimizations." 'Cause they saw a lot of people were still just kind of compiling, leaving that binary, letting it run for like four or five years. And then, that's it. And yeah, I'm going to stop ranting now.

Chris Adams: No, that's actually, I didn't realize, I was somewhat aware of things like the JVM, there's like hotspot or different kinds of flavors of the Java Virtual Machine to run this code, and it's somewhat similar to like in PHP land, like when a new version of PHP came out, because it made much better use of the underlying code, the underlying hardware, you saw a massive increase in performance.

And like, you kind of see something a bit like that with Python as well, with the whole global interpreter lock. Like, I can have a piece of Python that'll be running, and it won't be able to use all the other cores in my machine, in my computer, right? So, rather than lighting up the rest of the silicon, it's got just, it's, most of my computer is dark, basically, in that same kind of approach.

All right, yeah. Cool, alright, so that's like one of the approaches that we have, and this is one thing that you could plausibly do. I've shared a link to a blog post that I've, I was trying to explore this to find a way to explain it, to basically explain the fact that you can reduce the emissions associated with code without actually changing the code, by thinking about what options you might have in terms of, like you said here, like you change the VM or something like that, or change when you run it, or anything like this.

And I'll share a link to that, because I've kind of framed it in terms of If, there are three, three things you can change, basically. You can change the time of running something, which is kind of speaks to carbon awareness. You can change the speed, the amount of compute, computation you might be using, the number of cores you might be using something, or you might change the place, like where in the world you choose to run this for the carbon intensity of the underlying energy.

So I'll add that to the show notes because it might be another nice helpful addition to this. Alright, okay, that was quite, that was fun. Shall we look at the next story?

Asim Hussain: Go on. Yeah.

Chris Adams: Okay, the next story is, this is actually from The Guardian, and this is talking about the balancing some of the incentives of the kind of profits that are projected to come from deploying AI with something like a green levy on these profits, basically.

And this is actually, came from that, Left leaning organization, the International Monetary Fund, 

Asim Hussain: Oh, wonderful. Great. Great to see them in this space.

Chris Adams: And basically what they are, the argument from the IMF is basically saying, well, you've got all these very profitable AI firms, and they, and we know there's both the social and environmental impact that's taking place here. So what you should actually have is some kind of green AI win for tax, essentially, that will be used to fund some of the, sustainability initiatives.

And like, to be honest, I have a lot of sympathy for this because what we've seen from the largest providers in the last like year is that given the choice between investing in efficiency or investing in more capacity and building loads more data centers, we've seen all the big providers go for building more capacity and like emphasize profits rather than the environmental impact here.

So it looks like we're not the only people thinking about this. The IMF is thinking about this as well. And they're saying, about this. You need something a levee to do this. Do you have any kind of particular thoughts on this? First of all, Asim, because I was really surprised to see this come up from the IMF of all organizations.

Asim Hussain: Yes, I thought it was a very important point. I love the fact because the use of the word levy, and I remember us having the conversation last podcast about like use of the term levy rather than

tax instead of, explaining it, but then in the article actually uses the word tax, like all over the place. Yeah, I think it's really important. Like one of the things I'd say is that like, why is everybody so excited about AI in the first place? In my most cynical moments as as a software engineer, I would say, Our purpose in life is to either find solutions that help people waste more of their time or get rid of jobs and automation.

If you think about kind of why we have been like one of the most highly paid sectors for quite a significant amount of time, it's because building automations, yes, you could argue and helps you deliver kind of projects faster, but it also helps you to do more with fewer employees. It decreases the.

You know, the earnings potential, the, a lot of this stuff from that perspective, and now there's a big hoorah in our space. Cause like AI is now gone and

our automation has now gone the point where even our jobs are now we're all really like nervous and upset. And, but, you know, this has been the pressure that we've been applying to the rest of the world with all the industry for decades and decades.

And it's just now coming, to us and affecting us. So, you know, we, don't really have a leg to stand on, I'd say. We also. There's nothing we can do. It's happening is the only other thing I'd say. There's no, you know, we have to just accept it and move forward. But yeah, I really liked the idea of that because like, let me put it another way.

Like there's this huge kind of bro down AI tech bro showdown with like, well, I think Sam Altman posted something a year ago now, which deeply disturbed me. And he said he wouldn't be surprised, I think within the next couple of years, if there's a one person unicorn startup, which is a single, like, billion dollar organization run by one human being. And I was thinking to myself, like, that might actually be true. And I would, you know, I do, there is a chance that would be true. might happen in the future, but how do I feel about that? What is the human impact to that? I mean, what is the green impact to that? So I'm now going beyond green because I think that there's like this AI is going to make a few people and organizations immense amounts of power and wealth. How do we have ways to, redistribute all of that and to kind of add a level of fairness to, the rest of society? Is it okay? And so from a green level, absolutely. But I'd also argue from a societal level as well, like, like, you know, like when we talked, you know, how about this? When we spoke about the green transition, it was impossible for us to have like a proper conversation about the green transition without having a real conversation and talking about how we're going to transition the people who are employed in the fossil fuel industry over to other areas. I don't see having, I don't see us having that conversation here as well.

Like it's just ignored. And so I think that's something that we need to have is like, is if you're, if you want to have. The opportunity to get this much power and money and wealth, I think it should come with a certain amount of social responsibility to, you know, be a green levy in terms of the green ones, but I think it actually should be broader than that. It should be, you know,

Chris Adams: So address some of the. Some of the inevitable costs that might be incurred upon society to provide to, like, ease that transition. Okay,

Asim Hussain: yeah.

Chris Adams: I was not expecting you to go there, Asim, but 

Asim Hussain: Well, I feeling it. 

Chris Adams: Bang yeah, that's usually me kind of jumping up and down, actually. All right, okay, that 

Asim Hussain: How do you feel about it?

Chris Adams: I think, so you said this idea, like, I feel uncomfortable about a billion dollar startup with a single person.

And I, okay, how many, we're not that far from it, I think, because if, actually, no, we've been, so if you think, how many people work for WhatsApp? 

Asim Hussain: WhatsApp! I was WhatsApp, I think it 11 

Chris Adams: was purchased for $12.6 billion by Facebook a few years back, right? So that's not that far away on a kind of per person basis, but that's not a single person.

But you've got to realize that like, you know, if that was probably a lot better for the people who own shares in that than the people who are working for this. And we have seen multiple cases. We've seen cases like when a company has to choose between keeping on staff to work on something and getting rid of them, and then spending multiples of the staff's wages on buying their own shares to kind of in increase the cost, increase the share price. We've seen the decision that people have been taking, and there is the, I think that this is a thing that needs to be addressed. 'Cause the current, if we're going to assume that, if we're gonna accept that digital is gonna be the, this thing which is just as important to our lives as access to water or you to or energy or anything like that then you probably want to have a discussion about okay well how are the dividends shared well how is the upside shared in an equitable fashion so that we don't end up with people rushing outside with guillotines or in the very least right like it's not good for social cohesion basically so that's my the view that i might actually have on some of this

Asim Hussain: No, that's a, really, I think that's something that people, I've been, I don't know, should I say this?

Chris Adams: I'm going to stop you because it's coming up to 40 we've got one story, so we can, talk about societal accounting, all to carbon accounting, this new paper, which I think is really interesting and we both were nerding out about it before this call. So this is a new paper from Google, Carbon Accounting in the Cloud, a methodology for allocating emissions across data center users.

So, We can totally talk about the societal aspects of cloud computing here. But this one here is really interesting because this looks like one of the most interesting papers about how you apportion responsibility for your use of cloud services when, using these things. And for the longest time, we've had a real struggle because we haven't had access to any of these numbers.

And this paper really. Lays out a bunch of really interesting ideas with lots of really helpful diagrams, and it dies into how inside Google, people allocate carbon emissions for both internal use, but also for cloud customers. It's a really, fun paper, but it is also quite a significant piece of work.

Read. Like, me and Asim were quite excited about it, but we realized this is almost like a kind of book club kind of paper to read through, basically. 

Asim Hussain: Yeah. Yeah. Yeah. 

Chris Adams: So, Asim, I'll hand over to you because, there's a couple of things I'd like to draw attention to, but I suspect there's some things that caught your eye as well, or at least maybe you might have some context about why the two of us might be so excited about this.

Asim Hussain: Well, I just think, I think it's really exciting. Well, I get really excited when any organization does such a deep, thorough analysis of their emissions. The thing I'm kind of like going through as I go through this is like, part of me is like, I'm going to try and like represent some of this stuff as an impact framework manifest File because I can read an impact framework manifest file and I can compare it and I can look at it and I can know what's going on. Like one of the first things that the paper outlines, the approach that Google developed to quantify location based emissions of its individual products. And now I'm now like, I now need to dive into this paper to understand, well, yes, I'm understanding the, my definition of location based. However, I'm also seeing references to CFE, which to me doesn't factor as

Chris Adams: CFE being carbon-free energy 

Asim Hussain: carbon-free energy So like, there's like a lot of nuance to this stuff. and yeah, I I would probably, as an experiment for me to try and understand the paper, I might try and represent some of this stuff as a manifest file. 'cause for me that's quite useful as a way of, learning something in, in a way, in such a way where I can compare and contrast it to other methods and methodologies as well.

But it looks very, interesting, very exciting. And when Google first worked on its carbon dashboard as well. They were the only organization to have done it the slightly different way, which is kind of like bottom up, like from products and services up towards the top, and now IBM has actually done actually to give IBM some credit as well. And they've got great white paper as well. If you're not ready, I'll send it over to you, Chris. they've done another approach is kind of bottom up as well. And so they're the only two organizations that have gone bottom up. The other ones have gone top down and I'm always excited when people go bottom up, because then you get the data with the granularity, the level of products and services that people actually use, which they drives.

Which is what you need to drive emissions reductions. So that's it. I haven't read, I haven't read almost any of this So I'm interested to get Chris's, if you've got any time, I don't know. What are your hot

Chris Adams: So, the thing that really caught my eye from this is that it talks about some of the ideas that Google has been doing that other organizations aren't so open about. So, for example, you have like a given amount of capacity that might be available inside this. Now, what Google have done previously is they've kind of said, well, We know that we've got a certain amount of, kind of, energy that's coming from green sources and we've said that we want to have a percentage of our energy that is always running at, say, 100 percent carbon free, for example, right, and they, Google have an approach where they say we count something as carbon free if it's matched at the time of use and if it's I think it's on the same grid.

So it's not literally a solar power, but a set of panels on the data center. It might be a wind farm that's where you could plausibly deliver the power to that place. And they use this to kind of represent the amount of clean capacity as something which they call a virtual capacity curve, because it changes over the time of day, basically.

So they talk about in this paper, there's some production, there's some production loads, like that always have to run and always have to respond very, quickly. And there's things where they've got a Bit of freedom in how they move it around and I think this is actually quite interesting because they talk about where they have some flexibility inside this and they talk about how they account for a bunch of that because it's the first time I've seen a paper, A, talk about this, but also talk about the fact that there's like, a set amount of kind of idle power then there's amount of power that will kind of ramp up and down based on the amount of use you're introducing they do they speak about a bunch of really interesting things inside this and the thing that i think there's like there's a couple of figures which i really find like really quite fascinating actually and the fact that there's like one thing like If you are at all interested in, like, Sankey diagrams, they've got this really cool Sankey diagram of saying, well, this is all the kind of power that goes into running machines, running the overhead.

This is how it gets proportioned across all the different services. And this is how these end up being allocated to both our internal use, but also cloud customers and stuff like that. It's a really, fun read. And I'm probably going to spend like, I think, an afternoon or maybe the weekend making some more notes on this.

'cause there is a bunch of stuff which is beyond my can, like some of the equations are. I, don't have the, I don't have the ability to kind of make sense of those. I am looking forward to reading this nonetheless, because it's really nice to see something like this, not least because by putting this into the public domain, it's now raised the bar for some of the other providers to be more transparent about this.

Because if you're looking at, say, Amazon, you're looking at Amazon's calculator, you don't have scope 3 emissions, so that could be up to 99 percent of your emissions not accounted for in the numbers. So if they look suspiciously good on the Amazon dashboard, maybe they are suspiciously good. But also you look at the resolution.

This is something where they providing information at both the location and a product line value. So let's say I'm using Cloud Run or one particular kind of storage. I can see it at that kind of resolution. And that's that kind of location. In some other providers, you might see Europe and then compute.

So, there is nowhere near that kind of resolution. So, people talking about this is how we do it. This is how it's possible. This is what you should be expecting from other providers. I think it's really, good. And they also do mention the fact that they're using high time resolution. So, they say, "We're using data from electricity maps to help us work out these hourly curves, so that we know at what times of day, what the kind of carbon intensity for the power might be, so that we know that we've got this much kind of green compute that we can plausibly use," and in a defensible and transparent way, say, "yes, this really is running on renewable energy, according to the way that we talk about this."

And like, they do refer to like, they, you don't need to agree with the approach that they use in order to at least understand where they're coming from, because there's plus points and minus points with using the approach here versus having something totally location based like we've spoke about before.

So, yeah, that's kind of my initial thoughts when I see this, actually.

Asim Hussain: We need to get this to the point where it's kind of easily absorbable and understandable by folks in such a way that they can actually, because you know, otherwise it's just, oh, right. There's a great paper we'll use, you know, but I think you need to understand the nuance of a lot of this stuff.

I don't

Chris Adams: Well, there's two things, but what I think, so... you know, there's a, the real time cloud working group that Adrian Corkcroft is 

Asim Hussain: Oh yeah. Yeah. 

Chris Adams: He's been really pushing on a bunch of this stuff. This feels like one of the abs, absolutely worth sharing to that group because they've been doing a really good job of actually collating this data so it can be used.

And like, a scene like that was the thing that kind of fed into the impact framework stuff, right? So there is a path for this. And like, There's a job to decode some of this and make it easier for lay people because, yeah, like, Asim, we've been talking about this for literally five, six, five years to get to, for us to understand why it was exciting.

But yeah, you do need a job to actually make this easier for people new to the field because there's lots of developers who are kind of coming into this kind of sustainable software field.

Asim Hussain: you know, we should also have and this, we're now talking about GSF work. I know I've actually got to drop like very soon, but this is interesting because as I just asked you that question, I think an answer came to my head as well, which is like standardization. So, you know, as we like to, even as a real time cloud project is kind of evolving, like one of the things it's trying to figure out and work through is like, like you mentioned CFE and your definition of CFE.

And I can tell you right now, I've heard different definitions of CFE from other organizations, which don't. Count the, it to be, it doesn't have to be the grid. It could be anywhere. So I think some of this stuff, it might be interesting to have conversation with Google, like other ways to standardize some of the terminology, the methodology, the equations to this, as soon as you can create a standard, maybe something we can push into ISO or something like that. That kind of also in a way also forces the, not only simplifies everything for everybody, cause they're like, "well I don't really understand what the standard is, but I can see that it's got wide adoption and it's a standard and competitors have got together and agreed on this standard." So I don't really, you know, and there seems to be a wide body of people who support it. I don't need to look at this. This equation is so thick. I'm staring at this equ

Woo! 

Chris Adams: it's quite a man, 

Asim Hussain: It's a quite, a yeah, it's quite a lot. Selecting it has like 43 components in the selection.

But... 

Chris Adams: the thing that we can maybe talk about is that there are standards. We don't need to be doing all this work ourselves. Like energy TAG is one standard that is essentially written into European law and American laws around hydrogen now, like hydrogen production. So there's things that, you know, we wouldn't be starting from zero.

We could be using some of that stuff, but you're right. 

Asim Hussain: We how we push 

Chris Adams: Yeah. I need to also. Thank you. Trying to rush us through for the last few bits, because there's a few things inside this that, as hosts, I need to be doing, and I sadly can't talk about the marginal carbon intensity of baked bread, because we had a really lovely follow from Dr Daniel Schien, who responded about the low carbon bread and high carbon bread thing. So maybe we'll, actually, we should commit right now to do an article about the, about how green the energy is, and how you talk about that, because this is what we just spoke about. Right now, Dr schien raised a number of really good points and linked it and shared some really helpful resources with us.

Okay.

Asim Hussain: we should get a proper conversation together with, if we can, like with maybe EM and some

Chris Adams: Yeah, we have some people inside the organization who've been doing that. Alright, let's look at events. So, stuff that's coming up. HotCarbon is a workshop on sustainable computer systems. This is happening on July the 9th. It's free to attend virtually. You can turn up in person if you're in California.

It's really, good. And I, there's, they have videos online and really fascinating papers. It's really worth reading. It's like absolute cutting edge stuff. There's also a, the IEEE. They have a cloud conference on the 7th through to the 13th. This is in Shenzhen, China. And for the first time I've seen, there appears to be something like a sustainable AI track, or sustainable computing. You are? Oh, 

Asim Hussain: to be there. Yeah. I'm going to be there. Yeah. Yeah. I'm going to be there. I was invited by, well, the, anyway. So yeah, I'm going to be, I'm going to be, I'm going to be over there. I'm going to talk about sustainable AI in the cloud. There's going to be a whole track, several panels, discussion topics.

And

Chris Adams: Oh, wow! Cool!

Asim Hussain: You know, I don't think we particularly speak too much to, I'm a big believer in that this is a global challenge and a global issue. And yeah, most of our conversations happen in the Western world. So one of the things I'm personally trying to do is to try and

Chris Adams: Bring the other 1,5 1 point something billion people into that. Okay, great. Okay, so other two things we had. There is a, so outside of programming, this was shared with us. The masterclass on becoming a sustainable UX designer. This is one thing that's been led, I believe, by Thorsten. Oh, Thorsten, I'm afraid I forgot your name.

Thorsten Jonas. He's been helping organizing Sustainable UX as a community, which has the unfortunate short name of SUX, S U X, but it's, this is a virtual masterclass you can join. We'll share a link to that inside the show notes that's taking place, and he has a number of these coming up. And there's also Asim, I think this is yours, right?

The Software Measurement Landscape Workshop on the 9th of July? That's something that you've got, you're looking at? 

Asim Hussain: That's not, well, no, not, it's yeah, it's not something I'm particularly involved with myself, but the green, the, we have a meetup group in Brighton in the UK, organized by an organization called Root & Branch, which do a lot of green software, the kind of green software startup, and they're doing a workshop on software measurement.

Chris Adams: Folks, ah, so Cardamon were the people behind, sorry, Root & Branch were behind the Rust based software tool called Cardamon? I think Cardamon, it's monitor for some of this carbon related things. Okay, that makes sense now, putting two and two together. Okay. And we have one last wrap up before I need to go, actually.

So, this is the, this week in Green Tech, where it's basically you and me, and occasional guests talking about the news. We have a, there's a new podcast, or a new podcast related to the Green Software stuff called CXO Bytes, and this is, I believe, Sanjay Poddar, the chair of the Green Software Foundation.

He'll be talking a little bit about some of this and speaking at the, I guess, CIO level, something like that. That's what we have. So, there's a first episode coming out in July, for this and, yeah, I figure we should tell people about that because it's something that's coming up too. Alright.

Asim Hussain: That will be, that's a separate, just everybody knows that's a separate podcast, so you'll have to find it and put it in the show, no late. We have to find it and subscribe to it separately. And as Chris mentions, like Sanjay, I'll be speaking more to the c-suite level of an organization and like one of his personal beliefs, and I do subscribe to this as well, if you want change in an organization, it needs to come from both directions or it will just stall in the middle. So he's very keen 

Chris Adams: Getting by on it at the management level, at the very top, yeah? The big cheeses, 

Asim Hussain: do is big cheeses and we do the witherbread whatever. That's 

Chris Adams: Or the mushrooms, if you prefer, the 

Asim Hussain: right. There you go. Wither, wither, yeah.

Chris Adams: breaking down barriers to adoption, like you break down dying logs, right? Okay. But Asim, really, enjoyed this, mate, I'd really love to see you again, and get well soon. I hope the Nurofen and coffee thing doesn't have to be a daily thing, and that you get better over the weekend, alright?

Take care of yourself, mate.

Asim Hussain: Wonderful. You too.

Chris Adams: Okay, ta ra!

Asim Hussain: Catch you there.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode.



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]]> The Week in Green Software: FinOps, GreenOps and the Cloud The Week in Green Software: FinOps, GreenOps and the Cloud Thu, 20 Jun 2024 07:00:00 GMT 44:38 x0ln9zx0 false https://podcasts.castplus.fm/e/pnlxkvpn-the-week-in-green-software-finops-greenops-and-the-cloud 68dc7d17597bc7d53fb83a31 68dc7cef46a2532cdd8d1c23 On this episode of TWiGS, host Anne Currie is joined by Navveen Balani of Accenture and fellow GSF member. This conversation navigates the landscapes of, and intersections between GreenOps, DevOps, and FinOps, as well as the vital role of Infrastructure as Code in marrying financial and ecological efficiencies in cloud operations. Lastly, they tackle the intersection of cybersecurity and AI development, emphasizing the need for green software principles to fortify AI systems while minimizing energy use. 74 On this episode of TWiGS, host Anne Currie is joined by Navveen Balani of Accenture and fellow GSF member. This conversation navigates the landscapes of, and intersections between GreenOps, DevOps, and FinOps, as well as the vital role of Infrastructure as Code in marrying financial and ecological efficiencies in cloud operations. Lastly, they tackle the intersection of cybersecurity and AI development, emphasizing the need for green software principles to fortify AI systems while minimizing energy use. 

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TRANSCRIPT BELOW:

Navveen Balani: Definitely, I would say there is some synergy between security and green software and certain, I would say, features of green software principles can also be applied to security domain, right, to make it more energy-efficient.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne Currie: Welcome to another edition of the Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. Today, I'm your host, Anne Currie. So you're not quite hearing the usual dulcet tones of Chris Adams. You will have to do with me instead. But as usual, we'll be talking about the world of green software and what's, what's going on at the moment.

And today we're going to talk a little bit about how being green matches with FinOps, which I think is very true. And I think that's a really important part of the story. So we'll be talking a little bit about that. We'll also be talking a little bit about code and code efficiency, which I'm much more...

You have to be very careful about code efficiency. So that's, that's the part that we need to be really careful about. What's the context there when we talk about code efficiency. And finally, we'll talk about the intersection of cybersecurity and AI developments, and we'll be talking a little bit about GreenOps.

And there is a new Green Software Foundation project, GreenOps project, which is aimed to look about how we can embrace AI and use AI without totally throwing all our green principles out of the window. And I think that is absolutely doable, but we need to think about it. We need to go in, in a very knowing way.

So as I said, I am your host today, Anne Currie. But first I'm going to introduce our guest, Navveen. Navveen, do you want to, do you want to introduce yourself?

Navveen Balani: Thank you Anne. Hi everyone. I'm Navveen Balani. I'm the Managing Director and Chief Technologist for the Techno and Sustainability Innovation Group at Accenture, working at the intersection of technology and sustainability. I'm also the co chair of the Standards Working Group and the Impact Engine Framework at the Green Software Foundation.

I'm a Google Cloud Certified Fellow, a LinkedIn Top Voice, and author of several reading books. Very glad to be part of this podcast.

Anne Currie: Thank you very much, Navveen. It's very good to have you. So just a bit of context for me. My name is Anne Currie. I am one of the co-chairs of the community group of the Green Software Foundation. And I am also one of the authors of the new O'Reilly book, Building Green Software. So that's, fills you in a little bit on my background.

So before we dive in to the articles this week that we're going to be talking about, it's just a reminder that everything we talk about will be linked in the show notes at the bottom of the episode. So you can, you can read the articles that we're talking about. You don't just have to rely on us telling you what was in the article.

So, well, let's, let's move first to the first article from today, which was in Computer Weekly. And it was called Green Coding. It was, it was a, basically a puff piece by a company called CloudBolt who do a look at code efficiency, cloud frequency. So taking it beyond FinOps, beyond dollars, pounds, and pennies.

So actually, but it was a very good article, I thought. I was very pleased to see it in Computer Weekly. So it was fundamentally, it was about how GreenOps and FinOps are very aligned. They're very combined. And, and I can, I'm in complete agreement on that. It's a good article. It doesn't say, tell you anything that you probably, that you won't know alreby knowingwing that FinOps and GreenOps are quite aligned and they're all aligned through the fact that in the end, being green, a lot of being green, not all of being green, but a lot of being green is about cutting down on how many machines you, and how much electricity you are using to run your systems, which, which generally speaking cuts down on the cost.

So cost is somewhat of a proxy measure. It's not a perfect proxy measure, but it's somewhat of a proxy measure. So the question that Chris Skipper, our excellent editor has left me and Navveen to discuss is about considering the role of infrastructure as code in enhancing cloud efficiency. How can developers ensure that their infrastructure as code implementations are aligned with sustainability practices to reduce both costs and environmental impact?

So Navveen. What are your thoughts on that subject?

There's, 

Navveen Balani: I think that's a great question. Yeah, agreed. Developers need to embed sustainability as part of the infrastructure as co implementation. And the frameworks that I suggest developers apply is based on our Software Carbon Intensity specification from our Green Software Foundation which also recently received the ISO standard recognition.

So, for those who does not know what SCI is, SCI is a specification to measure the carbon emission of any software application and it promotes three key levers. Writing energy-efficient code, using less hardware for same amount of work, and making applications carbon-aware. And if you apply this strategy to infrastructure and the code, first you start with writing energy-efficient code.

So developers can focus on optimizing resource utilization by right sizing resources and implementing auto scaling. This means allocating only what's necessary for each workload, and also adjusting dynamically based on the demand. Second strategy is around using less hardware for same amount of work.

This basically involves automating resource management like automating the shutdown of non-essential resources during off hours and starting them during peak times. Even conserve energy and cut cost. Also, tagging and monitoring resources usage helps identifying optimization opportunities and eliminates waste.

You can also go with serverless architectures in your ISE code, particularly, it's quite effective as they scale with demand and eliminate, let's say, any provisional requirements. And finally, the third strategy is how do you make applications more carbon-aware. And that's where, as part of your infrastructure code, you can say that I want to deploy a particular workload in a clean free region so you can basically take, apply strategies like region shifting and time shifting and selecting cloud regions which are running on renewables and also maybe deploying workloads or scheduling jobs when the carbon intensity is low.

So all of these strategies can be definitely applied and designed as part of your infrastructure code.

Anne Currie: That's a, that's a very thorough answer and there's loads to unpick in there. Lots of different things. And some things I think at the moment are very aligned with FinOps and cutting your costs. And some things are not aligned yet, but are almost certainly going to become aligned in the future. So for example, you talked about operational efficiency and automation, which is interesting.

Operational efficiency, if you use for your machines. And if you use less electricity, your bill goes down. So that's all good. So in that respect, your FinOps and GreenOps are really well aligned. You know, fewer machines, less stuff, less carbon goes into the atmosphere and it's all fantastically good. And in that respect, I would say that That, that FinOps, that, that, that, you know, your, your hosting bill is a really good proxy metric for your carbon emission.

But of course it's, it's, it's almost stupid, it's so obvious to say it, but it can't just use your cloud bill totally blindly as a, as a guide. Just, you know, what I, when I used to do start, startups in my youth, quite often Azure or, AWS would give you loads of free credits. And then of course, but that doesn't mean that it's carbon free.

So, so there are times when, you know, you just, but you just need to use your head, don't you? Sometimes, obviously, you've been given a discount, but it's not green. It's just a discount.

Navveen Balani: Yeah, yeah, I think that that's, that's a good point. Because if you look at cloud, right? Cloud has infinite resources, right? So, it doesn't mean that, so you have to use it responsibly, right? So, you can bake in energy efficiency and sustainability, right? So, definitely, you have to look at how can you lower the carbon emissions.

And now, and there are also dashboards available from cloud vendors, right? Which at least gives you, some approximation, right? How, what, what is your carbon footprint of your application?

Anne Currie: Yeah, yeah, yeah. I mean, yes, they do provide really good tools and, and, and it's, it's a cut cost off really is the best possible metric you could potentially use, but it's where you've got the tools, it's where you've got the data. So, you know, it's, it's quite good from that perspective. Sometimes you just have to take what's good enough.

And, but so something else you mentioned is automation and obviously the, the really good operations is all automated these days. It's auto scaling and it's using the you know, not just in the cloud, but on prem as well, but actually I, in the. You can just do a lot of stuff manually, you don't have to leap straight, if you, if automation is too scary and you know, it's too much of a leap, just going through and turning off machines at the weekends, even manually, identifying machines that are over, that are over provisioned can actually, bizarrely, I think, I suspect in, well, and in fact, I've seen It might be the biggest carbon reduction you ever do, it's the simplest thing and the least techie thing.

So what do you think about, even for automation?

Navveen Balani: Yeah, totally agree. I would say even just turning off machines. Yeah. I mean, that's just manually, right? Would definitely save also the cost also, as well as the carbon emission also, right? Man, especially if you've turned on GPUs, that would affect. So, yeah, But actually, I think, also, I think if you look at the infrastructure, right, it's, it's, if you, if you break down the infrastructure in two parts, right, production and non-production environment, you can definitely have a lot of savings on non-production environment, right, because it doesn't need to be on always.

Production definitely needs to be on 24 by 7, but you are definitely have a lot of improvements that you can done on your non-production environment, dev environments, people, right, and I've seen customers having more non-production environments, right, than production ones. 

Anne Currie: Indeed. And it's so ungreen. It's there's, there was another interesting thing that you talked about in your when you were talking about the SCI, which is carbon-awareness, which of course we know is the most, it's actually the most, it That's the code efficiency and operational efficiency are all good for kind of short term mitigating the harms, but in, but to actually take full advantage of that, you know, the soaring production of cheap energy for renewables, we need to demand shift to when the sun is shining or the wind's blowing.

But the interesting thing about that is that although that's by far the most interesting thing in being green, I would say, it's the bit that at the moment doesn't really save you any money because most countries don't yet have dynamic pricing. So dynamic pricing, so, so what's your thoughts? What are your thoughts on dynamic pricing and when it's coming?

So dynamic pricing is basically when the price of electricity changes through the day, depending on how expensive it was to produce, which usually means, you know, at times when the sun's shining and the wind's blowing, the power is cheaper than others. And that's, that's now very common in certain countries like Spain, but very uncommon in other countries.

Navveen Balani: I think definitely that's a good concept that will promote more sustainability. Typically, if you look at cloud providers, like let's be, to give you an example of Google cloud, it at least gives you now, if you're deploying something on a region, it tells you that it's, it's a low carbon region and hopefully in future you will have the dynamic parsing also, right? It will also give you the time when you should run the workloads. And there are a lot of workloads we doesn't need to run 24 by 7, like batch jobs. We get a lot of emails, right? Of all those millions of emails, right? You'll be sending, right? For promotions. All those can be run on time where, where there is least intensity.

And definitely if you have, if you have a cost, if the cloud provider gives you a cost that this is a good time window, and this is less costly and then all, all, all the activity which doesn't are not critical enough can definitely take care of the dynamic pricing. So I assume in future, I mean, we can see the trend, right?

Maybe where you have, I mean, it's all about data. If you have the data from the grids available to the providers in future, then we can definitely definitely tap on it.

Anne Currie: Yeah. Yeah. I agree. It's, it's a bit of a shame. It's, it's where there's the hole in the, in the alignment of green ops and fin ops at the moment. That's, that is incredibly green to demand shift, but you don't necessarily get money off for doing that. You know, moving to that green region is incredibly green.

But it doesn't necessarily save you money, but it will do once dynamic pricing comes in.

Navveen Balani: I also, I think if I also feel if regulations also are there, right, with regulations around carbon emissions reporting. Especially, I think, the EUA Act just talked about reporting the carbon emission, but it's a dog bone mitigation. But at some point of time, I would say, when you have a reporting mechanism also, and everybody have to comply for it, I see a lot of these trends coming, right, a new innovative way, right, to lower the carbon emission, right?

So I think regulations at some point of time will also enable, right, a lot of these, I say, innovations, right, to come up.

Anne Currie: Yeah, I agree. Oh, I meant something I meant to mention that, that's aligned with what you were saying earlier about automation. There's the CNCF, the Cloud Native Computing Foundation. So another of our, of the Linux foundations out there. They describe, and it's, GreenOps equals Fin, GreenOps equals FinOps plus GitOps.

So basically they're saying GreenOps is automated FinOps, which is an interesting one because it feels to me like they're really saying there that GreenOps is good FinOps. Uh, and oddly enough, FinOps often say, well, FinOps is just good ops. GreenOps is just good ops. It's, it's which is interesting, which I think that people often don't really appreciate. No, sorry, I'm taking the final word there and I need to, I will leave the final word to you, Navveen, on FinOps and GreenOps.

Navveen Balani: I'd like to end with what you said, right? So it's, we started with DevOps where you decide to automate something, then you, then the FinOps came, right? So we, because cloud resources were getting expensive, right? And now we have GreenOps, right? So all, we have to look at it in holistically, right? Across DevOps, FinOps, GreenOps, right?

And ensure you take care of both the cost and carbon, right? And keep it under control.

Anne Currie: Yes, totally agree. Totally agree. Right. So we're going to move on to the next. So that's the first article we're talking about there, I would say is extremely uncontroversial. Operational efficiency is like a total win all around. The next article, which is about why you should switch to green coding for a net zero feature, which is a LinkedIn article from the CEO of CSM Technologies, I think is vastly more controversial. Not because it's wrong that you should write a more code efficient, more efficient code. But because I think that there's a lot of context around it. So, so he's written a lovely article, links, as I say, links in the show notes, saying we should all be, be coding more efficiently, which is, which is nice.

But, and it ends with the, with the line, we should change, change, save the world one line of code at a time, which I find massively controversial because I think that when it comes to code efficiency, a lot of business, it's just not the right thing for a lot of businesses to do. It's too expensive. What they should be doing is putting pressure on suppliers to write their code efficient, you know, write the scaled code efficiency.

I think that it can really waste time going down, people going down that rabbit hole and their, their bosses were very right to say, "don't, I'm not going to do it" because it would put you out of business if you rewrote all your systems in Rust or C. So I think that is, it's, it's, it's an article that's true, but only true in certain contexts and not in others.

So Navveen, what's your, what's your thinking on it?

Navveen Balani: So I would say we need to look at this holistically, particularly around green software, focusing on, let's say... I would say three dimensions we have to take. First is having developer training and implementation of green software. Second, we need management buy-in. And third, I would say, it's the culture shift that needs to happen.

And if you look at green software, right, when we when we started, when you all started with the foundation, three years back, green software was a very relatively new area. So we need to provide training and certification in this area so that developers are aware of, right, how they embed sustainability in their day to day work.

Apart from, I would say, the training, a developer needs to have accessible tools, right. Now we have the SCI specification, the Impact Framework, Carbon-Aware SDK, right, which, and there are a lot of other open source tools also available now, which can make it more actionable and developers can actually embed them as part of their DevOps process.

And once the measurement is done apart from code, right, it's also the optimization piece we talked about, right in the infrastructure as code earlier also, right, how you take it all together, right, and try to optimize not just the code, right, but also the resources which are running the hardware, the resources which are powering those applications.

And as I would say green software practices gain traction, I would say securing management buy-in also is essential, right, for widespread production. For instance, highlighting the business benefits is crucial. For instance, implementing green coding, right, can lead to also significant cost savings by, let's say, reducing energy consumption and optimizing utilization.

And as you mentioned, right, it's not just Our footprint, but the scope, the footprint of our suppliers to ensure they also follow the same standard methodology. And that's where I think it comes to the culture change, right? That we all need to go through, particularly for, for green software. And we need to look at how we can embed green software, right?

Going forward in all our work, right? Similar to, let's say, similar, we do it for security, right? So when we, when we had security at 10 years back, right? Now we have security by default, right? We don't talk about that application needs to be secure. We assume application is secure by default. Similarly, if we embed green software, not just code, but across all, all the layers, then we can ensure maybe over the next four, five years where all applications, the new applications that we build, right?

Have a green software principles baked in. So I would say, I mean, it's basically a holistic approach that would be required, right, to, right, from enabling the development community, the tech community where the foundation also, like, for foundation, like, Green Software Foundation plays a critical role.

The management needs to buy in, and also the culture change that needs to happen, right? And the culture change also needs to happen, I would say, at the universities and schools, right, where they can start educating green software early on, right? Similar to the way we have learned object oriented programming, right?

That's by default. We have learned over the, I mean, over the last I would say decade, few decades, right? If we have green software, same as object oriented programming concept, then I think whatever application we build in future, right, we'll have green software baked in.

Anne Currie: Yeah, I, I may agree up to a point. I agree with you, but I think we, I, I'm a big believer in separating out two types of developers. Obviously there are loads of different types of developers in the world, but so, but two types of backend developers, so front end developers, this is almost a separate thing.

But backend developers, you've got people who just working in an enterprise and the code that they're producing is not going to ever be deployed. You know, it's, it's the code that they are writing is never going to be run by billions of people in their own data centers. And then you've got people who are writing platforms and the whole purpose of the platform is to try and get some billions of people to, or at least millions of people to write this code, to run this code.

And those people, they absolutely need to write efficient code. When I think everybody needs to, should be, could, should get used to getting out their performance profiler and just making sure there are no egregious performance problems with their code. Because performance problems are your code's slower and you're burning a load of carbon and it's total waste.

So all again, very aligned with the business. You want your systems to run fast. Your customers want your systems to run fast. So, so having a decently performance system is good. But beyond that, you probably don't want to be writing code yourself, which is massively efficient because that takes a long time, but you do want the platforms you're running on to have done, to have made that investment.

So it's, it's kind of like, there's a lot of context here, isn't it? Are you writing code for mass use or are you writing code that is not really for mass use, which is, which is interesting. I think that's a subtlety that we, like, for example, lovely article though this was, did not point out that difference.

Navveen Balani: That's a good point. So, yeah, especially applications, right? Package applications which will be used, let's say, by millions of developers worldwide or users worldwide definitely needs to make that in right to ensure that for instance simply like all the large language models a good example right all all generative applications will be used by millions of applications millions of developers so how can you make the AI more efficient right both on the user side who is creating let's say the prompts right to Create in an efficient way so that the round trip is reduced and secondly on the backend side, right?

How do you have a low cost efficient energy-efficient model? That's why you also seeing a lot of LLM models are now Talking about the small language models more energy-efficient more compact, right? There is a trend where I would say right where Organizations are now looking at energy efficiency also, right, as part of the applications or whatever work they have been doing.

Anne Currie: Yeah. And of course that's all driven by cost. It's taking us back to our previous thing about cost and cost and green are very aligned. And the good thing about, well, the bad thing about AI is very costly. The good thing is it's driving quite a lot of efficiency improvements. So, I mean, I talk about this every time I'm on here, that, that Python has got a lot more efficient because, because of AI, that they've rewritten all of the code, core, core libraries in Rust. And of course that's, that's a perfect example of they are the kind of people you want to be writing super efficient code. They can save the world one line of code at a time because so many people run Python. But you want to be getting that out of your platform and not having to do it yourself as a, as a Python user.

You don't want to have to change to Rust yourself. You want to be able to get the value of Rust whilst still using Python. But yes, yeah. So, so that is all very interesting stuff, but yeah, very nuanced. It's all at every degree of it. It's what my, to my mind is what makes green interesting, is it's not simple.

It's not trivial. You have to step back and you go, "where am I? What am I doing? You know, what's, where, what's, how do I fit into this? Where does mine, where is my effort best applied?" I mean, you're obviously part of the SCI, which covers all of the things, you know, operational efficiency and code efficiency and demand shifting and shaping.

What's your interest? What do you like the most out of those things?

Navveen Balani: I would say from a, I think from a developer standpoint, right, depending on your roles, right, so SCI, I would say is more inclusive in terms of, depending on roles, right, whether you are a developer, architect, data scientist, right. All are various parts to play, right, to reduce the carbon emission and make applications more energy-efficient.

So, depending on your role, for instance, if you are building, or you are a developer writing code, right, then you can really focus on energy efficiency. And it's not, as you mentioned, right, it's not just moving towards a C or C++ language, right, which is more efficient, but it's, so you have to basically look in the context of the work you are doing and trying to optimize it, right.

So you have to do that trade off as a developer and how, what libraries access to make it more efficient. Second, I would say the whole hardware optimization, I think in terms of where all the DevOps, cloud, cloud architects comes in. How there are various custom chipset from various vendors, right? How can you best utilize from an infrastructure point of view?

And third, I would say is more strategic in nature, right, in terms of how do you bake in the whole carbon away computing concept, because that's new. You need to have data providers, you need to tie up with various licenses which are actually costlier, right, if you look at getting the real time data, right, from various providers.

So how do you bake that in in the application to more of a strategy kind of work and thinking? So in that way, I would say it's, I mean, depending on your role and context, right? I mean, whether from developers to architect for data scientists, right? Each can find definitely a value for, in SCI and then try to reduce their scope of work.

Anne Currie: Yeah. It's so, it's interesting. When I first heard about the SCI, I was a bit, I was a bit dubious about its value, but I have completely changed my mind on that as time goes, especially because I teach, I teach people who are green software and, and, and. One of the things that often comes up is people wanting to be able to do like for like measurements.

And I think that I originally thought the SCI was about a standard that you could compare between applications. And that was where the value would lie. And I was a bit dubious that we could realistically do it. And, and now I've realized that, that I like that the SCI has stayed fairly woolly and loose.

It's more conceptual than it is a specific implementation. And I like that because really it means that companies could choose their SCI score, they can choose how to define their SCI score for their applications and choose what's appropriate. What it's going to, what it's going to, what the denominator is going to be.

So it's per user or per transaction or per, so everything, something that's specific to them, and then it's essentially for like, for like, so you can say, well, last year it was this, and the next year is this, and you can average over time so that you're, you, you know, saying you don't say, well, I'm comparing a sunny day with a non sunny day, or, you know, all those kinds of things.

What I like about the SCI is it's, is it's very kind of conceptual high level nature that forces people to think, "well, actually, how do these things apply to my system?" You've got to use your head. You can't just, you know, you can't just follow it. You can't follow it blindly because it doesn't make any sense if you do that.

You have to say, how does this apply? Forces you to think, which I like.

Navveen Balani: Yeah, I think that's a good concept. Particularly if you look at SCI, right? It's for an application, so you have better control rather than giving you the carbon emission of all the applications. Right. Which typically is given by various cloud providers given an application, then you have a better control, as you mentioned, right?

