Environment Variables

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.Learn more about our people:Anne Currie: LinkedIn | WebsiteJulian Gommlich: LinkedIn | WebsiteFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterResources:Power in Numbers: Mapping the electricity grid of the future w/ Olivier Corradi [31:02] Electricity Maps [31:58]Google’s huge new Essex datacentre to emit 570,000 tonnes of CO2 a year [41:06] Compute Gardener SchedulerScalable Platform for Reporting Usage and Cloud Emissions Events:BetterSoftware – October 3 · Turin, ItalySustainable AI: Energy, Water, and the Future of Growth – October 6 · San Francisco, USA Sustainable Coding: Rust Meets the Right to Repair – October 16 · ’s-Hertogenbosch, Netherlands Hosted on Acast. See acast.com/privacy for more information.

Didi Hoffmann, CTO and co-founder of Green Coding Solutions, dives deep into the intersection of software and sustainability. He shares his journey from Linux kernel development to advocating for measurable software energy usage. Didi explains the creation of PowerLetrics, an open-source tool for tracking process-level energy consumption, and the importance of integrating energy metrics into operating systems. They also discuss the Blue Angel certification and the need for continuous sustainability checks in software development.

Boris Gamazaychikov, Head of AI Sustainability at Salesforce, discusses the critical intersection of artificial intelligence and environmental responsibility. He highlights the varying energy impacts of AI models and the launch of the groundbreaking AI Energy Score for benchmarking. Boris explains how specific models can lead to massive efficiency gains, the necessity of aligning AI workloads with renewable energy, and the importance of transparency in AI development. He encourages embracing AI while advocating for smarter, sustainable strategies in the tech landscape.

WIN FREE TICKETS TO GREEN IO LONDON:CLICK THIS LINK AND COMMENT BELOW TO WIN Learn more about our people:Chris Skipper: LinkedIn | WebsiteGosia Fricze: LinkedIn | WebsiteFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterResources:Green Software Movement | GSF [04:33] Green Software Practitioner Course | GSF [17:56] Environment Variables Podcast | Ep 84 Backstage: SOFT (Previously TOSS) Project [24:42] Events:Green IO London Conference September 23 & 24 2025 [20:37] Events - Green Software Movement | GSFIf you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!Connect with us on Twitter, Github and LinkedIn! Hosted on Acast. See acast.com/privacy for more information.

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. Learn more about our people:Chris Adams: LinkedIn | GitHub | WebsiteAsim Hussain: LinkedIn | WebsiteFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterNews:A Gift from Hugging Face on Earth Day: ChatUI-Energy Lets You See Your AI Chat’s Energy Impact Live [04:02]Our contribution to a global environmental standard for AI | Mistral AI [19:47]AI Energy Score Leaderboard - a Hugging Face Space by AIEnergyScore [30:42]Challenges Related to Approximating the Energy Consumption of a Website | IEEE [55:14]National Drought Group meets to address “nationally significant” water shortfall - GOV.UK Resources:GitHub - huggingface/chat-ui: Open source codebase powering the HuggingChat app [07:47]General policy framework for the ecodesign of digital services version 2024 [29:37]Software Carbon Intensity (SCI) Specification Project | GSF [37:35]Neural scaling law - Wikipedia [45:26]Software Carbon Intensity for Artificial Intelligence | GSF [52:25]Announcement:Green Software Movement | GSF [01:01:45] If you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!Connect with us on Twitter, Github and LinkedIn!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. IAsim 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 wantAsim 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 personallyChris 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 inChris 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, soChris 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 somethinglike 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, perhapsAsim 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 upChris 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'sChris 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 forChris 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 benchmarkChris 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 toChris 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'sChris 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 perChris 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 yearAsim 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, capitalAsim 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 thatChris 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 wrapChris 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.   Hosted on Acast. See acast.com/privacy for more information.