You can define your own boundary and architecture and calculate the SCS core, right? And the intent is to. Basically, as you deploy new versions, right, the intent is the SCI, you should look at how you can reduce the SCI score, right? We can't achieve zero, but definitely across releases, right? How can you make it have a lower SCI score?

And the point you made about the comparison also, right? So you're comparing your application versus your previous application that you have deployed. It's not about creating two applications from two different organizations, right? We're not there yet. It's about currently using this methodology, right, for your own application and trying ways to reduce it.

Anne Currie: Yeah, which makes all the sense to me. So many years ago in my, when I was more youthful, I used to work in retail and in retail, like for like comparisons are very important. You want to be able to say, well, this year we've made more money than last year, but you can't just say this year we made more money than last year.

You can, but it's not all that useful. What they actually want to do is say, it's per thing. So in retail it's often the kind of per square foot of retail space, balanced for kind of like, well, how expensive was that retail space? You know, so you're not comparing it and say, well, this year we made more money on the same on, on the same amount of floor space, but, you know, it was in London versus it was in the middle of, you know, of the desert. It's, it's kind of like you, you've got to, you've gotta come up with your own, like, for like measure so that you can say, well, is our business improving or is our business getting worse? And the SCI is exactly the same. It's, it's the concept of like, for, like, it's for you to check Ron North's for you to check against other people.

So yeah, it's yeah, I, I, I, I've been completely won over to the SCI. I was highly dubious to start with. Right.

Navveen Balani: Good example. Yeah, that's a very good example of a retail. I'll also use that.

Anne Currie: Right. So now we'll go to the final, the final thing we're going to talk about today, which is, we just touched it from a slightly, again, a slightly more holistic perspective. So this was an article in a Silicon Republic and it was a Q&A with the chief security officer of a, an AI company. It was talking about.

Basically, her premise, and I totally agree with it, is that security, cyber security is very aligned with being green. It wasn't, it's not, it's a bit thin as an article, it doesn't give you an awful lot of information, but I think basically, yeah, that there's the, the idea that, that I think we should be discussing is, is security and, and green aligned?

And you, you, Navveen, you've talked a little bit about that, about in terms building security in is, is like, it is, we've, we've learned to do that and we should learn to do green things, build green things in, in, in the same way. But separately to that, are there security benefits to being green? What do you think?

Navveen Balani: I would say, yeah, definitely there's synergies between security and green principles. So I like to again give that example of SCI, right, if you want to break down the methodology into three parts, right, making applications more energy-efficient, right. So, if you look at the security algorithms, right, how can you use, how can you basically optimize the security algorithms to, let's say, use fewer computation resources.

Particularly, if you look at the security stack, right, they also have evolved from various encryption and cryptography software, right, and now I think you have various key ciphers available across different dimensions. So, they're already following this, I would say, backtest, right, of making encryption security, right, more performance and more easier to adapt. So in that case, I would say it's more aligned towards the algorithms that they're using are more efficient, right? As compared to what it might be, let's say five years, 10 years down the line for, for security protocols. And similarly, I would also say new strategies can also be applied for security scanning.

For instance, vulnerability scanning is one commonly used, right, to identify any threats, maybe in cloud or maybe in desktops and other systems, right, that can actually run, take the advantage of running it on a time, right, where the carbon intensity is low. So in that way, it can apply certain green software principles.

To run all those scans where the carbon intensity is low and also save on the carbon emissions. So, definitely I would say there is some synergy between security and green software. And certain, I would say, features of green software principles can also be applied to secure the domain, right, to make it more energy-efficient.

Yeah,

Anne Currie: And so, so something that actually I'm, I, well, you did mention a little bit earlier is, is security is the perfect example of where hardware, using the right hardware for the job massively cuts your emissions. So dedicate the right kind of chips that are designed for for encryption are just so much more efficient than using general purpose or CPUs for, for that, for that.

So, yeah, we wouldn't be able to do what we do these days if it wasn't for dedicated chips. Oh, and so, and oddly enough, this does also map to some of the stuff that we said at the beginning about manual ops and manual FinOps, that's, it's amazing how many systems are through machines that are kind of like, nobody's, everyone's kind of forgotten about them.

They're not keeping them patched. They don't really do anything useful anymore. Those are your backdoor. Those are the ways that people break in and they're just wasting electricity. So even in building green software one of my co-authors, she, she brought up the fact that, that it's interesting that security, well, that are very much an example of a waste of electricity.

They, they're, they're wasting. So, so something like a denial of service attack, the whole purpose is to burn your electricity in your systems and burn your system so that your systems don't have anything, any time to do the thing that they're designed to do. You know, the thing that has value for you, instead, it's just burning your systems up burning electricity, running up all your bills to do something which is bad for you.

So having a secure system that, and things like applying the latest patches so that you're less, less exposed to denial of service attacks is green because denial of service attacks are very ungreen, they are very dirty. Same, it's, it is quite interesting, isn't it, from that?

Navveen Balani: Yeah, that's why I think the provisioning the right hardware, virtual machines, especially, right, provide various cloud providers. Right. They all, they now provide a managed services, right, to detect various denial attacks. And I assume, right, the underlying hardware that they are using, which is, which should be used by millions of applications, right,

would be definitely more sustainable, more energy-efficient, right. And, and more scalable.

Anne Currie: Yeah. Yeah. Securities are really interesting in that, that's it. So FinOps is just really a, a, a pretty much, except with, for, you know, your free Azure credits or whatever, is pretty much a group, the direct proxy measurement for carbon emissions. And, and likely to become more so in the future when we get dynamic pricing.

But security is not a direct proxy measurement is just, it's just that there are a lot of, you know, best practice in ops is also best practice in secure ops is also best practice in green ops. You know, they're, they're, they're kind of like, you can't use the number of hacks you have, then the number of attacks you, you fall foul to as a proxy measure, you, well, maybe you could, I think that would be a bit complicated.

Proxy measures for carbon is how many times your data gets stolen, aligned rather than proxy. It's interesting. I mean, so we just, we've talked about those, those three things, but have you run across anything interesting at the moment that's, that you think that our listeners should, should hear about? And

Navveen Balani: So, yeah, I would say for the, on the Green Software Foundations, specifically, right, we are working on green AI and so we are trying to look at how we can extend SCI to take I mean, how we can do SCI measurements for large language models generative AI models. So this is something we are actively, I would say, working towards from the foundation perspective.

And if you look at from an SCI perspective, right, we want to have various extensions to SCI, for instance. How do you do SCI for web applications, backend applications? And make it more easy to measure, right, different parts of the code and make it easily available to developers so that developers can measure their, their part of the overall carbon footprint, right so we can make it more accessible.

So that's, that's one thing I think we had from a foundation perspective, looking at how we can make it SCMO extensible to various other use cases.

Anne Currie: That's, that's very interesting. That is the, yeah. 'cause obviously Ai, AI is the, is the workload of the, it's on everybody's lips at the moment. It is... and so I, I saw very interesting charts from all the, the Economist, I think the, the other week that, that said, you know, that's, that showed the enormous amounts of power that was currently being used on ai, but still less than the amounts of power being used on Bitcoin.

So it is just worth reminding. And of course, bitcoin is very aligned with our last conversation about security and what people wanting and people who are attacking you wanting to run up your energy bills, because quite often what they want to do is mine Bitcoin on your, on your machines that you're not properly watching.

So security sweeps are a pretty good way of identifying machines that are burning power totally unnecessarily. From political... I, I quite liked to keep my eyes not just on the Al news, but the really good news news last week from a political rather than a technical perspective was the world's just got its first climate science trained president in the new female president of Mexico is a, is a climate scientist by, by trade and training.

So, I, I, I would be very interested to see what affects that out on the country. Any other interesting political news that's good news, do you think?

Navveen Balani: No, I think I've yet to catch up on,

Anne Currie: Well, I'm quite nosy, so I keep my, I keep my eyes open on all things. I would say there's, there's loads, actually, there's, there's a lot of good news going on at the moment. Texas is now a massive solar producing state. It's, so, yeah, there, there is, it's, The world is changing in a positive way. I, I like to, to, to keep reminding everybody that we are not doomers at the Green Software Foundation.

We, we are doing this because we believe it will have an effect.

Navveen Balani: I totally, I would say, especially with the foundation, right? It's all our collective journey that we've gone through. I mean, we started three years back, we didn't have, we didn't have any specification tools, right? Three years down the line, we have the first specification tool. Software carbon intensity specification, which is now an ISO standard.

We have various tools now, carbon SDK, impact engine framework. And I, I know that 1230 projects already in the pipeline on the foundation, right, which will make the world, I would say, a better place, right. In terms of sustainability, right. For all the work that we do. So, yeah, it's basically a shared responsibility, right.

Climate change is basically a shared responsibility, right. And from our perspective, developers, all we can do is contribute, right, by using the three SCI principles, which I talked about, right? Which I again repeat is either write better energy-efficient code, use hardware wisely, and make applications carbon-aware.

Anne Currie: And of course, actually, not just write it, cause you might not be the one who's writing, it's more important that you use it. So as I'll, I'll constantly, cause Python is such a good, good example of the moment, at the moment, because of their big, big revising stuff. If you people who upgrade the latest versions of Python that are much more, more efficient will be saving a lot compared to people who don't upgrade.

And that's, those kinds of things are the kind of things that will immediately be unearthed by running the SCI like for like. I mean, a really big change might be on your like for like is that you upgraded to more recent versions of a particular library or a particular set of tools that you're using that are more, more efficient.

SCI isn't just about what code you write, it's about what code you use. And that is almost certainly going to be where you get the biggest, biggest value, the biggest return. Anyway, sorry, now I'm doing my, I'm trying to take the last word again, and I'm going to leave the last word to you, Navveen.

Navveen Balani: So yeah, very happy to be part of this podcast. Enjoyed this conversation talking about, I think, three different aspects. I would, I would say. And to all the viewers is, yeah, thank you for listening in and have a good day.

Anne Currie: Navveen, awesome. Thank you very much for coming on this podcast. And a final reminder that all the resources are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. And see you all soon at some point, if they ever let me back in again as a guest host.

Good bye.

Navveen Balani: Goodbye. Thank you, everyone.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> On this episode of TWiGS, host Anne Currie is joined by Navveen Balani of Accenture and fellow GSF member. This conversation navigates the landscapes of, and intersections between GreenOps, DevOps, and FinOps, as well as the vital role of Infrastructure as Code in marrying financial and ecological efficiencies in cloud operations. Lastly, they tackle the intersection of cybersecurity and AI development, emphasizing the need for green software principles to fortify AI systems while minimizing energy use. 

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TRANSCRIPT BELOW:

Navveen Balani: Definitely, I would say there is some synergy between security and green software and certain, I would say, features of green software principles can also be applied to security domain, right, to make it more energy-efficient.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams.

Anne Currie: Welcome to another edition of the Week in Green Software, where we bring you the latest news and updates from the world of sustainable software development. Today, I'm your host, Anne Currie. So you're not quite hearing the usual dulcet tones of Chris Adams. You will have to do with me instead. But as usual, we'll be talking about the world of green software and what's, what's going on at the moment.

And today we're going to talk a little bit about how being green matches with FinOps, which I think is very true. And I think that's a really important part of the story. So we'll be talking a little bit about that. We'll also be talking a little bit about code and code efficiency, which I'm much more...

You have to be very careful about code efficiency. So that's, that's the part that we need to be really careful about. What's the context there when we talk about code efficiency. And finally, we'll talk about the intersection of cybersecurity and AI developments, and we'll be talking a little bit about GreenOps.

And there is a new Green Software Foundation project, GreenOps project, which is aimed to look about how we can embrace AI and use AI without totally throwing all our green principles out of the window. And I think that is absolutely doable, but we need to think about it. We need to go in, in a very knowing way.

So as I said, I am your host today, Anne Currie. But first I'm going to introduce our guest, Navveen. Navveen, do you want to, do you want to introduce yourself?

Navveen Balani: Thank you Anne. Hi everyone. I'm Navveen Balani. I'm the Managing Director and Chief Technologist for the Techno and Sustainability Innovation Group at Accenture, working at the intersection of technology and sustainability. I'm also the co chair of the Standards Working Group and the Impact Engine Framework at the Green Software Foundation.

I'm a Google Cloud Certified Fellow, a LinkedIn Top Voice, and author of several reading books. Very glad to be part of this podcast.

Anne Currie: Thank you very much, Navveen. It's very good to have you. So just a bit of context for me. My name is Anne Currie. I am one of the co-chairs of the community group of the Green Software Foundation. And I am also one of the authors of the new O'Reilly book, Building Green Software. So that's, fills you in a little bit on my background.

So before we dive in to the articles this week that we're going to be talking about, it's just a reminder that everything we talk about will be linked in the show notes at the bottom of the episode. So you can, you can read the articles that we're talking about. You don't just have to rely on us telling you what was in the article.

So, well, let's, let's move first to the first article from today, which was in Computer Weekly. And it was called Green Coding. It was, it was a, basically a puff piece by a company called CloudBolt who do a look at code efficiency, cloud frequency. So taking it beyond FinOps, beyond dollars, pounds, and pennies.

So actually, but it was a very good article, I thought. I was very pleased to see it in Computer Weekly. So it was fundamentally, it was about how GreenOps and FinOps are very aligned. They're very combined. And, and I can, I'm in complete agreement on that. It's a good article. It doesn't say, tell you anything that you probably, that you won't know alreby knowingwing that FinOps and GreenOps are quite aligned and they're all aligned through the fact that in the end, being green, a lot of being green, not all of being green, but a lot of being green is about cutting down on how many machines you, and how much electricity you are using to run your systems, which, which generally speaking cuts down on the cost.

So cost is somewhat of a proxy measure. It's not a perfect proxy measure, but it's somewhat of a proxy measure. So the question that Chris Skipper, our excellent editor has left me and Navveen to discuss is about considering the role of infrastructure as code in enhancing cloud efficiency. How can developers ensure that their infrastructure as code implementations are aligned with sustainability practices to reduce both costs and environmental impact?

So Navveen. What are your thoughts on that subject?

There's, 

Navveen Balani: I think that's a great question. Yeah, agreed. Developers need to embed sustainability as part of the infrastructure as co implementation. And the frameworks that I suggest developers apply is based on our Software Carbon Intensity specification from our Green Software Foundation which also recently received the ISO standard recognition.

So, for those who does not know what SCI is, SCI is a specification to measure the carbon emission of any software application and it promotes three key levers. Writing energy-efficient code, using less hardware for same amount of work, and making applications carbon-aware. And if you apply this strategy to infrastructure and the code, first you start with writing energy-efficient code.

So developers can focus on optimizing resource utilization by right sizing resources and implementing auto scaling. This means allocating only what's necessary for each workload, and also adjusting dynamically based on the demand. Second strategy is around using less hardware for same amount of work.

This basically involves automating resource management like automating the shutdown of non-essential resources during off hours and starting them during peak times. Even conserve energy and cut cost. Also, tagging and monitoring resources usage helps identifying optimization opportunities and eliminates waste.

You can also go with serverless architectures in your ISE code, particularly, it's quite effective as they scale with demand and eliminate, let's say, any provisional requirements. And finally, the third strategy is how do you make applications more carbon-aware. And that's where, as part of your infrastructure code, you can say that I want to deploy a particular workload in a clean free region so you can basically take, apply strategies like region shifting and time shifting and selecting cloud regions which are running on renewables and also maybe deploying workloads or scheduling jobs when the carbon intensity is low.

So all of these strategies can be definitely applied and designed as part of your infrastructure code.

Anne Currie: That's a, that's a very thorough answer and there's loads to unpick in there. Lots of different things. And some things I think at the moment are very aligned with FinOps and cutting your costs. And some things are not aligned yet, but are almost certainly going to become aligned in the future. So for example, you talked about operational efficiency and automation, which is interesting.

Operational efficiency, if you use for your machines. And if you use less electricity, your bill goes down. So that's all good. So in that respect, your FinOps and GreenOps are really well aligned. You know, fewer machines, less stuff, less carbon goes into the atmosphere and it's all fantastically good. And in that respect, I would say that That, that FinOps, that, that, that, you know, your, your hosting bill is a really good proxy metric for your carbon emission.

But of course it's, it's, it's almost stupid, it's so obvious to say it, but it can't just use your cloud bill totally blindly as a, as a guide. Just, you know, what I, when I used to do start, startups in my youth, quite often Azure or, AWS would give you loads of free credits. And then of course, but that doesn't mean that it's carbon free.

So, so there are times when, you know, you just, but you just need to use your head, don't you? Sometimes, obviously, you've been given a discount, but it's not green. It's just a discount.

Navveen Balani: Yeah, yeah, I think that that's, that's a good point. Because if you look at cloud, right? Cloud has infinite resources, right? So, it doesn't mean that, so you have to use it responsibly, right? So, you can bake in energy efficiency and sustainability, right? So, definitely, you have to look at how can you lower the carbon emissions.

And now, and there are also dashboards available from cloud vendors, right? Which at least gives you, some approximation, right? How, what, what is your carbon footprint of your application?

Anne Currie: Yeah, yeah, yeah. I mean, yes, they do provide really good tools and, and, and it's, it's a cut cost off really is the best possible metric you could potentially use, but it's where you've got the tools, it's where you've got the data. So, you know, it's, it's quite good from that perspective. Sometimes you just have to take what's good enough.

And, but so something else you mentioned is automation and obviously the, the really good operations is all automated these days. It's auto scaling and it's using the you know, not just in the cloud, but on prem as well, but actually I, in the. You can just do a lot of stuff manually, you don't have to leap straight, if you, if automation is too scary and you know, it's too much of a leap, just going through and turning off machines at the weekends, even manually, identifying machines that are over, that are over provisioned can actually, bizarrely, I think, I suspect in, well, and in fact, I've seen It might be the biggest carbon reduction you ever do, it's the simplest thing and the least techie thing.

So what do you think about, even for automation?

Navveen Balani: Yeah, totally agree. I would say even just turning off machines. Yeah. I mean, that's just manually, right? Would definitely save also the cost also, as well as the carbon emission also, right? Man, especially if you've turned on GPUs, that would affect. So, yeah, But actually, I think, also, I think if you look at the infrastructure, right, it's, it's, if you, if you break down the infrastructure in two parts, right, production and non-production environment, you can definitely have a lot of savings on non-production environment, right, because it doesn't need to be on always.

Production definitely needs to be on 24 by 7, but you are definitely have a lot of improvements that you can done on your non-production environment, dev environments, people, right, and I've seen customers having more non-production environments, right, than production ones. 

Anne Currie: Indeed. And it's so ungreen. It's there's, there was another interesting thing that you talked about in your when you were talking about the SCI, which is carbon-awareness, which of course we know is the most, it's actually the most, it That's the code efficiency and operational efficiency are all good for kind of short term mitigating the harms, but in, but to actually take full advantage of that, you know, the soaring production of cheap energy for renewables, we need to demand shift to when the sun is shining or the wind's blowing.

But the interesting thing about that is that although that's by far the most interesting thing in being green, I would say, it's the bit that at the moment doesn't really save you any money because most countries don't yet have dynamic pricing. So dynamic pricing, so, so what's your thoughts? What are your thoughts on dynamic pricing and when it's coming?

So dynamic pricing is basically when the price of electricity changes through the day, depending on how expensive it was to produce, which usually means, you know, at times when the sun's shining and the wind's blowing, the power is cheaper than others. And that's, that's now very common in certain countries like Spain, but very uncommon in other countries.

Navveen Balani: I think definitely that's a good concept that will promote more sustainability. Typically, if you look at cloud providers, like let's be, to give you an example of Google cloud, it at least gives you now, if you're deploying something on a region, it tells you that it's, it's a low carbon region and hopefully in future you will have the dynamic parsing also, right? It will also give you the time when you should run the workloads. And there are a lot of workloads we doesn't need to run 24 by 7, like batch jobs. We get a lot of emails, right? Of all those millions of emails, right? You'll be sending, right? For promotions. All those can be run on time where, where there is least intensity.

And definitely if you have, if you have a cost, if the cloud provider gives you a cost that this is a good time window, and this is less costly and then all, all, all the activity which doesn't are not critical enough can definitely take care of the dynamic pricing. So I assume in future, I mean, we can see the trend, right?

Maybe where you have, I mean, it's all about data. If you have the data from the grids available to the providers in future, then we can definitely definitely tap on it.

Anne Currie: Yeah. Yeah. I agree. It's, it's a bit of a shame. It's, it's where there's the hole in the, in the alignment of green ops and fin ops at the moment. That's, that is incredibly green to demand shift, but you don't necessarily get money off for doing that. You know, moving to that green region is incredibly green.

But it doesn't necessarily save you money, but it will do once dynamic pricing comes in.

Navveen Balani: I also, I think if I also feel if regulations also are there, right, with regulations around carbon emissions reporting. Especially, I think, the EUA Act just talked about reporting the carbon emission, but it's a dog bone mitigation. But at some point of time, I would say, when you have a reporting mechanism also, and everybody have to comply for it, I see a lot of these trends coming, right, a new innovative way, right, to lower the carbon emission, right?

So I think regulations at some point of time will also enable, right, a lot of these, I say, innovations, right, to come up.

Anne Currie: Yeah, I agree. Oh, I meant something I meant to mention that, that's aligned with what you were saying earlier about automation. There's the CNCF, the Cloud Native Computing Foundation. So another of our, of the Linux foundations out there. They describe, and it's, GreenOps equals Fin, GreenOps equals FinOps plus GitOps.

So basically they're saying GreenOps is automated FinOps, which is an interesting one because it feels to me like they're really saying there that GreenOps is good FinOps. Uh, and oddly enough, FinOps often say, well, FinOps is just good ops. GreenOps is just good ops. It's, it's which is interesting, which I think that people often don't really appreciate. No, sorry, I'm taking the final word there and I need to, I will leave the final word to you, Navveen, on FinOps and GreenOps.

Navveen Balani: I'd like to end with what you said, right? So it's, we started with DevOps where you decide to automate something, then you, then the FinOps came, right? So we, because cloud resources were getting expensive, right? And now we have GreenOps, right? So all, we have to look at it in holistically, right? Across DevOps, FinOps, GreenOps, right?

And ensure you take care of both the cost and carbon, right? And keep it under control.

Anne Currie: Yes, totally agree. Totally agree. Right. So we're going to move on to the next. So that's the first article we're talking about there, I would say is extremely uncontroversial. Operational efficiency is like a total win all around. The next article, which is about why you should switch to green coding for a net zero feature, which is a LinkedIn article from the CEO of CSM Technologies, I think is vastly more controversial. Not because it's wrong that you should write a more code efficient, more efficient code. But because I think that there's a lot of context around it. So, so he's written a lovely article, links, as I say, links in the show notes, saying we should all be, be coding more efficiently, which is, which is nice.

But, and it ends with the, with the line, we should change, change, save the world one line of code at a time, which I find massively controversial because I think that when it comes to code efficiency, a lot of business, it's just not the right thing for a lot of businesses to do. It's too expensive. What they should be doing is putting pressure on suppliers to write their code efficient, you know, write the scaled code efficiency.

I think that it can really waste time going down, people going down that rabbit hole and their, their bosses were very right to say, "don't, I'm not going to do it" because it would put you out of business if you rewrote all your systems in Rust or C. So I think that is, it's, it's, it's an article that's true, but only true in certain contexts and not in others.

So Navveen, what's your, what's your thinking on it?

Navveen Balani: So I would say we need to look at this holistically, particularly around green software, focusing on, let's say... I would say three dimensions we have to take. First is having developer training and implementation of green software. Second, we need management buy-in. And third, I would say, it's the culture shift that needs to happen.

And if you look at green software, right, when we when we started, when you all started with the foundation, three years back, green software was a very relatively new area. So we need to provide training and certification in this area so that developers are aware of, right, how they embed sustainability in their day to day work.

Apart from, I would say, the training, a developer needs to have accessible tools, right. Now we have the SCI specification, the Impact Framework, Carbon-Aware SDK, right, which, and there are a lot of other open source tools also available now, which can make it more actionable and developers can actually embed them as part of their DevOps process.

And once the measurement is done apart from code, right, it's also the optimization piece we talked about, right in the infrastructure as code earlier also, right, how you take it all together, right, and try to optimize not just the code, right, but also the resources which are running the hardware, the resources which are powering those applications.

And as I would say green software practices gain traction, I would say securing management buy-in also is essential, right, for widespread production. For instance, highlighting the business benefits is crucial. For instance, implementing green coding, right, can lead to also significant cost savings by, let's say, reducing energy consumption and optimizing utilization.

And as you mentioned, right, it's not just Our footprint, but the scope, the footprint of our suppliers to ensure they also follow the same standard methodology. And that's where I think it comes to the culture change, right? That we all need to go through, particularly for, for green software. And we need to look at how we can embed green software, right?

Going forward in all our work, right? Similar to, let's say, similar, we do it for security, right? So when we, when we had security at 10 years back, right? Now we have security by default, right? We don't talk about that application needs to be secure. We assume application is secure by default. Similarly, if we embed green software, not just code, but across all, all the layers, then we can ensure maybe over the next four, five years where all applications, the new applications that we build, right?

Have a green software principles baked in. So I would say, I mean, it's basically a holistic approach that would be required, right, to, right, from enabling the development community, the tech community where the foundation also, like, for foundation, like, Green Software Foundation plays a critical role.

The management needs to buy in, and also the culture change that needs to happen, right? And the culture change also needs to happen, I would say, at the universities and schools, right, where they can start educating green software early on, right? Similar to the way we have learned object oriented programming, right?

That's by default. We have learned over the, I mean, over the last I would say decade, few decades, right? If we have green software, same as object oriented programming concept, then I think whatever application we build in future, right, we'll have green software baked in.

Anne Currie: Yeah, I, I may agree up to a point. I agree with you, but I think we, I, I'm a big believer in separating out two types of developers. Obviously there are loads of different types of developers in the world, but so, but two types of backend developers, so front end developers, this is almost a separate thing.

But backend developers, you've got people who just working in an enterprise and the code that they're producing is not going to ever be deployed. You know, it's, it's the code that they are writing is never going to be run by billions of people in their own data centers. And then you've got people who are writing platforms and the whole purpose of the platform is to try and get some billions of people to, or at least millions of people to write this code, to run this code.

And those people, they absolutely need to write efficient code. When I think everybody needs to, should be, could, should get used to getting out their performance profiler and just making sure there are no egregious performance problems with their code. Because performance problems are your code's slower and you're burning a load of carbon and it's total waste.

So all again, very aligned with the business. You want your systems to run fast. Your customers want your systems to run fast. So, so having a decently performance system is good. But beyond that, you probably don't want to be writing code yourself, which is massively efficient because that takes a long time, but you do want the platforms you're running on to have done, to have made that investment.

So it's, it's kind of like, there's a lot of context here, isn't it? Are you writing code for mass use or are you writing code that is not really for mass use, which is, which is interesting. I think that's a subtlety that we, like, for example, lovely article though this was, did not point out that difference.

Navveen Balani: That's a good point. So, yeah, especially applications, right? Package applications which will be used, let's say, by millions of developers worldwide or users worldwide definitely needs to make that in right to ensure that for instance simply like all the large language models a good example right all all generative applications will be used by millions of applications millions of developers so how can you make the AI more efficient right both on the user side who is creating let's say the prompts right to Create in an efficient way so that the round trip is reduced and secondly on the backend side, right?

How do you have a low cost efficient energy-efficient model? That's why you also seeing a lot of LLM models are now Talking about the small language models more energy-efficient more compact, right? There is a trend where I would say right where Organizations are now looking at energy efficiency also, right, as part of the applications or whatever work they have been doing.

Anne Currie: Yeah. And of course that's all driven by cost. It's taking us back to our previous thing about cost and cost and green are very aligned. And the good thing about, well, the bad thing about AI is very costly. The good thing is it's driving quite a lot of efficiency improvements. So, I mean, I talk about this every time I'm on here, that, that Python has got a lot more efficient because, because of AI, that they've rewritten all of the code, core, core libraries in Rust. And of course that's, that's a perfect example of they are the kind of people you want to be writing super efficient code. They can save the world one line of code at a time because so many people run Python. But you want to be getting that out of your platform and not having to do it yourself as a, as a Python user.

You don't want to have to change to Rust yourself. You want to be able to get the value of Rust whilst still using Python. But yes, yeah. So, so that is all very interesting stuff, but yeah, very nuanced. It's all at every degree of it. It's what my, to my mind is what makes green interesting, is it's not simple.

It's not trivial. You have to step back and you go, "where am I? What am I doing? You know, what's, where, what's, how do I fit into this? Where does mine, where is my effort best applied?" I mean, you're obviously part of the SCI, which covers all of the things, you know, operational efficiency and code efficiency and demand shifting and shaping.

What's your interest? What do you like the most out of those things?

Navveen Balani: I would say from a, I think from a developer standpoint, right, depending on your roles, right, so SCI, I would say is more inclusive in terms of, depending on roles, right, whether you are a developer, architect, data scientist, right. All are various parts to play, right, to reduce the carbon emission and make applications more energy-efficient.

So, depending on your role, for instance, if you are building, or you are a developer writing code, right, then you can really focus on energy efficiency. And it's not, as you mentioned, right, it's not just moving towards a C or C++ language, right, which is more efficient, but it's, so you have to basically look in the context of the work you are doing and trying to optimize it, right.

So you have to do that trade off as a developer and how, what libraries access to make it more efficient. Second, I would say the whole hardware optimization, I think in terms of where all the DevOps, cloud, cloud architects comes in. How there are various custom chipset from various vendors, right? How can you best utilize from an infrastructure point of view?

And third, I would say is more strategic in nature, right, in terms of how do you bake in the whole carbon away computing concept, because that's new. You need to have data providers, you need to tie up with various licenses which are actually costlier, right, if you look at getting the real time data, right, from various providers.

So how do you bake that in in the application to more of a strategy kind of work and thinking? So in that way, I would say it's, I mean, depending on your role and context, right? I mean, whether from developers to architect for data scientists, right? Each can find definitely a value for, in SCI and then try to reduce their scope of work.

Anne Currie: Yeah. It's so, it's interesting. When I first heard about the SCI, I was a bit, I was a bit dubious about its value, but I have completely changed my mind on that as time goes, especially because I teach, I teach people who are green software and, and, and. One of the things that often comes up is people wanting to be able to do like for like measurements.

And I think that I originally thought the SCI was about a standard that you could compare between applications. And that was where the value would lie. And I was a bit dubious that we could realistically do it. And, and now I've realized that, that I like that the SCI has stayed fairly woolly and loose.

It's more conceptual than it is a specific implementation. And I like that because really it means that companies could choose their SCI score, they can choose how to define their SCI score for their applications and choose what's appropriate. What it's going to, what it's going to, what the denominator is going to be.

So it's per user or per transaction or per, so everything, something that's specific to them, and then it's essentially for like, for like, so you can say, well, last year it was this, and the next year is this, and you can average over time so that you're, you, you know, saying you don't say, well, I'm comparing a sunny day with a non sunny day, or, you know, all those kinds of things.

What I like about the SCI is it's, is it's very kind of conceptual high level nature that forces people to think, "well, actually, how do these things apply to my system?" You've got to use your head. You can't just, you know, you can't just follow it. You can't follow it blindly because it doesn't make any sense if you do that.

You have to say, how does this apply? Forces you to think, which I like.

Navveen Balani: Yeah, I think that's a good concept. Particularly if you look at SCI, right? It's for an application, so you have better control rather than giving you the carbon emission of all the applications. Right. Which typically is given by various cloud providers given an application, then you have a better control, as you mentioned, right?

You can define your own boundary and architecture and calculate the SCS core, right? And the intent is to. Basically, as you deploy new versions, right, the intent is the SCI, you should look at how you can reduce the SCI score, right? We can't achieve zero, but definitely across releases, right? How can you make it have a lower SCI score?

And the point you made about the comparison also, right? So you're comparing your application versus your previous application that you have deployed. It's not about creating two applications from two different organizations, right? We're not there yet. It's about currently using this methodology, right, for your own application and trying ways to reduce it.

Anne Currie: Yeah, which makes all the sense to me. So many years ago in my, when I was more youthful, I used to work in retail and in retail, like for like comparisons are very important. You want to be able to say, well, this year we've made more money than last year, but you can't just say this year we made more money than last year.

You can, but it's not all that useful. What they actually want to do is say, it's per thing. So in retail it's often the kind of per square foot of retail space, balanced for kind of like, well, how expensive was that retail space? You know, so you're not comparing it and say, well, this year we made more money on the same on, on the same amount of floor space, but, you know, it was in London versus it was in the middle of, you know, of the desert. It's, it's kind of like you, you've got to, you've gotta come up with your own, like, for like measure so that you can say, well, is our business improving or is our business getting worse? And the SCI is exactly the same. It's, it's the concept of like, for, like, it's for you to check Ron North's for you to check against other people.

So yeah, it's yeah, I, I, I, I've been completely won over to the SCI. I was highly dubious to start with. Right.

Navveen Balani: Good example. Yeah, that's a very good example of a retail. I'll also use that.

Anne Currie: Right. So now we'll go to the final, the final thing we're going to talk about today, which is, we just touched it from a slightly, again, a slightly more holistic perspective. So this was an article in a Silicon Republic and it was a Q&A with the chief security officer of a, an AI company. It was talking about.

Basically, her premise, and I totally agree with it, is that security, cyber security is very aligned with being green. It wasn't, it's not, it's a bit thin as an article, it doesn't give you an awful lot of information, but I think basically, yeah, that there's the, the idea that, that I think we should be discussing is, is security and, and green aligned?

And you, you, Navveen, you've talked a little bit about that, about in terms building security in is, is like, it is, we've, we've learned to do that and we should learn to do green things, build green things in, in, in the same way. But separately to that, are there security benefits to being green? What do you think?

Navveen Balani: I would say, yeah, definitely there's synergies between security and green principles. So I like to again give that example of SCI, right, if you want to break down the methodology into three parts, right, making applications more energy-efficient, right. So, if you look at the security algorithms, right, how can you use, how can you basically optimize the security algorithms to, let's say, use fewer computation resources.

Particularly, if you look at the security stack, right, they also have evolved from various encryption and cryptography software, right, and now I think you have various key ciphers available across different dimensions. So, they're already following this, I would say, backtest, right, of making encryption security, right, more performance and more easier to adapt. So in that case, I would say it's more aligned towards the algorithms that they're using are more efficient, right? As compared to what it might be, let's say five years, 10 years down the line for, for security protocols. And similarly, I would also say new strategies can also be applied for security scanning.

For instance, vulnerability scanning is one commonly used, right, to identify any threats, maybe in cloud or maybe in desktops and other systems, right, that can actually run, take the advantage of running it on a time, right, where the carbon intensity is low. So in that way, it can apply certain green software principles.

To run all those scans where the carbon intensity is low and also save on the carbon emissions. So, definitely I would say there is some synergy between security and green software. And certain, I would say, features of green software principles can also be applied to secure the domain, right, to make it more energy-efficient.

Yeah,

Anne Currie: And so, so something that actually I'm, I, well, you did mention a little bit earlier is, is security is the perfect example of where hardware, using the right hardware for the job massively cuts your emissions. So dedicate the right kind of chips that are designed for for encryption are just so much more efficient than using general purpose or CPUs for, for that, for that.

So, yeah, we wouldn't be able to do what we do these days if it wasn't for dedicated chips. Oh, and so, and oddly enough, this does also map to some of the stuff that we said at the beginning about manual ops and manual FinOps, that's, it's amazing how many systems are through machines that are kind of like, nobody's, everyone's kind of forgotten about them.

They're not keeping them patched. They don't really do anything useful anymore. Those are your backdoor. Those are the ways that people break in and they're just wasting electricity. So even in building green software one of my co-authors, she, she brought up the fact that, that it's interesting that security, well, that are very much an example of a waste of electricity.

They, they're, they're wasting. So, so something like a denial of service attack, the whole purpose is to burn your electricity in your systems and burn your system so that your systems don't have anything, any time to do the thing that they're designed to do. You know, the thing that has value for you, instead, it's just burning your systems up burning electricity, running up all your bills to do something which is bad for you.

So having a secure system that, and things like applying the latest patches so that you're less, less exposed to denial of service attacks is green because denial of service attacks are very ungreen, they are very dirty. Same, it's, it is quite interesting, isn't it, from that?

Navveen Balani: Yeah, that's why I think the provisioning the right hardware, virtual machines, especially, right, provide various cloud providers. Right. They all, they now provide a managed services, right, to detect various denial attacks. And I assume, right, the underlying hardware that they are using, which is, which should be used by millions of applications, right,

would be definitely more sustainable, more energy-efficient, right. And, and more scalable.

Anne Currie: Yeah. Yeah. Securities are really interesting in that, that's it. So FinOps is just really a, a, a pretty much, except with, for, you know, your free Azure credits or whatever, is pretty much a group, the direct proxy measurement for carbon emissions. And, and likely to become more so in the future when we get dynamic pricing.

But security is not a direct proxy measurement is just, it's just that there are a lot of, you know, best practice in ops is also best practice in secure ops is also best practice in green ops. You know, they're, they're, they're kind of like, you can't use the number of hacks you have, then the number of attacks you, you fall foul to as a proxy measure, you, well, maybe you could, I think that would be a bit complicated.

Proxy measures for carbon is how many times your data gets stolen, aligned rather than proxy. It's interesting. I mean, so we just, we've talked about those, those three things, but have you run across anything interesting at the moment that's, that you think that our listeners should, should hear about? And

Navveen Balani: So, yeah, I would say for the, on the Green Software Foundations, specifically, right, we are working on green AI and so we are trying to look at how we can extend SCI to take I mean, how we can do SCI measurements for large language models generative AI models. So this is something we are actively, I would say, working towards from the foundation perspective.

And if you look at from an SCI perspective, right, we want to have various extensions to SCI, for instance. How do you do SCI for web applications, backend applications? And make it more easy to measure, right, different parts of the code and make it easily available to developers so that developers can measure their, their part of the overall carbon footprint, right so we can make it more accessible.

So that's, that's one thing I think we had from a foundation perspective, looking at how we can make it SCMO extensible to various other use cases.

Anne Currie: That's, that's very interesting. That is the, yeah. 'cause obviously Ai, AI is the, is the workload of the, it's on everybody's lips at the moment. It is... and so I, I saw very interesting charts from all the, the Economist, I think the, the other week that, that said, you know, that's, that showed the enormous amounts of power that was currently being used on ai, but still less than the amounts of power being used on Bitcoin.

So it is just worth reminding. And of course, bitcoin is very aligned with our last conversation about security and what people wanting and people who are attacking you wanting to run up your energy bills, because quite often what they want to do is mine Bitcoin on your, on your machines that you're not properly watching.

So security sweeps are a pretty good way of identifying machines that are burning power totally unnecessarily. From political... I, I quite liked to keep my eyes not just on the Al news, but the really good news news last week from a political rather than a technical perspective was the world's just got its first climate science trained president in the new female president of Mexico is a, is a climate scientist by, by trade and training.

So, I, I, I would be very interested to see what affects that out on the country. Any other interesting political news that's good news, do you think?

Navveen Balani: No, I think I've yet to catch up on,

Anne Currie: Well, I'm quite nosy, so I keep my, I keep my eyes open on all things. I would say there's, there's loads, actually, there's, there's a lot of good news going on at the moment. Texas is now a massive solar producing state. It's, so, yeah, there, there is, it's, The world is changing in a positive way. I, I like to, to, to keep reminding everybody that we are not doomers at the Green Software Foundation.

We, we are doing this because we believe it will have an effect.

Navveen Balani: I totally, I would say, especially with the foundation, right? It's all our collective journey that we've gone through. I mean, we started three years back, we didn't have, we didn't have any specification tools, right? Three years down the line, we have the first specification tool. Software carbon intensity specification, which is now an ISO standard.

We have various tools now, carbon SDK, impact engine framework. And I, I know that 1230 projects already in the pipeline on the foundation, right, which will make the world, I would say, a better place, right. In terms of sustainability, right. For all the work that we do. So, yeah, it's basically a shared responsibility, right.

Climate change is basically a shared responsibility, right. And from our perspective, developers, all we can do is contribute, right, by using the three SCI principles, which I talked about, right? Which I again repeat is either write better energy-efficient code, use hardware wisely, and make applications carbon-aware.

Anne Currie: And of course, actually, not just write it, cause you might not be the one who's writing, it's more important that you use it. So as I'll, I'll constantly, cause Python is such a good, good example of the moment, at the moment, because of their big, big revising stuff. If you people who upgrade the latest versions of Python that are much more, more efficient will be saving a lot compared to people who don't upgrade.

And that's, those kinds of things are the kind of things that will immediately be unearthed by running the SCI like for like. I mean, a really big change might be on your like for like is that you upgraded to more recent versions of a particular library or a particular set of tools that you're using that are more, more efficient.

SCI isn't just about what code you write, it's about what code you use. And that is almost certainly going to be where you get the biggest, biggest value, the biggest return. Anyway, sorry, now I'm doing my, I'm trying to take the last word again, and I'm going to leave the last word to you, Navveen.

Navveen Balani: So yeah, very happy to be part of this podcast. Enjoyed this conversation talking about, I think, three different aspects. I would, I would say. And to all the viewers is, yeah, thank you for listening in and have a good day.

Anne Currie: Navveen, awesome. Thank you very much for coming on this podcast. And a final reminder that all the resources are in the show description below, and you can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. And see you all soon at some point, if they ever let me back in again as a guest host.

Good bye.

Navveen Balani: Goodbye. Thank you, everyone.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again and see you in the next episode.



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]]> The Week in Green Software: Carbon Hack 24 Recap The Week in Green Software: Carbon Hack 24 Recap Thu, 13 Jun 2024 08:00:00 GMT 1:03:53 713p86n0 false https://podcasts.castplus.fm/e/x8149q5n-the-week-in-green-software-carbon-hack-24-recap 68dc7d197be17a7f01356c7a 68dc7cef46a2532cdd8d1c23 TWiGS host Chris Adams is joined by Asim Hussain the executive director of the GSF to talk about the recent hackathon hosted by the GSF : Carbon Hack 24. Asim goes through some of his favourite projects that featured work with the Impact Framework including some surprising choices! They also cover some interesting news from the world of cloud service providers and the new CSDDD developments. Asim also talks about how mushrooms are out and bread is in! 73 TWiGS host Chris Adams is joined by Asim Hussain the executive director of the GSF to talk about the recent hackathon hosted by the GSF : Carbon Hack 24. Asim goes through some of his favourite projects that featured work with the Impact Framework including some surprising choices! They also cover some interesting news from the world of cloud service providers and the new CSDDD developments. Asim also talks about how mushrooms are out and bread is in!

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TRANSCRIPT BELOW:
Asim Hussain: And I'm just sitting there thinking, we are old. These are the kids of the future, you know, maybe gave me so much hope, like these are 16 year old kids, and they were doing some incredibly advanced green software measurement, reporting, zoom, understanding curtailment, understanding like how to measure this stuff.

And I was like, ooh, we've got a, we've got a good future ahead of us.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, the podcast where we talk about sustainable software development. I'm your host, Chris Adams. This week, our format is This Week in Green Software, where rather than doing a deep dive into some of the specifics of green software, we're going to look at some of the news stories that have been making the rounds and provide a bit of commentary and analysis on this. And joining me today is my good friend Asim Hussain, the executive director of the Green Software Foundation. Asim, I'll hand over to you to introduce yourself a bit more than what I've just shared so far, if that's okay with you.

Asim Hussain: Yeah, of course. Yeah. Thanks. I really love these, having these podcast episodes with you and talking through this material. Asim Hussain, Executive Director of the Green Software Foundation and been, you know, lucky enough to be at the intersection of sustainability and software for quite a few years. So based in the UK and excited to be on the podcast again.

Chris Adams: Thank you, Asim. And when I last spoke to you on the podcast, the recurring thing is your history of growing all kinds of delicious mushrooms. How are the myco friends doing?

Asim Hussain: Not good, not good. I've lost, my green thumb or my blue thumb. Maybe if that's how you would, talk about it. But yeah, no, I've lost my, I've lost the thumb. I now bake bread is, the thing that I do, which is equally disappointing, to be honest with you. I just love doing stuff, which is like very, the people, you put a lot of effort in and then you get kind of like middling results is kind of my, seems to be my hobbies of, the day, hobbies du jour.

Yeah.

Chris Adams: Okay, so you've been culturing um, what's it, butter, sourdough and things like that?

Asim Hussain: Yeah, sourdough right now, yeah. I do have a sourdough starter that I, was gonna say keep refreshed, but I probably should say like, keep on the verge of starvation all the time. But yeah, no, it's actually quite fun. I make, I bake bread every weekend and I give it a go. Yeah.

Chris Adams: you for sharing that. Presumably, this is going to be carbon aware bread that you're using, that you're baking when the energy is green, because in the UK, you have the baking forecast. Are you familiar with the baking forecast?

Asim Hussain: I am not aware of the baking forecast.

Chris Adams: Ah, dude, this is so cool.

Asim Hussain: Now that you've said it, I can't believe I'm not using the baking forecast. Because it is, a lot of, that's the thing my wife questions me on. She's like, does the financials of this work out? And I'm like, I don't know, but there's healthier bread than the stuff you get in the supermarket.

Chris Adams: So for those who are curious, and I promise we will talk about green software, this is a kind of segue towards some of the ideas we might talk about later. In the UK, there is a website called the Baking Forecast, named after the shipping forecast, and the idea behind it is to look at the carbon intensity of the electricity that might be going into the oven when you're doing any kind of heating.

And this means that you can then decide to time your baking to be zero or very low carbon bread by making sure you bake when there's lots of renewable energies on the grid. Or, alternatively, if you are not paying attention, you can end up with very, high carbon bread by baking when there's lots of fossil fuels on the grid.

Now fortunately, sunnier, we've got some more solar and wind coming in, it's not like the depths of winter, so it's probably greener breaking today than it was in November or December. But yeah, this is one thing that we should, that I think is, only the UK has this right now. And we'll have to share a link in the show notes because it's a really, cool project.

Asim Hussain: That's cool. You could also end up with really high carbon bread if you leave it in the oven for too long.

Chris Adams: Oh yeah,

Asim Hussain: wait, wait. Oh my God. Where is it? Where is it? I don't know which one it is. You do it.

Chris Adams: I think I know what you're after.

Asim Hussain: Oh, there we go. Oh no.

Chris Adams: This is what you're after?

Asim Hussain: That was it.

Chris Adams: Yes, it, yeah, as, you, Asim has discovered the sound effects on the, on Riverside, the podcast platform we're using. So-

Asim Hussain: You told me about it.

Chris Adams: Yeah, I do apologize. So if we have, we might be like two children at the front of an aircraft playing with this, flip, flicking all the switches, but we'll try our best to not make listening to this too insufferable.

Okay, so we've, mentioned about the baking forecast as a way to talk about carbon aware use of electricity and things like that. Shall we look at some of the stories together, Asim?

Asim Hussain: Yeah, let's do it.

Chris Adams: Oh, if you're new to this podcast, my name is Chris Adams, or if I'm in trouble, Christopher Adams. I am the executive director of the Green Web Foundation, a small Dutch non profit focused on reaching a fossil free internet by 2030.

I'm also one of the chairs of the policy working group inside the Green Software Foundation. The other thing I'll share is that at the end of this show, we'll be showing a link with all the show notes, and it's usually written in Markdown, and we do accept pull requests, so if there's a thing that we've linked to that you think is incorrect, or if there's things you'd like to add, then it's open source and you can do that.

And I think that's the main thing I'll share. The stuff we talk about will be linked. And I guess we should probably find something to talk about then, shouldn't we?

Asim Hussain: Yeah. Something that's not bread related.

Chris Adams: Alright then, well in that case, shall we move on to, I want to make a joke about biochar, but let's just move on from, yeah, alright, so let's, focus on the first story, which is GreenOps, FinOps, and the Sustainable Cloud.

This is from, uh, I think it's actually originally from Forrester, a big analyst firm, who have now kind of woken up to this idea that If you're tracking cost, in many cases you might be tracking carbon. And this is the main thing they're talking about here. There's a few kind of key takeaways and key points inside this.

And this basically does talk about things like, say, you can reduce emissions through kind of looking at kind of existing cost bills, for example. There's different steps you can take. They talk a little bit about some of this stuff. And they also provide a very, high level idea that Right now, we do have inconsistency across the large cloud providers, so Google and Microsoft, they might report all of the emissions, or all of the emissions according to the kind of GHG protocol, which is like the gold standard of reporting, giving you scope 1, 2, and 3, where the majority of the emissions for what we do is probably in scope 3, and talking about how Amazon is doing this, and they've recently, we now know on the grapevine that they've started to rehire people.

But at present, if you look at the dashboards or the calculators of Amazon, you're only going to look at scope one and two, which on average means that you're missing a significant chunk of the picture. So that basically means if you dashboards, and you think, oh, my numbers are really low, that might not be the full story.

All right, Asim, I trust you had a chance to look over it. So are there any particular takeaways or hot takes you had on this one that you might share with us?

Asim Hussain: Well, I don't know if I had any particular hot takes is stuff I've heard before and kind of stuff I've heard mentioned kind of in quite a few times over the past few years. And what I was actually interested in is perhaps just having a more. I'd actually love to hear your thoughts and maybe we can like walk through some of the ideas and some of the question marks I have are just about the whole premise of this link to cost and carbon and like, there's a few things I think of when I think about that.

One of the things I think of is, well, isn't, the cost of things the reason why we're in this problem in the first place? You know, how can costs, how can money both be the cause of the problem and the solution to the problem at the same time? And then, like, you know, you know, yes, there is this argument that kind of reducing cost reduces carbon, there's this kind of correlation.

I've spoken about it extensively, in my past, but kind of I'm trying to refine my thinking in this space. We know that there's somewhat of a correlation there. But we also know there's things you can do which can dramatically reduce carbon, which don't affect your bottom, the cost at all in the slightest.

So what is the, how big of an opportunity is that and is therefore focusing on cost kind of distracting us from things that we can do, which can dramatically reduce our carbon that they've got nothing to do with the cost. And another thing just kind of pops up in my mind as well, which is I remember I was speaking to just the energy purchasing department at Microsoft years and years ago. And I actually asked them, can I quote, quote you on this.

Chris Adams: And this is because you worked at Microsoft.

Asim Hussain: I used to work at Microsoft. Yeah. Yeah. And a rule of thumb that they gave me was, which was the, this is pre AI, was that the cost of running a data center is only 10 percent energy. The actual dollar cost, 10 percent energy, 50 percent depreciation of assets.

So you bought some chips, and they're gonna be worth zero in five years, so they're depreciating of assets. And everything else was like, you know, people and everything else was number one. And so like, when you think about that, certain parts like that emits carbon into the atmosphere, all of those-

Chris Adams: The creation of, oh, each of these, right?

Asim Hussain: The energy emits carbon, but it's only 10%.

You know, the human beings running around emit carbon, the actual physical infrastructure like emit carbon when it's created. And then what happens in an organization is you normalize that into a dollar value, which you then charge for services that run off there. But does that dollar value, like represent, you know, accurately, you know, like the carbon emissions put it that way like there's probably a lot of nuance here yes. Reducing your cost reduces emissions, but maybe reducing costs In this way has unbelievably emissions reductions and reducing, you know, reducing $10 in another area, eh, probably doesn't have that much impact at all.

So I'm kind of a little concerned with how I don't really think the link is kind of that well understood and I'm worried a little bit that if we focus too much on cost reduction, we might be able to reduce costs in ways that have no impact on carbon emissions reductions. That's kind of like where I'm thinking about.

Chris Adams: Mm.

Asim Hussain: I was just wondering what your thoughts were, actually.

Chris Adams: So, I think there's two things. I'll share a question to that, but you said two things which I think are really interesting there. First of all, the cost of energy as, like, that's a part of what the data center might be, right? And you also said, like, depreciation of assets being one of the big costs.

And that, I'm assuming that basically means, like, I've got a server, it's going to last over, say, three or four years, and because of that I split the cost over four years, and that's how I, that's, you know, it's-

Asim Hussain: Yeah. Accounting wise that let me see exactly how they figure out accounting wise. Yeah.

Chris Adams: All right, so there are two things. Most of these discussions seem to be about energy usage, with the idea being that, I think you're right, there's totally mismatch between these two things, because we are, in many ways, we don't see any of this stuff, we don't have the visibility in a bunch of these things.

And one thing that we've, one thing that I guess we've seen a number of large companies start talking about now is saying, well, we are looking to extend the life of our servers, and we're doing this as a way, as a green measure, right? That sounds great, yeah? But if you extend the life of your servers, you've gone from 50 percent depreciation to maybe 30 percent or 40 percent depreciation, right?

I mean, from a financial point of view, that basically, I mean, Amazon did this. It put like billions onto their, on to their balance sheet because they now look way more profitable than they were before because they don't have to kind of write it off. So, yes, there are some things being shared for this but whether how that actually factors into some of the calculations people use.

I mean, I don't know. There was an announcement there. We haven't seen any changes whatsoever in any of the kind of cloud dashboards, so you would have expected that to make a difference if you did have, if we did see that, and both Amazon has done this, Google has done this, and Microsoft had done this, right?

So, I would argue that looking at calculators, there's a gap here between what we're seeing here and what we are being taught to optimize for. And there's also another question. Yeah.

Asim Hussain: Yeah, no, it, and there's also, I remember just as you were saying that, I remember this, and this is years, this is information is years old, but I was chatting to the, uh, the Xbox team at Microsoft and like one of the things, I mean, I still, no, I don't have an Xbox. I finally got rid of it. That initial estimation

Chris Adams: You sorely miss. Your love to Xbox, that is. Yeah.

Asim Hussain: Yeah, love.

Chris Adams: Oh no, you don't work for them now. You're okay. You can say anything.

Asim Hussain: I'm okay. I can, get rid of it, I think. I think. Yeah, you wouldn't get, you wouldn't get a free Xbox if you went to Microsoft, and I always thought they should give you one. But, it was, like, they assumed that it would only last, like, four, three or four years. I can't, I'm, don't quote, no one quote me on this.

But the actual lifespan of kind of these consoles turned out to be a lot longer. And people, I don't know how old Xbox is now, like, it must be like six, seven years. But it's still, like, pretty, you know, still pretty active. The decision was to kind of still keep, from an accounting perspective, still keep it kind of three, four years, even though the reality was it was being used for longer.

So those decisions are being made as well. But it's interesting that's part of the, yeah, I suppose that's part of the story we don't really, The point I'm coming from is this, is a $1 reduction in your costs, how do you link that to an actual carbon emission? Because coming back to my point of like the energy versus the embodied, like how much of the emissions is linked to the energy versus the embodied and therefore how much of your cost is linked to reducing the energy versus reducing the embodied?

Does that make sense? It's

Chris Adams: This comes to basically disclosure of cost structure, which is a bit of a kind of like jargony term but basically without having visibility into how this stuff is paid for because in many cases we have very proprietary services, it's really hard to basically, be sure, like, is this really happening?

And even when we talk about things like, say, cloud, moving to cloud compute, moving to, say, serverless, all these other tools, which are kind of higher up the stack, right? There is, it's not like the profit margins for cloud at the very bottom, like paying for compute per, on a per hour basis, will be different from things higher up.

And we don't have much in the way of visibility into any of that stuff. I mean, I, as I understand it. The higher up the stack you go, the more value you add. A bit like if you're buying refined, if you're buying oil, and then you're selling, say, refined kerosene or something like that, it's going to be a different price, right?

So you can think of, I think we have maybe some mental models we can apply for some of this. And this is actually possibly one way that you can say, well, if we have If we're running a bunch of companies, and we have a set amount of resources, and we cannot use more resources, like more inputs, there are ways to still continue to increase the amount of kind of value and profits being made by shifting people to kind of go higher up the stack to get them to purchase, maybe say, a serverless thing, or something like, something which allows you to kind of cram more stuff onto the same infrastructure.

But the problem is, In order for you to do that, you need to not build more infrastructure. And what we've just seen is the opposite of that happening right now. And I think this is what, I think you're somewhat right. It's easy to talk about cloud costs because it's, you've already got the numbers there and you don't have to, yeah.

Asim Hussain: It's like one of the most obviously disclosed, like, facts about your cloud usage is how much it's going to cost you.

Chris Adams: Yeah. And like, if you're in an organization, it's probably easier to start optimizing a number that you already have than to pay for someone to, or even pay for a solution for someone to do this stuff. Because I've invested like. We've, uh, okay, I promise we'll get back to the other stories a little bit later on, all right?

But inside the Green Web Foundation, we've been looking to, like, audit all of our own services recently, or just, like, work out our own kind of, look at our own annual emissions. And the thing we found is that, let's say we've, uh, the majority of our impact, because we're a quite small organization, comes from, not necessarily from the services we're paying for on an hourly basis, like, we use Hetzner, and we don't, Which we're spending maybe, I guess, maybe a thousand euros a year on Hetzner in total?

Asim Hussain: What's Hetzner?

Chris Adams: Hetzner is a German cloud computing service. So, about half of all of Mastodon run on Hetzner service because they're so cheap. So much cheaper, and in Europe at least. They've been one of the greener ones to use, because they build all their own software. And if you look at their prices, they're typically, like, you know how DigitalOcean will sometimes be cheaper than, say, the big cloud providers?

They are cheaper than DigitalOcean, once again. So as a result, we're a cash strapped non profit, we're going to go for the cheapest green, option we can find. And the cheapest green option that We can find that has cloud like APIs and is used and works quite well. So we've been using those ones, and as a result, cloud makes a small amount of our kind of digital estate.

What ends up being a larger thing is the infrastructure we use, like the laptops and the monitors we have inside our own houses, inside our own offices. But, even if we buy those from circular providers, so my laptop is from BackMarket, which is like a kind of second hand provider, and likewise, Hannah does the same thing with hers, and all this.

Hannah's my, she's my colleague who's been leading on this work here. We've done this, but under most of the ways there's no guidance on, okay, is it someone else's carbon? Do you know, has someone else banked it so therefore it's free for you because they've depreciated the carbon there? There isn't much in the way of guidance or real stuff that you can adopt.

Like, you can might, you might be able to sight a paper, but that's not the same as there being, say, guidance that you might use for your own reporting to say, this is why we've chosen these numbers here, for example. And like, this is what, these are some of the problems. There isn't this recognized way to account for circular versus buying new stuff, for example, and this does speak to this idea of, well, okay, how do you estimate this stuff and work out what is the most effective thing, which, you know, which levers should you be pulling if you want to reduce the emissions from software or digital services in general?

Asim Hussain: So that would be like an example of, well, yeah, cause I've even had these conversations before we're talking about like, what'd you do? I think with all these like hyperscalers, like once they actually reach end of life, they're not broken.

Chris Adams: Mm

Asim Hussain: the, equation that they're running is like, we've got per square foot of infrastructure, of real estate, what is the profitability? And at a certain point, it becomes more profitable to take out this old server, which is perfectly fine.

Chris Adams: Yeah, usable. Working.

Asim Hussain: But you can just put something else, which is more profitable per square foot for that given situation. So that, I mean, people think these things are breaking and then no, they're not, they're just perfectly fine and working.

They might be more inefficient. Then we've had like conversations in the past with, I've had people going, well, if I was to take that server. End of life server and build my own data center. Could I argue that is zero? Could I argue that it has zero embodied emissions? Is that like a reasonable statement to argue?

And, I don't know. I don't know what the answer to that question is.

Chris Adams: The question, the answer is likely to be about, oh God, this is so nerdy, mate. I'm so sorry. But like, what is the depreciation that we've just spoke about now? You said half of the cost in a data center is a depreciation. What's, how do you split that? How do you kind of, Amortize the carbon over these years and then how do you then share that with someone else?

So there's a company called Open, I think they're called Open Sesame or Sesame. They basically build data centers or like racks and stuff out of all of the, uh, Yeah, decommissioned stuff from Facebook and all these open compute projects, because all the companies, because you know how these servers are designed, and know how they're specced, it's actually quite common to buy these, and these tend to be cheaper than what you might have elsewhere.

And in many cases, it's not like, It's not like they rust, right? They really, they're, kept in kind of quite good con condition and they are working in a lot of cases, but the, they make the argument that, well, this is circular, so this is gonna be greener what you have, what you've been using elsewhere, but whether you are able, but yeah, it's, a number question.

It's an open question about how you account for that stuff inside, if you're purchasing, say, computing from these folks compared to people from somewhere else, because a lot of the time, order for in you to do this, you probably need organizations to be really transparent about where they got their servers from, and that's not always what companies are incentivized to do, basically, right now.

Asim Hussain: but then we get to that same argument of like, at the end of the day, that's going to be sold to somebody as like a dollar cost, like dollar per hour of a CPU. and if we just look at cost as a proxy for carbon, then you could argue there that will, they might have to end up selling that service at a rate that is actually quite comparable to a brand new server.

Chris Adams: Yeah.

Asim Hussain: This is where I think there's this kind of, I don't know if it's a breakdown or there just needs to be a lot more thought put into it. It's almost like there's a, that you pay a dollar value. There's a black box. And then at the other end, we know there's some sort of carbon emissions. And I think there's a lot of nuance there.

And maybe what I'm saying is that actually cost isn't a great proxy for carbon. Because really, maybe that is, I'm kind of refining my thinking as I'm talking to you. Maybe cost isn't a great proxy for carbon. Or maybe you put it this way. Three, four, five years ago I was advocating for it because we had nothing else.

Chris Adams: Mm.

Asim Hussain: There were so few other options, but I'd actually argue now, we have the capacity and capability, even if a cloud provider, somebody themselves don't even know what the carbon emissions are. There are models that now exist where you can estimate from what you can see about. But the infrastructure that you're using and make your own assumptions and make your own judgments as to what the carbon emissions are.

So I think I suppose where I'm thinking now is we can actually move beyond costs. I suppose that's where I'm thinking.

Chris Adams: Well, the thing I might share with you, and if you forgive the plug, so I've just been travelling all around Western Europe by train, and I literally started on the 31st of May, and I arrived at this conference called DjangoCon, which is a conference all around DjangoCon, Django, which is a very popular framework that initially powered, say, Instagram, and like, part of the NHS website, all this stuff here, yeah.

Asim Hussain: I used to be a Django developer. That was like my job. Yeah. My job, my paid for profession for quite a few years was a Django developer.

Chris Adams: Wow, I did not know that. Okay, no, that's a, okay, that's a story I'm going to use then, because I think we can bring this back to the cross thing for a second. Anyway, I did a talk, I, there was very short notice, I was going to deliver a workshop about how to, basically, how to use some of the tools available to kind of green your Django stack.

Right, so we were using a tool called Green Metrics Tool, which as Ana, one of the, like, I guess the founder of this, the guy who, the lead maintainer, he says the name is descriptive more than creative, but you kind of know what it's for, right? I quite like that. Anyway, that does give you figures for energy usage and embodied carbon and it does provide these and even provides like SCI figures. So there's now like a workshop and a deck and I will show a link to it to see how you can take an existing project and come up with SCI figures for various user journeys and things that you do have. Asim, I didn't have time to talk about the Impact Framework, and I, once I know enough about the Impact Framework, I'd be really up for using it.

But one of the key things I had to do was I was doing this workshop and then literally the day before I arrived, we had someone with a visa problem, so they couldn't do their talk. So basically the organizers asked me, said, Chris, are you, do you have a talk ready or are you, would you be up for filling this spot?

We have a 30 minute slot to talk about this. And I basically had 24 hours to take the workshop, turn it into a presentation. And the thing, the reason I'm talking about this is that I presented a kind of taxonomy of ways to think about these tools that you have. And I described things in terms of usage based and cost based specifically for this reason.

So you would use cost based tools to do your first round of disclosure or to work out like a baseline because until you, and until you've been able to demonstrate any value from doing this, it's going to be really hard to argue for like weeks of developer time to come up with some numbers. Right?

Whereas this at least gives you a number that you do have. And then I spoke about how you would use usage based tools to start figuring out, okay, well, what changes can I make to reduce some of this stuff? And yeah, I'll share the link to the deck, because it's, it allows us to create the, you know, we need, like, taxonomies to realize that we're not conflating these ideas too much.

Because a lot of the time, you're doing different jobs, and you're asking, you're being asked by different people. So, like, for example, the whole kind of Software Carbon Intensity thing, initially, it was, as I understand it was created partly because the sustainability, like the head of sustainability asks for some numbers, and if you're going to report along these numbers, you don't really see much in the way of incentives to show how you're changing in future, right?

it doesn't incentivize changes at the engineering level.

Asim Hussain: Yeah, yeah, yeah.

Chris Adams: right? So therefore you need a, you know, one of the thrusts would say, well, let's come up with a metric to show that we are planning forward. And we're taking some steps so that if you're an engineer, you have an idea of what's, what, you can do rather than just outsourcing all to energy procurement, because that-

Asim Hussain: Yeah,

Chris Adams: isn't necessarily the full solution, for example.

Asim Hussain: I mean, the way, I mean, I really love your taxonomy of cost based versus user based. I think that, yeah, I like that actually. And user based being more, perhaps more, more granular and more action. Cause it kind of gives you more, more specific things you can do. Whereas cost is like this really high level thing.

Just to bring it back to what you said about the SCI, the, one of the things, And I really, I didn't coin this, uh, but when it was coined, I loved it, but they described it as measurement for reporting versus measurement for action. And that's, I think, an interesting way of looking at kind of the different measures, uh, in this space.

And, you know, we talk about regulations. We need to talk about regulation. We want regulations a lot, but once you start talking about regulations, the measure that you have is a measurement for reporting and the, behavior it triggers is what, number can I disclose that I can defend? What can I defend? Whereas a measurement for action is kind of what number do I need to drive action change. And that's when we think about the SCI, it's a measurement for action. I don't know whether, where, it will need to land to be something, you know, to be, you know, To be, you know, defensible.

Like, I don't know why I'm gonna go off on a massive tangent right now, but-

Chris Adams: Go with it, let's go with it.

Asim Hussain: Go with it. Let's go with it. We know Krav Maga. You know Krav Maga?

Chris Adams: Wow, yeah, the Israeli martial art you're talking about?

Asim Hussain: The Israeli martial art. Like, I've always, there's actually a place nearby and I've always wanted to learn.

Chris Adams: I did not expect you to go in that direction, let's go with

Asim Hussain: Well, but let's just go with it.

Because here's an interesting thing about it. And one of the things that the founder of Krav Maga said, this is will never be allowed to be done in like a competitive environment. There will never be the world championship of Krav Maga. There will never be like this, you know, global Krav Maga, blah, blah, blah.

Because by, by that's when you start bringing in rules and the actual nature of the sport changes. So it doesn't become good at what it's supposed to do.

Chris Adams: Ah, yeah, so for people who are not familiar, Krav Maga is, it's like, oh, it's like punch him in the love spuds and all the stuff like that. It does all the kinds of things which you wouldn't do in a, kind of formalized mode, because they are, they're basically weapons of war, rather than actually weapons of art, right?

So it's more like, there's more martial than the art part, perhaps.

Asim Hussain: I mean if I'm gonna learn a martial art, it's gonna be there so I can like actually get out of trouble, so I don't mind like poking somebody in the eye or kicking them where they shouldn't be kicked.

But like, but I think that might, that's just one of my thinking and I'm, and I, this is just my thinking and this is a consensus based organization, so I just want to really be clear that I'm just expressing kind of some thoughts have and they might change in the future as well. You know, can you have both things?

Can you have a measure which is good for action and good for reporting? Or does, you know, as soon as you bring a regulation into a measure, does it then transform the nature of it, so it, you know, it doesn't become good as an action driving measure and it can only serve as a , as a measure for reporting.

That's kind of some of the thinking we've gone, I don't know how we got to this.

Chris Adams: No, this is a graceful segue into the next story, Asim, don't worry. All

Asim Hussain: But yeah, that, that's some of the thinking I had.

Chris Adams: I don't have a, I won't try to directly answer that, but what I will do is name check the new story, the next story we have, which is from Linklaters, the law firm, and they talk about, so this is going to be a bit of jargon.

Asim Hussain: Oh, yeah.

Chris Adams: I'm afraid, so this is a, this was a new story from actually a few weeks back, say, the new CSDDD compromise finally accepted by member states.

That is impenetrable to most people who are programmers, but the short version is that there is a piece of law. called, we already have a piece of law called the Corporate Sustainability Reporting Directive, which is a bit like, which essentially compels organizations of a certain size to disclose their carbon emissions on an annual basis, just like they disclose their financial things and their financial figures.

Different parts of the European Union are now, okay, for want subclassing the directive and turning it into their own law, right? So, France has been the first of the countries in Europe to actually do this. And Egalité, CSAD, yeah, like, they have a really hardcore version of the law, where if you are a corporate director, and you don't disclose, and you take steps to block disclosure, You have jail, you know, there are jail time sentences and stuff like that now.

There's some really, hardcore things, right? And that's like the disclosure thing. But the thing that's interesting is that this plays nicely with this new law, which is the Corporate Sustainable, Corporate, I think it's Corporate Supply Chain Due Diligence Directive. And this introduced some new demands.

So you basically need to like, as you'd imagine, Have some due diligence in your supply chain, but they also need to demonstrate how this fits into some of the laws or some of the kind of longer term goals of being a company active in, like Europe, where Europe has set targets of saying, well, you need to reduce emissions by, you know, we aim as a kind of union to reduce emissions by 55 percent and who knows, maybe even 90 percent by 2040.

And as a result, you know, you now have to publish a climate transition plan, which shows what your steps are to actually help get there. And, uh, this I think is interesting because this now means that you have basically a mechanism for accountability each year, I mean, kind of anyway, but you've also got something to show that you're looking forward.

So you've got forward looking and backward looking. And I think this is quite helpful for the conversations around, say, a consequential model like The Software Carbon Intensity thing, plus some of the kind of somewhat backward looking models that you might have with the GHG. And I think the GHG protocol, and this is how I now think about this, you will say that you might use the SCI to plan forward and say, well, am I going to do this versus that?

And that allows me to talk about, it's not just my organization, like, uh, are there places where impact is taking place where I don't have to put it on my books, but I know it's a significant problem, right? Because the current state of the law doesn't actually incentivize you to do that. And, uh, particularly if you're like, say, a media company, for example, where, you know, most of the impact is in a router in someone's home, or in someone's end user device, like their big ass television, you might not try and tell someone.

Hey, can you please stop using your big ass television and use a, tablet, right? Yeah, you might do that, but like, it's, that's going to be a hard thing for you to kind of argue is inside your kind of organizational boundary, for example, for, reporting. So this is why having two ways of looking at this is actually quite helpful.

And this is how I've expressed the difference between an SCI and the GHG protocol. The fact that they are complimentary to each other, but very but different.

Asim Hussain: Yeah, and I think that's, I never meant to throw shade on any kind of measurement for reporting, like it's all, needed. it's just different audiences. It's just different audiences, different needs, different, other things. So would you say the CSDDD is, because I just think the way you just described it, there's, it's, different to how some of the ways I've heard it described, but the, does it go beyond the organizational boundary?

So the climate transition plan that you have to expose, is it how it supports the EU's aggregate goal, or is it just how you, your organization plays, how its emissions has to reduce? Because if, it's an aggregate goal, then the kind of debate that you're just describing, which is like, I'm making my TV more efficient, but who cares? It's not part of my or something. That's not part of my effectively changing Help showing how you're helping the climate transition by things outside of your value chain.

Chris Adams: I need to stress, I'm not a lawyer. All right. Okay. And uh, I think I see, I think you mentioned that there might be someone who is a lawyer who does look at this, that I think it would be really nice to have someone who, with that deeper domain expertise to kind of talk about this 'cause. I'm mindful that because I have an English accent, it sounds like I know what I, that I am confident and, uh, about what I'm talking about when I might not be that well informed.

Asim Hussain: In a lot of you're very well informed in a lot of areas. But anyway, yeah.

Chris Adams: Alright. So yeah, I'm definitely not a lawyer in this con in, this context. Uh, but the, I guess the key, you know, your question is do you use 'em differently and would you be, I mean, is that the question that you had? Like, uh, uh, is that, maybe you could just repeat that to make sure I understand the question.

Asim Hussain: Well, I think it's interesting. I think it's, I think, because a lot of these questions are kind of like insular to an organization. Like what are you doing? What's your organization? How is your organization reducing its own like emissions? And then the conversation is basically coached around, "well, that's not my problem. That's not me. I, even though you could do something materially to reduce those emissions, it's just not part of my value chain. So I'm not going to be focused in on it." Is it, I presume because it's a business reporting directive. It is kind of very much like how, like you've, made, how are you as an organization going to your emissions in line with the EU's target by 2040?

Chris Adams: So, okay. That's, yeah. I think I understand your question now. So, uh, as I'm aware so far. The CSDDD is basically, they're still all focused very much on your own organization, right? So it, I think most of the supply chain due diligence part is about your upstream, right? Not necessarily so much stuff that you have downstream, for example.

So, you know, you might talk about who you purchase things from, but it might not be about to what extent am I incentivizing or am I accelerating the production of fossil fuels, for example, right? So right now, let's say, when we saw this, when we saw, I think Will Alpine, you know, he was a guest before, and so, so, so, so, so he mentioned this a while ago, and I think way, way back in the first or second episode.

He spoke about, okay, we need to be doing, you know, we, need to be using, we need to be responsible for our use of AI. Yes, we need to think about the green part, but we also think about these parts elsewhere. He's been like really, he's been leading on a bunch of this stuff and talking about this. Yeah.

Yeah. So, the yes.

Asim Hussain: Scope 4, he got me onto the idea of scope 4 emissions, which is like Very interesting.

Chris Adams: Yeah, so he, so like, he's been talking about this in quite kind of clear ways for quite some time. So, the scope 4 stuff that you've just mentioned, don't know to what extent that's actually included in this right now. But if you're talking about your plan, it's likely that if you're going to have a transition plan, you probably would have very good reasons to talk about what your transition plan looks like because you're helping transition the entire economy.

So it may be that you might refer to this, but this is where I'm outside of my kind of comfort zone, basically.

Asim Hussain: We can, lobby right now for what we'd want. I mean, what the ideal solution I would say is not lobby. That's the wrong word. We don't, do lobbying, but you know, you know, There is this overall aggregate goal that we need to achieve. And that's it. I love this. It was Henry Richardson that said that.

I remember it so specifically. I know the person that said it. And I know in what meeting they said it. was such a great statement. We were talking about the SCI. And we were talking about this whole idea of double counting. You know, double counting, when it comes to reportings and all this stuff, it's so significant because you're an organization.

You don't want to, you know, You don't want to be responsible for somebody else's emissions. You don't want double counting. But he said, actually. As a humanity, we want double counting because what does double counting mean? Double counting means that there's more than one person incentivized to reduce that emissions, right?

That's, the advantage somewhat of double counting. And actually, if you think of scope 3 in a way is double, counting across the board because somebody's scope 1 and 2 is somebody else's scope 3. But I actually completely forgot where I was gonna go with this.

Chris Adams: I'll try and rescue Asim before we move to the next story. So, we were talking a little bit about, and bring it back to digital services, right? The, so, I can't name the organization that we did some of this work for, but we were talking about a large media company, we were talking with a large media company, and we were helping them understand this, because some of the jargon, I'm afraid, that people use for this is like an attributional approach, which is like, are they my emissions? Versus a consequential approach, which is, do these emissions, you know, is this activity going to increase or reduce emissions? And when you're looking at media, for example, essentially, if you look at the attributional part and the GSG protocol, what you have to report on, right, you are incentivized to care about the data center more than anything else, right?

Because that's kind of inside your boundary at the moment, or what. Most organizations report as their boundary, right? Not every organization. But if you were to look at the consequential approach, you'd be looking at, yeah, all the things mentioned before. And like, the, this is quite a significant thing because for consuming digital media for consumers, the data center's making, Less than 10 percent, right?

It's tiny compared to the, well, the energy use at least of the, yeah, and uh, if you look at the energy use, it's going to be, you know, more than two thirds is coming from all the stuff at the end users, which is outside of your, you know, it's not on your book, so why would you care right now?

Asim Hussain: Well, it's an externality in a way, isn't it? Yeah.

Chris Adams: So this is, this is why I think it's interesting to see these two things have some interplay, and we, I think we need some wording for this right now.

Like, the one I'm trying to popularize is, you have climate disclosure, Which is one of my missions. And then there's climate response. What was my plan to reduce this? Right? Climate disclosure, you want your missions to be small. Your climate response, you want it to be really big. really, like, massive and ambitious and, like, loud and everything like that.

And that's what I think you can get behind.

Asim Hussain: But I think that's what Scope 4, that's why I think Scope 4 is a really interesting concept. It is, I've forgotten how it's defined exactly, but you know, things like your response, like things that you are doing outside of anything to do with making money or products, like that is what's counted in Scope 4.

But also it's like the impact you're having, the impact your, business and the work that you're doing is having on the fundamental problem of climate change. So, i. e. like, if you're enabling something which helps people to find another oil reserve, that's a massive scope for. That would like overshadow anything that you're producing in this world.

But scope for could also be negative, because if you're doing lots of work that actually sucks out carbon from the atmosphere. You can actually have a, like a, scope four, which a note, scope one, two, three, can't be negative, like, unless you're not doing, unless you're just not breathing or just sitting very still, but like, it can't be, but scope four could potentially be positive or negative and it shows your, like, how you, really like impacting the world, like.

Chris Adams: Yeah, so the thing we could possibly link to, I'm going to add to the show notes the episode we did, the podcast episode, which was all about AI and ML impacting climate change way, way, back in the day, I think June 13th. Oh wow, it's literally two years ago.

Asim Hussain: Is it two years?

Chris Adams: Yeah, we're recording on June 12th.

Asim Hussain: About AI?!.

Chris Adams: No, we've spoken about it before, but this is the, this is one I'm really, actually, I really loved this one. This was recorded on June the 13th, and this included, it was Abhishek Gupta, who's now, like, inside CAT, inside Climate Action Tech, which is a community that we're both part of. He's being funded on a micro grant to come up with some sustainable AI principles, but Will, Will Alpine, he, well, Will Buchanan is what he, before he got married, he was called Will Buchanan.

He mentioned a bunch of this stuff. And we also have Lynn Kaack, who was one of the co founders of Climate Change AI, which is another one of the really, in my view, really fantastic organizations who have real authority in talking about that. They mentioned a bunch of this stuff, so if you want to know a bit more about AI, look at these things from two years ago, because a lot of that still stands, and it's very, it's, I, learned so much from that one, basically.

So yes.

Asim Hussain: I get pinged by organizations all the time going, "oh, we want to talk about, or media, we want to talk about AI and, oh, AI and sustainability is suddenly a problem." And I'm like, we've been talking about it for a long time, like well before chat GPT came on the market as AI being a, hot topic of conversation in sustainability circles.

Chris Adams: Well, maybe the thing we should also link to in then Asim is, A, there's, so the GSF has actually started to endorse some existing legislation in America, to make this a bit clearer. But also, in Europe, I think something like a million and a half or a million euros, there was a tender a few years ago, to basically start researching some of this stuff that you might refer to as Scope 4, basically.

Like Scope 4 is, it's an idea, but it's not, there's no standard around it right now. So there was some work there to start looking at. This to see how you might quantify the positive and negative impacts of various steps. Now, because you've got, this was funded by the Green Digital Coalition explicitly to kind of measure the positive impacts, it's not surprising that every single intervention is only positive.

So it's not like there's a methodology for talking, oh, if we use AI to extract oil and gas, does this mean that we've, you know, there is nothing like that. And I would be very happy to see something like that because those climate emergency, see all this stuff here, right?

Asim Hussain: Well, that's one of the reasons I was thinking about this recently is kind of where we're starting to, well, we've somewhat measured our emissions. I'm starting to like, now that we're growing, right.

Chris Adams: This is the GSF you're about?

Asim Hussain: GSF. Yeah. And I suppose the green web is the same, isn't it? Like if you were to employ just one more person, your emissions would jump up significantly.

And so like, how do you meaning, meaningfully have a plan? Or what do you even say when you're like a very small organization that's just growing? Like, I mean, forget if you're

Chris Adams: If you hire five people, you've doubled your

Asim Hussain: have like you've doubled your emissions and you've set yourself a target to reduce that, what does that really mean?

And I think for, especially for organizations like you and me, like Green Web and Green Software, like if there was a scope for, our scope for is significant. Because the activities that we do as an organization, I don't mean scope 4, our negative scope 4's are significant. Because the act, us existing, arguably, hopefully, reduce the overall emissions of the world.

And so in a way you could, you know, it'd be interesting to look at organizations from their scope 4, because then you're investing in kind of a different component. There could be like startups out there who aren't like in the green software space, but have a negative, if you have a negative scope 4.

The more you grow, the more emissions get reduced from the atmosphere. It's interesting.

Chris Adams: I'm I'll be honest, dude, I'm a little bit I'm sceptical of this scope 4 idea because it very much, when I've seen people talk about it, they use it as a way to either say well this is why it's okay for us to deal with these really damaging things elsewhere, right, it's used to basically draw attention away from other things, or I just don't think like It's not, it's never going to balance out.

It's okay for things to be like, orthogonal, in two different, moving along two different axes, right? And I think it, that makes more sense. What you can do instead, is actually be responsible about the impact that you do actually have. And the, there's a, an approach used by the New Climate Institute, who I think, which I think is really interesting.

What they do, they basically say, look, We're a small non profit, our job is to be at, say, COP 28, 29, 26 or something and essentially work with policy makers to set the rules that end up impacting, influencing ginormous organizations that are way, larger than us. And what they do instead, they basically say, well, we have to fly there, we have to do something like this, we're going to impose an internal cost of maybe 100 euros per ton that we put into a kind of, I forget the term they use, Let's just call it a carbon war chest, right?

A climate war chest. And then they use that to fund other systemically effective things. And, like, this feels like a much more sensible way to do it. And this dude, like, dude, like, you know how the GSF, where the GSF came from, right? The funding for the GSF came from the internal carbon levy inside Microsoft.

You told me this before.

Asim Hussain: Well, actually, no,

Chris Adams: Oh,

Asim Hussain: I didn't, get there. No, there is an internal carbon levy in Microsoft, which is then used, to fund green measures but the actual, like, technically the money from this actually came from a different bucket.

Chris Adams: Ah, dude, I, ah, that would have been such a good story.

Asim Hussain: It's unfortunately not a good story, yeah.

Will Alpine, a lot of his work he did at Microsoft was actually funded from that war chest. He knew how to navigate that space and get money from that war chest.

Chris Adams: So this feels like a more sensible way to do it, right? The idea of saying, well, you're not trying to say, you're not trying to get back into eco heaven, you're being an adult about the emissions, and about that there is an impact being caused, and then you're talking about, well, what is the way that I can do it, which doesn't give you my sugar rush of saying, I'm carbon neutral again, but says, well, we have a societal goal to get to, so let's think about it in that sense.

I'm, this was, I mean, Microsoft is using an internal carbon price. It's not the same as a carbon tax, but it does revolve like, it's a discussion about how you allocate time and money to projects, rather.

Asim Hussain: It's a tax because different departments have to pay different amounts depending on how much carbon they've been deemed to have omitted. So it is kind of like a tax.

Chris Adams: Yes, this is something we can-

We don't want to use the word tax? Oh, is this another topic?

It's not so much that, it's, I mean, it's more that, yeah, you might not want to use the word tax in various places, because certain people are in favor of tax, certain people are not in favor of tax, right? And like, there are loads of ways where people allocate funds to various things inside this.

I mean, like, there's a reason inside Microsoft they use it, they called it a levy, right?

Asim Hussain: Oh yeah.

Chris Adams: But like, we, you know, there are loads of places where we allocate a percentage of some funds to funding something else. So this is why I've stayed away from using the term tax inside this, because in the way I see it, governments get to levy taxes, they get to like raise taxes and stuff like that, but organizations don't necessarily, and there are loads of cases where if you're inside an organization, you're going to allocate this much to kind of keeping your staff happy.

Is that a staff happiness tax? Or if I'm paying for cloud, right, and it's a chunk of my, let's say I'm building a digital service and I'm paying Amazon a chunk, or Microsoft, is that a Microsoft tax? I'm not sure it is. It's, I'm paying for something, right? Yeah.

Asim Hussain: Good point. Now that you've said the word tax like five times, every time you said it, I get a-

Chris Adams: You, sir, have said the word tax. I have not used the word tax at all.

Asim Hussain: Have you not? Okay. Well, I think it, I think anyway.