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.Learn more about our people:Chris Adams: LinkedIn | GitHub | WebsiteScott Chamberlin: LinkedIn | WebsiteFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterNews:Set a carbon fee in Sustainability Manager | Microsoft [26:45]Making an Impact with Microsoft's Carbon Fee | Microsoft Report [28:40] AI Training Load Fluctuations at Gigawatt-scale – Risk of Power Grid Blackout? – SemiAnalysis [49:12]Resources:Chris’s question on LinkedIn about understanding the energy usage from personal use of Generative AI tools [01:56]Neuralwatt Demo on YouTube [02:04]Charting the path towards sustainable AI with Azure Machine Learning resource metrics | Will Alpine [24:53] NVApi - Nvidia GPU Monitoring API | smcleod.net [29:44]Azure Machine Learning monitoring data reference | Microsoft Environment Variables Episode 63 - Greening Serverless with Kate Goldenring [31:18]NVIDIA to Acquire GPU Orchestration Software Provider Run:ai [33:20]Run.AINVIDIA Run:ai Documentation  GitHub - huggingface/AIEnergyScore: AI Energy Score: Initiative to establish comparable energy efficiency ratings for AI models. [56:20]Carbon accounting in the Cloud: a methodology for allocating emissions across data center users If you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!Connect with us on Twitter, Github and LinkedIn!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 kindScott 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. SoChris 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. SoChris 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 runwhere 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 happenswhen 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 tendScott 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.  Hosted on Acast. See acast.com/privacy for more information.

In this discussion, Sean Varley, Chief Evangelist at Ampere Computing, dives into the urgent need for energy efficiency in AI. He highlights how power caps and utilization are more critical than sheer compute power. Varley showcases Ampere’s innovative chip designs that enable sustainable cloud-native computing, and discusses partnerships, like the one with Rakuten, which have resulted in significant energy savings. The conversation also touches on the importance of machine utilization and innovative cooling solutions for greener data centers.

Adrian Cockcroft, former VP of Cloud Architecture Strategy at AWS and a microservices pioneer at Netflix, discusses the evolution of cloud sustainability. He highlights the shift from monolithic systems to serverless computing, emphasizing carbon tracking as a crucial metric for developers. Adrian also shares insights on the transparency challenges cloud providers face regarding energy data. Additionally, he delves into his passion for building generative AI-powered smart home systems, merging technology with eco-friendly practices.

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:Chris Skipper: LinkedIn | WebsiteNavveen Balani: LinkedInFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterResources:Software Standards Working Group [00:18]GSF Directory | Projects [01:06]https://wiki.greensoftware.foundation/proj-mycelium [03:57]Software Carbon Intensity (SCI) Specification | GSF [04:18] Impact Framework [08:09]Carbon Aware SDK [09:11]Green Software Patterns [09:32]Awesome Green Software | GitHub [10:11]Software Carbon Intensity for AI [10:58]Software Carbon Intensity for Web [12:24]Events:Developer Week 2025 (July 3 · Mannheim) [13:20]Green IO Munich (July 3-4) [13:35]EVOLVE [25]: Shaping Tomorrow (July 4 · Brighton) [13:51]Grid-Aware Websites (July 6 at 7:00 pm CEST · Amsterdam) [14:03]Master JobRunr v8: A Live-Coding Webinar (July 6 · Virtual) [14:20]Blue Angle for Software / Carbon Aware Computing (July 9 at 6:30 pm CEST · Berlin) [14:30]Shaping Progress Responsibly—AI and Sustainability (July 10 at 6:00 pm CEST · Frankfurt am Main) [14:41]Green Data Center for Green Software (July 15 at 6:30 pm CEST · Hybrid · Karlsruhe) [14:52]If you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!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 theenvironmental impact of software. What makes it unique is its focus on three core levels,energy efficiency, hardware efficiency, and carbon awareness. Energy efficiencylooks 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 itis 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 ofthe 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 asSCI 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. Hosted on Acast. See acast.com/privacy for more information.

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:Chris Adams: LinkedIn | GitHub | WebsiteAnne Currie: LinkedInChris Skipper: LinkedInPindy Bhullar: LinkedInLiya Mathew: LinkedInAsim Hussain: LinkedInHolly Cummins: LinkedInCharles Tripp: LinkedInDawn Nafus: LinkedInMax Schulze: LinkedInKillian Daly: LinkedInJames Martin: LinkedInFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterResources:Backstage: TOSS Project (02:26)Backstage: Green Software Patterns (04:51)The Week in Green Software: Obscuring AI’s Real Carbon Output (07:41)The Week in Green Software: Sustainable AI Progress (09:51)AI Energy Measurement for Beginners (12:57)The Economics of AI (15:22)How to Tell When Energy Is Green with Killian Daly (17:47)How to Explain Software to Normal People with James Martin (20:29)If you enjoyed this episode then please either:Follow, rate, and review on Apple PodcastsFollow and rate on SpotifyWatch our videos on The Green Software Foundation YouTube Channel!Connect with us on Twitter, Github and LinkedIn!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 geoChris 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. HolisticallyChris 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. 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