Chris Adams: I talk about internal, I talk about internal carbon pricing, because this is internalizing the costs that are otherwise being shifted onto society, and that seems a kind of economic and kind of grown up way to think about this.

Asim Hussain: Yeah.

Chris Adams: Yeah, uh, yeah, I'm not using that word either for very good reason, because there's another thing. Anyway, we've totally gone off script. This has been fun, though. Let's, should we go back to where we are? So we were going to talk about CarbonHack, some of the aftermath and learning points from that before we wrap up.

So are there any particular projects? So maybe it might be worth you just briefly summarizing what CarbonHack actually was, and then maybe if we talk about one or two projects. So, two projects that really caught your eye, then we can wrap up after that because we've this, has been, this was going to be a short, one and we're running about 50 minutes already, basically.

Asim Hussain: That's okay. People must have some long journeys ahead of them. Summer holidays coming along. Yeah, no, it was really good. So CarbonHack this year, we focused on Impact Framework, but more specifically focused around measuring, like how do we actually measure software, so it's really exciting. So, I'm really excited to be here where we can actually talk about this.

So, really quickly, Impact Framework is a framework that we've been building here to a very low level. You can measure, print, kind of most things with it, but also provide that evidence of the measurement in this impact manifest file. So there's a couple of, we had a couple of prizes. We had the ones I was very excited about.

I was inspired by all of them, to be honest with you. But the one I was really keen to see what people It was we just threw it out there. We were like, well, let's just see how people respond to this one. And it was beyond carbon. Because we've talked about carbon all the way through this episode.

But, you know, as we know, the sustainability challenge we had to have ahead of us is actually far bigger than just carbon. And we just left it fairly open. Kind of anything that kind of measures the environmental impacts beyond carbon was effectively it. And there was some really interesting submissions.

Like the one that won was, as we might guess, like water, like they, they measured the impact of water. And it also triggered some like really interesting conversations around, you know, the, I know there's been some research in this space, but like, at what level are you measuring it? Are you measuring the water impact?

Like at the data center? Kind of like primary. Primary and secondary, primary water and secondary water kind of was like the concept and it was just exciting. But now, what that means now is that there's now a plugin for Impact Framework where you can just, there's actually two, but you can drop it in and just say like, I just want to estimate my water impacts of my workload.

You can just drop it in and do it. Another, team was from a team called Grasp and I loved it because, you know, one of the things that plugin measures. Actually does a couple of different beyond carbon categories, but the three main ones are death.

Chris Adams: Okay.

Asim Hussain: Premature death, social cost of carbon, plus a dollar value on like the carbon emissions and displacement, which is kind of interesting as well.

Like how many people and all this stuff is like one of the, you have to submit like citations and research to prove and evidence kind of where these numbers are coming from. But yeah, you know, like social cost of carbon, we know like what is the cost of carbon as we take it down through the generations and, you know, all this other stuff

Chris Adams: So that's the harm caused by, on society, by the people, right? And like, this is what the, that was 50 under Barack Obama's time in America, went down to 1.

Asim Hussain: No, I think it's two.

Chris Adams: or 2, and then that's gone up to 190 now. No, I think it's actually back to 50. But there, I think the, it's now shifted to a very, a much, Like, fourfold increase, or nearly a fourfold increase in the US, for example.

But this is like, and in Europe it's around 100 right now, that's the figure.

Asim Hussain: I don't, yeah. I don't believe they use what Biden decided and wrote on an exec. It's it was more, more from research.

Chris Adams: Oh, I'm not saying that's like, that's not Biden saying this, that's like, the group who was allocated to do this work, these are the things they've recommended for this. And that's how they've, that's what the number looks like is being mentioned. I'll share a link to that as well, actually, because that's actually This is, really helpful for the internal carbon pricing stuff we discussed before.

If you're going to think about, okay, how do I create a carbon war chest to fund the work like Will Alpine did, or founding an organization, or doing any kind of systemic work. Yes.

Asim Hussain: Yeah, but I think that's kind of like where it's interesting. cause you know, sometimes I do feel that. We sanitize this conversation too much. Like it's too sanitized. Like we talk about carbon, we talk about this, talk about that. But actually, the reason why we're here is this is there's a lot of human suffering that is happening and will increasingly happen because of the work that we're doing and the work. There is, that value in this whole, whole question.

Are you trying to like add a cost to carbon? How many people will likely be displaced because of rising sea levels

Chris Adams: Always like climate migration being informed.

Asim Hussain: Climate migration. Yeah. so like, and of course there's also research which talks about what is the increased death rate, you know, You have to look at kind of, not just carbon emissions, but air quality and things like that.

So there's these, so these are the impacts. I mean, I, and I know it's kind of, it can be hard for some people to swallow, but it is, you know, something I think about a lot. I actually have a big skull next to me. It's like, I'm very stoic.

Chris Adams: You are full of surprises. Wow, you've got kind of Shakespearean kind of Wow.

Asim Hussain: A little

Chris Adams: Alas, poor Yorick. Wow. I did not see that coming at all.

Asim Hussain: Well, you, it's a stoicism thing.

It's like, if you, contemplate death a lot, you, live life more. But those are two, two, two, two really good plugins there. But there's just been, it's hard to like really pick up on the rest. There's some really good work done to using Impact Framework, not as like, not building a plugin for it, but just leveraging it to examine what the emissions of machine learning is.

 And the important thing about that is, is the output of it is this kind of very standardized manifest file, which anybody can read. You're

Chris Adams: Like an auditable or repeatable

Asim Hussain: audible, readable, and you're just reading this manifest file. And as soon as you look at this manifest file, if you know now how to read these manifest files, you can go, Okay.

So, straight away, I know you did this, you used that coefficient. I get the kind of measurement that you are. I can, that's an interesting number you generated. So that's one of the things we want to drive from it. And the best, yeah, there's some great work from, I've forgotten the name of the team now.

But the, they did work on the, trying to figure out the, using impact frame in a very In a usual way, which is like in logistics, can you drive, can you create a manifest file which represents the emissions of like a fleet of cars, like delivering Amazon packages all over? Can there be a manifest file for that?

And, uh, I think the winner was they built, it's called green. Oh, no, I want to mention two other teams and they both got the same name, which is very confusing in the hackathon. Find a called green and meet green. Oh, I forgot the actual name of the team. It was green something, but they, uh, they built Kubernetes.

So an interface into Kubernetes and the reason why it won, it was just like Kubernetes is so ubiquitous on the internet. And now if you've got, and it wasn't using, there was a separate one for Kepler, but this is very specifically for Kubernetes, independence of Kepler, taking the observations from there.

And you can compute the, environmental impact of that. And I think, actually, I shouldn't have mentioned it, that my favorite solution was a team called Greener Meet. My absolute, I all, I'm sorry, all of the rest of you, you all weren't my favorite solution. My favorite solution was a team called Greener Meet.

Chris Adams: Green Me, as in, like, Greener You, Greener Me, or Greenami, like Konami?

Asim Hussain: Greener Meet, as in having a meeting.

Chris Adams: Oh, Greener Meet, as in zoom, not like.

Asim Hussain: Zoom. And it was exactly

Chris Adams: Okay.

Asim Hussain: And we had an under 18s prize, which we really spent a long time with lawyers trying to figure out, can we do an under 18s prize? But we managed to get one together. And it was a team of 16 year olds from school in California.

And they created, they used the Impact Framework to actually measure The environmental impact of Zoom. So they literally like they, they got Python libraries. They, ran Zoom calls. They could get the utilization. They compared it to Watttime and they even found they were like, they even switched on and off features of Zoom to show like how that changed the emissions profile of your call.

And even I loved it. They found a curtailment that they were having a meeting and cause they're in CAISO. They

Chris Adams: CAISO being Californian Independent Systems Operator.

Asim Hussain: I knew you'd know that.

Chris Adams: And that's basically the grid operators in California you're talking about.

Asim Hussain: And California has a lot of renewables, as you know, so they had a moment in the meeting where they curtailed, which means that the emissions are effectively zero for the call now because they're factoring that in.

It was really good work, and we tried to give them the prize on the call. They, we were delayed. They had to go to class. So we couldn't give the kids the prize, and if we can, maybe we can bring them on this podcast. I'd love to speak to them. I just, it gave me such, honestly, you watch this, And I'm sorry to the rest of the prize winners, but I think this was the best video.

It was the best presented. And I'm just sitting there thinking, these are the, now we're going to, now we're going to, " the kids of today!" - we are old. These are the kids of the future. You know, maybe it gave me so much hope, like these are 16 year old kids and they were doing some incredibly advanced green software, measurement, reporting, zoom, understanding curtailment, understanding like how to measure this stuff.

And I was like, ooh, we've got a, we've got a good future ahead of us. I think, I don't think, yeah.

Chris Adams: Asim, this sounds really cool, but I have to ask, right, why are we relying on a bunch of literal children to do this work when Zoom is a multi billion dollar publicly traded company that does have to report on its own figures like this, and they are doing, Zoom doesn't expose these numbers themselves, right?

Like they will tell you how much you've saved, but you can't see the figures from this. We're relying on children to do the work of essentially publicly traded companies here, right? Right. This feels like a, we, I think this is really, cool. But this has an open question about what they would do maybe Zoom or they might join an organi organization like the GSF and say, well, this is how we do measure this stuff. And, uh, 'cause if not, I think we might need to refer to the work of children until that happens. Because this is just like, I mean, if, this embarrasses them to actually being more transparent about how this is being used, I think, we should be doing it because it's kind of bonkers that you are relying, like, it's nice to have like youth of today, but one of these groups has literally access to billions of dollars of capital and one of these groups is doing it in their spare time and couldn't even accept the thing because they had to leave for class. Yeah.

Asim Hussain: For class. Yeah. Yeah. I mean, that's a fair point. But at the same time, I mean, I want to counter your point for a second as well. I mean, I'm, you know, I have multiple views. Like, do you really want to be waiting all the time? Like, we're just waiting all the time. We're just sitting and waiting and waiting and waiting, for everybody to be transparent about all of their emissions all the time.

If we can just take that power back into our hands, And we why not wait for people to be transparent, but actually like, look, this is a model that I've created. I think this is the emissions profile of your product. I'm going to publish it. If you feel somewhat different, that's the beauty of open source.

If you feel somewhat different, create a pull request and be, as transparent as me. I think in a way we, it is good. That we're leading the way, it is good that the kids are leading that way in the future because it kind of brings power back into our hands, if that makes sense.

Chris Adams: I think what it sounds like you're suggesting is in the absence of information, it might be worth using the age old internet trick of ask a question on Stack Overflow, answer it with an incorrect answer, then hope that people will actually come up with better answers, because that's the tried and tested way to get a good answer on Stack Overflow.

You answer it incorrectly, then someone goes, no, you're getting it wrong. Now I'm doing it, because they're so incensed that someone is wrong on the internet. So this basically, I think what we can use that, we can harness this natural resource and we can say, we're only going to use the worker of these children, and we're for talking about Zoom calls until someone comes in with actual better figures and ideally the organizations with full access to this, like maybe Zoom themselves, or maybe a competitor of Zoom.

So if you'd like to do this, I'm chris@greenweb.org, no, I'm very happy to have this conversation because this does feel like it's necessary. Anyway, oh, blimey, I've just seen the time. Asim, we've, gone a bit over, but this has been really, fun and,

Asim Hussain: Yeah. It has been!

Chris Adams: I, this is, We've got another one of these in a few weeks time.

So, Asim, really lovely chatting with you, nice to catch up, and sorry to hear about the mushrooms, but I guess the Mushroom Kingdom's loss is the Bread Kingdom's gain. So, I should probably wrap up now. Folks, if you've made it this far, we will be sharing links Pretty much everything we've discussed and which you can find at podcast.greensoftware.foundation. And thanks again for listening. My name is Chris Adams and this is Asim Hussain of the Green Web Foundation and Green Software Foundation respectively. All right. Thanks, Asim, mate. Take care of yourself. Okay. Bye.

Asim Hussain: Cheers. See you later.

Chris Adams: Hey, everyone. Thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again, and see you in the next episode!




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]]> TWiGS host Chris Adams is joined by Asim Hussain the executive director of the GSF to talk about the recent hackathon hosted by the GSF : Carbon Hack 24. Asim goes through some of his favourite projects that featured work with the Impact Framework including some surprising choices! They also cover some interesting news from the world of cloud service providers and the new CSDDD developments. Asim also talks about how mushrooms are out and bread is in!

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TRANSCRIPT BELOW:
Asim Hussain: And I'm just sitting there thinking, we are old. These are the kids of the future, you know, maybe gave me so much hope, like these are 16 year old kids, and they were doing some incredibly advanced green software measurement, reporting, zoom, understanding curtailment, understanding like how to measure this stuff.

And I was like, ooh, we've got a, we've got a good future ahead of us.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, the podcast where we talk about sustainable software development. I'm your host, Chris Adams. This week, our format is This Week in Green Software, where rather than doing a deep dive into some of the specifics of green software, we're going to look at some of the news stories that have been making the rounds and provide a bit of commentary and analysis on this. And joining me today is my good friend Asim Hussain, the executive director of the Green Software Foundation. Asim, I'll hand over to you to introduce yourself a bit more than what I've just shared so far, if that's okay with you.

Asim Hussain: Yeah, of course. Yeah. Thanks. I really love these, having these podcast episodes with you and talking through this material. Asim Hussain, Executive Director of the Green Software Foundation and been, you know, lucky enough to be at the intersection of sustainability and software for quite a few years. So based in the UK and excited to be on the podcast again.

Chris Adams: Thank you, Asim. And when I last spoke to you on the podcast, the recurring thing is your history of growing all kinds of delicious mushrooms. How are the myco friends doing?

Asim Hussain: Not good, not good. I've lost, my green thumb or my blue thumb. Maybe if that's how you would, talk about it. But yeah, no, I've lost my, I've lost the thumb. I now bake bread is, the thing that I do, which is equally disappointing, to be honest with you. I just love doing stuff, which is like very, the people, you put a lot of effort in and then you get kind of like middling results is kind of my, seems to be my hobbies of, the day, hobbies du jour.

Yeah.

Chris Adams: Okay, so you've been culturing um, what's it, butter, sourdough and things like that?

Asim Hussain: Yeah, sourdough right now, yeah. I do have a sourdough starter that I, was gonna say keep refreshed, but I probably should say like, keep on the verge of starvation all the time. But yeah, no, it's actually quite fun. I make, I bake bread every weekend and I give it a go. Yeah.

Chris Adams: you for sharing that. Presumably, this is going to be carbon aware bread that you're using, that you're baking when the energy is green, because in the UK, you have the baking forecast. Are you familiar with the baking forecast?

Asim Hussain: I am not aware of the baking forecast.

Chris Adams: Ah, dude, this is so cool.

Asim Hussain: Now that you've said it, I can't believe I'm not using the baking forecast. Because it is, a lot of, that's the thing my wife questions me on. She's like, does the financials of this work out? And I'm like, I don't know, but there's healthier bread than the stuff you get in the supermarket.

Chris Adams: So for those who are curious, and I promise we will talk about green software, this is a kind of segue towards some of the ideas we might talk about later. In the UK, there is a website called the Baking Forecast, named after the shipping forecast, and the idea behind it is to look at the carbon intensity of the electricity that might be going into the oven when you're doing any kind of heating.

And this means that you can then decide to time your baking to be zero or very low carbon bread by making sure you bake when there's lots of renewable energies on the grid. Or, alternatively, if you are not paying attention, you can end up with very, high carbon bread by baking when there's lots of fossil fuels on the grid.

Now fortunately, sunnier, we've got some more solar and wind coming in, it's not like the depths of winter, so it's probably greener breaking today than it was in November or December. But yeah, this is one thing that we should, that I think is, only the UK has this right now. And we'll have to share a link in the show notes because it's a really, cool project.

Asim Hussain: That's cool. You could also end up with really high carbon bread if you leave it in the oven for too long.

Chris Adams: Oh yeah,

Asim Hussain: wait, wait. Oh my God. Where is it? Where is it? I don't know which one it is. You do it.

Chris Adams: I think I know what you're after.

Asim Hussain: Oh, there we go. Oh no.

Chris Adams: This is what you're after?

Asim Hussain: That was it.

Chris Adams: Yes, it, yeah, as, you, Asim has discovered the sound effects on the, on Riverside, the podcast platform we're using. So-

Asim Hussain: You told me about it.

Chris Adams: Yeah, I do apologize. So if we have, we might be like two children at the front of an aircraft playing with this, flip, flicking all the switches, but we'll try our best to not make listening to this too insufferable.

Okay, so we've, mentioned about the baking forecast as a way to talk about carbon aware use of electricity and things like that. Shall we look at some of the stories together, Asim?

Asim Hussain: Yeah, let's do it.

Chris Adams: Oh, if you're new to this podcast, my name is Chris Adams, or if I'm in trouble, Christopher Adams. I am the executive director of the Green Web Foundation, a small Dutch non profit focused on reaching a fossil free internet by 2030.

I'm also one of the chairs of the policy working group inside the Green Software Foundation. The other thing I'll share is that at the end of this show, we'll be showing a link with all the show notes, and it's usually written in Markdown, and we do accept pull requests, so if there's a thing that we've linked to that you think is incorrect, or if there's things you'd like to add, then it's open source and you can do that.

And I think that's the main thing I'll share. The stuff we talk about will be linked. And I guess we should probably find something to talk about then, shouldn't we?

Asim Hussain: Yeah. Something that's not bread related.

Chris Adams: Alright then, well in that case, shall we move on to, I want to make a joke about biochar, but let's just move on from, yeah, alright, so let's, focus on the first story, which is GreenOps, FinOps, and the Sustainable Cloud.

This is from, uh, I think it's actually originally from Forrester, a big analyst firm, who have now kind of woken up to this idea that If you're tracking cost, in many cases you might be tracking carbon. And this is the main thing they're talking about here. There's a few kind of key takeaways and key points inside this.

And this basically does talk about things like, say, you can reduce emissions through kind of looking at kind of existing cost bills, for example. There's different steps you can take. They talk a little bit about some of this stuff. And they also provide a very, high level idea that Right now, we do have inconsistency across the large cloud providers, so Google and Microsoft, they might report all of the emissions, or all of the emissions according to the kind of GHG protocol, which is like the gold standard of reporting, giving you scope 1, 2, and 3, where the majority of the emissions for what we do is probably in scope 3, and talking about how Amazon is doing this, and they've recently, we now know on the grapevine that they've started to rehire people.

But at present, if you look at the dashboards or the calculators of Amazon, you're only going to look at scope one and two, which on average means that you're missing a significant chunk of the picture. So that basically means if you dashboards, and you think, oh, my numbers are really low, that might not be the full story.

All right, Asim, I trust you had a chance to look over it. So are there any particular takeaways or hot takes you had on this one that you might share with us?

Asim Hussain: Well, I don't know if I had any particular hot takes is stuff I've heard before and kind of stuff I've heard mentioned kind of in quite a few times over the past few years. And what I was actually interested in is perhaps just having a more. I'd actually love to hear your thoughts and maybe we can like walk through some of the ideas and some of the question marks I have are just about the whole premise of this link to cost and carbon and like, there's a few things I think of when I think about that.

One of the things I think of is, well, isn't, the cost of things the reason why we're in this problem in the first place? You know, how can costs, how can money both be the cause of the problem and the solution to the problem at the same time? And then, like, you know, you know, yes, there is this argument that kind of reducing cost reduces carbon, there's this kind of correlation.

I've spoken about it extensively, in my past, but kind of I'm trying to refine my thinking in this space. We know that there's somewhat of a correlation there. But we also know there's things you can do which can dramatically reduce carbon, which don't affect your bottom, the cost at all in the slightest.

So what is the, how big of an opportunity is that and is therefore focusing on cost kind of distracting us from things that we can do, which can dramatically reduce our carbon that they've got nothing to do with the cost. And another thing just kind of pops up in my mind as well, which is I remember I was speaking to just the energy purchasing department at Microsoft years and years ago. And I actually asked them, can I quote, quote you on this.

Chris Adams: And this is because you worked at Microsoft.

Asim Hussain: I used to work at Microsoft. Yeah. Yeah. And a rule of thumb that they gave me was, which was the, this is pre AI, was that the cost of running a data center is only 10 percent energy. The actual dollar cost, 10 percent energy, 50 percent depreciation of assets.

So you bought some chips, and they're gonna be worth zero in five years, so they're depreciating of assets. And everything else was like, you know, people and everything else was number one. And so like, when you think about that, certain parts like that emits carbon into the atmosphere, all of those-

Chris Adams: The creation of, oh, each of these, right?

Asim Hussain: The energy emits carbon, but it's only 10%.

You know, the human beings running around emit carbon, the actual physical infrastructure like emit carbon when it's created. And then what happens in an organization is you normalize that into a dollar value, which you then charge for services that run off there. But does that dollar value, like represent, you know, accurately, you know, like the carbon emissions put it that way like there's probably a lot of nuance here yes. Reducing your cost reduces emissions, but maybe reducing costs In this way has unbelievably emissions reductions and reducing, you know, reducing $10 in another area, eh, probably doesn't have that much impact at all.

So I'm kind of a little concerned with how I don't really think the link is kind of that well understood and I'm worried a little bit that if we focus too much on cost reduction, we might be able to reduce costs in ways that have no impact on carbon emissions reductions. That's kind of like where I'm thinking about.

Chris Adams: Mm.

Asim Hussain: I was just wondering what your thoughts were, actually.

Chris Adams: So, I think there's two things. I'll share a question to that, but you said two things which I think are really interesting there. First of all, the cost of energy as, like, that's a part of what the data center might be, right? And you also said, like, depreciation of assets being one of the big costs.

And that, I'm assuming that basically means, like, I've got a server, it's going to last over, say, three or four years, and because of that I split the cost over four years, and that's how I, that's, you know, it's-

Asim Hussain: Yeah. Accounting wise that let me see exactly how they figure out accounting wise. Yeah.

Chris Adams: All right, so there are two things. Most of these discussions seem to be about energy usage, with the idea being that, I think you're right, there's totally mismatch between these two things, because we are, in many ways, we don't see any of this stuff, we don't have the visibility in a bunch of these things.

And one thing that we've, one thing that I guess we've seen a number of large companies start talking about now is saying, well, we are looking to extend the life of our servers, and we're doing this as a way, as a green measure, right? That sounds great, yeah? But if you extend the life of your servers, you've gone from 50 percent depreciation to maybe 30 percent or 40 percent depreciation, right?

I mean, from a financial point of view, that basically, I mean, Amazon did this. It put like billions onto their, on to their balance sheet because they now look way more profitable than they were before because they don't have to kind of write it off. So, yes, there are some things being shared for this but whether how that actually factors into some of the calculations people use.

I mean, I don't know. There was an announcement there. We haven't seen any changes whatsoever in any of the kind of cloud dashboards, so you would have expected that to make a difference if you did have, if we did see that, and both Amazon has done this, Google has done this, and Microsoft had done this, right?

So, I would argue that looking at calculators, there's a gap here between what we're seeing here and what we are being taught to optimize for. And there's also another question. Yeah.

Asim Hussain: Yeah, no, it, and there's also, I remember just as you were saying that, I remember this, and this is years, this is information is years old, but I was chatting to the, uh, the Xbox team at Microsoft and like one of the things, I mean, I still, no, I don't have an Xbox. I finally got rid of it. That initial estimation

Chris Adams: You sorely miss. Your love to Xbox, that is. Yeah.

Asim Hussain: Yeah, love.

Chris Adams: Oh no, you don't work for them now. You're okay. You can say anything.

Asim Hussain: I'm okay. I can, get rid of it, I think. I think. Yeah, you wouldn't get, you wouldn't get a free Xbox if you went to Microsoft, and I always thought they should give you one. But, it was, like, they assumed that it would only last, like, four, three or four years. I can't, I'm, don't quote, no one quote me on this.

But the actual lifespan of kind of these consoles turned out to be a lot longer. And people, I don't know how old Xbox is now, like, it must be like six, seven years. But it's still, like, pretty, you know, still pretty active. The decision was to kind of still keep, from an accounting perspective, still keep it kind of three, four years, even though the reality was it was being used for longer.

So those decisions are being made as well. But it's interesting that's part of the, yeah, I suppose that's part of the story we don't really, The point I'm coming from is this, is a $1 reduction in your costs, how do you link that to an actual carbon emission? Because coming back to my point of like the energy versus the embodied, like how much of the emissions is linked to the energy versus the embodied and therefore how much of your cost is linked to reducing the energy versus reducing the embodied?

Does that make sense? It's

Chris Adams: This comes to basically disclosure of cost structure, which is a bit of a kind of like jargony term but basically without having visibility into how this stuff is paid for because in many cases we have very proprietary services, it's really hard to basically, be sure, like, is this really happening?

And even when we talk about things like, say, cloud, moving to cloud compute, moving to, say, serverless, all these other tools, which are kind of higher up the stack, right? There is, it's not like the profit margins for cloud at the very bottom, like paying for compute per, on a per hour basis, will be different from things higher up.

And we don't have much in the way of visibility into any of that stuff. I mean, I, as I understand it. The higher up the stack you go, the more value you add. A bit like if you're buying refined, if you're buying oil, and then you're selling, say, refined kerosene or something like that, it's going to be a different price, right?

So you can think of, I think we have maybe some mental models we can apply for some of this. And this is actually possibly one way that you can say, well, if we have If we're running a bunch of companies, and we have a set amount of resources, and we cannot use more resources, like more inputs, there are ways to still continue to increase the amount of kind of value and profits being made by shifting people to kind of go higher up the stack to get them to purchase, maybe say, a serverless thing, or something like, something which allows you to kind of cram more stuff onto the same infrastructure.

But the problem is, In order for you to do that, you need to not build more infrastructure. And what we've just seen is the opposite of that happening right now. And I think this is what, I think you're somewhat right. It's easy to talk about cloud costs because it's, you've already got the numbers there and you don't have to, yeah.

Asim Hussain: It's like one of the most obviously disclosed, like, facts about your cloud usage is how much it's going to cost you.

Chris Adams: Yeah. And like, if you're in an organization, it's probably easier to start optimizing a number that you already have than to pay for someone to, or even pay for a solution for someone to do this stuff. Because I've invested like. We've, uh, okay, I promise we'll get back to the other stories a little bit later on, all right?

But inside the Green Web Foundation, we've been looking to, like, audit all of our own services recently, or just, like, work out our own kind of, look at our own annual emissions. And the thing we found is that, let's say we've, uh, the majority of our impact, because we're a quite small organization, comes from, not necessarily from the services we're paying for on an hourly basis, like, we use Hetzner, and we don't, Which we're spending maybe, I guess, maybe a thousand euros a year on Hetzner in total?

Asim Hussain: What's Hetzner?

Chris Adams: Hetzner is a German cloud computing service. So, about half of all of Mastodon run on Hetzner service because they're so cheap. So much cheaper, and in Europe at least. They've been one of the greener ones to use, because they build all their own software. And if you look at their prices, they're typically, like, you know how DigitalOcean will sometimes be cheaper than, say, the big cloud providers?

They are cheaper than DigitalOcean, once again. So as a result, we're a cash strapped non profit, we're going to go for the cheapest green, option we can find. And the cheapest green option that We can find that has cloud like APIs and is used and works quite well. So we've been using those ones, and as a result, cloud makes a small amount of our kind of digital estate.

What ends up being a larger thing is the infrastructure we use, like the laptops and the monitors we have inside our own houses, inside our own offices. But, even if we buy those from circular providers, so my laptop is from BackMarket, which is like a kind of second hand provider, and likewise, Hannah does the same thing with hers, and all this.

Hannah's my, she's my colleague who's been leading on this work here. We've done this, but under most of the ways there's no guidance on, okay, is it someone else's carbon? Do you know, has someone else banked it so therefore it's free for you because they've depreciated the carbon there? There isn't much in the way of guidance or real stuff that you can adopt.

Like, you can might, you might be able to sight a paper, but that's not the same as there being, say, guidance that you might use for your own reporting to say, this is why we've chosen these numbers here, for example. And like, this is what, these are some of the problems. There isn't this recognized way to account for circular versus buying new stuff, for example, and this does speak to this idea of, well, okay, how do you estimate this stuff and work out what is the most effective thing, which, you know, which levers should you be pulling if you want to reduce the emissions from software or digital services in general?

Asim Hussain: So that would be like an example of, well, yeah, cause I've even had these conversations before we're talking about like, what'd you do? I think with all these like hyperscalers, like once they actually reach end of life, they're not broken.

Chris Adams: Mm

Asim Hussain: the, equation that they're running is like, we've got per square foot of infrastructure, of real estate, what is the profitability? And at a certain point, it becomes more profitable to take out this old server, which is perfectly fine.

Chris Adams: Yeah, usable. Working.

Asim Hussain: But you can just put something else, which is more profitable per square foot for that given situation. So that, I mean, people think these things are breaking and then no, they're not, they're just perfectly fine and working.

They might be more inefficient. Then we've had like conversations in the past with, I've had people going, well, if I was to take that server. End of life server and build my own data center. Could I argue that is zero? Could I argue that it has zero embodied emissions? Is that like a reasonable statement to argue?

And, I don't know. I don't know what the answer to that question is.

Chris Adams: The question, the answer is likely to be about, oh God, this is so nerdy, mate. I'm so sorry. But like, what is the depreciation that we've just spoke about now? You said half of the cost in a data center is a depreciation. What's, how do you split that? How do you kind of, Amortize the carbon over these years and then how do you then share that with someone else?

So there's a company called Open, I think they're called Open Sesame or Sesame. They basically build data centers or like racks and stuff out of all of the, uh, Yeah, decommissioned stuff from Facebook and all these open compute projects, because all the companies, because you know how these servers are designed, and know how they're specced, it's actually quite common to buy these, and these tend to be cheaper than what you might have elsewhere.

And in many cases, it's not like, It's not like they rust, right? They really, they're, kept in kind of quite good con condition and they are working in a lot of cases, but the, they make the argument that, well, this is circular, so this is gonna be greener what you have, what you've been using elsewhere, but whether you are able, but yeah, it's, a number question.

It's an open question about how you account for that stuff inside, if you're purchasing, say, computing from these folks compared to people from somewhere else, because a lot of the time, order for in you to do this, you probably need organizations to be really transparent about where they got their servers from, and that's not always what companies are incentivized to do, basically, right now.

Asim Hussain: but then we get to that same argument of like, at the end of the day, that's going to be sold to somebody as like a dollar cost, like dollar per hour of a CPU. and if we just look at cost as a proxy for carbon, then you could argue there that will, they might have to end up selling that service at a rate that is actually quite comparable to a brand new server.

Chris Adams: Yeah.

Asim Hussain: This is where I think there's this kind of, I don't know if it's a breakdown or there just needs to be a lot more thought put into it. It's almost like there's a, that you pay a dollar value. There's a black box. And then at the other end, we know there's some sort of carbon emissions. And I think there's a lot of nuance there.

And maybe what I'm saying is that actually cost isn't a great proxy for carbon. Because really, maybe that is, I'm kind of refining my thinking as I'm talking to you. Maybe cost isn't a great proxy for carbon. Or maybe you put it this way. Three, four, five years ago I was advocating for it because we had nothing else.

Chris Adams: Mm.

Asim Hussain: There were so few other options, but I'd actually argue now, we have the capacity and capability, even if a cloud provider, somebody themselves don't even know what the carbon emissions are. There are models that now exist where you can estimate from what you can see about. But the infrastructure that you're using and make your own assumptions and make your own judgments as to what the carbon emissions are.

So I think I suppose where I'm thinking now is we can actually move beyond costs. I suppose that's where I'm thinking.

Chris Adams: Well, the thing I might share with you, and if you forgive the plug, so I've just been travelling all around Western Europe by train, and I literally started on the 31st of May, and I arrived at this conference called DjangoCon, which is a conference all around DjangoCon, Django, which is a very popular framework that initially powered, say, Instagram, and like, part of the NHS website, all this stuff here, yeah.

Asim Hussain: I used to be a Django developer. That was like my job. Yeah. My job, my paid for profession for quite a few years was a Django developer.

Chris Adams: Wow, I did not know that. Okay, no, that's a, okay, that's a story I'm going to use then, because I think we can bring this back to the cross thing for a second. Anyway, I did a talk, I, there was very short notice, I was going to deliver a workshop about how to, basically, how to use some of the tools available to kind of green your Django stack.

Right, so we were using a tool called Green Metrics Tool, which as Ana, one of the, like, I guess the founder of this, the guy who, the lead maintainer, he says the name is descriptive more than creative, but you kind of know what it's for, right? I quite like that. Anyway, that does give you figures for energy usage and embodied carbon and it does provide these and even provides like SCI figures. So there's now like a workshop and a deck and I will show a link to it to see how you can take an existing project and come up with SCI figures for various user journeys and things that you do have. Asim, I didn't have time to talk about the Impact Framework, and I, once I know enough about the Impact Framework, I'd be really up for using it.

But one of the key things I had to do was I was doing this workshop and then literally the day before I arrived, we had someone with a visa problem, so they couldn't do their talk. So basically the organizers asked me, said, Chris, are you, do you have a talk ready or are you, would you be up for filling this spot?

We have a 30 minute slot to talk about this. And I basically had 24 hours to take the workshop, turn it into a presentation. And the thing, the reason I'm talking about this is that I presented a kind of taxonomy of ways to think about these tools that you have. And I described things in terms of usage based and cost based specifically for this reason.

So you would use cost based tools to do your first round of disclosure or to work out like a baseline because until you, and until you've been able to demonstrate any value from doing this, it's going to be really hard to argue for like weeks of developer time to come up with some numbers. Right?

Whereas this at least gives you a number that you do have. And then I spoke about how you would use usage based tools to start figuring out, okay, well, what changes can I make to reduce some of this stuff? And yeah, I'll share the link to the deck, because it's, it allows us to create the, you know, we need, like, taxonomies to realize that we're not conflating these ideas too much.

Because a lot of the time, you're doing different jobs, and you're asking, you're being asked by different people. So, like, for example, the whole kind of Software Carbon Intensity thing, initially, it was, as I understand it was created partly because the sustainability, like the head of sustainability asks for some numbers, and if you're going to report along these numbers, you don't really see much in the way of incentives to show how you're changing in future, right?

it doesn't incentivize changes at the engineering level.

Asim Hussain: Yeah, yeah, yeah.

Chris Adams: right? So therefore you need a, you know, one of the thrusts would say, well, let's come up with a metric to show that we are planning forward. And we're taking some steps so that if you're an engineer, you have an idea of what's, what, you can do rather than just outsourcing all to energy procurement, because that-

Asim Hussain: Yeah,

Chris Adams: isn't necessarily the full solution, for example.

Asim Hussain: I mean, the way, I mean, I really love your taxonomy of cost based versus user based. I think that, yeah, I like that actually. And user based being more, perhaps more, more granular and more action. Cause it kind of gives you more, more specific things you can do. Whereas cost is like this really high level thing.

Just to bring it back to what you said about the SCI, the, one of the things, And I really, I didn't coin this, uh, but when it was coined, I loved it, but they described it as measurement for reporting versus measurement for action. And that's, I think, an interesting way of looking at kind of the different measures, uh, in this space.

And, you know, we talk about regulations. We need to talk about regulation. We want regulations a lot, but once you start talking about regulations, the measure that you have is a measurement for reporting and the, behavior it triggers is what, number can I disclose that I can defend? What can I defend? Whereas a measurement for action is kind of what number do I need to drive action change. And that's when we think about the SCI, it's a measurement for action. I don't know whether, where, it will need to land to be something, you know, to be, you know, To be, you know, defensible.

Like, I don't know why I'm gonna go off on a massive tangent right now, but-

Chris Adams: Go with it, let's go with it.

Asim Hussain: Go with it. Let's go with it. We know Krav Maga. You know Krav Maga?

Chris Adams: Wow, yeah, the Israeli martial art you're talking about?

Asim Hussain: The Israeli martial art. Like, I've always, there's actually a place nearby and I've always wanted to learn.

Chris Adams: I did not expect you to go in that direction, let's go with

Asim Hussain: Well, but let's just go with it.

Because here's an interesting thing about it. And one of the things that the founder of Krav Maga said, this is will never be allowed to be done in like a competitive environment. There will never be the world championship of Krav Maga. There will never be like this, you know, global Krav Maga, blah, blah, blah.

Because by, by that's when you start bringing in rules and the actual nature of the sport changes. So it doesn't become good at what it's supposed to do.

Chris Adams: Ah, yeah, so for people who are not familiar, Krav Maga is, it's like, oh, it's like punch him in the love spuds and all the stuff like that. It does all the kinds of things which you wouldn't do in a, kind of formalized mode, because they are, they're basically weapons of war, rather than actually weapons of art, right?

So it's more like, there's more martial than the art part, perhaps.

Asim Hussain: I mean if I'm gonna learn a martial art, it's gonna be there so I can like actually get out of trouble, so I don't mind like poking somebody in the eye or kicking them where they shouldn't be kicked.

But like, but I think that might, that's just one of my thinking and I'm, and I, this is just my thinking and this is a consensus based organization, so I just want to really be clear that I'm just expressing kind of some thoughts have and they might change in the future as well. You know, can you have both things?

Can you have a measure which is good for action and good for reporting? Or does, you know, as soon as you bring a regulation into a measure, does it then transform the nature of it, so it, you know, it doesn't become good as an action driving measure and it can only serve as a , as a measure for reporting.

That's kind of some of the thinking we've gone, I don't know how we got to this.

Chris Adams: No, this is a graceful segue into the next story, Asim, don't worry. All

Asim Hussain: But yeah, that, that's some of the thinking I had.

Chris Adams: I don't have a, I won't try to directly answer that, but what I will do is name check the new story, the next story we have, which is from Linklaters, the law firm, and they talk about, so this is going to be a bit of jargon.

Asim Hussain: Oh, yeah.

Chris Adams: I'm afraid, so this is a, this was a new story from actually a few weeks back, say, the new CSDDD compromise finally accepted by member states.

That is impenetrable to most people who are programmers, but the short version is that there is a piece of law. called, we already have a piece of law called the Corporate Sustainability Reporting Directive, which is a bit like, which essentially compels organizations of a certain size to disclose their carbon emissions on an annual basis, just like they disclose their financial things and their financial figures.

Different parts of the European Union are now, okay, for want subclassing the directive and turning it into their own law, right? So, France has been the first of the countries in Europe to actually do this. And Egalité, CSAD, yeah, like, they have a really hardcore version of the law, where if you are a corporate director, and you don't disclose, and you take steps to block disclosure, You have jail, you know, there are jail time sentences and stuff like that now.

There's some really, hardcore things, right? And that's like the disclosure thing. But the thing that's interesting is that this plays nicely with this new law, which is the Corporate Sustainable, Corporate, I think it's Corporate Supply Chain Due Diligence Directive. And this introduced some new demands.

So you basically need to like, as you'd imagine, Have some due diligence in your supply chain, but they also need to demonstrate how this fits into some of the laws or some of the kind of longer term goals of being a company active in, like Europe, where Europe has set targets of saying, well, you need to reduce emissions by, you know, we aim as a kind of union to reduce emissions by 55 percent and who knows, maybe even 90 percent by 2040.

And as a result, you know, you now have to publish a climate transition plan, which shows what your steps are to actually help get there. And, uh, this I think is interesting because this now means that you have basically a mechanism for accountability each year, I mean, kind of anyway, but you've also got something to show that you're looking forward.

So you've got forward looking and backward looking. And I think this is quite helpful for the conversations around, say, a consequential model like The Software Carbon Intensity thing, plus some of the kind of somewhat backward looking models that you might have with the GHG. And I think the GHG protocol, and this is how I now think about this, you will say that you might use the SCI to plan forward and say, well, am I going to do this versus that?

And that allows me to talk about, it's not just my organization, like, uh, are there places where impact is taking place where I don't have to put it on my books, but I know it's a significant problem, right? Because the current state of the law doesn't actually incentivize you to do that. And, uh, particularly if you're like, say, a media company, for example, where, you know, most of the impact is in a router in someone's home, or in someone's end user device, like their big ass television, you might not try and tell someone.

Hey, can you please stop using your big ass television and use a, tablet, right? Yeah, you might do that, but like, it's, that's going to be a hard thing for you to kind of argue is inside your kind of organizational boundary, for example, for, reporting. So this is why having two ways of looking at this is actually quite helpful.

And this is how I've expressed the difference between an SCI and the GHG protocol. The fact that they are complimentary to each other, but very but different.

Asim Hussain: Yeah, and I think that's, I never meant to throw shade on any kind of measurement for reporting, like it's all, needed. it's just different audiences. It's just different audiences, different needs, different, other things. So would you say the CSDDD is, because I just think the way you just described it, there's, it's, different to how some of the ways I've heard it described, but the, does it go beyond the organizational boundary?

So the climate transition plan that you have to expose, is it how it supports the EU's aggregate goal, or is it just how you, your organization plays, how its emissions has to reduce? Because if, it's an aggregate goal, then the kind of debate that you're just describing, which is like, I'm making my TV more efficient, but who cares? It's not part of my or something. That's not part of my effectively changing Help showing how you're helping the climate transition by things outside of your value chain.

Chris Adams: I need to stress, I'm not a lawyer. All right. Okay. And uh, I think I see, I think you mentioned that there might be someone who is a lawyer who does look at this, that I think it would be really nice to have someone who, with that deeper domain expertise to kind of talk about this 'cause. I'm mindful that because I have an English accent, it sounds like I know what I, that I am confident and, uh, about what I'm talking about when I might not be that well informed.

Asim Hussain: In a lot of you're very well informed in a lot of areas. But anyway, yeah.

Chris Adams: Alright. So yeah, I'm definitely not a lawyer in this con in, this context. Uh, but the, I guess the key, you know, your question is do you use 'em differently and would you be, I mean, is that the question that you had? Like, uh, uh, is that, maybe you could just repeat that to make sure I understand the question.

Asim Hussain: Well, I think it's interesting. I think it's, I think, because a lot of these questions are kind of like insular to an organization. Like what are you doing? What's your organization? How is your organization reducing its own like emissions? And then the conversation is basically coached around, "well, that's not my problem. That's not me. I, even though you could do something materially to reduce those emissions, it's just not part of my value chain. So I'm not going to be focused in on it." Is it, I presume because it's a business reporting directive. It is kind of very much like how, like you've, made, how are you as an organization going to your emissions in line with the EU's target by 2040?

Chris Adams: So, okay. That's, yeah. I think I understand your question now. So, uh, as I'm aware so far. The CSDDD is basically, they're still all focused very much on your own organization, right? So it, I think most of the supply chain due diligence part is about your upstream, right? Not necessarily so much stuff that you have downstream, for example.

So, you know, you might talk about who you purchase things from, but it might not be about to what extent am I incentivizing or am I accelerating the production of fossil fuels, for example, right? So right now, let's say, when we saw this, when we saw, I think Will Alpine, you know, he was a guest before, and so, so, so, so, so he mentioned this a while ago, and I think way, way back in the first or second episode.

He spoke about, okay, we need to be doing, you know, we, need to be using, we need to be responsible for our use of AI. Yes, we need to think about the green part, but we also think about these parts elsewhere. He's been like really, he's been leading on a bunch of this stuff and talking about this. Yeah.

Yeah. So, the yes.

Asim Hussain: Scope 4, he got me onto the idea of scope 4 emissions, which is like Very interesting.

Chris Adams: Yeah, so he, so like, he's been talking about this in quite kind of clear ways for quite some time. So, the scope 4 stuff that you've just mentioned, don't know to what extent that's actually included in this right now. But if you're talking about your plan, it's likely that if you're going to have a transition plan, you probably would have very good reasons to talk about what your transition plan looks like because you're helping transition the entire economy.

So it may be that you might refer to this, but this is where I'm outside of my kind of comfort zone, basically.

Asim Hussain: We can, lobby right now for what we'd want. I mean, what the ideal solution I would say is not lobby. That's the wrong word. We don't, do lobbying, but you know, you know, There is this overall aggregate goal that we need to achieve. And that's it. I love this. It was Henry Richardson that said that.

I remember it so specifically. I know the person that said it. And I know in what meeting they said it. was such a great statement. We were talking about the SCI. And we were talking about this whole idea of double counting. You know, double counting, when it comes to reportings and all this stuff, it's so significant because you're an organization.

You don't want to, you know, You don't want to be responsible for somebody else's emissions. You don't want double counting. But he said, actually. As a humanity, we want double counting because what does double counting mean? Double counting means that there's more than one person incentivized to reduce that emissions, right?

That's, the advantage somewhat of double counting. And actually, if you think of scope 3 in a way is double, counting across the board because somebody's scope 1 and 2 is somebody else's scope 3. But I actually completely forgot where I was gonna go with this.

Chris Adams: I'll try and rescue Asim before we move to the next story. So, we were talking a little bit about, and bring it back to digital services, right? The, so, I can't name the organization that we did some of this work for, but we were talking about a large media company, we were talking with a large media company, and we were helping them understand this, because some of the jargon, I'm afraid, that people use for this is like an attributional approach, which is like, are they my emissions? Versus a consequential approach, which is, do these emissions, you know, is this activity going to increase or reduce emissions? And when you're looking at media, for example, essentially, if you look at the attributional part and the GSG protocol, what you have to report on, right, you are incentivized to care about the data center more than anything else, right?

Because that's kind of inside your boundary at the moment, or what. Most organizations report as their boundary, right? Not every organization. But if you were to look at the consequential approach, you'd be looking at, yeah, all the things mentioned before. And like, the, this is quite a significant thing because for consuming digital media for consumers, the data center's making, Less than 10 percent, right?

It's tiny compared to the, well, the energy use at least of the, yeah, and uh, if you look at the energy use, it's going to be, you know, more than two thirds is coming from all the stuff at the end users, which is outside of your, you know, it's not on your book, so why would you care right now?

Asim Hussain: Well, it's an externality in a way, isn't it? Yeah.

Chris Adams: So this is, this is why I think it's interesting to see these two things have some interplay, and we, I think we need some wording for this right now.

Like, the one I'm trying to popularize is, you have climate disclosure, Which is one of my missions. And then there's climate response. What was my plan to reduce this? Right? Climate disclosure, you want your missions to be small. Your climate response, you want it to be really big. really, like, massive and ambitious and, like, loud and everything like that.

And that's what I think you can get behind.

Asim Hussain: But I think that's what Scope 4, that's why I think Scope 4 is a really interesting concept. It is, I've forgotten how it's defined exactly, but you know, things like your response, like things that you are doing outside of anything to do with making money or products, like that is what's counted in Scope 4.

But also it's like the impact you're having, the impact your, business and the work that you're doing is having on the fundamental problem of climate change. So, i. e. like, if you're enabling something which helps people to find another oil reserve, that's a massive scope for. That would like overshadow anything that you're producing in this world.

But scope for could also be negative, because if you're doing lots of work that actually sucks out carbon from the atmosphere. You can actually have a, like a, scope four, which a note, scope one, two, three, can't be negative, like, unless you're not doing, unless you're just not breathing or just sitting very still, but like, it can't be, but scope four could potentially be positive or negative and it shows your, like, how you, really like impacting the world, like.

Chris Adams: Yeah, so the thing we could possibly link to, I'm going to add to the show notes the episode we did, the podcast episode, which was all about AI and ML impacting climate change way, way, back in the day, I think June 13th. Oh wow, it's literally two years ago.

Asim Hussain: Is it two years?

Chris Adams: Yeah, we're recording on June 12th.

Asim Hussain: About AI?!.

Chris Adams: No, we've spoken about it before, but this is the, this is one I'm really, actually, I really loved this one. This was recorded on June the 13th, and this included, it was Abhishek Gupta, who's now, like, inside CAT, inside Climate Action Tech, which is a community that we're both part of. He's being funded on a micro grant to come up with some sustainable AI principles, but Will, Will Alpine, he, well, Will Buchanan is what he, before he got married, he was called Will Buchanan.

He mentioned a bunch of this stuff. And we also have Lynn Kaack, who was one of the co founders of Climate Change AI, which is another one of the really, in my view, really fantastic organizations who have real authority in talking about that. They mentioned a bunch of this stuff, so if you want to know a bit more about AI, look at these things from two years ago, because a lot of that still stands, and it's very, it's, I, learned so much from that one, basically.

So yes.

Asim Hussain: I get pinged by organizations all the time going, "oh, we want to talk about, or media, we want to talk about AI and, oh, AI and sustainability is suddenly a problem." And I'm like, we've been talking about it for a long time, like well before chat GPT came on the market as AI being a, hot topic of conversation in sustainability circles.

Chris Adams: Well, maybe the thing we should also link to in then Asim is, A, there's, so the GSF has actually started to endorse some existing legislation in America, to make this a bit clearer. But also, in Europe, I think something like a million and a half or a million euros, there was a tender a few years ago, to basically start researching some of this stuff that you might refer to as Scope 4, basically.

Like Scope 4 is, it's an idea, but it's not, there's no standard around it right now. So there was some work there to start looking at. This to see how you might quantify the positive and negative impacts of various steps. Now, because you've got, this was funded by the Green Digital Coalition explicitly to kind of measure the positive impacts, it's not surprising that every single intervention is only positive.

So it's not like there's a methodology for talking, oh, if we use AI to extract oil and gas, does this mean that we've, you know, there is nothing like that. And I would be very happy to see something like that because those climate emergency, see all this stuff here, right?

Asim Hussain: Well, that's one of the reasons I was thinking about this recently is kind of where we're starting to, well, we've somewhat measured our emissions. I'm starting to like, now that we're growing, right.

Chris Adams: This is the GSF you're about?

Asim Hussain: GSF. Yeah. And I suppose the green web is the same, isn't it? Like if you were to employ just one more person, your emissions would jump up significantly.

And so like, how do you meaning, meaningfully have a plan? Or what do you even say when you're like a very small organization that's just growing? Like, I mean, forget if you're

Chris Adams: If you hire five people, you've doubled your

Asim Hussain: have like you've doubled your emissions and you've set yourself a target to reduce that, what does that really mean?

And I think for, especially for organizations like you and me, like Green Web and Green Software, like if there was a scope for, our scope for is significant. Because the activities that we do as an organization, I don't mean scope 4, our negative scope 4's are significant. Because the act, us existing, arguably, hopefully, reduce the overall emissions of the world.

And so in a way you could, you know, it'd be interesting to look at organizations from their scope 4, because then you're investing in kind of a different component. There could be like startups out there who aren't like in the green software space, but have a negative, if you have a negative scope 4.

The more you grow, the more emissions get reduced from the atmosphere. It's interesting.

Chris Adams: I'm I'll be honest, dude, I'm a little bit I'm sceptical of this scope 4 idea because it very much, when I've seen people talk about it, they use it as a way to either say well this is why it's okay for us to deal with these really damaging things elsewhere, right, it's used to basically draw attention away from other things, or I just don't think like It's not, it's never going to balance out.

It's okay for things to be like, orthogonal, in two different, moving along two different axes, right? And I think it, that makes more sense. What you can do instead, is actually be responsible about the impact that you do actually have. And the, there's a, an approach used by the New Climate Institute, who I think, which I think is really interesting.

What they do, they basically say, look, We're a small non profit, our job is to be at, say, COP 28, 29, 26 or something and essentially work with policy makers to set the rules that end up impacting, influencing ginormous organizations that are way, larger than us. And what they do instead, they basically say, well, we have to fly there, we have to do something like this, we're going to impose an internal cost of maybe 100 euros per ton that we put into a kind of, I forget the term they use, Let's just call it a carbon war chest, right?

A climate war chest. And then they use that to fund other systemically effective things. And, like, this feels like a much more sensible way to do it. And this dude, like, dude, like, you know how the GSF, where the GSF came from, right? The funding for the GSF came from the internal carbon levy inside Microsoft.

You told me this before.

Asim Hussain: Well, actually, no,

Chris Adams: Oh,

Asim Hussain: I didn't, get there. No, there is an internal carbon levy in Microsoft, which is then used, to fund green measures but the actual, like, technically the money from this actually came from a different bucket.

Chris Adams: Ah, dude, I, ah, that would have been such a good story.

Asim Hussain: It's unfortunately not a good story, yeah.

Will Alpine, a lot of his work he did at Microsoft was actually funded from that war chest. He knew how to navigate that space and get money from that war chest.

Chris Adams: So this feels like a more sensible way to do it, right? The idea of saying, well, you're not trying to say, you're not trying to get back into eco heaven, you're being an adult about the emissions, and about that there is an impact being caused, and then you're talking about, well, what is the way that I can do it, which doesn't give you my sugar rush of saying, I'm carbon neutral again, but says, well, we have a societal goal to get to, so let's think about it in that sense.

I'm, this was, I mean, Microsoft is using an internal carbon price. It's not the same as a carbon tax, but it does revolve like, it's a discussion about how you allocate time and money to projects, rather.

Asim Hussain: It's a tax because different departments have to pay different amounts depending on how much carbon they've been deemed to have omitted. So it is kind of like a tax.

Chris Adams: Yes, this is something we can-

We don't want to use the word tax? Oh, is this another topic?

It's not so much that, it's, I mean, it's more that, yeah, you might not want to use the word tax in various places, because certain people are in favor of tax, certain people are not in favor of tax, right? And like, there are loads of ways where people allocate funds to various things inside this.

I mean, like, there's a reason inside Microsoft they use it, they called it a levy, right?

Asim Hussain: Oh yeah.

Chris Adams: But like, we, you know, there are loads of places where we allocate a percentage of some funds to funding something else. So this is why I've stayed away from using the term tax inside this, because in the way I see it, governments get to levy taxes, they get to like raise taxes and stuff like that, but organizations don't necessarily, and there are loads of cases where if you're inside an organization, you're going to allocate this much to kind of keeping your staff happy.

Is that a staff happiness tax? Or if I'm paying for cloud, right, and it's a chunk of my, let's say I'm building a digital service and I'm paying Amazon a chunk, or Microsoft, is that a Microsoft tax? I'm not sure it is. It's, I'm paying for something, right? Yeah.

Asim Hussain: Good point. Now that you've said the word tax like five times, every time you said it, I get a-

Chris Adams: You, sir, have said the word tax. I have not used the word tax at all.

Asim Hussain: Have you not? Okay. Well, I think it, I think anyway.

Chris Adams: I talk about internal, I talk about internal carbon pricing, because this is internalizing the costs that are otherwise being shifted onto society, and that seems a kind of economic and kind of grown up way to think about this.

Asim Hussain: Yeah.

Chris Adams: Yeah, uh, yeah, I'm not using that word either for very good reason, because there's another thing. Anyway, we've totally gone off script. This has been fun, though. Let's, should we go back to where we are? So we were going to talk about CarbonHack, some of the aftermath and learning points from that before we wrap up.

So are there any particular projects? So maybe it might be worth you just briefly summarizing what CarbonHack actually was, and then maybe if we talk about one or two projects. So, two projects that really caught your eye, then we can wrap up after that because we've this, has been, this was going to be a short, one and we're running about 50 minutes already, basically.

Asim Hussain: That's okay. People must have some long journeys ahead of them. Summer holidays coming along. Yeah, no, it was really good. So CarbonHack this year, we focused on Impact Framework, but more specifically focused around measuring, like how do we actually measure software, so it's really exciting. So, I'm really excited to be here where we can actually talk about this.

So, really quickly, Impact Framework is a framework that we've been building here to a very low level. You can measure, print, kind of most things with it, but also provide that evidence of the measurement in this impact manifest file. So there's a couple of, we had a couple of prizes. We had the ones I was very excited about.

I was inspired by all of them, to be honest with you. But the one I was really keen to see what people It was we just threw it out there. We were like, well, let's just see how people respond to this one. And it was beyond carbon. Because we've talked about carbon all the way through this episode.

But, you know, as we know, the sustainability challenge we had to have ahead of us is actually far bigger than just carbon. And we just left it fairly open. Kind of anything that kind of measures the environmental impacts beyond carbon was effectively it. And there was some really interesting submissions.

Like the one that won was, as we might guess, like water, like they, they measured the impact of water. And it also triggered some like really interesting conversations around, you know, the, I know there's been some research in this space, but like, at what level are you measuring it? Are you measuring the water impact?

Like at the data center? Kind of like primary. Primary and secondary, primary water and secondary water kind of was like the concept and it was just exciting. But now, what that means now is that there's now a plugin for Impact Framework where you can just, there's actually two, but you can drop it in and just say like, I just want to estimate my water impacts of my workload.

You can just drop it in and do it. Another, team was from a team called Grasp and I loved it because, you know, one of the things that plugin measures. Actually does a couple of different beyond carbon categories, but the three main ones are death.

Chris Adams: Okay.

Asim Hussain: Premature death, social cost of carbon, plus a dollar value on like the carbon emissions and displacement, which is kind of interesting as well.

Like how many people and all this stuff is like one of the, you have to submit like citations and research to prove and evidence kind of where these numbers are coming from. But yeah, you know, like social cost of carbon, we know like what is the cost of carbon as we take it down through the generations and, you know, all this other stuff

Chris Adams: So that's the harm caused by, on society, by the people, right? And like, this is what the, that was 50 under Barack Obama's time in America, went down to 1.

Asim Hussain: No, I think it's two.

Chris Adams: or 2, and then that's gone up to 190 now. No, I think it's actually back to 50. But there, I think the, it's now shifted to a very, a much, Like, fourfold increase, or nearly a fourfold increase in the US, for example.

But this is like, and in Europe it's around 100 right now, that's the figure.

Asim Hussain: I don't, yeah. I don't believe they use what Biden decided and wrote on an exec. It's it was more, more from research.

Chris Adams: Oh, I'm not saying that's like, that's not Biden saying this, that's like, the group who was allocated to do this work, these are the things they've recommended for this. And that's how they've, that's what the number looks like is being mentioned. I'll share a link to that as well, actually, because that's actually This is, really helpful for the internal carbon pricing stuff we discussed before.

If you're going to think about, okay, how do I create a carbon war chest to fund the work like Will Alpine did, or founding an organization, or doing any kind of systemic work. Yes.

Asim Hussain: Yeah, but I think that's kind of like where it's interesting. cause you know, sometimes I do feel that. We sanitize this conversation too much. Like it's too sanitized. Like we talk about carbon, we talk about this, talk about that. But actually, the reason why we're here is this is there's a lot of human suffering that is happening and will increasingly happen because of the work that we're doing and the work. There is, that value in this whole, whole question.

Are you trying to like add a cost to carbon? How many people will likely be displaced because of rising sea levels

Chris Adams: Always like climate migration being informed.

Asim Hussain: Climate migration. Yeah. so like, and of course there's also research which talks about what is the increased death rate, you know, You have to look at kind of, not just carbon emissions, but air quality and things like that.

So there's these, so these are the impacts. I mean, I, and I know it's kind of, it can be hard for some people to swallow, but it is, you know, something I think about a lot. I actually have a big skull next to me. It's like, I'm very stoic.

Chris Adams: You are full of surprises. Wow, you've got kind of Shakespearean kind of Wow.

Asim Hussain: A little

Chris Adams: Alas, poor Yorick. Wow. I did not see that coming at all.

Asim Hussain: Well, you, it's a stoicism thing.

It's like, if you, contemplate death a lot, you, live life more. But those are two, two, two, two really good plugins there. But there's just been, it's hard to like really pick up on the rest. There's some really good work done to using Impact Framework, not as like, not building a plugin for it, but just leveraging it to examine what the emissions of machine learning is.

 And the important thing about that is, is the output of it is this kind of very standardized manifest file, which anybody can read. You're

Chris Adams: Like an auditable or repeatable

Asim Hussain: audible, readable, and you're just reading this manifest file. And as soon as you look at this manifest file, if you know now how to read these manifest files, you can go, Okay.

So, straight away, I know you did this, you used that coefficient. I get the kind of measurement that you are. I can, that's an interesting number you generated. So that's one of the things we want to drive from it. And the best, yeah, there's some great work from, I've forgotten the name of the team now.

But the, they did work on the, trying to figure out the, using impact frame in a very In a usual way, which is like in logistics, can you drive, can you create a manifest file which represents the emissions of like a fleet of cars, like delivering Amazon packages all over? Can there be a manifest file for that?

And, uh, I think the winner was they built, it's called green. Oh, no, I want to mention two other teams and they both got the same name, which is very confusing in the hackathon. Find a called green and meet green. Oh, I forgot the actual name of the team. It was green something, but they, uh, they built Kubernetes.

So an interface into Kubernetes and the reason why it won, it was just like Kubernetes is so ubiquitous on the internet. And now if you've got, and it wasn't using, there was a separate one for Kepler, but this is very specifically for Kubernetes, independence of Kepler, taking the observations from there.

And you can compute the, environmental impact of that. And I think, actually, I shouldn't have mentioned it, that my favorite solution was a team called Greener Meet. My absolute, I all, I'm sorry, all of the rest of you, you all weren't my favorite solution. My favorite solution was a team called Greener Meet.

Chris Adams: Green Me, as in, like, Greener You, Greener Me, or Greenami, like Konami?

Asim Hussain: Greener Meet, as in having a meeting.

Chris Adams: Oh, Greener Meet, as in zoom, not like.

Asim Hussain: Zoom. And it was exactly

Chris Adams: Okay.

Asim Hussain: And we had an under 18s prize, which we really spent a long time with lawyers trying to figure out, can we do an under 18s prize? But we managed to get one together. And it was a team of 16 year olds from school in California.

And they created, they used the Impact Framework to actually measure The environmental impact of Zoom. So they literally like they, they got Python libraries. They, ran Zoom calls. They could get the utilization. They compared it to Watttime and they even found they were like, they even switched on and off features of Zoom to show like how that changed the emissions profile of your call.

And even I loved it. They found a curtailment that they were having a meeting and cause they're in CAISO. They

Chris Adams: CAISO being Californian Independent Systems Operator.

Asim Hussain: I knew you'd know that.

Chris Adams: And that's basically the grid operators in California you're talking about.

Asim Hussain: And California has a lot of renewables, as you know, so they had a moment in the meeting where they curtailed, which means that the emissions are effectively zero for the call now because they're factoring that in.

It was really good work, and we tried to give them the prize on the call. They, we were delayed. They had to go to class. So we couldn't give the kids the prize, and if we can, maybe we can bring them on this podcast. I'd love to speak to them. I just, it gave me such, honestly, you watch this, And I'm sorry to the rest of the prize winners, but I think this was the best video.

It was the best presented. And I'm just sitting there thinking, these are the, now we're going to, now we're going to, " the kids of today!" - we are old. These are the kids of the future. You know, maybe it gave me so much hope, like these are 16 year old kids and they were doing some incredibly advanced green software, measurement, reporting, zoom, understanding curtailment, understanding like how to measure this stuff.

And I was like, ooh, we've got a, we've got a good future ahead of us. I think, I don't think, yeah.

Chris Adams: Asim, this sounds really cool, but I have to ask, right, why are we relying on a bunch of literal children to do this work when Zoom is a multi billion dollar publicly traded company that does have to report on its own figures like this, and they are doing, Zoom doesn't expose these numbers themselves, right?

Like they will tell you how much you've saved, but you can't see the figures from this. We're relying on children to do the work of essentially publicly traded companies here, right? Right. This feels like a, we, I think this is really, cool. But this has an open question about what they would do maybe Zoom or they might join an organi organization like the GSF and say, well, this is how we do measure this stuff. And, uh, 'cause if not, I think we might need to refer to the work of children until that happens. Because this is just like, I mean, if, this embarrasses them to actually being more transparent about how this is being used, I think, we should be doing it because it's kind of bonkers that you are relying, like, it's nice to have like youth of today, but one of these groups has literally access to billions of dollars of capital and one of these groups is doing it in their spare time and couldn't even accept the thing because they had to leave for class. Yeah.

Asim Hussain: For class. Yeah. Yeah. I mean, that's a fair point. But at the same time, I mean, I want to counter your point for a second as well. I mean, I'm, you know, I have multiple views. Like, do you really want to be waiting all the time? Like, we're just waiting all the time. We're just sitting and waiting and waiting and waiting, for everybody to be transparent about all of their emissions all the time.

If we can just take that power back into our hands, And we why not wait for people to be transparent, but actually like, look, this is a model that I've created. I think this is the emissions profile of your product. I'm going to publish it. If you feel somewhat different, that's the beauty of open source.

If you feel somewhat different, create a pull request and be, as transparent as me. I think in a way we, it is good. That we're leading the way, it is good that the kids are leading that way in the future because it kind of brings power back into our hands, if that makes sense.

Chris Adams: I think what it sounds like you're suggesting is in the absence of information, it might be worth using the age old internet trick of ask a question on Stack Overflow, answer it with an incorrect answer, then hope that people will actually come up with better answers, because that's the tried and tested way to get a good answer on Stack Overflow.

You answer it incorrectly, then someone goes, no, you're getting it wrong. Now I'm doing it, because they're so incensed that someone is wrong on the internet. So this basically, I think what we can use that, we can harness this natural resource and we can say, we're only going to use the worker of these children, and we're for talking about Zoom calls until someone comes in with actual better figures and ideally the organizations with full access to this, like maybe Zoom themselves, or maybe a competitor of Zoom.

So if you'd like to do this, I'm chris@greenweb.org, no, I'm very happy to have this conversation because this does feel like it's necessary. Anyway, oh, blimey, I've just seen the time. Asim, we've, gone a bit over, but this has been really, fun and,

Asim Hussain: Yeah. It has been!

Chris Adams: I, this is, We've got another one of these in a few weeks time.

So, Asim, really lovely chatting with you, nice to catch up, and sorry to hear about the mushrooms, but I guess the Mushroom Kingdom's loss is the Bread Kingdom's gain. So, I should probably wrap up now. Folks, if you've made it this far, we will be sharing links Pretty much everything we've discussed and which you can find at podcast.greensoftware.foundation. And thanks again for listening. My name is Chris Adams and this is Asim Hussain of the Green Web Foundation and Green Software Foundation respectively. All right. Thanks, Asim, mate. Take care of yourself. Okay. Bye.

Asim Hussain: Cheers. See you later.

Chris Adams: Hey, everyone. Thanks for listening. Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts.

And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser.

Thanks again, and see you in the next episode!




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]]> The Week in Green Software: Building Green Software 2 The Week in Green Software: Building Green Software 2 Thu, 30 May 2024 08:00:00 GMT 49:27 815696y0 false https://podcasts.castplus.fm/e/6nrr4rmn-the-week-in-green-software-building-green-software-2 68dc7d14d5c8a1f9b326f65c 68dc7cef46a2532cdd8d1c23 In this episode of the TWiGS, host Chris Adams welcomes back Anne Currie, Sara Bergman, and Sarah Hsu, authors of the book Building Green Software. They dive into the latest updates and hot topics at the intersection of sustainability and software engineering. The discussion highlights the importance of making software and hardware more efficient and explores cutting-edge topics like serverless computing on Kubernetes with WebAssembly, the circular economy for electronics, and the potential for dynamic pricing in cloud services based on renewable energy availability. The episode emphasizes the ongoing energy transition and the need for innovative solutions to reduce the carbon footprint of technology. 72 In this episode of the TWiGS, host Chris Adams welcomes back Anne Currie, Sara Bergman, and Sarah Hsu, authors of the book Building Green Software. They dive into the latest updates and hot topics at the intersection of sustainability and software engineering. The discussion highlights the importance of making software and hardware more efficient and explores cutting-edge topics like serverless computing on Kubernetes with WebAssembly, the circular economy for electronics, and the potential for dynamic pricing in cloud services based on renewable energy availability. The episode emphasizes the ongoing energy transition and the need for innovative solutions to reduce the carbon footprint of technology.

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TRANSCRIPT BELOW:
Anne Currie: That is the nature of renewables, is that they are variably available, and we just have to, we have to take advantage of that, not fight against it, and not constantly wish we were in the fossil fuel age.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to the Green Software Foundation podcast, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. In this episode, we welcome back the authors of the book, Building Green Software, Anne Currie, Sara Bergman, and Sarah Hsu, for an episode of This Week in Green Software,

our roundup of what's happening and hot at the intersection of sustainability and software engineering. So Anne, Sara, Sarah, I know that you've been on the podcast before, but I wanted to just provide a bit of space for people who are new to this to let you introduce yourselves. Anne, it's okay if I give this floor to you before we run through the usual roster?

Anne Currie: Yeah, thanks Chris. So, my name is Anne Currie. I am the CEO of a training company, Strategically Green, and I have been in the tech industry for pretty much 30 years now, and I am one of the co-chairs of the Green Software Foundation Community Group.

Chris Adams: Cool. Thank you, Anne. All right. Sara, is it okay if I hand over the floor to you next?

Sara Bergman: Sure. Thanks for having me back. I always enjoy being on the podcast. So my name is Sara Bergman. I am a senior software engineer at Microsoft, where I work with the Microsoft 365 products, which is very fun and exciting. I'm also one of the co-authors of the book Building Green Software. And other things that are new in my life is I'm recently back at work after maternity leave.

So I'm still figuring it out, you know, what is life now?

Chris Adams: Well, welcome back and congratulations on, yeah, the new instances of Sara, I suppose. Yeah.

Okay. And moving on to other Sarah. Sarah Hsu, if I give space to you to introduce yourself.

Sarah Hsu: Hi everyone. It's nice to be back. So my name is LSara Hsu. I am an SRE working for a financial institution. I'm also a project lead for the green software course for the GSF and similar to Anne and Sara, we recently just published a book by O'Reilly called Building Green Software. Very excited to be back.

Chris Adams: Cool. Nice to see you again. And Sarah Hsu, we met in person for the first time when you were in Berlin delivering a keynote for, was it CamudaCon, the conference there?

Sarah Hsu: Yes. A Process Orchestration Conference, which is exactly what we need, right? Because their motto is automation. And automation is the foundation of modern software systems. So...

Chris Adams: thank you for that, Sara. I was also pleased for us to both realize that we had arms and legs and were more than just a YouTube square, basically, or a square in a Zoom call. So yes, that was lovely as well. Okay, for folks, if you've never been listening to this podcast before, my name is Chris Adams. I am the executive director of the Green Web Foundation.

It's a Dutch nonprofit focused on reaching a fossil-free internet by 2030. I also work as one of the policy chairs of the Green Software Foundation Policy working group as well. And before we dive into some of the stories, that's the format of this show, we'll be sharing links to the show notes and all the stories that we do discuss.

So there'll be on GitHub and Markdown, what else, for you to look through. Alright, so the format we normally do is do a round up of news stories, but you three, because I have the pleasure of speaking to people who've written a book about green software, we figured we'd make it a little bit more interesting for listeners and touch on some of the topics in the book, so we've got a bit of a kind of nice way in to cover some of that content. Does that sound okay to you folks?

Anne Currie: Great.

Chris Adams: Alright then, so the first story that we have is actually about a new project called SpinKube. So this is hyper efficient serverless on Kubernetes powered by WebAssembly. Now a few podcasts ago before, we did a whole story all about Wasm, WebAssembly, and why it's an interesting piece of software. But, previously, back then, it was only available on Nomad, which is a similar scheduling tool, but not the same as Kubernetes. And this story, as I understand it, is basically the idea of providing some of these kinds of tools for Kubernetes, the most popular scheduling tool for this. This basically means that if you thought that was a cool idea, you've got access to it yourselves. And Anne, I think I might hand over to you for this, because this really touches on some of the things you spoke about in your book about operational efficiency versus coding efficiency. Maybe I will hand over to you here.

Anne Currie: It really does. So Wasm is something that I find absolutely fascinating. It's both, as you say, both from, at the moment, the operational efficiency perspective, because they're really focused on how you can bin pack as much work onto each machine as possible. So you can reach that magic 50 percent utilization number and then get even higher, which is very hard to do without a tool to help you.

Very hard to do. But beyond that, what also is, is they have ambitions even further, which is that they want to start actually optimizing the code that you write in WebAssembly as well, so that under your feet, it will become more efficient. And we all know that writing efficient code is really, really hard.

There's a, there's a huge impact on developer productivity. And we, we cover this quite a lot in the operational efficiency and the code efficiency chapters of our book. And one of the senior engineers, Danielle Lancashire at Fermion, who's behind all this Wasm, one of the, one of the groups behind all this movement in Wasm came and talked to me, talked for me at a conference in London a few months ago, and she was actually talking about code efficiency, the code efficiency improvements that come from Wasm.

Now they're a bit down the road. The moment they're focusing on operational efficiency, but I really like to see a platform with a vision for, because in the end, we're all going to have to run on much, much less power at times when the sun isn't shining and the wind isn't blowing. That is the nature of renewables, is that they are variably available and we just have to, we have to take advantage of that, not fight against it and not constantly wish we were in the fossil fuel age.

And so I really like to see a platform which is thinking about operational efficiency now and also thinking about code efficiency down the road. So I was, I like Wasm from that perspective. I think they're doing a lot of nice work.

Chris Adams: Yeah, we have some nods here. I just realized when I spoke about this story, I dived straight in without really explaining why some people find this actually exciting in the first place. So when we spoke about things like, say, serverless, and one of the, as I understand it, one of the key things behind this tool is it's like a very fast version of a serverless platform that spins up and down quite quickly.

So we've had previous generations where you might need to have a bit of a wait before a piece of software can start running before you can really use it. And this is like one of the key things that Wasm has made available. And that allows for, like you said, time, it basically means that there's less wasted time, but it also provides, opens the door for newer, more efficient platforms like this. Okay, before we move on to the next story, Sarah, Sara, is there anything that you want to add to this?

Anne Currie: Actually, before we move on, I'm going to just hark back to something that Sarah said just before this, which is that the benefits of modern ops and automation is that a big part of automation, of modern automation is the ability to spin up quickly because things like auto replacing, just having stuff sat around waiting and for you to fail over to with autoscaling, which is vastly more operationally efficient, relies on things being able to spin up fast.

If you can't have fast instantiation, you can't have a lot of these modern automation, this modern progress in automation. So yeah, it is a really good thing.

Sara Bergman: Also, I think one thing that has been said before and can always be said again is the benefit of the sort of platform that makes it easier for people because people want to be green, but sometimes it's a lot of work and anything that can make it less work to be greener is a great thing and should be celebrated.

And on the ops side of things, not every software developer or software person is like, highly interested in that. There are some people who love it and like go all in, and there's some who like, "I just want to write my code and like deploy it in some way to my users." So therefore things like this is so important to, to help bridge that gap in a green way.

Sarah Hsu: Yeah, yeah. I a hundred percent agree. Like software engineers, we're inherently lazy people, right? Like none of like, not speaking for you guys, I'm not a security expert, but I know how important a secure application is, right? I'm waiting for this tooling framework and best practice from the security people. So I think that's the gap we really trying to fill, that "how do we make everyone else have that ability to be green at their fingertip without having all the knowledge of like being really green?" And like, yeah, I feel like we can talk about operation efficiency to... I mean, to, 'til tomorrow, yeah, because it really is the lowest hanging fruit and people don't realize how many things we're already doing or have like knock on green benefits, like exactly what Anne said about reliability and resiliency.

Yeah. They all like come down to like automation and how do you utilize automation? Right. Anyway, I think we should stop. We can always come back to it. Let's get through all the other stuff. And then we come back to talk about Green Ops.

Chris Adams: Okay, all duly noted. All right, so we've spoken a little bit about operational efficiency, which is the running of servers. Spoke a bit about coding efficiency, which is what a lot of people might reach for first, or people wouldn't typically think about. And, but there's other ways you might talk about this.

So let's move to the next story. Sara, I might actually ask you about this one. So this one is a story from Grist magazine, which basically is talking, it covers the staggering quantities of like, Transition metals that we're currently throwing out when we could be mining them. And while we'll share a link to this in the show notes, one of the key things that the four of us can see right now is a chart showing some of the minerals that we hear associated with a transition away from fossil fuels to greener forms of power. There was a report by the UN called the Global E-Waste Monitor and they've shared some of these stats for the first time. And one of the key things is basically that we are throwing out something in the region of 62 million tons of electronics. And when you look at the actual mineral content of that, in some ways it's actually comparable to the demand for new kinds of metals that we have.

So the charts that we'll link to here, things like, say, copper, it's not that, it's quite similar to each other. Some things like cobalt or neodymium. These are, we're basically looking at the amount of minerals that could be circular. Some cases are not the same. So things like lithium, for example, we still, there's still a lot of demand and there's not nearly enough that's in circulation. But this feels like this provides an interesting flip side to the whole discussion around what we do with our stuff. And I think the term that I saw in the book, which was hardware efficiency. So I kind of wanted to like, see what you folks thought about this, in particular Sara, because this is one thing we spoke about in a previous podcast, like, this feels like there's more than just "hold on to your kit," for example, there's maybe a chance to talk about things in a more circular fashion.

Sara Bergman: Absolutely. And I think the key word there is circularity. E-waste is the fastest growing waste stream in the world, which is saying a lot. And it is growing for several reasons. Like we consume more, we have more devices, like more devices are smart devices. My, like kitchen fan has an app. I don't know why it needs an app, but you know, there, there are an increasing amount of what can be classified as electronic, not what can be classified, but what is electronic.

I see the same with like toys and things. So that's one thing. And also we crave the newer and the newer things and things have shorter and shorter Life cycles or the use phase is short and shorter. So it's, it is fast growing and before, in a time before time... 

no, but when it was not the case that it was so fast at growing, we could maybe afford to mine everything.

But now when the consumption is so high and the throwout rate is so high, we really need to start thinking in a circular way. Because, in the end, there is only so much metal in the ground and our earth is finite. It's not something that grows back, at least not very quickly. So having this thinking, I think it's going to be, it needs to be a game changer.

And I know several countries have adopted what's called the right to repair, which is basically that you 

Chris Adams: is big thing in Europe. 

Sara Bergman: Yeah, it's a big thing in Europe. China has, no sorry, not China, India has a similar legislation. New York has a similar legislation. And that's really good. And that's about what you said, holding onto your kit longer.

But that's really only one side of it. I think the right to recycle should be equally hotly debated, perhaps even more hotly debated. Because we, the hardware industry has been forced to make hardware kind of hard to recycle because we are demanding better, yet smaller devices, or bigger screens, but thinner, lighter hardware.

And that's really complicated to produce. And it makes it really hard to recycle. Also in this article that you linked, let me find the number. But, what they said, the percentage of ethically recycled waste was also staggeringly low. It's like a business as usual case was only 20%. It's, well, you're all going to have to go to the show notes the article for yourself to find the percentage.

What I'm meaning to say is that this is incredibly important and something that is an additional dimension to the conversation beyond holding on to your hardware. It's also the reuse and recycle part of the story.

Anne Currie: It's a sales feature as well. It's recycling and it's a sales feature. I just bought a new phone, new, it was an old Pixel 7a, because my previous one had run out of security patches, the classic way that you have to throw away a working piece of kit. So I was quite annoyed because I was having to throw away a perfectly working piece of kit.

So I bought 7a. But the 7a sent me all the stuff to do a, to send my old phone back, and I got 25 percent off and I thought, "well, actually, that was pretty good." I actually felt quite good about that in the end. I thought, well, it's going to be recycled. I got 25 percent off. All right. That was, it was, it felt like a feature that they were offering me.

Chris Adams: Well, this is, maybe this touches one of the key ideas that, if you're moving away from linear economy, once you've, dug these minerals up once. One of the ideas is that once they're in the kind of sphere that we are in, right, you don't need to have, it's not that going into the atmosphere like say fossil fuels for example.

You can have these things circular. Now, this is one thing that we don't have quite the policy support for yet, but this is one thing that we could definitely be seeing more of in future, and this feels like the direction we might be heading towards if things work out, basically.

Sarah Hsu: Yeah. I also think it's an age old thing, right? We should find the thing that fits your purpose. It's similar to say, we want to find the VM instance that fits my purpose. I feel like people shouldn't want the latest and the greatest. Like my laptop is not that great, but I don't do that much on my laptop.

So like, I think people also need to start having that mindset. Like, "Oh, I don't need like the fastest, like the most, you know, cores, power laptop, choose to go on Zoom call, do a little bit of coding." And yeah, I think that's another bit of the things that also really fit into operational efficiency, you know, where you want to find things that fits your purpose.

And I think that applies to hardware as well.

Chris Adams: I agree, actually. All right. Thank you for that, Sara. All right. So, we're going to move to the next story now. This is a story from Carbon Brief. And, Anne, I'm going to hand over to you for this one because this is one thing we discussed before. So, this is a story from Carbon Brief who basically mentioned that earlier on this month, no, last month, actually, now.

The UK's electricity grid operated for a whole hour with just two, almost no power coming from fossil fuels. Now this is a record low for the country and this hopefully is a sign of things to come. So Anne, I know that we discussed this here, but maybe you could talk, touch on like why you found this interesting and what it's kind of telling us as software engineers perhaps.

Anne Currie: Yeah, I mean, obviously this is an incredibly positive story, and it's actually the direction that almost everybody's going in. If you haven't had a play around with electricitymaps.com to have a look at how green all the grids of the world are, it's absolutely fascinating. You'll learn a lot. And if you step back in time, it shows you what the carbon intensity, the average carbon intensity is of every grid in the world that they can get data on, which is a surprisingly large number of grids.

You'll see that if you, and then you can go back in time and step forward and you'll see that everything is becoming greener and greener. The energy transition is happening. You know, it's not just something that will be happening in the future. It is happening, but it won't be easy. Oh, I saw an excellent Uruguay is apparently completely a hundred percent green for eight solid months this, this year.

So there are, but that is because, but the interesting thing on this is that every country does it in a different way and every country being the green intensity of their grid, it varies over time in different ways. It depends what you're using to generate the power. So places like Uruguay have loads of solar and wind, which is fantastic, but they also have tons and tons of hydro and the hydro is used to smooth out the times when the wind isn't blowing and the sun isn't shining.

In the UK, we don't have so much in the way of stored, so it's great if it's windy, and if it's sunny, but mostly in the UK, so if it's windy, we do an awful lot of offshore wind, rather, not onshore wind, but offshore wind.

Sara Bergman: I thought going to say, we don't do a lot of sun here.

Anne Currie: We do not do a lot of sunny here. It's actually, it's not as bad as, I've got solar panels, and through the summer, it's actually, well, through half the year, that will cover all my household usage.

It's oddly much better than you'd think, but nonetheless, in the UK, it's mostly wind. But there's not so much in the way of storage. So it's, the UK is an absolutely excellent example of we're going to have to get good at using power that's variably available and cheap when it's available and either demand shift or demand shape so that we are using the renewable power and using less of non renewable stuff.

So it is absolutely fascinating. It's a great story. It's a really good move forward, but not all grids are identically green. They all have difference. Different ways of doing it and results.

Chris Adams: Alright, I've got a pop quiz for you then, because you're talking about the fact that the grids change, and sometimes they're going to be greener because there's more clean generation on the grid versus other times, right? We know in the UK, for example, there's groups like Octopus Energy, who basically will change the cost of your power, depending on the time of day.

In some cases, they even pay you to use this. And we see this in other parts of the world. When do you reckon we're going to start seeing cloud providers do this in the net? Because we know it happens, and we see this stuff, but none of these signals are passed on to software engineers yet.

Anne Currie: No, and every time I'm speaking to somebody from a cloud provider, I ask about it. Normally the people are quite green people and they want it to happen, but I think it'll be a way off. Now, it's, it's interesting. I spoke to somebody who was working for a cloud provider, but was a software engineer who used to be in the insurance industry.

And he was saying to me, "Oh, well, you know, I can totally see it being to the advantage of the cloud providers to start doing dynamic tariffs, time of use tariffs, because then they have another product they can sell." So the people who can't do it, they will sell them insurance with a price cap. And obviously they'll charge a load of money for that.

If you know, but, and then that is a product. Now, well, is that what they're gonna do? They'd be quite sensible to do it. It's, you know, the cloud providers are very good at making money and it won't be cheap. So I would love to see it, but I think it's going to be years before it happens.

Chris Adams: Okay, so you're thinking three years at least for you, Anne, yeah? Okay. Any other takers for anything faster?

Sara Bergman: Yeah, no, I think, well, working for a cloud provider, I just want to say, I don't know. But what I think all, at least the major cloud providers are fairly open with their use of PPAs, so Power Purchase Agreements, as a way of, yeah, meeting the green energy needs, because they have data centers sort of where they need to have data centers and then use PPAs to, to handle their Scope 2 emissions.

And I think because all of them at least are so seemingly tied to those agreements, it is a contradiction between those and billing a customer for actual usage. So I think it could be like a complicated thing for them is what I suspect. But I also think it's something that customers would really appreciate.

So I'm hoping it will be sooner, but no, I'm with Anne.

Chris Adams: Okay. Years away. And Sarah, you've got something to say, it looks like.

Sarah Hsu: Oh, I was just going to say, probably not in the near future, but we just need one person, one cloud provider to do it and everyone else be like, "right, we need to do it too."

Chris Adams: Do you know what? I had a conversation from people outside of the big three, the big cloud providers. So, in Texas, there's a company called Build AI. They've been doing some work to basically, they'll provide you computing, but at certain peak times, you don't have access to it. And as a result, they're able to have much lower costs for this stuff.

So I'll share a link to that. And there's a company called Saluna, also in Texas as well. And what they do is they speak to generators, people who run like clean energy. And they'll basically say, we will give you a floor price under which you will never, you know, we'll always get give you something like that.

And using that, they are able to provide these kind of services. So we are seeing this start to develop, just not with the big providers. And we'll, I guess the next question will be, at what point do these new providers get bought by the big providers to protect the margins? Because that might be the logical thing to do if you have these kind of funds. All right, let's move on from that, because that was a, we spent a bunch of time talking about carbon awareness, and there's a few other stories that we have up here.

Anne Currie: But carbon awareness is the most important thing.

Chris Adams: It's definitely

Sara Bergman: And operational efficiency.

Chris Adams: Alright, ok, so let's move to the next story then. So, we have another story here, this is actually probably the nerdiest story we have here. This one is actually a link to an issue in the OpenTelemetry repository. Basically, there is a standard called OpenTelemetry which is designed to make it easy to understand what the, I guess it's to make Infrastructure Observable, and Sara, I might need some help from you on why observability is important.

But this one is basically put forward by, I think, one of the people who's inside the Green Software Foundation to start agreeing some sustainability metrics to expose in all the kind of tooling that we currently do have. This was really interesting because this feels like, A, this is something that I saw discussed in the book, but also for people who are not familiar with OpenTelemetry, OpenOps, or even Observability in general, is anyone who might want to go, like, enlighten us or at least give us some points about why this might be interesting from a Green Software perspective? Sarah, I might hand over to you because you wrote part of this chapter for the book, I believe.

Sarah Hsu: Yes. So I guess observability was born out of necessity because things are so complicated now. Microservices is made out of our world. And sometimes one requests have to travel like the entirety of a street of like a hundred houses before they actually reach a part of their journey. And it's really impossible to figure out what and where things have gone wrong, right? Metrics is for when you know something is going to go wrong and then you set up a metric to monitor that. But then in this unknown world, it's really hard to figure out who is going to break, for example, like, oh, I can't, I forgot that guy's name, but someone from Honeycomb.

You guys know how much I love Honeycomb. He gave a really good example. Like if you're like a, like an iOS developer and you support like 10 different phones in 10 different countries, and suddenly one version of the phone in this country is going to break. How do you know to monitor that using metric?

So I think that's a really good example, like why we need observability and observability borrowed its thinking from control theory, which is like, we are trying to understand the internal state of a system by just looking at its outputs. And outputs here are like telemetry. And telemetry, you've got logging, you've got metrics, you've got traces.

And so basically OpenTelemetry is, it's like a set of framework. It's about the creation and then the management. All of those telemetry, it's actually not a backend. So it's not like Prometheus. It's not Jaeger. It's not like Grafana cloud. It's just a convention, which is really good. I remember when we were all in QCon a few years ago and Daniel was talking, Daniel from Skyscanner was talking about their OpenTelemetry migration plan or something.

They basically went from like 300 different components down to 150 because they used OpenTelemetry as a standard, as an auto collector. And then that's the way, become one stop shop for all the telemetry. It's like, you don't need developers sending three different telemetries to three different backend systems.

And then one big thing, everyone's being on call here. One big thing we find difficult is how do I context link everything? Why I need to basically, "oh, this logs happen at 1:21 PM. Right. Let me go find the traces that also happen. But what if time shift, right?" And sometimes like Something is in a different time zone.

So anyway, that's a massive rant about why we need observability. So it's, I know, sorry, it's my job guys. And then I guess one big thing we talk about in the book is that green software needs to be ready with observability. Like we need to be with it. Right. So for example, in this complicated microservices world, we want to be able to know which component, which process.

It's emitting the most carbon. Right. And that's where we want to be. And we need OpenTelemetry. We need people like OpenTelemetry to help us get there. So it's like absolutely amazing to see so many people are standing up and then it's like, Hey, we should add this like semantic thing.

Chris Adams: I'm really excited about seeing this because I've used Honeycomb to understand what was broken about some applications I've been running before. And I was always a little bit wary about saying, well, okay, I'm not sure about, I want to be totally tied to one provider. And this here seems, and we have seen some providers who have started to make some CO2 figures for this. There's, confusingly, a, so a company called Sentry that do provide this and they even propose like a HTTP header for CO2 per request, right? But to see this at a kind of standard level, this feels like it might make it easier for a larger set of providers to come up with and at least make it easier to kind of see some of this because I think this is something that came out the book was that we, you need to be, you need to observe this, but it's often quite difficult to get the underlying numbers from some providers, and this is something that we need a bit, we need some more work with, or we need some progress on, basically.

Sarah Hsu: Yeah. And I guess one big thing about OpenTelemetry is that it, because it is just a framework and it's vendor neutral, I think sometimes people forget how important staying vendor neutral is. So yeah, I think that's why it's so important that we locked in with OpenTelemetry now, because it is going to be the solutions for this observability space going forward.

Sara Bergman: Absolutely agree, because I think sometimes when people talk about software, they think of a specific type of software that runs in the public cloud. But that is not the entirety of software that exists out there. There's so much software who runs on different places and nowhere near the cloud. And that software is equally, equally important.

So, having something that fits more than just the one most popular scenario is incredibly important. And, and I just want to say that, I think this is important because I don't think anyone should get away, quote unquote, from doing sustainability work because, oh, I don't know my number. That should not be an excuse.

Anne Currie: No, I totally agree. But, but even if it's impossible to get your number, there's still so much you can do without the numbers. as well. And yeah, it's amazing how people go, "Oh, I can't get the numbers to get" well, just work on your operational efficiency.

Sarah Hsu: Yeah. Yeah. And like one of the biggest takeaway I gave at the CamundaCon in Berlin was that you can think of BinOps as a really natural evolution of DevOps and FinOps. FinOps is the optimization with money. We basically need to do the same for sustainability. And there are so many things we already can do and yeah, people should really pack themselves on their back because they didn't realize the transition is going to be much smoother than they thought.

I 

Chris Adams: Okay, I'm glad you mentioned FinOps actually, Sarah, because this is talking about OpenTelemetry, some ways to expose some of the figures into this. As I understand it, there is a is it focus, which is the standard that the FinOps groups are pushing for trying to come up with like standardized cost, cloud cost figures, because I understand, as I understand it, there are some people pushing to put some CO2 figures in those as well, so that you'd be able to get some of these ideas from not just billing, but also from operational figures.

So, because in some cases, one view will give you a slightly different view than the other, for example. We've got this, and we're just moving to the last of the stories that we have today. One of the largest providers has published their sustainability report. Microsoft published their 2024 environmental sustainability report in the last week or so, and there's a lot in it, actually.

So, they're one of the large providers, and they have various commitments about getting to net zero by a certain times, but there's actually quite an interesting amount of data for the nerds inside this. Sara, I might hand over to you, because I suspect that you've been poring over this in quite a lot of detail, actually.

Sara Bergman: Thank you. Yeah, I think I love it when this report comes out every year, because even though I work for Microsoft, there's no way I can keep up with everything that's Like, it's just too big. So I always learn so much. But I think for me, I'm a measurement geek, in case that wasn't obvious already.

So some of the things I thought was super interesting was firstly, that the PUE of the data centers was published. I don't think Microsoft has ever published that number, but they said that this year, the design rating of the new data center is 1,12. And I think that's pretty impressive. That's pretty close to 1, which is, you know, the dream where all the energy that goes in goes to actual compute and to know other resources.

Chris Adams: I'm glad you mentioned this, because I wanted you to actually just, for folks who are not familiar with what PUE means, maybe you could just, like, expand on that a little bit, and say why people, why you might even care about that number being high or low, for example.

Sara Bergman: Yeah, absolutely. So P U E stands for power usage effectiveness. Or if, yeah, effectiveness. Yeah. I always mix up the E words. Power usage effectiveness. And I think in the sort of early days of green software, this was the number people spent a lot of time focusing on. It is a number that's higher than 1.

So it measures all the power that goes into your data centers and how much goes to actual compute. If, if all the energy or all the power that goes into your data center goes to meaningful compute operations, your PUE = 1. Only half of what goes into the data center goes to actual useful compute.

Then your PUE number is 2. 

Chris Adams: I see. 

Sara Bergman: There's been a race to get to one, where you waste basically as little power as possible.

Chris Adams: And when you say little power, you're talking about, like, keeping the machine from overheating and melting, or stuff like that, right? Like keeping cool enough?

Sara Bergman: Yeah, cooling is one thing that consumes a lot of extra power. It's probably the biggest consumption of power, but they're like, they're lighting in the data center also, that's also consumes power. So anything that's in there that doesn't contribute to compute, go into this rate.

Chris Adams: Do servers need to see? I mean, they don't need to be in

the, like, it's not like they have 

Sara Bergman: there people who work there.

Chris Adams: Okay, that's fair enough. Okay, so this is, so in this report, they're publishing this information, which has traditionally been a thing that you do not see, all right?

That's one of the key things from this.

Sara Bergman: exactly. And I thought that was interesting. And in the sort of early days of green software, this was a number of people obsessed a bit about because I think there was like this, I don't want to say miscomprehension, but this notion, at least, that if you just got the PUE to 1, all the problems would be solved, like, magically, everything would be so efficient that we would not have to care about everything else.

People were, like, hyper focused on data center design. And I'm not saying it doesn't matter. Of course it matters. Of course it's important. But it's an area where, over time, we paid down massive knowledge and spend time on. And in the end, that's only one side, because if whatever compute operations you run are vastly inefficient in themselves, it doesn't matter if all the power going in goes to compute if the compute is wasteful in its matter.

So it is one part of the puzzle, but it is not the most important. But I'm glad to see it getting so close to one. I think it was very impressive.

Chris Adams: Yeah, this is interesting to see this because there's a project called Realtime Cloud which is run inside the Green Software Foundation and one of the key things that people are looking to do is figure out the PUE for all the different regions from all the different cloud providers right now. And they've got a carbon intensity figures for each hour of compute. But now they're able to use, because there's this information published at a data center level or actually a region level, you've got a level of transparency that you don't see from the other two so far. I think. Google might be sharing some of this, but we still have a kind of like patchy spreadsheet listing this stuff.

What I'll do is I'll share a link to the spreadsheet for this, because this is one of the things that once you have this, that should allow you to start being able to kind of optimise what the carbon intensity and of the compute you're using based on these kinds of figures here. This doesn't touch everything though, and I wanted to just leave the floor open. And Sarah, is there anything that caught your eyes on this, because there is more to this report than just PUE, for example.

Sarah Hsu: Don't want to be the bearer of bad news, though, although I remember hearing this from Anne, so I'm just their messenger. I'm not the bearer. I remember you and Anne mentioned that because PUE has got a lot of branding around it at the moment, and people know that it's like the sort of like the efficiency factor for a data center.

So like people then don't want PUE to go up, even though they need cooling. So they end up using water to do the cooling. So they actually trying to get PUE to as little as possible, closer to one as possible as Sara mentioned, but then they compensate that with using water and water is another like sources that's just as sacred.

Is that how you say that word? Yeah. But anyway, I will like, and talk about this because I remember I heard this from you.

Anne Currie: Yeah, no, as you say, we do fetishize PUE a little bit. It's a measurement and everybody loves to meet it. But the other thing is it's not a carbon aware measurement because it's just a flat number. So it doesn't say, "Oh, well, actually, you know, this is what we did when the sun, when the carbon intensity of the grid was high.

And this is what we did when the carbon intensity of the grid was low." And actually you want, we need behavior to change. Between those two, two times. So it's nice, you know, it tells us something, but we need to make sure we, with all things, you know, it's context specific. We, it doesn't provide a lot of context and we might need more context.

Well, we will need more context in the future.

Chris Adams: Okay, so better than nothing. So two cheers rather than three cheers. So good to see this. But as Sara mentioned, I believe the technical term is impact transfer when you go from one factor like energy to impacting water, for example. And in some places, you may be, you may have data centers sited in regions of water stress or pulling power from like local aquifers and things like that. If that's used for drinking water or agriculture or things like that, that's not really ideal. But there's a big discussion about what happens with this water. In some cases, it might be, I think the term is withdrawn and waste and consumed. So you have water that might be taken in and some of it might be discharged, given back.

But if it's at a different temperature, that might not be ideal for fish. And stuff like that. Or if it's consumed and like just evaporated away, that's not ideal either. So there is a whole bunch here. And maybe this is one of the things we need, we should be hoping to see, or we should be looking to see more of. There's, okay, if we've got data center level PUEs, should we be thinking about water of usage effectiveness and other things like that?

Sarah Hsu: I think there's one, I don't know what to call them, but I know they're called Scaleway. And I think you did a thing for them that they, I think now they have like a landing page where you can see the PUE and the WUE. So I think people are start doing it, but again, like much smaller scale provider instead of like cloud providers, 

Chris Adams: I'm really glad you mentioned this actually, Sarah. So, the thing you're referring to, we'll share a link to this. So, Scaleway, they're basically one of the largest cloud providers in France, and they also operate in a number of other countries, but they basically do have these dashboards. But the thing that's, the thing about these dashboards, they were initially created by by Facebook in 2014. So there is nothing stopping every data center exposing these numbers from a technical point of view. And like, if you're looking at a policy level, what they have there is totally something that could be done. It's something that we could be seeing if people chose to be disclosing some of that. And we might see some of this come out as a result of new laws that may be landing or that have already landed in some countries.

For example, Germany has this. But there are more on the horizon, actually. Anything to add from there, perhaps, Anne, you're about to just say something, yeah.

Anne Currie: I am. So, so Scaleway, I think it's kind of related to stuff we talked about earlier. I was on this podcast with Scaleway where I learned a lot of stuff about what they were up to. And there's some interesting things that they're doing totally away from PUE and WUE reporting. So, in Europe, there's a group called the SDIA, which is an acronym.

I can never remember what it stands for. 

Chris Adams: Sustainable Digital Infrastructure Alliance,

Anne Currie: Excellent. And their raison d'etre is to try and get European data, non hypercloud data centers to start learning from the American hyperclouds and actually delivering some of those services. And one of the, one of the things they're pushing for is actually starting to orchestrate and offer services on your data centers that will provide operational efficiency and code efficiency in the same way that the hyperscalers do. And that generally, you know, your bog standard data centers don't. And that's a really, that's a really bad thing about bog standard data centers.

They don't do this because it's very hard to do. If you can get your data center to provide it, then that makes life much easier. And interesting, on the podcast that I was on with Scaleway, they had a, one of their engineers on who was working on their serverless offering. So a bit like Wasm we were talking about earlier, services that improve operational efficiency and code efficiency provided through your data center. That's what we want. That's where the cloud is good with being, is potentially green, but only if you adopt them. And all these things, if you just lift and shift into the cloud, if you lift and shift into good data centers like Scaleway, you don't get any benefit really, or don't get much benefit.

You need to be moving over to efficient services.

Sara Bergman: Yeah. And I think that was another thing that Microsoft mentioned in the sustainability report that they are working on several ways to increase resource utilization. And just to mention too, they have these power aware workload allocations, and they're also like smartly allocating CPU cores for internal workloads.

Because don't forget Microsoft is also a software provider, not just a cloud provider. And I thought that was really good that they called out because sometimes when we say things like this, yeah, the hyperscalers are good at this, people question it, like, "oh, is it really worth their time?" So I thought it was good to see them calling out specific things that they're doing that can also hopefully inspire others to do the same with their data centers.

Chris Adams: So here's one question I have there about this report. So, yes, it's very good that you see some transparency here. And yes, there are definitely cases where moving to the cloud can be more, can be greater. But we've seen, both Microsoft and Google and Amazon, their emissions climb. Year on year. And this year, we're seeing emissions 30 percent higher this year than last year, which is not going to make it easy to get to net zero.

And this is one of the key things that Microsoft themselves have been talking about, saying, yes, this is a real challenge for us, and a significant chunk of our emissions are in our supply chain. Scope 3 is the largest source. So this is one thing that I think that it's, it feels like when we're talking about this, green software doesn't really have that much to say about right now in terms of the actual creation of data centers. And this is something that I kind of want to open the floor up, like how do we actually deal with this fact that things can be more efficient, but still growing in absolute terms? Is this something that we can be doing or do we need to be having discussions about absolute resource limits, for example, and things like this, or how are we going to get here? I'm going to hand to Anne because you've got something, but you're about to say something I think.

Anne Currie: I am always about to say something. I think this is a really, the whole, the whole thing of, you know, the degrowth, people shouldn't be allowed to, we shouldn't ban people from doing things, it's a very unpopular argument and it is why people, you know, are not, even though everybody now believes in climate change, they don't want to have to give up all the things they want in life.

Now, efficiency can often really deliver you the same standard or less. I mean, it's not all, I mean, Jevons paradox, we all know about Jevons paradox. The Jevons paradox is you, you, things become more efficient and where there's untapped demand, where people really wanted to use them and they couldn't previously because they were too expensive and now they can.

That's a, that's actually, that's a big improvement in life. But it's not a guaranteed lock in. Everything that you make more efficient doesn't always result in overall there being more usage. So we use a lot less electricity to run household appliances now than we used to. They've become more efficient and there is only a certain number of times that you can wash the dishes with the dishwasher or wash, you know.

At some point, demand does become 

Chris Adams: there's an upper limit to how often I want to vacuum my room, for example. Yeah,

Anne Currie: me, it's quite a low number of times I want to vacuum my room. I'm more worried about things like Bitcoin, where there is no upper limit. I mean, so what worries me about things like Bitcoin is there is no, literally no upper limit to how much you might want to do it.

There is no, there's no point at which you go, "Oh, do you know, I'm happy now, I've just done all the things I wanted it to do." So with those things, where there's no upper limit, I think. And they potentially don't have a great deal of benefit for most of society. We might want to go down the China route and start banning them.

But for things where eventually our upper limit will be reached and they are providing societal value, I'm minded to let them grow a bit, at least. I don't know. I don't know what everybody else thinks.

Chris Adams: No, this is actually, this is, you're right, this is a complicated question to deal with, and this is one of the things that we, one of the things that's probably bigger than green software that we need to be aware of when we talk about this, because if we make things more efficient, then we've got to figure out, okay, what's the flip side of this, like you mentioned with Jevon's paradox? We do have an episode with Vlad Kouraoume, where we dive deeply into this if you're interested in listening, and we'll share a link to that. There's a number of other things we might want to talk about for this, but what we'll do is we'll share a link to the report so that there's plenty for us to be discussing. All right, I think that takes us to the stories that we do actually have. We're coming up to time. Folks, I want to say thank you so much for kind of coming onto this. If people were curious about these terms that we've been using, like operation efficiency, coding efficiency, things like that, this is what's outlined in the book that you three have been working on.

Is that correct?

Anne Currie: It is indeed. Yes. Building Green Software, the new book from O'Reilly. 

Chris Adams: Anne, there's one question I want to ask because we spoke about this a few months ago. So this is currently available right now, and you can get it via O'Reilly, but there was a discussion about this actually going. Available into the commons eventually. Is there one thing we could just touch on some of that, because this is a really cool thing about this that I was really excited to hear about when you first shared this with me, Anne.

Anne Currie: Like, I feel terrible about this because this is, we just haven't had time to do it. 'Cause we're crazily busy. We've got to take the code, but we, we do have a license. We negotiated, O'Reilly very kindly allowed us to make it available under the O'Reilly Creative Commons license, which is a, a kind of read only license, but totally that's, that's still absolutely fine.

That's great. And so we just need to do it, but we haven't got around to doing it yet. Cause we have to do some tidying up and actually publish the thing, but it will at some point be available under a Creative Commons license.

Chris Adams: Cool, alright, brilliant. Well, thank you for that, Anne. Well, folks, this takes us to time. If people do want to find out about, if they're interested in what you have to say, or what you've been looking at, folks, are there any maybe just do a quick whip round of where people should be looking. Anne, if I hand it over to you first, then to the Saras. Anne, if someone has listened to you and they want to find out more about what you're up to, is there a website you direct people to or a network or anything?

Anne Currie: If you want to find out more about me, then LinkedIn is where I tend to hang out these days, and I'm very happy to chat to people and answer questions. And/or our website strategically.green will give us an idea, and I do an awful lot of public training as well, so you can always sign up for that.

Chris Adams: Brilliant. Thank you for that, Anne. And I'm going to go for Sarah with an H, if that's okay. So where should people, if they want to learn about your things, or maybe hear about your talk, where should, where should they be looking?

Sarah Hsu: Similarly, LinkedIn, I call LinkedIn the grownup version of Instagram,

Chris Adams: Yeah.

Sarah Hsu: but yeah, LinkedIn and yeah, I do fair bit of public speaking. So if you're ever catch us or catch one of us, don't forget to come get the book signed and because it's such fun, who can get to collect all three signatures. Because we are never in the same place once.

Anne Currie: We're never in the same 

Chris Adams: Okay, sara, and over to you. If people want to find out some of the work you're working on or things, where would you direct people's attention to?

Sara Bergman: Yeah, I'm also on grown up Instagram, aka LinkedIn, 

Chris Adams: Yeah.

Sara Bergman: but follow me, don't send me a friend request because I'm terrible, or what's it called, a contact request maybe, because I've not been good at accepting or rejecting people, so now it's an uncomfortable list and I just ignore it, it's a red flag with me.

So yeah, you can follow me and that that's the best way probably. And yeah, I'm speaking at NDC Oslo in about, yeah, in June. So if you have the book, come and get it signed. It will be lovely to meet folks. I also have a few copies that I can hand out if anyone catches me. h 

Chris Adams: Ohere., exciting. Thank you for sharing that little one. All right, then. Well, folks, it sounds like I guess we'll see if people want to follow what you're up to. Millennial Twitter, LinkedIn is the place to go to. All right. What we'll do, if you have any of this interesting, folks, we are going to, and if you're listening to this for the first time, we'll be sharing the show notes with all the links to the projects that we've mentioned here, along with some of the other episodes where we touch on some of the things like Jevon's paradox, or some of the finer points of serverless. All right, folks, this has been fun. Thank you so much for giving us the time and,yeah, have a lovely day, folks. See you around, all right? 

Anne Currie: Cheerio everybody. Bye

Sarah Hsu: Bye guys. Lovely seeing you

Sara Bergman: Thank you. Bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!



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]]> In this episode of the TWiGS, host Chris Adams welcomes back Anne Currie, Sara Bergman, and Sarah Hsu, authors of the book Building Green Software. They dive into the latest updates and hot topics at the intersection of sustainability and software engineering. The discussion highlights the importance of making software and hardware more efficient and explores cutting-edge topics like serverless computing on Kubernetes with WebAssembly, the circular economy for electronics, and the potential for dynamic pricing in cloud services based on renewable energy availability. The episode emphasizes the ongoing energy transition and the need for innovative solutions to reduce the carbon footprint of technology.

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TRANSCRIPT BELOW:
Anne Currie: That is the nature of renewables, is that they are variably available, and we just have to, we have to take advantage of that, not fight against it, and not constantly wish we were in the fossil fuel age.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Chris Adams. Hello, and welcome to the Green Software Foundation podcast, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. In this episode, we welcome back the authors of the book, Building Green Software, Anne Currie, Sara Bergman, and Sarah Hsu, for an episode of This Week in Green Software,

our roundup of what's happening and hot at the intersection of sustainability and software engineering. So Anne, Sara, Sarah, I know that you've been on the podcast before, but I wanted to just provide a bit of space for people who are new to this to let you introduce yourselves. Anne, it's okay if I give this floor to you before we run through the usual roster?

Anne Currie: Yeah, thanks Chris. So, my name is Anne Currie. I am the CEO of a training company, Strategically Green, and I have been in the tech industry for pretty much 30 years now, and I am one of the co-chairs of the Green Software Foundation Community Group.

Chris Adams: Cool. Thank you, Anne. All right. Sara, is it okay if I hand over the floor to you next?

Sara Bergman: Sure. Thanks for having me back. I always enjoy being on the podcast. So my name is Sara Bergman. I am a senior software engineer at Microsoft, where I work with the Microsoft 365 products, which is very fun and exciting. I'm also one of the co-authors of the book Building Green Software. And other things that are new in my life is I'm recently back at work after maternity leave.

So I'm still figuring it out, you know, what is life now?

Chris Adams: Well, welcome back and congratulations on, yeah, the new instances of Sara, I suppose. Yeah.

Okay. And moving on to other Sarah. Sarah Hsu, if I give space to you to introduce yourself.

Sarah Hsu: Hi everyone. It's nice to be back. So my name is LSara Hsu. I am an SRE working for a financial institution. I'm also a project lead for the green software course for the GSF and similar to Anne and Sara, we recently just published a book by O'Reilly called Building Green Software. Very excited to be back.

Chris Adams: Cool. Nice to see you again. And Sarah Hsu, we met in person for the first time when you were in Berlin delivering a keynote for, was it CamudaCon, the conference there?

Sarah Hsu: Yes. A Process Orchestration Conference, which is exactly what we need, right? Because their motto is automation. And automation is the foundation of modern software systems. So...

Chris Adams: thank you for that, Sara. I was also pleased for us to both realize that we had arms and legs and were more than just a YouTube square, basically, or a square in a Zoom call. So yes, that was lovely as well. Okay, for folks, if you've never been listening to this podcast before, my name is Chris Adams. I am the executive director of the Green Web Foundation.

It's a Dutch nonprofit focused on reaching a fossil-free internet by 2030. I also work as one of the policy chairs of the Green Software Foundation Policy working group as well. And before we dive into some of the stories, that's the format of this show, we'll be sharing links to the show notes and all the stories that we do discuss.

So there'll be on GitHub and Markdown, what else, for you to look through. Alright, so the format we normally do is do a round up of news stories, but you three, because I have the pleasure of speaking to people who've written a book about green software, we figured we'd make it a little bit more interesting for listeners and touch on some of the topics in the book, so we've got a bit of a kind of nice way in to cover some of that content. Does that sound okay to you folks?

Anne Currie: Great.

Chris Adams: Alright then, so the first story that we have is actually about a new project called SpinKube. So this is hyper efficient serverless on Kubernetes powered by WebAssembly. Now a few podcasts ago before, we did a whole story all about Wasm, WebAssembly, and why it's an interesting piece of software. But, previously, back then, it was only available on Nomad, which is a similar scheduling tool, but not the same as Kubernetes. And this story, as I understand it, is basically the idea of providing some of these kinds of tools for Kubernetes, the most popular scheduling tool for this. This basically means that if you thought that was a cool idea, you've got access to it yourselves. And Anne, I think I might hand over to you for this, because this really touches on some of the things you spoke about in your book about operational efficiency versus coding efficiency. Maybe I will hand over to you here.

Anne Currie: It really does. So Wasm is something that I find absolutely fascinating. It's both, as you say, both from, at the moment, the operational efficiency perspective, because they're really focused on how you can bin pack as much work onto each machine as possible. So you can reach that magic 50 percent utilization number and then get even higher, which is very hard to do without a tool to help you.

Very hard to do. But beyond that, what also is, is they have ambitions even further, which is that they want to start actually optimizing the code that you write in WebAssembly as well, so that under your feet, it will become more efficient. And we all know that writing efficient code is really, really hard.

There's a, there's a huge impact on developer productivity. And we, we cover this quite a lot in the operational efficiency and the code efficiency chapters of our book. And one of the senior engineers, Danielle Lancashire at Fermion, who's behind all this Wasm, one of the, one of the groups behind all this movement in Wasm came and talked to me, talked for me at a conference in London a few months ago, and she was actually talking about code efficiency, the code efficiency improvements that come from Wasm.

Now they're a bit down the road. The moment they're focusing on operational efficiency, but I really like to see a platform with a vision for, because in the end, we're all going to have to run on much, much less power at times when the sun isn't shining and the wind isn't blowing. That is the nature of renewables, is that they are variably available and we just have to, we have to take advantage of that, not fight against it and not constantly wish we were in the fossil fuel age.

And so I really like to see a platform which is thinking about operational efficiency now and also thinking about code efficiency down the road. So I was, I like Wasm from that perspective. I think they're doing a lot of nice work.

Chris Adams: Yeah, we have some nods here. I just realized when I spoke about this story, I dived straight in without really explaining why some people find this actually exciting in the first place. So when we spoke about things like, say, serverless, and one of the, as I understand it, one of the key things behind this tool is it's like a very fast version of a serverless platform that spins up and down quite quickly.

So we've had previous generations where you might need to have a bit of a wait before a piece of software can start running before you can really use it. And this is like one of the key things that Wasm has made available. And that allows for, like you said, time, it basically means that there's less wasted time, but it also provides, opens the door for newer, more efficient platforms like this. Okay, before we move on to the next story, Sarah, Sara, is there anything that you want to add to this?

Anne Currie: Actually, before we move on, I'm going to just hark back to something that Sarah said just before this, which is that the benefits of modern ops and automation is that a big part of automation, of modern automation is the ability to spin up quickly because things like auto replacing, just having stuff sat around waiting and for you to fail over to with autoscaling, which is vastly more operationally efficient, relies on things being able to spin up fast.

If you can't have fast instantiation, you can't have a lot of these modern automation, this modern progress in automation. So yeah, it is a really good thing.

Sara Bergman: Also, I think one thing that has been said before and can always be said again is the benefit of the sort of platform that makes it easier for people because people want to be green, but sometimes it's a lot of work and anything that can make it less work to be greener is a great thing and should be celebrated.

And on the ops side of things, not every software developer or software person is like, highly interested in that. There are some people who love it and like go all in, and there's some who like, "I just want to write my code and like deploy it in some way to my users." So therefore things like this is so important to, to help bridge that gap in a green way.

Sarah Hsu: Yeah, yeah. I a hundred percent agree. Like software engineers, we're inherently lazy people, right? Like none of like, not speaking for you guys, I'm not a security expert, but I know how important a secure application is, right? I'm waiting for this tooling framework and best practice from the security people. So I think that's the gap we really trying to fill, that "how do we make everyone else have that ability to be green at their fingertip without having all the knowledge of like being really green?" And like, yeah, I feel like we can talk about operation efficiency to... I mean, to, 'til tomorrow, yeah, because it really is the lowest hanging fruit and people don't realize how many things we're already doing or have like knock on green benefits, like exactly what Anne said about reliability and resiliency.

Yeah. They all like come down to like automation and how do you utilize automation? Right. Anyway, I think we should stop. We can always come back to it. Let's get through all the other stuff. And then we come back to talk about Green Ops.

Chris Adams: Okay, all duly noted. All right, so we've spoken a little bit about operational efficiency, which is the running of servers. Spoke a bit about coding efficiency, which is what a lot of people might reach for first, or people wouldn't typically think about. And, but there's other ways you might talk about this.

So let's move to the next story. Sara, I might actually ask you about this one. So this one is a story from Grist magazine, which basically is talking, it covers the staggering quantities of like, Transition metals that we're currently throwing out when we could be mining them. And while we'll share a link to this in the show notes, one of the key things that the four of us can see right now is a chart showing some of the minerals that we hear associated with a transition away from fossil fuels to greener forms of power. There was a report by the UN called the Global E-Waste Monitor and they've shared some of these stats for the first time. And one of the key things is basically that we are throwing out something in the region of 62 million tons of electronics. And when you look at the actual mineral content of that, in some ways it's actually comparable to the demand for new kinds of metals that we have.

So the charts that we'll link to here, things like, say, copper, it's not that, it's quite similar to each other. Some things like cobalt or neodymium. These are, we're basically looking at the amount of minerals that could be circular. Some cases are not the same. So things like lithium, for example, we still, there's still a lot of demand and there's not nearly enough that's in circulation. But this feels like this provides an interesting flip side to the whole discussion around what we do with our stuff. And I think the term that I saw in the book, which was hardware efficiency. So I kind of wanted to like, see what you folks thought about this, in particular Sara, because this is one thing we spoke about in a previous podcast, like, this feels like there's more than just "hold on to your kit," for example, there's maybe a chance to talk about things in a more circular fashion.

Sara Bergman: Absolutely. And I think the key word there is circularity. E-waste is the fastest growing waste stream in the world, which is saying a lot. And it is growing for several reasons. Like we consume more, we have more devices, like more devices are smart devices. My, like kitchen fan has an app. I don't know why it needs an app, but you know, there, there are an increasing amount of what can be classified as electronic, not what can be classified, but what is electronic.

I see the same with like toys and things. So that's one thing. And also we crave the newer and the newer things and things have shorter and shorter Life cycles or the use phase is short and shorter. So it's, it is fast growing and before, in a time before time... 

no, but when it was not the case that it was so fast at growing, we could maybe afford to mine everything.

But now when the consumption is so high and the throwout rate is so high, we really need to start thinking in a circular way. Because, in the end, there is only so much metal in the ground and our earth is finite. It's not something that grows back, at least not very quickly. So having this thinking, I think it's going to be, it needs to be a game changer.

And I know several countries have adopted what's called the right to repair, which is basically that you 

Chris Adams: is big thing in Europe. 

Sara Bergman: Yeah, it's a big thing in Europe. China has, no sorry, not China, India has a similar legislation. New York has a similar legislation. And that's really good. And that's about what you said, holding onto your kit longer.

But that's really only one side of it. I think the right to recycle should be equally hotly debated, perhaps even more hotly debated. Because we, the hardware industry has been forced to make hardware kind of hard to recycle because we are demanding better, yet smaller devices, or bigger screens, but thinner, lighter hardware.

And that's really complicated to produce. And it makes it really hard to recycle. Also in this article that you linked, let me find the number. But, what they said, the percentage of ethically recycled waste was also staggeringly low. It's like a business as usual case was only 20%. It's, well, you're all going to have to go to the show notes the article for yourself to find the percentage.

What I'm meaning to say is that this is incredibly important and something that is an additional dimension to the conversation beyond holding on to your hardware. It's also the reuse and recycle part of the story.

Anne Currie: It's a sales feature as well. It's recycling and it's a sales feature. I just bought a new phone, new, it was an old Pixel 7a, because my previous one had run out of security patches, the classic way that you have to throw away a working piece of kit. So I was quite annoyed because I was having to throw away a perfectly working piece of kit.

So I bought 7a. But the 7a sent me all the stuff to do a, to send my old phone back, and I got 25 percent off and I thought, "well, actually, that was pretty good." I actually felt quite good about that in the end. I thought, well, it's going to be recycled. I got 25 percent off. All right. That was, it was, it felt like a feature that they were offering me.

Chris Adams: Well, this is, maybe this touches one of the key ideas that, if you're moving away from linear economy, once you've, dug these minerals up once. One of the ideas is that once they're in the kind of sphere that we are in, right, you don't need to have, it's not that going into the atmosphere like say fossil fuels for example.

You can have these things circular. Now, this is one thing that we don't have quite the policy support for yet, but this is one thing that we could definitely be seeing more of in future, and this feels like the direction we might be heading towards if things work out, basically.

Sarah Hsu: Yeah. I also think it's an age old thing, right? We should find the thing that fits your purpose. It's similar to say, we want to find the VM instance that fits my purpose. I feel like people shouldn't want the latest and the greatest. Like my laptop is not that great, but I don't do that much on my laptop.

So like, I think people also need to start having that mindset. Like, "Oh, I don't need like the fastest, like the most, you know, cores, power laptop, choose to go on Zoom call, do a little bit of coding." And yeah, I think that's another bit of the things that also really fit into operational efficiency, you know, where you want to find things that fits your purpose.

And I think that applies to hardware as well.

Chris Adams: I agree, actually. All right. Thank you for that, Sara. All right. So, we're going to move to the next story now. This is a story from Carbon Brief. And, Anne, I'm going to hand over to you for this one because this is one thing we discussed before. So, this is a story from Carbon Brief who basically mentioned that earlier on this month, no, last month, actually, now.

The UK's electricity grid operated for a whole hour with just two, almost no power coming from fossil fuels. Now this is a record low for the country and this hopefully is a sign of things to come. So Anne, I know that we discussed this here, but maybe you could talk, touch on like why you found this interesting and what it's kind of telling us as software engineers perhaps.

Anne Currie: Yeah, I mean, obviously this is an incredibly positive story, and it's actually the direction that almost everybody's going in. If you haven't had a play around with electricitymaps.com to have a look at how green all the grids of the world are, it's absolutely fascinating. You'll learn a lot. And if you step back in time, it shows you what the carbon intensity, the average carbon intensity is of every grid in the world that they can get data on, which is a surprisingly large number of grids.

You'll see that if you, and then you can go back in time and step forward and you'll see that everything is becoming greener and greener. The energy transition is happening. You know, it's not just something that will be happening in the future. It is happening, but it won't be easy. Oh, I saw an excellent Uruguay is apparently completely a hundred percent green for eight solid months this, this year.

So there are, but that is because, but the interesting thing on this is that every country does it in a different way and every country being the green intensity of their grid, it varies over time in different ways. It depends what you're using to generate the power. So places like Uruguay have loads of solar and wind, which is fantastic, but they also have tons and tons of hydro and the hydro is used to smooth out the times when the wind isn't blowing and the sun isn't shining.

In the UK, we don't have so much in the way of stored, so it's great if it's windy, and if it's sunny, but mostly in the UK, so if it's windy, we do an awful lot of offshore wind, rather, not onshore wind, but offshore wind.

Sara Bergman: I thought going to say, we don't do a lot of sun here.

Anne Currie: We do not do a lot of sunny here. It's actually, it's not as bad as, I've got solar panels, and through the summer, it's actually, well, through half the year, that will cover all my household usage.

It's oddly much better than you'd think, but nonetheless, in the UK, it's mostly wind. But there's not so much in the way of storage. So it's, the UK is an absolutely excellent example of we're going to have to get good at using power that's variably available and cheap when it's available and either demand shift or demand shape so that we are using the renewable power and using less of non renewable stuff.

So it is absolutely fascinating. It's a great story. It's a really good move forward, but not all grids are identically green. They all have difference. Different ways of doing it and results.

Chris Adams: Alright, I've got a pop quiz for you then, because you're talking about the fact that the grids change, and sometimes they're going to be greener because there's more clean generation on the grid versus other times, right? We know in the UK, for example, there's groups like Octopus Energy, who basically will change the cost of your power, depending on the time of day.

In some cases, they even pay you to use this. And we see this in other parts of the world. When do you reckon we're going to start seeing cloud providers do this in the net? Because we know it happens, and we see this stuff, but none of these signals are passed on to software engineers yet.

Anne Currie: No, and every time I'm speaking to somebody from a cloud provider, I ask about it. Normally the people are quite green people and they want it to happen, but I think it'll be a way off. Now, it's, it's interesting. I spoke to somebody who was working for a cloud provider, but was a software engineer who used to be in the insurance industry.

And he was saying to me, "Oh, well, you know, I can totally see it being to the advantage of the cloud providers to start doing dynamic tariffs, time of use tariffs, because then they have another product they can sell." So the people who can't do it, they will sell them insurance with a price cap. And obviously they'll charge a load of money for that.

If you know, but, and then that is a product. Now, well, is that what they're gonna do? They'd be quite sensible to do it. It's, you know, the cloud providers are very good at making money and it won't be cheap. So I would love to see it, but I think it's going to be years before it happens.

Chris Adams: Okay, so you're thinking three years at least for you, Anne, yeah? Okay. Any other takers for anything faster?

Sara Bergman: Yeah, no, I think, well, working for a cloud provider, I just want to say, I don't know. But what I think all, at least the major cloud providers are fairly open with their use of PPAs, so Power Purchase Agreements, as a way of, yeah, meeting the green energy needs, because they have data centers sort of where they need to have data centers and then use PPAs to, to handle their Scope 2 emissions.

And I think because all of them at least are so seemingly tied to those agreements, it is a contradiction between those and billing a customer for actual usage. So I think it could be like a complicated thing for them is what I suspect. But I also think it's something that customers would really appreciate.

So I'm hoping it will be sooner, but no, I'm with Anne.

Chris Adams: Okay. Years away. And Sarah, you've got something to say, it looks like.

Sarah Hsu: Oh, I was just going to say, probably not in the near future, but we just need one person, one cloud provider to do it and everyone else be like, "right, we need to do it too."

Chris Adams: Do you know what? I had a conversation from people outside of the big three, the big cloud providers. So, in Texas, there's a company called Build AI. They've been doing some work to basically, they'll provide you computing, but at certain peak times, you don't have access to it. And as a result, they're able to have much lower costs for this stuff.

So I'll share a link to that. And there's a company called Saluna, also in Texas as well. And what they do is they speak to generators, people who run like clean energy. And they'll basically say, we will give you a floor price under which you will never, you know, we'll always get give you something like that.

And using that, they are able to provide these kind of services. So we are seeing this start to develop, just not with the big providers. And we'll, I guess the next question will be, at what point do these new providers get bought by the big providers to protect the margins? Because that might be the logical thing to do if you have these kind of funds. All right, let's move on from that, because that was a, we spent a bunch of time talking about carbon awareness, and there's a few other stories that we have up here.

Anne Currie: But carbon awareness is the most important thing.

Chris Adams: It's definitely

Sara Bergman: And operational efficiency.

Chris Adams: Alright, ok, so let's move to the next story then. So, we have another story here, this is actually probably the nerdiest story we have here. This one is actually a link to an issue in the OpenTelemetry repository. Basically, there is a standard called OpenTelemetry which is designed to make it easy to understand what the, I guess it's to make Infrastructure Observable, and Sara, I might need some help from you on why observability is important.

But this one is basically put forward by, I think, one of the people who's inside the Green Software Foundation to start agreeing some sustainability metrics to expose in all the kind of tooling that we currently do have. This was really interesting because this feels like, A, this is something that I saw discussed in the book, but also for people who are not familiar with OpenTelemetry, OpenOps, or even Observability in general, is anyone who might want to go, like, enlighten us or at least give us some points about why this might be interesting from a Green Software perspective? Sarah, I might hand over to you because you wrote part of this chapter for the book, I believe.

Sarah Hsu: Yes. So I guess observability was born out of necessity because things are so complicated now. Microservices is made out of our world. And sometimes one requests have to travel like the entirety of a street of like a hundred houses before they actually reach a part of their journey. And it's really impossible to figure out what and where things have gone wrong, right? Metrics is for when you know something is going to go wrong and then you set up a metric to monitor that. But then in this unknown world, it's really hard to figure out who is going to break, for example, like, oh, I can't, I forgot that guy's name, but someone from Honeycomb.

You guys know how much I love Honeycomb. He gave a really good example. Like if you're like a, like an iOS developer and you support like 10 different phones in 10 different countries, and suddenly one version of the phone in this country is going to break. How do you know to monitor that using metric?

So I think that's a really good example, like why we need observability and observability borrowed its thinking from control theory, which is like, we are trying to understand the internal state of a system by just looking at its outputs. And outputs here are like telemetry. And telemetry, you've got logging, you've got metrics, you've got traces.

And so basically OpenTelemetry is, it's like a set of framework. It's about the creation and then the management. All of those telemetry, it's actually not a backend. So it's not like Prometheus. It's not Jaeger. It's not like Grafana cloud. It's just a convention, which is really good. I remember when we were all in QCon a few years ago and Daniel was talking, Daniel from Skyscanner was talking about their OpenTelemetry migration plan or something.

They basically went from like 300 different components down to 150 because they used OpenTelemetry as a standard, as an auto collector. And then that's the way, become one stop shop for all the telemetry. It's like, you don't need developers sending three different telemetries to three different backend systems.

And then one big thing, everyone's being on call here. One big thing we find difficult is how do I context link everything? Why I need to basically, "oh, this logs happen at 1:21 PM. Right. Let me go find the traces that also happen. But what if time shift, right?" And sometimes like Something is in a different time zone.

So anyway, that's a massive rant about why we need observability. So it's, I know, sorry, it's my job guys. And then I guess one big thing we talk about in the book is that green software needs to be ready with observability. Like we need to be with it. Right. So for example, in this complicated microservices world, we want to be able to know which component, which process.

It's emitting the most carbon. Right. And that's where we want to be. And we need OpenTelemetry. We need people like OpenTelemetry to help us get there. So it's like absolutely amazing to see so many people are standing up and then it's like, Hey, we should add this like semantic thing.

Chris Adams: I'm really excited about seeing this because I've used Honeycomb to understand what was broken about some applications I've been running before. And I was always a little bit wary about saying, well, okay, I'm not sure about, I want to be totally tied to one provider. And this here seems, and we have seen some providers who have started to make some CO2 figures for this. There's, confusingly, a, so a company called Sentry that do provide this and they even propose like a HTTP header for CO2 per request, right? But to see this at a kind of standard level, this feels like it might make it easier for a larger set of providers to come up with and at least make it easier to kind of see some of this because I think this is something that came out the book was that we, you need to be, you need to observe this, but it's often quite difficult to get the underlying numbers from some providers, and this is something that we need a bit, we need some more work with, or we need some progress on, basically.

Sarah Hsu: Yeah. And I guess one big thing about OpenTelemetry is that it, because it is just a framework and it's vendor neutral, I think sometimes people forget how important staying vendor neutral is. So yeah, I think that's why it's so important that we locked in with OpenTelemetry now, because it is going to be the solutions for this observability space going forward.

Sara Bergman: Absolutely agree, because I think sometimes when people talk about software, they think of a specific type of software that runs in the public cloud. But that is not the entirety of software that exists out there. There's so much software who runs on different places and nowhere near the cloud. And that software is equally, equally important.

So, having something that fits more than just the one most popular scenario is incredibly important. And, and I just want to say that, I think this is important because I don't think anyone should get away, quote unquote, from doing sustainability work because, oh, I don't know my number. That should not be an excuse.

Anne Currie: No, I totally agree. But, but even if it's impossible to get your number, there's still so much you can do without the numbers. as well. And yeah, it's amazing how people go, "Oh, I can't get the numbers to get" well, just work on your operational efficiency.

Sarah Hsu: Yeah. Yeah. And like one of the biggest takeaway I gave at the CamundaCon in Berlin was that you can think of BinOps as a really natural evolution of DevOps and FinOps. FinOps is the optimization with money. We basically need to do the same for sustainability. And there are so many things we already can do and yeah, people should really pack themselves on their back because they didn't realize the transition is going to be much smoother than they thought.

I 

Chris Adams: Okay, I'm glad you mentioned FinOps actually, Sarah, because this is talking about OpenTelemetry, some ways to expose some of the figures into this. As I understand it, there is a is it focus, which is the standard that the FinOps groups are pushing for trying to come up with like standardized cost, cloud cost figures, because I understand, as I understand it, there are some people pushing to put some CO2 figures in those as well, so that you'd be able to get some of these ideas from not just billing, but also from operational figures.

So, because in some cases, one view will give you a slightly different view than the other, for example. We've got this, and we're just moving to the last of the stories that we have today. One of the largest providers has published their sustainability report. Microsoft published their 2024 environmental sustainability report in the last week or so, and there's a lot in it, actually.

So, they're one of the large providers, and they have various commitments about getting to net zero by a certain times, but there's actually quite an interesting amount of data for the nerds inside this. Sara, I might hand over to you, because I suspect that you've been poring over this in quite a lot of detail, actually.

Sara Bergman: Thank you. Yeah, I think I love it when this report comes out every year, because even though I work for Microsoft, there's no way I can keep up with everything that's Like, it's just too big. So I always learn so much. But I think for me, I'm a measurement geek, in case that wasn't obvious already.

So some of the things I thought was super interesting was firstly, that the PUE of the data centers was published. I don't think Microsoft has ever published that number, but they said that this year, the design rating of the new data center is 1,12. And I think that's pretty impressive. That's pretty close to 1, which is, you know, the dream where all the energy that goes in goes to actual compute and to know other resources.

Chris Adams: I'm glad you mentioned this, because I wanted you to actually just, for folks who are not familiar with what PUE means, maybe you could just, like, expand on that a little bit, and say why people, why you might even care about that number being high or low, for example.

Sara Bergman: Yeah, absolutely. So P U E stands for power usage effectiveness. Or if, yeah, effectiveness. Yeah. I always mix up the E words. Power usage effectiveness. And I think in the sort of early days of green software, this was the number people spent a lot of time focusing on. It is a number that's higher than 1.

So it measures all the power that goes into your data centers and how much goes to actual compute. If, if all the energy or all the power that goes into your data center goes to meaningful compute operations, your PUE = 1. Only half of what goes into the data center goes to actual useful compute.

Then your PUE number is 2. 

Chris Adams: I see. 

Sara Bergman: There's been a race to get to one, where you waste basically as little power as possible.

Chris Adams: And when you say little power, you're talking about, like, keeping the machine from overheating and melting, or stuff like that, right? Like keeping cool enough?

Sara Bergman: Yeah, cooling is one thing that consumes a lot of extra power. It's probably the biggest consumption of power, but they're like, they're lighting in the data center also, that's also consumes power. So anything that's in there that doesn't contribute to compute, go into this rate.

Chris Adams: Do servers need to see? I mean, they don't need to be in

the, like, it's not like they have 

Sara Bergman: there people who work there.

Chris Adams: Okay, that's fair enough. Okay, so this is, so in this report, they're publishing this information, which has traditionally been a thing that you do not see, all right?

That's one of the key things from this.

Sara Bergman: exactly. And I thought that was interesting. And in the sort of early days of green software, this was a number of people obsessed a bit about because I think there was like this, I don't want to say miscomprehension, but this notion, at least, that if you just got the PUE to 1, all the problems would be solved, like, magically, everything would be so efficient that we would not have to care about everything else.

People were, like, hyper focused on data center design. And I'm not saying it doesn't matter. Of course it matters. Of course it's important. But it's an area where, over time, we paid down massive knowledge and spend time on. And in the end, that's only one side, because if whatever compute operations you run are vastly inefficient in themselves, it doesn't matter if all the power going in goes to compute if the compute is wasteful in its matter.

So it is one part of the puzzle, but it is not the most important. But I'm glad to see it getting so close to one. I think it was very impressive.

Chris Adams: Yeah, this is interesting to see this because there's a project called Realtime Cloud which is run inside the Green Software Foundation and one of the key things that people are looking to do is figure out the PUE for all the different regions from all the different cloud providers right now. And they've got a carbon intensity figures for each hour of compute. But now they're able to use, because there's this information published at a data center level or actually a region level, you've got a level of transparency that you don't see from the other two so far. I think. Google might be sharing some of this, but we still have a kind of like patchy spreadsheet listing this stuff.

What I'll do is I'll share a link to the spreadsheet for this, because this is one of the things that once you have this, that should allow you to start being able to kind of optimise what the carbon intensity and of the compute you're using based on these kinds of figures here. This doesn't touch everything though, and I wanted to just leave the floor open. And Sarah, is there anything that caught your eyes on this, because there is more to this report than just PUE, for example.

Sarah Hsu: Don't want to be the bearer of bad news, though, although I remember hearing this from Anne, so I'm just their messenger. I'm not the bearer. I remember you and Anne mentioned that because PUE has got a lot of branding around it at the moment, and people know that it's like the sort of like the efficiency factor for a data center.

So like people then don't want PUE to go up, even though they need cooling. So they end up using water to do the cooling. So they actually trying to get PUE to as little as possible, closer to one as possible as Sara mentioned, but then they compensate that with using water and water is another like sources that's just as sacred.

Is that how you say that word? Yeah. But anyway, I will like, and talk about this because I remember I heard this from you.

Anne Currie: Yeah, no, as you say, we do fetishize PUE a little bit. It's a measurement and everybody loves to meet it. But the other thing is it's not a carbon aware measurement because it's just a flat number. So it doesn't say, "Oh, well, actually, you know, this is what we did when the sun, when the carbon intensity of the grid was high.

And this is what we did when the carbon intensity of the grid was low." And actually you want, we need behavior to change. Between those two, two times. So it's nice, you know, it tells us something, but we need to make sure we, with all things, you know, it's context specific. We, it doesn't provide a lot of context and we might need more context.

Well, we will need more context in the future.

Chris Adams: Okay, so better than nothing. So two cheers rather than three cheers. So good to see this. But as Sara mentioned, I believe the technical term is impact transfer when you go from one factor like energy to impacting water, for example. And in some places, you may be, you may have data centers sited in regions of water stress or pulling power from like local aquifers and things like that. If that's used for drinking water or agriculture or things like that, that's not really ideal. But there's a big discussion about what happens with this water. In some cases, it might be, I think the term is withdrawn and waste and consumed. So you have water that might be taken in and some of it might be discharged, given back.

But if it's at a different temperature, that might not be ideal for fish. And stuff like that. Or if it's consumed and like just evaporated away, that's not ideal either. So there is a whole bunch here. And maybe this is one of the things we need, we should be hoping to see, or we should be looking to see more of. There's, okay, if we've got data center level PUEs, should we be thinking about water of usage effectiveness and other things like that?

Sarah Hsu: I think there's one, I don't know what to call them, but I know they're called Scaleway. And I think you did a thing for them that they, I think now they have like a landing page where you can see the PUE and the WUE. So I think people are start doing it, but again, like much smaller scale provider instead of like cloud providers, 

Chris Adams: I'm really glad you mentioned this actually, Sarah. So, the thing you're referring to, we'll share a link to this. So, Scaleway, they're basically one of the largest cloud providers in France, and they also operate in a number of other countries, but they basically do have these dashboards. But the thing that's, the thing about these dashboards, they were initially created by by Facebook in 2014. So there is nothing stopping every data center exposing these numbers from a technical point of view. And like, if you're looking at a policy level, what they have there is totally something that could be done. It's something that we could be seeing if people chose to be disclosing some of that. And we might see some of this come out as a result of new laws that may be landing or that have already landed in some countries.

For example, Germany has this. But there are more on the horizon, actually. Anything to add from there, perhaps, Anne, you're about to just say something, yeah.

Anne Currie: I am. So, so Scaleway, I think it's kind of related to stuff we talked about earlier. I was on this podcast with Scaleway where I learned a lot of stuff about what they were up to. And there's some interesting things that they're doing totally away from PUE and WUE reporting. So, in Europe, there's a group called the SDIA, which is an acronym.

I can never remember what it stands for. 

Chris Adams: Sustainable Digital Infrastructure Alliance,

Anne Currie: Excellent. And their raison d'etre is to try and get European data, non hypercloud data centers to start learning from the American hyperclouds and actually delivering some of those services. And one of the, one of the things they're pushing for is actually starting to orchestrate and offer services on your data centers that will provide operational efficiency and code efficiency in the same way that the hyperscalers do. And that generally, you know, your bog standard data centers don't. And that's a really, that's a really bad thing about bog standard data centers.

They don't do this because it's very hard to do. If you can get your data center to provide it, then that makes life much easier. And interesting, on the podcast that I was on with Scaleway, they had a, one of their engineers on who was working on their serverless offering. So a bit like Wasm we were talking about earlier, services that improve operational efficiency and code efficiency provided through your data center. That's what we want. That's where the cloud is good with being, is potentially green, but only if you adopt them. And all these things, if you just lift and shift into the cloud, if you lift and shift into good data centers like Scaleway, you don't get any benefit really, or don't get much benefit.

You need to be moving over to efficient services.

Sara Bergman: Yeah. And I think that was another thing that Microsoft mentioned in the sustainability report that they are working on several ways to increase resource utilization. And just to mention too, they have these power aware workload allocations, and they're also like smartly allocating CPU cores for internal workloads.

Because don't forget Microsoft is also a software provider, not just a cloud provider. And I thought that was really good that they called out because sometimes when we say things like this, yeah, the hyperscalers are good at this, people question it, like, "oh, is it really worth their time?" So I thought it was good to see them calling out specific things that they're doing that can also hopefully inspire others to do the same with their data centers.

Chris Adams: So here's one question I have there about this report. So, yes, it's very good that you see some transparency here. And yes, there are definitely cases where moving to the cloud can be more, can be greater. But we've seen, both Microsoft and Google and Amazon, their emissions climb. Year on year. And this year, we're seeing emissions 30 percent higher this year than last year, which is not going to make it easy to get to net zero.

And this is one of the key things that Microsoft themselves have been talking about, saying, yes, this is a real challenge for us, and a significant chunk of our emissions are in our supply chain. Scope 3 is the largest source. So this is one thing that I think that it's, it feels like when we're talking about this, green software doesn't really have that much to say about right now in terms of the actual creation of data centers. And this is something that I kind of want to open the floor up, like how do we actually deal with this fact that things can be more efficient, but still growing in absolute terms? Is this something that we can be doing or do we need to be having discussions about absolute resource limits, for example, and things like this, or how are we going to get here? I'm going to hand to Anne because you've got something, but you're about to say something I think.

Anne Currie: I am always about to say something. I think this is a really, the whole, the whole thing of, you know, the degrowth, people shouldn't be allowed to, we shouldn't ban people from doing things, it's a very unpopular argument and it is why people, you know, are not, even though everybody now believes in climate change, they don't want to have to give up all the things they want in life.

Now, efficiency can often really deliver you the same standard or less. I mean, it's not all, I mean, Jevons paradox, we all know about Jevons paradox. The Jevons paradox is you, you, things become more efficient and where there's untapped demand, where people really wanted to use them and they couldn't previously because they were too expensive and now they can.

That's a, that's actually, that's a big improvement in life. But it's not a guaranteed lock in. Everything that you make more efficient doesn't always result in overall there being more usage. So we use a lot less electricity to run household appliances now than we used to. They've become more efficient and there is only a certain number of times that you can wash the dishes with the dishwasher or wash, you know.

At some point, demand does become 

Chris Adams: there's an upper limit to how often I want to vacuum my room, for example. Yeah,

Anne Currie: me, it's quite a low number of times I want to vacuum my room. I'm more worried about things like Bitcoin, where there is no upper limit. I mean, so what worries me about things like Bitcoin is there is no, literally no upper limit to how much you might want to do it.

There is no, there's no point at which you go, "Oh, do you know, I'm happy now, I've just done all the things I wanted it to do." So with those things, where there's no upper limit, I think. And they potentially don't have a great deal of benefit for most of society. We might want to go down the China route and start banning them.

But for things where eventually our upper limit will be reached and they are providing societal value, I'm minded to let them grow a bit, at least. I don't know. I don't know what everybody else thinks.

Chris Adams: No, this is actually, this is, you're right, this is a complicated question to deal with, and this is one of the things that we, one of the things that's probably bigger than green software that we need to be aware of when we talk about this, because if we make things more efficient, then we've got to figure out, okay, what's the flip side of this, like you mentioned with Jevon's paradox? We do have an episode with Vlad Kouraoume, where we dive deeply into this if you're interested in listening, and we'll share a link to that. There's a number of other things we might want to talk about for this, but what we'll do is we'll share a link to the report so that there's plenty for us to be discussing. All right, I think that takes us to the stories that we do actually have. We're coming up to time. Folks, I want to say thank you so much for kind of coming onto this. If people were curious about these terms that we've been using, like operation efficiency, coding efficiency, things like that, this is what's outlined in the book that you three have been working on.

Is that correct?

Anne Currie: It is indeed. Yes. Building Green Software, the new book from O'Reilly. 

Chris Adams: Anne, there's one question I want to ask because we spoke about this a few months ago. So this is currently available right now, and you can get it via O'Reilly, but there was a discussion about this actually going. Available into the commons eventually. Is there one thing we could just touch on some of that, because this is a really cool thing about this that I was really excited to hear about when you first shared this with me, Anne.

Anne Currie: Like, I feel terrible about this because this is, we just haven't had time to do it. 'Cause we're crazily busy. We've got to take the code, but we, we do have a license. We negotiated, O'Reilly very kindly allowed us to make it available under the O'Reilly Creative Commons license, which is a, a kind of read only license, but totally that's, that's still absolutely fine.

That's great. And so we just need to do it, but we haven't got around to doing it yet. Cause we have to do some tidying up and actually publish the thing, but it will at some point be available under a Creative Commons license.

Chris Adams: Cool, alright, brilliant. Well, thank you for that, Anne. Well, folks, this takes us to time. If people do want to find out about, if they're interested in what you have to say, or what you've been looking at, folks, are there any maybe just do a quick whip round of where people should be looking. Anne, if I hand it over to you first, then to the Saras. Anne, if someone has listened to you and they want to find out more about what you're up to, is there a website you direct people to or a network or anything?

Anne Currie: If you want to find out more about me, then LinkedIn is where I tend to hang out these days, and I'm very happy to chat to people and answer questions. And/or our website strategically.green will give us an idea, and I do an awful lot of public training as well, so you can always sign up for that.

Chris Adams: Brilliant. Thank you for that, Anne. And I'm going to go for Sarah with an H, if that's okay. So where should people, if they want to learn about your things, or maybe hear about your talk, where should, where should they be looking?

Sarah Hsu: Similarly, LinkedIn, I call LinkedIn the grownup version of Instagram,

Chris Adams: Yeah.

Sarah Hsu: but yeah, LinkedIn and yeah, I do fair bit of public speaking. So if you're ever catch us or catch one of us, don't forget to come get the book signed and because it's such fun, who can get to collect all three signatures. Because we are never in the same place once.

Anne Currie: We're never in the same 

Chris Adams: Okay, sara, and over to you. If people want to find out some of the work you're working on or things, where would you direct people's attention to?

Sara Bergman: Yeah, I'm also on grown up Instagram, aka LinkedIn, 

Chris Adams: Yeah.

Sara Bergman: but follow me, don't send me a friend request because I'm terrible, or what's it called, a contact request maybe, because I've not been good at accepting or rejecting people, so now it's an uncomfortable list and I just ignore it, it's a red flag with me.

So yeah, you can follow me and that that's the best way probably. And yeah, I'm speaking at NDC Oslo in about, yeah, in June. So if you have the book, come and get it signed. It will be lovely to meet folks. I also have a few copies that I can hand out if anyone catches me. h 

Chris Adams: Ohere., exciting. Thank you for sharing that little one. All right, then. Well, folks, it sounds like I guess we'll see if people want to follow what you're up to. Millennial Twitter, LinkedIn is the place to go to. All right. What we'll do, if you have any of this interesting, folks, we are going to, and if you're listening to this for the first time, we'll be sharing the show notes with all the links to the projects that we've mentioned here, along with some of the other episodes where we touch on some of the things like Jevon's paradox, or some of the finer points of serverless. All right, folks, this has been fun. Thank you so much for giving us the time and,yeah, have a lovely day, folks. See you around, all right? 

Anne Currie: Cheerio everybody. Bye

Sarah Hsu: Bye guys. Lovely seeing you

Sara Bergman: Thank you. Bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners. To find out more about the Green Software Foundation, please visit greensoftware.foundationon. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!



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]]> Environment Variables Year Two Roundup Environment Variables Year Two Roundup Thu, 23 May 2024 08:00:00 GMT 31:20 k18m8nn0 false https://podcasts.castplus.fm/e/rn7yrvvn-environment-variables-year-two-roundup 68dc7d06597bc7d53fb83464 68dc7cef46a2532cdd8d1c23 Join us for a special episode of Environment Variables as we celebrate over a year two years of bringing you the best insights on Green Software! In this episode, we explore the key insights and voices that have contributed to the weaving of sustainability through our conversations this year. Tune in for a refresher on the most interesting discussions on the progress, challenges, and future of green software development. 71 Join us for a special episode of Environment Variables as we celebrate over a year two years of bringing you the best insights on Green Software! In this episode, we explore the key insights and voices that have contributed to the weaving of sustainability through our conversations this year. Tune in for a refresher on the most interesting discussions on the progress, challenges, and future of green software development.


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TRANSCRIPT BELOW:
Chris Skipper: Hello and welcome to this special Year 2 Roundup episode of Environment Variables. I'm Chris Skipper, the producer behind the scenes. As we mark the second anniversary of this podcast, it's a perfect moment to reflect on the journey we've undertaken with the Green Software Foundation over the last year, and how that has been encapsulated through our episodes.

From its inception, Environment Variables has aimed to be more than just a podcast. It's a platform for advocacy and education on sustainable software practices. Over the past year, We've seen the Green Software Foundation grow and evolve, and we've been right there to document and discuss each milestone.

This podcast has not only followed the foundation's developments, but also mirrored the broader shifts towards sustainability and tech, bringing these insights right to your ears. Today, rather than revisiting our top episodes, we will explore how the themes of sustainability have woven through our discussions.

Highlighting key insights and the impactful voices that have contributed to this dialogue. You'll hear about the progress, the challenges, and what lies ahead for green software development. For your listening pleasure, as always, links to each of the episodes will be down in the show notes below. Or, if you want to listen to all of the episodes of Environment Variables, you can head to podcast.greensoftware.foundation, preferably after this episode, please, to hear them all. So, without further ado, let's dive into the collective journey of the past year with Environment Variables.
 

Chris Skipper: To kick us off, let's start off with an episode of one of our segments entitled the week in green software, or as we like to call it twigs,

This segment delivers a concentrated blast of the latest in green software news, keeping our listeners informed and engaged with current trends and advancements in the field.

In fact, it's a nice touch that this episode covers AWS and Scope3 emissions data, as the very first episode of Environment Variables way back in April of 2022 also covered Amazon's Customer Carbon Footprint tool. In this snippet, our host Chris Adams discusses a fundamental concept in environmental accountability, the Greenhouse Gas Protocol, which is the de facto standard for measuring the carbon footprint of any organization or activity.

Chris breaks down this somewhat complex subject using a relatable analogy involving coffee, explaining the three scopes of emissions. He uses everyday examples to illustrate Scope 1, Scope 2, and Scope 3 emissions, making it accessible for both newcomers and seasoned professionals, which has often become the role of twigs.

This approach not only simplifies the understanding of these emissions, but also highlights the significant impact of Scope 3 emissions, which often constitute the majority of an organization's environmental impact. Let's listen to Chris explain this in more detail.

Chris Adams: So I'll just quickly, for those who are new to the subject or folks who have never heard of GHG, the greenhouse gas protocol, essentially, this is a way, the kind of de facto standard for measuring the carbon footprint of any organization or any activity. And you typically split it into three kind of buckets of emissions.

And because we're nerds or developers and drink coffee, we can use hot beverages as the mechanism for understanding the difference between scope one, two, and three. You can think of scope one, which is from combusting fossil fuel. That's a bit like turning on gas to heat up water so you can have a nice cup of coffee, right?

Scope two, it's like turning on an electric kettle. So someone is setting fire to something to heat up some water somewhere to generate electricity so that you can heat up a kettle. So it's all the emissions associated.

Scope 3 is a bit like walking into a Starbucks or a Third Wave coffee shop and then buying a cup of coffee. So you're not involved in actually farming beans or burning anything, but there is definitely a supply chain associated so that you can have coffee. So these are the three kinds of scopes. And typically, Scope 1 and 2 are quite the common ones that organizations tend to report on, but for these, for lots and lots of organizations, scope 3 can make up 80 percent plus of the environmental impact.

And this is why we've been talking about it as being quite a big deal because if you do not have 80 percent of your reported numbers, they may look somewhat different to the other providers.

Chris Skipper: In the past year, Environment Variables has also introduced a new segment called Fact Check. This segment focuses on verifying claims and clarifying misconceptions related to the environmental impact of technology, providing our listeners with accurate and actionable information. In episode 29, Host Chris Adams discusses the role of artificial intelligence in optimizing the environmental impact of software.

He is joined by experts Colleen Josephson and Miguel Ponce de Leon from VMware, who delve into the intricacies of sustainability in virtualization and networking, as well as VMware's internal efforts towards decarbonization. In the snippet we're about to hear, Colleen Josephson highlights the challenges associated with training AI models in the telecommunications sector, a field where VMware has significant expertise.

She points out the substantial energy required to train these models and raises critical questions about the energy savings versus the environmental cost of AI, emphasizing the importance of evaluating the longevity and efficiency of AI models before committing resources. Let's listen to Colleen Josephson.

Explain these complex trade offs.

Colleen Josephson: Training is a very expensive process nowadays. Whether it's in the telco space, because, again, VMware, as you hinted, we have a long history of virtualization and cloud, but that also has become very relevant to telecommunications. We need to train the models that we use to make these decisions to try and save energy.

And the process of having so much data and training it, it can be really power consuming. So I think one of the things that stands out to me is, what is your anticipated energy savings? savings. Once you've deployed this model, how long do you anticipate that this model will be good for? And do you need to retrain it?

All of those you want to have some idea of so that you can calculate whether or not it was worth the energy to train this model in the first place.

Chris Skipper: Continuing the insightful discussion on the role of technology in environmental sustainability, we turn to Miguel Ponce de Leon, who shares his experience working on a collaborative project in Ireland. This initiative, a partnership between VMware and a local grid utility hosting a data center, focuses on integrating measurements from renewable energy sources directly into data center operations.

This effort not only enhances the understanding of energy usage within the data center. but also promotes actionable strategies for optimizing energy efficiency. Let's hear Miguel elaborate on how this collaboration helps data center operators become better environmental stewards by enabling precise monitoring and proactive management of resources.

Miguel Ponce de Leon: So one of the things I can mention is that we are working with a grid utility in Ireland and with that grid utility that also hosts a data center if that so happens. We're also working with an accelerator program, a program that is helping startups to look at how you can not just link but actually be able to take the correct measurements from the green sources, the wind farm locations, and the usage within the data center for its performance.

So, again, here it's about leveraging not just the research, we'd say, that would come from research performing organizations or from the offices of CTO of VMware, but also looking at start ups and start ups within the space and being able to link this. And that is helping the utilities. understand what type of usage, and imagine it's a utility that has their own data center.

So it's helping them be a good citizen even within their own environment, but being able to measure it and then being able to take action on it, right? Because that's the important thing is, okay, you've got your baseline, but what can I change about what I'm delivering within that data center? Even down to the containers, how can I move my clusters and pods over and maybe consolidate some of the pods?

We're even moving some of that research as well to look even with the pods. It's been available. How many of the CPUs are they using within the cluster? So again, it's about being able to help data center owners being good citizens around that space.

Chris Skipper: This next snippet comes from episode 32, which was all about the State of Green Software Survey, with lead researcher at the GSF, Tamara Kneese. The State of Green Software Survey serves as a political resource for the Green Software Foundation, offering crucial insights into the involving landscape of sustainable software practices.

By highlighting trends like the carbon footprint of crypto mining and the need for stringent regulations for generative AI, the report informs and influences stakeholders across the tech industry, from developers to policy makers.

In this snippet, Tamara emphasizes the report's role in enhancing the visibility of green software initiatives.

 

Tamara Kneese: So one of the main goals was really to raise the profile of green software. And I was really interested to see the percentage of developers who actually had some degree of awareness of green software. And so it makes sense that a lot of the people who filled out our survey already were somewhat aware of it.

and already interested. Although there were a number of people who replied in the comments that this was the very first time they'd been exposed to green software. And so by putting out this public report that can be taken up by the press, that can be taken up by policy makers, that can be taken up by academic researchers, it is a way of really getting the word out about green software.

Thinking about the reporting Court as a mechanism for evangelizing green software is really part of what we wanted to do. And we also wanted to understand after knowing that 92% of developers who surveyed said that they were concerned about climate change and wanna do something about it. So what do they need to actually make that happen?

What resources, tools. and other forms of support do they need to take action. And another key element of this is reaching out to ICT industry leaders to the C suite who really want to know how and why they should make green software part of their organization. And really trying to emphasize the business case for green software from their perspective was another really key part of this survey.

Chris Skipper: As we continue to look back on the previous year's journey, our next segment brings you insights from the Green Software Foundation's HotCarbon event, which took place on World Environment Day, June 5th, 2023. In episode 39, host Chris Adams, alongside Executive Director of the Green Software Foundation, Asim Hussain, Delved deep into a mailbag session, addressing questions that remained from this engaging live virtual event.

Their discussion begins with a look at the challenges of quantifying energy consumed by various computer components in the software carbon intensity specification, and transitions into real world applications of measuring SCI and CI/CD pipelines. The efficiency of GPUs and innovative uses of data center cooling water.

In our first snippet, Asim highlights a memorable talk from the previous year's Hot Carbon event, emphasizing the critical role of green software in managing the burgeoning demands on cloud infrastructure without the need to expand physical resources. Let's hear Asim explain this further.

Asim Hussain: It shows how important the work that we're talking about is. It's like, actually, it's one of the really great talks from last year's HotCarbon, which I loved, which was, I've forgotten, I've got to apologize, I'm not going to remember which one it was, but it was talking about how projecting forward kind of compute growth and how green software was a way of being able to handle the additional usage and load of the cloud without actually having to build more servers.

Because fundamentally we are constrained at the rate with which we can actually increase the cloud. But the growth is growing significantly as well. So like being more efficient actually allows you to, to deal with growth. So I think that sounds like what you're describing. So you have to be green, you have to use green software if you want a realistic chance of generative AI being as ubiquitous as you want it to be.

Chris Skipper: In this second snippet from Asim, he emphasizes the importance of broadening the dialogue beyond carbon to include other critical resources like water, acknowledging that managing environmental impacts often involves navigating trade offs between different sustainability goals. Let's listen to that now.

Asim Hussain: There might be situations where it's mutually the opposite, being more carbon efficient might actually make you more water intensive. Like for instance, doing things that reduce carbon emissions might require more water consumption, which is why I think it's exciting that we're actually are starting to have this conversation right now because I think we're so focused on carbon.

And we're optimizing for carbon, but actually the landscape is much more complicated. It's much more of a surface where you're trying to minimize the environmental impacts of your choices, and you might have to make trade offs versus one versus the other. If there's a water scarcity right now, you might have to increase your carbon emissions.

I'm excited that this is where the conversation is evolving to, because once we add water to the mix, we can add other things.

Chris Skipper: As we continue our exploration, our next episode takes a fascinating leap and dared to go where no podcast, or at least this podcast, had gone before, into the realm of science fiction and its role in envisioning a green future. In episode 42, host Anne Currie is joined by Joe Lindsay Walton, a research fellow in Arts, Climate and Technology at the University of Sussex.

Together, they delve into the imaginative horizons that science fiction opens for tackling climate change, the practical application of these ideas to green software, and the impact of speculative futures on our environmental strategies. In this snippet, Joe Lindsay Walton questions who really holds the reins in crafting a global strategy against climate change.

Let's listen to Joe's interesting take.

Jo Lindsay Walton: Are these orbital data storage facilities, are they going to Out compete the earthbound data centers that are using the dirty energy. Who actually holds the big picture of global strategy here, of addressing the urgent issue of climate change? Is it the conference of parties? Kind of, but they're mired in all these geopolitical rivalries.

Is it the scientists? The IPC? Yes, but they're constrained by the remit of political neutrality and face challenges around communication. Is it the finance and markets? They're waking up to something. They're trying to incorporate climate into these risk management methodologies that they don't really play all that nicely with.

Is it science fiction? Yes, we're drawing in a really interdisciplinary way. We've talked about Kim Stanley Robinson throwing everything at climate change, but it is ultimately a story. I'm not really sure who does hold the big picture. And if I was to try and summarize it in a crude way, it seems that we're hoping to adjust the rules of the game.

We haven't even adjusted them yet, but we're hoping to adjust the rules of the game so that Goods and services and enterprises and value chains and industries and sectors and whole communities and regions that are incompatible with a broadly livable planet are going to be destroyed in the Schumpeterian whirlwind of creative destruction, will crash and burn.

And I think there's a lot of emphasis. on the creation side of that, building data centers on the moon or in orbit, but not enough imaginative, creative, realistic thinking about the destruction side of it. There's this expectation that enterprises are going to snitch on themselves. Oh, we've tested for impairment.

We're reporting against this particular standard. All our assets are stranded. We're just going to shut up shop. Goodbye. So I think I would be interested in more science fictional Thinking about the potential pain of switching from carbon intensive activities to the sustainable ones. Not just the focus on the kind of shiny new possibilities, but also the focus on what it's like to shut up shop.

Chris Skipper: Environment Variables also dared to break boundaries on the academic front. Our next snippet comes from episode 47, titled, New Research Horizons, which takes us to the forefront of innovation in green software. Host Chris Adams is joined by Dr. Daniel Sheehan from the University of Bristol to delve into the evolving landscape of digital sustainability.

Their discussion spans from the implications of historical studies to the transformative potential of recent research, offering listeners insights into how new findings are reshaping our approach to sustainability in technology. In this snippet, Daniel discusses the nuances of energy consumption across different media delivery platforms, emphasizing the complexities in interpreting data that could potentially mislead consumers about the energy impacts of their viewing choices.

 

Daniel Schien: The academic publication that I'm sure you will link to it in the show notes. There's a graph that compares the energy intensity of those four different modes of delivery of television from the BC. So IP, cable, satellite, and terrestrial. And even though they, they differ. So between, if I remember correctly, between 60 watt hours to 180 watt hours, that's in the year 2016, there's a potential step in the interpretation of those results that consumers might take, that needs more support.

If you see this graph, you might I think as a consumer, if I change from streaming to watching something via terrestrial broadcast, I am going to save 100 watt hours per viewer hour. However, that would not be a correct interpretation because all of those delivery modes, they are provided by an infrastructure that is inherently inelastic in its energy consumption.

Chris Skipper: Jumping to November and Decarbonize Software 2023, an essential event for the Green Software Foundation took place. This annual gathering is crucial for those passionate about reducing the environmental impact of software. Bringing together experts, practitioners, and innovators worldwide to exchange insights, breakthroughs, and strategies for sustainable software development.

With COP28 kicking off on the 30th of November in Dubai, we wanted to highlight projects which are driving momentum towards a low emission and climate resilient world. At COP28, global leaders discussed how to reduce global greenhouse gas emissions by 43 percent by 2030.

In sync with this ambitious goal, Decarbonize Software served as a vibrant forum to promote collective action and shared knowledge. in our pursuit of greener software practices at the forefront of climate action. In episode 53, I was joined by Sophie Trinder and Adam Jackson of the GSF to give a rundown of what happened.

 In this next snippet, Sophie gives a rundown of one talk from the event titled Responsible AI, a fireside chat in which Jesse Mccrosky, Head of Sustainability and Social Change at ThoughtWorks, talked about integrating real time environmental impact metrics into our everyday software tools.

 

Sophie Trinder: Jesse painted this hypothetical picture. What if there was a carbon counter or there was a water gallons clock at the top of ChatGPT? Would you use it differently? Would you only use it when it was essential? Maybe you would write shorter prompts? Would you ask it to write its answers briefer? Similarly, he painted another picture where, what if a software developer's screen went red?

If the developer made a decision that, yeah, might make them save some time, might make them slightly more efficient, but what if it came with a huge carbon cost? So their screen went bright red when there was a carbon cost associated with the code that they were writing. I think it was just a really interesting way to Start thinking about being more transparent with that data so that we can be more conscious with our decision making.

Chris Skipper: Continuing our dive into the impactful insights from Decarbonize Software 2023, next we'll hear from Adam Jackson. In this snippet, Adam eloquently draws parallels between well crafted software and green software, emphasizing the necessity of built in quality throughout the entire software lifecycle.

Let's listen to how Adam articulates the holistic approach needed to integrate sustainability into software development.

Adam Jackson: I often tell folks when they ask me what green software actually is, that there's a lot of parallels with well crafted software. So well crafted software like green software focuses on built in quality throughout the complete software life cycles and architecture, development, operations, and getting all of the stakeholders together to take responsibility.

And we need the same thing for green software. So at the GSF, we often talk about software practitioners. And this is a broad term that goes beyond developers alone. It includes product managers, uh, program managers, designers, UX, testers, IT operations. And what takes the time, and GSF does have some materials that can help here, is developing the best practice, the processes, and the learnings that bring all of these people together.

into a really holistic software life cycle and I think that's why we're really keen to get organizations to hear and listen to others even if they're competitors. What's the best practice that we can all share that's gonna deliver green outcomes?

Chris Skipper: Transitioning now to our next insightful episode of The Week in Green Software, we delve into the pioneering sustainability strategies at one of the tech world's giants and GSF member, Google. In episode 55, Chris Adams is joined by Savannah Goodman from Google.

Who shares the ambitious climate goals set by the company. Google aims to achieve net zero emissions by 2030 and to operate on 24 7 carbon free energy by the same year. Savannah explains the complexity of moving from annual global matching to local hourly matching of energy use. illustrating the innovative approaches Google is deploying to meet these goals.

This episode not only highlights Google's efforts, but also discusses broader implications for the tech industry's push towards a more sustainable future. Let's hear from Savannah on how Google is tackling these ambitious targets.

Savannah Goodman: Google has two main climate goals. One of them is to be net zero by 2030. The other is to be running on 24 7 carbon free energy by 2030. And just to clarify too, 24 7 carbon free energy is much more complex. And I'm going to talk to you about how we've been able to make this a little bit more complex than the annual matching schemes that have been most common to date, because we're essentially moving from global annual matching to local hourly matching.

And so you can imagine how, especially over a global system, how complex that is. And there's no playbook, but we see these goals as a way to actually help scale new global solutions that drive broader system wide decarbonization because we're actually aligning our own goals with what the grid

Needs.

Chris Skipper: Next, we focus on episode 58 that dives deep into the Green Software Foundation's impact framework, probably the biggest highlight of the previous year. Host Asim Hussain is joined by Srini Rakanathan and Naveen Balani. who are at the forefront of developing this transformative tool. They discuss the challenges and solutions in creating a framework versatile enough to assess the environmental impact of software across various platforms, from large cloud providers to personal devices.

This conversation sheds light on the critical need for standardized models that can adapt to different environments without compromising on maintenance and adoption. Listen in as Srini shares Insights into the decision to implement model plugins for more effective integration and broader applicability in green software efforts.

Srini Rakhunathan: With the original concept that we had, where we wanted to cover all, we would have had to build multiple flavors of the impact framework. And that would have caused issues in maintenance, that would have caused issues in adoption. I think the standardization of a model plug in was more important. a decision that we took once we realized that one model is not going to cut the cake for all of us and you had different models depending on whether you are hosting it on AWS or Azure or GCP or your laptop or even your mobile devices.

But if someone wants to just look at the raw emissions from the software, agnostic of the hardware, you could do that only if you have a very thin measurement tools.

Chris Skipper: Finally, let's finish off on another episode of Twigs. In episode 63, we focus In episode 63, Chris Adams, Asim Hussain, and Anne Currie delve into the evolving landscape of AI legislation and its implications for green software. This first snippet from Asim highlights the accelerating integration of AI technologies and their significant energy demands, which could dramatically reshape global emission profiles by 2040.

Asim Hussain: The growth in AI has been significant. We all know on this call that the previous dirty secret of data centers where most of those servers were idle, in a future of AI, those chips are not going to be idle. They're going to be running at a hundred percent. So like, I think we've spoken on this call previously about, you know, various previous reports that talked about, you know, given the current trajectory by 2040, the tech sector will be like 14 percent of global emissions.

I wonder if anybody's doing any analysis. To revisit, well, now, given what we now know about the complete AI will take over everything. It is taking over everything right now. How does that look now? Where will we be in 2040 with the current growth in AI? Will tech be half of all emissions? And will we just be sitting there, you know, being carried around by robots and being fed by little tubes like that robot show?

But I think, I strongly suspect that they factor that in, and I wonder if it's an underestimate.

Chris Skipper: Our final snippet comes from Anne as she contemplates the future of green software practices.

Anne Currie: All discussion we're doing around this is great, fantastically great, but I always think about taking it back to my Maturity Matrix projects on the GSF Maturity Matrix project. You need to do different things at different times, so this information is useful to you in different things at different times of your journey.

So for most of us at the moment, we're not doing anything at all. Just, you know, you really don't need that much data. You know, your scope 1, scope 2, scope 3 are not immediately critical to most people who just need to turn off the machines they're not using anymore, do a bit of right sizing, have a think about what they're up to.

To start to think about, are they in green regions? What are the future, what platforms are they choosing? Are they likely to be aligned with the green future? To start with, where most of us are at the moment, you don't need a great deal of data to, to really make a huge difference. So it's absolutely fantastic that they're doing this work and that every, that we're doing this work.

Everybody's doing this work, but we don't want to lose sight of the fact that. For almost everybody at the moment, we don't even need data. We just need to start to, to use best operational practices.

Chris Skipper: What does the future hold for green software? Is it all doom and gloom? Absolutely not. The GSF is actively working on expanding its initiatives, pushing for widespread adoption of sustainable practices across the tech industry.

You can expect this podcast to bring you all the significant updates showcasing both the challenges and the innovative solutions that are making a real difference. Thank you for joining us on this look back on the last year of Environment Variables. Make sure to follow us on your preferred podcast platform to stay updated on all future episodes.

And don't forget to check out the show notes for links to further information discussed today. A final reminder. You can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. See you all in the next episode. Bye for now!

 

Hosted on Acast. See acast.com/privacy for more information.

]]> Join us for a special episode of Environment Variables as we celebrate over a year two years of bringing you the best insights on Green Software! In this episode, we explore the key insights and voices that have contributed to the weaving of sustainability through our conversations this year. Tune in for a refresher on the most interesting discussions on the progress, challenges, and future of green software development.


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TRANSCRIPT BELOW:
Chris Skipper: Hello and welcome to this special Year 2 Roundup episode of Environment Variables. I'm Chris Skipper, the producer behind the scenes. As we mark the second anniversary of this podcast, it's a perfect moment to reflect on the journey we've undertaken with the Green Software Foundation over the last year, and how that has been encapsulated through our episodes.

From its inception, Environment Variables has aimed to be more than just a podcast. It's a platform for advocacy and education on sustainable software practices. Over the past year, We've seen the Green Software Foundation grow and evolve, and we've been right there to document and discuss each milestone.

This podcast has not only followed the foundation's developments, but also mirrored the broader shifts towards sustainability and tech, bringing these insights right to your ears. Today, rather than revisiting our top episodes, we will explore how the themes of sustainability have woven through our discussions.

Highlighting key insights and the impactful voices that have contributed to this dialogue. You'll hear about the progress, the challenges, and what lies ahead for green software development. For your listening pleasure, as always, links to each of the episodes will be down in the show notes below. Or, if you want to listen to all of the episodes of Environment Variables, you can head to podcast.greensoftware.foundation, preferably after this episode, please, to hear them all. So, without further ado, let's dive into the collective journey of the past year with Environment Variables.
 

Chris Skipper: To kick us off, let's start off with an episode of one of our segments entitled the week in green software, or as we like to call it twigs,

This segment delivers a concentrated blast of the latest in green software news, keeping our listeners informed and engaged with current trends and advancements in the field.

In fact, it's a nice touch that this episode covers AWS and Scope3 emissions data, as the very first episode of Environment Variables way back in April of 2022 also covered Amazon's Customer Carbon Footprint tool. In this snippet, our host Chris Adams discusses a fundamental concept in environmental accountability, the Greenhouse Gas Protocol, which is the de facto standard for measuring the carbon footprint of any organization or activity.

Chris breaks down this somewhat complex subject using a relatable analogy involving coffee, explaining the three scopes of emissions. He uses everyday examples to illustrate Scope 1, Scope 2, and Scope 3 emissions, making it accessible for both newcomers and seasoned professionals, which has often become the role of twigs.

This approach not only simplifies the understanding of these emissions, but also highlights the significant impact of Scope 3 emissions, which often constitute the majority of an organization's environmental impact. Let's listen to Chris explain this in more detail.

Chris Adams: So I'll just quickly, for those who are new to the subject or folks who have never heard of GHG, the greenhouse gas protocol, essentially, this is a way, the kind of de facto standard for measuring the carbon footprint of any organization or any activity. And you typically split it into three kind of buckets of emissions.

And because we're nerds or developers and drink coffee, we can use hot beverages as the mechanism for understanding the difference between scope one, two, and three. You can think of scope one, which is from combusting fossil fuel. That's a bit like turning on gas to heat up water so you can have a nice cup of coffee, right?

Scope two, it's like turning on an electric kettle. So someone is setting fire to something to heat up some water somewhere to generate electricity so that you can heat up a kettle. So it's all the emissions associated.

Scope 3 is a bit like walking into a Starbucks or a Third Wave coffee shop and then buying a cup of coffee. So you're not involved in actually farming beans or burning anything, but there is definitely a supply chain associated so that you can have coffee. So these are the three kinds of scopes. And typically, Scope 1 and 2 are quite the common ones that organizations tend to report on, but for these, for lots and lots of organizations, scope 3 can make up 80 percent plus of the environmental impact.

And this is why we've been talking about it as being quite a big deal because if you do not have 80 percent of your reported numbers, they may look somewhat different to the other providers.

Chris Skipper: In the past year, Environment Variables has also introduced a new segment called Fact Check. This segment focuses on verifying claims and clarifying misconceptions related to the environmental impact of technology, providing our listeners with accurate and actionable information. In episode 29, Host Chris Adams discusses the role of artificial intelligence in optimizing the environmental impact of software.

He is joined by experts Colleen Josephson and Miguel Ponce de Leon from VMware, who delve into the intricacies of sustainability in virtualization and networking, as well as VMware's internal efforts towards decarbonization. In the snippet we're about to hear, Colleen Josephson highlights the challenges associated with training AI models in the telecommunications sector, a field where VMware has significant expertise.

She points out the substantial energy required to train these models and raises critical questions about the energy savings versus the environmental cost of AI, emphasizing the importance of evaluating the longevity and efficiency of AI models before committing resources. Let's listen to Colleen Josephson.

Explain these complex trade offs.

Colleen Josephson: Training is a very expensive process nowadays. Whether it's in the telco space, because, again, VMware, as you hinted, we have a long history of virtualization and cloud, but that also has become very relevant to telecommunications. We need to train the models that we use to make these decisions to try and save energy.

And the process of having so much data and training it, it can be really power consuming. So I think one of the things that stands out to me is, what is your anticipated energy savings? savings. Once you've deployed this model, how long do you anticipate that this model will be good for? And do you need to retrain it?

All of those you want to have some idea of so that you can calculate whether or not it was worth the energy to train this model in the first place.

Chris Skipper: Continuing the insightful discussion on the role of technology in environmental sustainability, we turn to Miguel Ponce de Leon, who shares his experience working on a collaborative project in Ireland. This initiative, a partnership between VMware and a local grid utility hosting a data center, focuses on integrating measurements from renewable energy sources directly into data center operations.

This effort not only enhances the understanding of energy usage within the data center. but also promotes actionable strategies for optimizing energy efficiency. Let's hear Miguel elaborate on how this collaboration helps data center operators become better environmental stewards by enabling precise monitoring and proactive management of resources.

Miguel Ponce de Leon: So one of the things I can mention is that we are working with a grid utility in Ireland and with that grid utility that also hosts a data center if that so happens. We're also working with an accelerator program, a program that is helping startups to look at how you can not just link but actually be able to take the correct measurements from the green sources, the wind farm locations, and the usage within the data center for its performance.

So, again, here it's about leveraging not just the research, we'd say, that would come from research performing organizations or from the offices of CTO of VMware, but also looking at start ups and start ups within the space and being able to link this. And that is helping the utilities. understand what type of usage, and imagine it's a utility that has their own data center.

So it's helping them be a good citizen even within their own environment, but being able to measure it and then being able to take action on it, right? Because that's the important thing is, okay, you've got your baseline, but what can I change about what I'm delivering within that data center? Even down to the containers, how can I move my clusters and pods over and maybe consolidate some of the pods?

We're even moving some of that research as well to look even with the pods. It's been available. How many of the CPUs are they using within the cluster? So again, it's about being able to help data center owners being good citizens around that space.

Chris Skipper: This next snippet comes from episode 32, which was all about the State of Green Software Survey, with lead researcher at the GSF, Tamara Kneese. The State of Green Software Survey serves as a political resource for the Green Software Foundation, offering crucial insights into the involving landscape of sustainable software practices.

By highlighting trends like the carbon footprint of crypto mining and the need for stringent regulations for generative AI, the report informs and influences stakeholders across the tech industry, from developers to policy makers.

In this snippet, Tamara emphasizes the report's role in enhancing the visibility of green software initiatives.

 

Tamara Kneese: So one of the main goals was really to raise the profile of green software. And I was really interested to see the percentage of developers who actually had some degree of awareness of green software. And so it makes sense that a lot of the people who filled out our survey already were somewhat aware of it.

and already interested. Although there were a number of people who replied in the comments that this was the very first time they'd been exposed to green software. And so by putting out this public report that can be taken up by the press, that can be taken up by policy makers, that can be taken up by academic researchers, it is a way of really getting the word out about green software.

Thinking about the reporting Court as a mechanism for evangelizing green software is really part of what we wanted to do. And we also wanted to understand after knowing that 92% of developers who surveyed said that they were concerned about climate change and wanna do something about it. So what do they need to actually make that happen?

What resources, tools. and other forms of support do they need to take action. And another key element of this is reaching out to ICT industry leaders to the C suite who really want to know how and why they should make green software part of their organization. And really trying to emphasize the business case for green software from their perspective was another really key part of this survey.

Chris Skipper: As we continue to look back on the previous year's journey, our next segment brings you insights from the Green Software Foundation's HotCarbon event, which took place on World Environment Day, June 5th, 2023. In episode 39, host Chris Adams, alongside Executive Director of the Green Software Foundation, Asim Hussain, Delved deep into a mailbag session, addressing questions that remained from this engaging live virtual event.

Their discussion begins with a look at the challenges of quantifying energy consumed by various computer components in the software carbon intensity specification, and transitions into real world applications of measuring SCI and CI/CD pipelines. The efficiency of GPUs and innovative uses of data center cooling water.

In our first snippet, Asim highlights a memorable talk from the previous year's Hot Carbon event, emphasizing the critical role of green software in managing the burgeoning demands on cloud infrastructure without the need to expand physical resources. Let's hear Asim explain this further.

Asim Hussain: It shows how important the work that we're talking about is. It's like, actually, it's one of the really great talks from last year's HotCarbon, which I loved, which was, I've forgotten, I've got to apologize, I'm not going to remember which one it was, but it was talking about how projecting forward kind of compute growth and how green software was a way of being able to handle the additional usage and load of the cloud without actually having to build more servers.

Because fundamentally we are constrained at the rate with which we can actually increase the cloud. But the growth is growing significantly as well. So like being more efficient actually allows you to, to deal with growth. So I think that sounds like what you're describing. So you have to be green, you have to use green software if you want a realistic chance of generative AI being as ubiquitous as you want it to be.

Chris Skipper: In this second snippet from Asim, he emphasizes the importance of broadening the dialogue beyond carbon to include other critical resources like water, acknowledging that managing environmental impacts often involves navigating trade offs between different sustainability goals. Let's listen to that now.

Asim Hussain: There might be situations where it's mutually the opposite, being more carbon efficient might actually make you more water intensive. Like for instance, doing things that reduce carbon emissions might require more water consumption, which is why I think it's exciting that we're actually are starting to have this conversation right now because I think we're so focused on carbon.

And we're optimizing for carbon, but actually the landscape is much more complicated. It's much more of a surface where you're trying to minimize the environmental impacts of your choices, and you might have to make trade offs versus one versus the other. If there's a water scarcity right now, you might have to increase your carbon emissions.

I'm excited that this is where the conversation is evolving to, because once we add water to the mix, we can add other things.

Chris Skipper: As we continue our exploration, our next episode takes a fascinating leap and dared to go where no podcast, or at least this podcast, had gone before, into the realm of science fiction and its role in envisioning a green future. In episode 42, host Anne Currie is joined by Joe Lindsay Walton, a research fellow in Arts, Climate and Technology at the University of Sussex.

Together, they delve into the imaginative horizons that science fiction opens for tackling climate change, the practical application of these ideas to green software, and the impact of speculative futures on our environmental strategies. In this snippet, Joe Lindsay Walton questions who really holds the reins in crafting a global strategy against climate change.

Let's listen to Joe's interesting take.

Jo Lindsay Walton: Are these orbital data storage facilities, are they going to Out compete the earthbound data centers that are using the dirty energy. Who actually holds the big picture of global strategy here, of addressing the urgent issue of climate change? Is it the conference of parties? Kind of, but they're mired in all these geopolitical rivalries.

Is it the scientists? The IPC? Yes, but they're constrained by the remit of political neutrality and face challenges around communication. Is it the finance and markets? They're waking up to something. They're trying to incorporate climate into these risk management methodologies that they don't really play all that nicely with.

Is it science fiction? Yes, we're drawing in a really interdisciplinary way. We've talked about Kim Stanley Robinson throwing everything at climate change, but it is ultimately a story. I'm not really sure who does hold the big picture. And if I was to try and summarize it in a crude way, it seems that we're hoping to adjust the rules of the game.

We haven't even adjusted them yet, but we're hoping to adjust the rules of the game so that Goods and services and enterprises and value chains and industries and sectors and whole communities and regions that are incompatible with a broadly livable planet are going to be destroyed in the Schumpeterian whirlwind of creative destruction, will crash and burn.

And I think there's a lot of emphasis. on the creation side of that, building data centers on the moon or in orbit, but not enough imaginative, creative, realistic thinking about the destruction side of it. There's this expectation that enterprises are going to snitch on themselves. Oh, we've tested for impairment.

We're reporting against this particular standard. All our assets are stranded. We're just going to shut up shop. Goodbye. So I think I would be interested in more science fictional Thinking about the potential pain of switching from carbon intensive activities to the sustainable ones. Not just the focus on the kind of shiny new possibilities, but also the focus on what it's like to shut up shop.

Chris Skipper: Environment Variables also dared to break boundaries on the academic front. Our next snippet comes from episode 47, titled, New Research Horizons, which takes us to the forefront of innovation in green software. Host Chris Adams is joined by Dr. Daniel Sheehan from the University of Bristol to delve into the evolving landscape of digital sustainability.

Their discussion spans from the implications of historical studies to the transformative potential of recent research, offering listeners insights into how new findings are reshaping our approach to sustainability in technology. In this snippet, Daniel discusses the nuances of energy consumption across different media delivery platforms, emphasizing the complexities in interpreting data that could potentially mislead consumers about the energy impacts of their viewing choices.

 

Daniel Schien: The academic publication that I'm sure you will link to it in the show notes. There's a graph that compares the energy intensity of those four different modes of delivery of television from the BC. So IP, cable, satellite, and terrestrial. And even though they, they differ. So between, if I remember correctly, between 60 watt hours to 180 watt hours, that's in the year 2016, there's a potential step in the interpretation of those results that consumers might take, that needs more support.

If you see this graph, you might I think as a consumer, if I change from streaming to watching something via terrestrial broadcast, I am going to save 100 watt hours per viewer hour. However, that would not be a correct interpretation because all of those delivery modes, they are provided by an infrastructure that is inherently inelastic in its energy consumption.

Chris Skipper: Jumping to November and Decarbonize Software 2023, an essential event for the Green Software Foundation took place. This annual gathering is crucial for those passionate about reducing the environmental impact of software. Bringing together experts, practitioners, and innovators worldwide to exchange insights, breakthroughs, and strategies for sustainable software development.

With COP28 kicking off on the 30th of November in Dubai, we wanted to highlight projects which are driving momentum towards a low emission and climate resilient world. At COP28, global leaders discussed how to reduce global greenhouse gas emissions by 43 percent by 2030.

In sync with this ambitious goal, Decarbonize Software served as a vibrant forum to promote collective action and shared knowledge. in our pursuit of greener software practices at the forefront of climate action. In episode 53, I was joined by Sophie Trinder and Adam Jackson of the GSF to give a rundown of what happened.

 In this next snippet, Sophie gives a rundown of one talk from the event titled Responsible AI, a fireside chat in which Jesse Mccrosky, Head of Sustainability and Social Change at ThoughtWorks, talked about integrating real time environmental impact metrics into our everyday software tools.

 

Sophie Trinder: Jesse painted this hypothetical picture. What if there was a carbon counter or there was a water gallons clock at the top of ChatGPT? Would you use it differently? Would you only use it when it was essential? Maybe you would write shorter prompts? Would you ask it to write its answers briefer? Similarly, he painted another picture where, what if a software developer's screen went red?

If the developer made a decision that, yeah, might make them save some time, might make them slightly more efficient, but what if it came with a huge carbon cost? So their screen went bright red when there was a carbon cost associated with the code that they were writing. I think it was just a really interesting way to Start thinking about being more transparent with that data so that we can be more conscious with our decision making.

Chris Skipper: Continuing our dive into the impactful insights from Decarbonize Software 2023, next we'll hear from Adam Jackson. In this snippet, Adam eloquently draws parallels between well crafted software and green software, emphasizing the necessity of built in quality throughout the entire software lifecycle.

Let's listen to how Adam articulates the holistic approach needed to integrate sustainability into software development.

Adam Jackson: I often tell folks when they ask me what green software actually is, that there's a lot of parallels with well crafted software. So well crafted software like green software focuses on built in quality throughout the complete software life cycles and architecture, development, operations, and getting all of the stakeholders together to take responsibility.

And we need the same thing for green software. So at the GSF, we often talk about software practitioners. And this is a broad term that goes beyond developers alone. It includes product managers, uh, program managers, designers, UX, testers, IT operations. And what takes the time, and GSF does have some materials that can help here, is developing the best practice, the processes, and the learnings that bring all of these people together.

into a really holistic software life cycle and I think that's why we're really keen to get organizations to hear and listen to others even if they're competitors. What's the best practice that we can all share that's gonna deliver green outcomes?

Chris Skipper: Transitioning now to our next insightful episode of The Week in Green Software, we delve into the pioneering sustainability strategies at one of the tech world's giants and GSF member, Google. In episode 55, Chris Adams is joined by Savannah Goodman from Google.

Who shares the ambitious climate goals set by the company. Google aims to achieve net zero emissions by 2030 and to operate on 24 7 carbon free energy by the same year. Savannah explains the complexity of moving from annual global matching to local hourly matching of energy use. illustrating the innovative approaches Google is deploying to meet these goals.

This episode not only highlights Google's efforts, but also discusses broader implications for the tech industry's push towards a more sustainable future. Let's hear from Savannah on how Google is tackling these ambitious targets.

Savannah Goodman: Google has two main climate goals. One of them is to be net zero by 2030. The other is to be running on 24 7 carbon free energy by 2030. And just to clarify too, 24 7 carbon free energy is much more complex. And I'm going to talk to you about how we've been able to make this a little bit more complex than the annual matching schemes that have been most common to date, because we're essentially moving from global annual matching to local hourly matching.

And so you can imagine how, especially over a global system, how complex that is. And there's no playbook, but we see these goals as a way to actually help scale new global solutions that drive broader system wide decarbonization because we're actually aligning our own goals with what the grid

Needs.

Chris Skipper: Next, we focus on episode 58 that dives deep into the Green Software Foundation's impact framework, probably the biggest highlight of the previous year. Host Asim Hussain is joined by Srini Rakanathan and Naveen Balani. who are at the forefront of developing this transformative tool. They discuss the challenges and solutions in creating a framework versatile enough to assess the environmental impact of software across various platforms, from large cloud providers to personal devices.

This conversation sheds light on the critical need for standardized models that can adapt to different environments without compromising on maintenance and adoption. Listen in as Srini shares Insights into the decision to implement model plugins for more effective integration and broader applicability in green software efforts.

Srini Rakhunathan: With the original concept that we had, where we wanted to cover all, we would have had to build multiple flavors of the impact framework. And that would have caused issues in maintenance, that would have caused issues in adoption. I think the standardization of a model plug in was more important. a decision that we took once we realized that one model is not going to cut the cake for all of us and you had different models depending on whether you are hosting it on AWS or Azure or GCP or your laptop or even your mobile devices.

But if someone wants to just look at the raw emissions from the software, agnostic of the hardware, you could do that only if you have a very thin measurement tools.

Chris Skipper: Finally, let's finish off on another episode of Twigs. In episode 63, we focus In episode 63, Chris Adams, Asim Hussain, and Anne Currie delve into the evolving landscape of AI legislation and its implications for green software. This first snippet from Asim highlights the accelerating integration of AI technologies and their significant energy demands, which could dramatically reshape global emission profiles by 2040.

Asim Hussain: The growth in AI has been significant. We all know on this call that the previous dirty secret of data centers where most of those servers were idle, in a future of AI, those chips are not going to be idle. They're going to be running at a hundred percent. So like, I think we've spoken on this call previously about, you know, various previous reports that talked about, you know, given the current trajectory by 2040, the tech sector will be like 14 percent of global emissions.

I wonder if anybody's doing any analysis. To revisit, well, now, given what we now know about the complete AI will take over everything. It is taking over everything right now. How does that look now? Where will we be in 2040 with the current growth in AI? Will tech be half of all emissions? And will we just be sitting there, you know, being carried around by robots and being fed by little tubes like that robot show?

But I think, I strongly suspect that they factor that in, and I wonder if it's an underestimate.

Chris Skipper: Our final snippet comes from Anne as she contemplates the future of green software practices.

Anne Currie: All discussion we're doing around this is great, fantastically great, but I always think about taking it back to my Maturity Matrix projects on the GSF Maturity Matrix project. You need to do different things at different times, so this information is useful to you in different things at different times of your journey.

So for most of us at the moment, we're not doing anything at all. Just, you know, you really don't need that much data. You know, your scope 1, scope 2, scope 3 are not immediately critical to most people who just need to turn off the machines they're not using anymore, do a bit of right sizing, have a think about what they're up to.

To start to think about, are they in green regions? What are the future, what platforms are they choosing? Are they likely to be aligned with the green future? To start with, where most of us are at the moment, you don't need a great deal of data to, to really make a huge difference. So it's absolutely fantastic that they're doing this work and that every, that we're doing this work.

Everybody's doing this work, but we don't want to lose sight of the fact that. For almost everybody at the moment, we don't even need data. We just need to start to, to use best operational practices.

Chris Skipper: What does the future hold for green software? Is it all doom and gloom? Absolutely not. The GSF is actively working on expanding its initiatives, pushing for widespread adoption of sustainable practices across the tech industry.

You can expect this podcast to bring you all the significant updates showcasing both the challenges and the innovative solutions that are making a real difference. Thank you for joining us on this look back on the last year of Environment Variables. Make sure to follow us on your preferred podcast platform to stay updated on all future episodes.

And don't forget to check out the show notes for links to further information discussed today. A final reminder. You can visit podcast.greensoftware.foundation to listen to more episodes of Environment Variables. See you all in the next episode. Bye for now!

 

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]]> Greening Software Procurement Greening Software Procurement Thu, 09 May 2024 07:00:00 GMT 52:19 81x7lr30 false https://podcasts.castplus.fm/e/vnw4x928-greening-software-procurement 68dc7d127be17a7f013569ef 68dc7cef46a2532cdd8d1c23 Joining Chris Adams today is Mike Gifford, an accessibility and open web veteran, to look at the drivers adopting digital sustainability in the industry, learn from the field of accessibility and inclusive design how we can further sustainable software development. Mike tells us about the wins from the accessibility movement that we can learn from in this engaging episode. 70 Joining Chris Adams today is Mike Gifford, an accessibility and open web veteran, to look at the drivers adopting digital sustainability in the industry, learn from the field of accessibility and inclusive design how we can further sustainable software development. Mike tells us about the wins from the accessibility movement that we can learn from in this engaging episode.

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TRANSCRIPT BELOW:

Mike Gifford: Yeah. So that's an interesting approach to structure content so that you're hopefully trying to go off and encourage your, the organizations to push themselves to better understand their users and be able to build a website that meet the needs of their users.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to the Green Software Foundation podcast, Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. When we look at the drivers adopting digital sustainability, in industry. As much as we want to think people will build greener digital services out of the goodness of their own hearts, it's worth bearing in mind that in a commercial environment, other concerns frequently take precedence.

And if we want to see wider adoption of more sustainable practices in digital, a lot of the time we have to be somewhat strategic, learning from other fields, for example, and being thoughtful about how we spend the social and political capital we might have available to us. So where should we be looking for people who have done this before?

One commonly cited field is the field of accessibility and inclusive design, where over the last 20 years, we've seen accessibility move from a field where lone, heroic actions were the norm, to one where, in growing parts of the world, accessibility is a non-negotiable, mandatory requirement in new projects with the full force of the law behind it now.

So, are there wins from the accessibility movement that we can learn from to apply for digital sustainability? And where in the history of accessibility online should we be looking? With us today, we have Mike Gifford, an accessibility and open web veteran, to help us navigate these questions. Mike, thanks very much for joining.

Can I give you the floor to introduce yourself today?

Mike Gifford: Absolutely. I'm Mike Gifford. I'm a senior strategist at Civic Actions. I'm excited to be here. And this is, I'm definitely an active listener and I've really enjoyed all the. Discussions that have taken place from other leaders in this field. I'm also a Drupal core accessibility maintainer. Drupal runs about a million websites around the world.

And I've been trying to improve accessibility in that field. And increasingly trying to do work in sustainability there as well. At Civic Actions, I lead the accessibility practice area. I've also been involved in contributing to the W3C's sustainable web community groups, draft web sustainability guidelines.

That's a bit of a mouth mouthful, but it's essentially just a group of people within a standards body called the W3C that is trying to go off and create a set of best practices around sustainability, and I've also spearheaded the development of a tool called OpenACR. Which is a new way to try and help organize vendor claims around digital accessibility.

Chris Adams: Brilliant. Thank you for that. And for folks who are listening to this for the first time, we do have a podcast episode specifically with the W3C, some of the other members, we'll link to that in the show notes. If you're new to this podcast, my name is Chris Adams, as I mentioned before. I work at the Green Web Foundation. Foundation, where we are a Dutch nonprofit focused on reaching an entirely fossil-free internet by 2030. I also am one of the chairs of the Green Software Foundation's Policy Working Group, and one of the maintainers of a software library called CO2.js, which you can probably guess what that does from the name.

It makes it easy to work out the environmental impact of various digital services. Mike, before we start, I should ask, because when I have spoken to you before, I've generally assumed you're in Canada, but you mentioned you might not be, so first of all, where are you calling from, and how's the weather?

Mike Gifford: So I'm calling from outside of Carcassonne, France, and I've been here for just over five months and I've got another four months to go. My wife wanted to have a sabbatical between careers, and this was a good place for us to learn French and eat cheese, stumble across castles and explore the culture of Europe.

So that's why we're here. And the weather here generally has been really good, especially compared to Ottawa, but right now it really has been a cold, nasty week this week. And so it's not being very good and it'll actually be warmer in Ottawa later this week than it is here in, in, you know, outside of Carcassonne.

It's a lot of climate chaos is going on around the world. We're seeing it all over the place. It's inconsistent weather.

Chris Adams: Wow, I did not, realize that. I generally would assume that Ottawa and Carcassonne, I mean, they're more or less the same latitude, right? But they are definitely not the same temperature most of the time, and Ottawa's definitely colder normally, right?

Mike Gifford: Yes, absolutely. We can get down to minus 20, minus 30 Celsius in the winter. And in the summer we can get up to plus 30, sometimes 35 Celsius, but it's a, it's definitely, it gets much colder. It has a much heavier impact from the Arctic weather.

Chris Adams: Wow, so I guess, thank you Jetstream for the clement weather in Western Europe. Okay. Long may it stay, I suppose. Yeah. All right. So I think you're sitting comfortably. For folks who are listening, we have show notes, so we're going to mention various projects and we'll do our best to list every single project and website that we do.

So if you are listening along and you want to learn more about one of the things we have, please do check out the show notes at podcast.greensoftware.foundation for more. All right, then, Mike, sitting comfortably, I guess should we start, yeah? Okay, so I did this introduction talking a little bit about accessibility, but I didn't do a particularly good job of explaining what accessibility might be.

And I'm wondering, before we talk about sustainability, maybe you could just expand a little bit about what people tend to mean when they talk about accessibility in this context, because I think there are a few misconceptions that people tend to bring when they first hear about accessibility.

What is accessibility, and what are the kind of myths that we might want to think about that we should probably dispel?

Mike Gifford: First of all, people assume that accessibility is just for the blind and deaf community. Like this is who we're trying to go off and to meet. And there's just a small number of people who comply with that. Why do we need to really worry with this? This is just a, you know, a government overreach additional administration, but that's, it's actually not the case.

If you look at permanent disabilities, most countries in the Western world, a quarter of the population has some form of disability. That could be, and that's a permanent disability, so that could be somebody who's got low vision or no vision, people who have hearing problems, mobility problems, could be cognitive problems, people who have dyslexia, color blindness.

It's a whole range of different aspects. And I tend to think of it like just part of a human experience. Is it that, that when we're born, we have certain limitations and we grow up and we learn how to do things. And we have, you know, we have abilities that we have at this time. But they change over time, you know, when I have allergies, my eyesight isn't as good because I have blurry eyes because I've got allergies.

There's also situations where as we age, like as soon as you're over 40, your eyesight begins to degrade. Probably most people listening to this podcast have glasses. That's a form of assistive technology. If you didn't have glasses, you probably would, would have much more difficulty reading a book or reading a monitor or your phone for that matter.

There's also issues of, you know, temporary and situational disabilities. So if you're, if you're injured, for example, where you're carrying a baby or you're in a loud environment, it may be more difficult for you to go off and to hear the other information that's going on around you. The other thing is looking at people who might be in a place where there isn't a lot of internet, like where if you're using an older device, these are also places where there's limited elements people can access.

And if you're in, in, in rural Canada, you can have a lot of difficulty going off and getting the bandwidth go off and to download a web page. And that's not a disability as such, but it is a way that is people are being disabled because the assumption is that people who are using the technology are going to have super fast

Chris Adams: infinite bandwidth, newest iPhone, everything like that.

Mike Gifford: If you don't have the latest device and the highest bandwidth, then you're going to have a lot of challenges. And so many people outside of urban areas don't. And even inside of urban areas, there's places where you can get, there's dead zones in cities where there, or that the things are wrong or inconsistent.

So a lot of our assumptions are incorrect. And I like to think about it also in terms of just, ultimately, you know, the planet is going to be fine with, you know, with or without us, the planet will survive. It comes down to us as a species and as a species, as Western civilization or civilization, how do we want to organize ourselves to survive?

And the brass tacks is that we have to work on a very fragile planet and we have to work with the fact that we are fragile species and that we need to go off and think about it not us at the height of our abilities, 

Chris Adams: The whole gamut, 

Mike Gifford: abilities throughout the whole gamut of our lives, right? Hopefully we'll all grow old.

Hopefully we'll all be able to explore and experience worlds where we, we do have different abilities because we've managed to live to 80, 90, a hundred years old, but that will be a different experience for all of us.

Chris Adams: I see, okay, alright, that's actually quite an expansive and interesting way of framing it. I haven't actually thought about it in that way, and the idea of saying, well, accessibility is actually a kind of... the fact that you've shifted it somewhat to the situation you're in is actually something that I think that's that's quite helpful framing. Okay, so we've got a rough idea that accessibility isn't just about helping a small number of blind people It's actually a significant part of the population in many cases probably larger than 25 percent when you include the different situational aspects someone might actually be in.

Okay, and it sounds like you know, did you ever see the London Olympics opening where they've got Tim Berners Lee or like Timball to his friends saying, where he has this whole thing, like this is for everyone. He's talking about the web and the internet. There's a thing that it seems very much that you're kind of, there's a reference to that, I suppose.

So these sound like good things, right? But we know that historically, when people are working on digital projects, accessibility hasn't had the same kind of priority that say, shipping a new feature might be, or making sure a particular date is hit by a team working on something. So let's say that, as society, the things you've described sound like good things to be aiming for and wanting to include.

How has maybe the accessibility community made sure these do get prioritized? Because they don't immediately all sound like they would have an immediate short term kind of benefit, for example, or that you might explain to on an earnings call, for example. Maybe you could, say, lay some of that out there and we can explore some of that area as well.

Mike Gifford: You'd think that it really wouldn't take much more of a carrot than trying to go off and support your future self, because that's ultimately you're doing. It's just trying to go off and make sure whatever you, whatever abilities you wake up with tomorrow, which may not be the same as they are today, are going to be, but you're going to be able to use the technology around and interact with the world as best as you possibly can.

That's not much to add, but it's not how people think. People think, people are very much more geared on what's the sexy new thing. Like. At FOSDEM, I gave a talk on accessibility, or sure, on sustainability, and I was, I've given talks on both of them, but it was, and I think it may have actually been both of them, an integration of the two and that, that one.

But the previous two sessions were on AI and they were packed. Like they were, it was like standing room only. And then my talk came up around sustainability. I mean, the room wasn't empty, but it certainly emptied out. And yet this is one of the things that like, this is life. This is us. You know, AI is neat and all, but ultimately it's not the thing.

But in terms of answering your question, what caused accessibility to go off and actually become a, a thing that people are paying attention to is the efforts of people with disabilities who have lobbied long and hard and protested often with civil disobedience in order to go off and to make sure that their rights are respected, seeing accessibility as a civil right.

And so that they are able to go off and have the rights to access by law. And in most Western countries, people with disabilities have rights to work and to employment, to housing, and to be able to communicate with their government. But that process is not being well respected. The digital world is being very much a move fast and break things kind of world, and, and actually supporting the bulk of their users is something that generally most companies don't invest in, even most governments.

Most governments have, far less accessible websites than they say that they should or say that they do. and, and it's something that, that you know, it's only through protest and through awareness raising and decades of effort that, that we've been able to get to where we are right now. and, and that's something that is still not where we need to be.

We're still not at a point where there's a final site that we can say, "Yes, this is accessible." This is like, it is... sites are more accessible, there is more awareness, but it's, it's just like security. It's about perpetual vigilance. So how do we make sure that we're more accessible today than we were yesterday, and that people are pushing that, that type of a framework forward?

Unfortunately, many people have a checklist mind set. They want to go off and say, "has this been done? Yes, let's move on." And that just. Unfortunately, it doesn't help people with disabilities who are actually experts around their own experience of where they're facing barriers with digital interfaces and you know, they need to be able to find ways to engage with their governments or engage with their stores or their friends in ways that are just like everyone else.

But that has, that does require effort and maintenance.

Chris Adams: I see, okay, so it sounds like there's a degree of, like, hard won essentially campaigning on various levels, and there's degree of, like, organ and there's maybe a degree of actual, like, organization with the people who are able to say, "well, okay, we need to find a way to translate these into concrete things you can ask for," or that people who do want to do the right thing, so they know how to go about doing that.

Mike Gifford: Right. Which is, which is how the web content accessibility guidelines. And the Web Accessibility Initiative from the W3C got that started because they saw that this was a need to try and, and provide some guidance and direction that everyone could agree on. And that would be a universal point that would allow everyone to agree that this is an improvement that, that benefits people.

And creating standards is a huge challenge, but it's been really important for organizations and governments to being able to lean on those and build legislation around that. Some of which actually have teeth for a lot of agencies or organizations around the world. So the, having legislation that requires accessibility is a, it's a huge deal.

Chris Adams: I see, and okay, so there's one thing that you just spoke about just there was this idea that, okay, you're essentially able to kind of capture this idea that society values this thing, which is not maybe immediately helping in a quarterly earnings call, for example, but it's still something that people tend to value, and that has ended up being translated into various forms of law, and one of the things that helped with the creation of that law was essentially things like the existence of some of these standards so that people could then say, "well, we want to have, you know, because we value access for everyone rather than just a very specific set of people, we're going to refer to these standards here saying you need to at least hit this kind of bar."

That's how some of it came about. Okay. And if I understand correctly, you, some of the examples you used earlier on about the situational example, like when you've got a broken arm or in your, I think I've seen some of that mentioned from Microsoft, actually, in some of their inclusive design things, so presumably there's an argument about the regulatory certainty that gets provided and means that organizations themselves might want to invest in saying, "well, okay, this is how we are competing in this by, you know, we're going to meet this bar when other people aren't," for example.

That's another kind of argument people take. Okay.

Mike Gifford: And Microsoft has had a great deal of leadership around the thinking around inclusive design and trying to think about how do we make sure that we are thinking about the whole self and not just ours, everyone on their best days.

Chris Adams: Okay, cool. So it sounds like there is a bit of a push. And so one way that some of these were adopted was essentially going through the public sector route to kind of demonstrate that there's demand and there's something that people need to go for and that and by creating that demand that allowed people to then respond to this.

Okay, so I've, are there any like particular countries or examples you've heard of? Because I've heard of like stories like say, I don't know, the things that started in the public sector that then kind of, percolated down. So like maybe I think things with Kindles, Kindle books, for example, or something like that, are there any examples we might point to show how something that large organizations might have been prioritizing first, but this end up kind of shifting them to start prioritizing something in a way that they weren't previously prioritizing?

Mike Gifford: I think that one of the challenges has been that, that governments have initially tried to go off and create their own legislation and, and not to build on a common standard. And one of the issues with this is that the tech is often global. And so if you're trying to go off and create a legislation, like so many governments individually in government agencies just don't have the clout to go off and to go to Microsoft and say, "Yeah. We want you to go to this and implement these standards because these are, if you want to sell to us, you have to follow these standards." Like maybe Microsoft will say, "sure, we'd love to do that. We're happy to go from bend over backwards and make that happen." But most likely they won't. They'll say, "sorry, if you're, this is what the product is.

And if you try it, we'll charge you X amount to go up in additional costs to go up from to meet those standards". But it, but it's not something that, that we can cover because you're such a small entity, generally how, how government procurements work. But, but if you're able to go off and work on a global standard, then technology, and especially if technology companies are involved, like they are, they have been to the W3C, that there's, there's an opportunity to, to get them on board and make sure that a lot of the kinks are worked out of the process, when it comes to implementation.

So that there are things that we know that can be implemented and can be maintained by the technology that we work with, whether that's open source or proprietary, there needs to be an engagement around how we're working on those same set of standards.

Chris Adams: So, one thing that you've just spoke about there was this notion of, okay, you, there's some work that goes into creating standards, because that allows both organizations, at governmental level to say, "hi, we need you to meet at least this bar," but from the other point of view, for people who are trying to meet these standards, let's say like, okay, you're calling from Europe instead of Canada today, right?

Everyone having 28 standards per country in all of Europe, for example, having one makes it a bit easier for someone who's inside the company saying, "hey, can we even at least meet this bar? Because this one will mean that we can, we don't need to do it across all these other countries as well," for example.

That's one of the ideas around the kind of some of these global standards. Yeah.

Mike Gifford: Absolutely. Having a single standard is much easier to get compliance around, particularly if you get a few larger economies that are adopting it early. Like if the European Union is able to adopt a standard around digital sustainability, that's something that will have real impacts around the rest of the world, because a lot of people want to sell their products into Europe.

So. So that's just like the GDPR has had a large impact on privacy outside of Europe as well as inside of Europe because it was something that was, was agreed to amongst a larger group of countries that there was a, an opportunity to really make waves across the digital sector.

Chris Adams: Okay, so that's quite interesting. So we spoke, we were speaking a little bit about accessibility and essentially the same things, because there is maybe not a short term gain for this, you might talk about it in terms of, okay, there is, you're creating some certainty and saying, well, these are things you need to actually have, and I guess in the context of some recent laws we're seeing passed, you might see some of that start to play out in the form of, I think we have things like, say, the CSRD, the Corporate Social Responsibility Directive in Europe, as one example, but even in America, or even in, I think, California specifically, because California is a big enough economy in its own right.

You're seeing some regulations there which end up acting as a kind of driver to kind of move to rather than just have people inside companies saying, "we'll save you some money on your green service." It's like, "no, this is the thing that's actually going to be, this is the bar we need to meet if we want to kind of keep selling to these people here," for example, or "this is a risk that we can head off by making sure we're compliant with this."

That seems to be the argument you're essentially kind of making around that, right?

Mike Gifford: That's right. And I think that, that the, it'll be interesting to see as scope 3 emissions get, get, more closely monitored and evaluated the, the impact of digital, I think is going to be a lot bigger than it currently is because most people don't, don't own a data center, they use services. So if you're, if you're suddenly looking at those people who are managing all the different technology that we're engaging with our lives on a regular basis, that has a third party impact and in terms of virtual pre-emission changes, and I think that we, we're going to, it'll be more of a challenge to try and figure out how to regulate those and to address them.

The web doesn't really affect most people for their scope one or scope two emissions, but it will certainly affect for, for scope 3 emissions.

Chris Adams: Right, I'm just going to take a kind of step back out for this. So I just want to check. So when you've mentioned the words scope 3 and scope 2 and scope 1, I assume you're talking about, say, the Greenhouse Gas Protocol. This is a bit like an international standard for tracking carbon emissions for an organization, for example.

So where scope 1 might be me burning, say, gas and emissions caused from, I mean, I use the hot beverage example because that seems to be quite easy way to get your head around it. Scope 1 is me burning wood or coal to heat up a kettle so I can have coffee, right? Scope 2 is me consuming electricity, so someone else somewhere is burning coal or gas so I can have coffee.

And then scope 3 is the entire supply chain, so that's like me going into Starbucks so I can have coffee. And then, there's still a supply chain, but I'm not burning anything myself, but, you know, there is still someone accounting for the emissions that way, and that's what you're talking to when you talk about these kind of scope 1, 2, and 3 thing here, right?

Okay, brilliant. So, we spoke a little bit about how there are maybe some patterns, some ideas, and some lessons from the accessibility movement where, with the creation of some standards, we've then seen people tie this to, say, how projects get bought. Like, someone help people allocate time and money to do a piece of work and I think the one thing that when we spoke before you mentioned about how you saw this happen at a kind of public sector level first saying "we have to make this accessible because we have a mandate to serve all of our population not just the rich people basically or not just like the this one target market we've decided to segment and go after" essentially but that ends up creating a kind of norm which then is allows, say, other sectors or kind of more other private sectors to start looking for that as well.

And I think there have been examples of, was it the Domino's pizza, for example? The famous example where once people had set a norm that you actually had accessible websites, then was it, maybe you could expand on this one because I think I'm going to get it wrong, but I do remember like Domino's had to start rebuilding their websites and making sure their sites were also accessible to people because the norm had been set.

Mike Gifford: There's a, the US has an unusual piece around accessibility where if your website isn't accessible, you can be sued. There's people who've set up lawsuits and yeah, and some of them are not as legitimate as others, there's easy enough to find accessibility bugs in various different websites. But, but there are, there are people who are chasing the court system in order to try and get revenue from lawsuits.

So then that happens quite a lot in the states and that's a huge motivator around accessibility in the world actually, is the, the sphere of lawsuits from or within the United States. But domino's got sued and they were like, "we're not going to pay for this. We're just going to go off and channel just in the courts.

We don't think this is necessary." They did end up losing the case and they also spent quite a lot more in, in, legal fees than they did in actually hiring web developers who knew what they, they were doing and actually were able to fix the issue. But it was a, it was an interesting case in that, that they, just the egos of the senior management at Domino's.

Were like, "well, we don't want,