Intro

This Week in Green Software, the affable Anne Currie is joined by Sara Bergman, Senior Software Engineer at Microsoft and co-author of Building Green Software. Together, they dive into the complexities of sustainable data in relation to AI and cloud computing. They explore the environmental impact of managing and storing vast quantities of data, and question the feasibility of making these processes more eco-friendly. The discussion touches on cloud providers' carbon reporting, the importance of using AI responsibly, and how businesses can optimize their cloud use to minimize their environmental footprint. Tune in for an insightful conversation on balancing technological advancements with sustainability in the age of AI.

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TRANSCRIPT BELOW:

Sara Bergman: What data do we need to take in order to take meaningful action? Like, what is the level that, of course, yeah, if I could get minute by minute, like there's tons of stuff we could do and correlations we could draw, but what is the level of data that we would need to start taking meaningful action? And I think that could unlock a lot of good things.

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

I'm your host, Chris Adams.

Anne Currie: Hello, and my name is Anne and welcome to 

The Week in Green Software. So this week, you won't be hearing the usual dulcet tones of Chris Adams. I'll be joining you, Anne Currie, and we'll be delving into the tricky, but interesting world of sustainable data, whether it's possible to store and manage huge quantities of data, which we will need now, particularly for AI, in a way that's eco friendly.

Or is that impossible? Well, look, I'm going to leap through to the end and say, we have to do it. And therefore it is not impossible. It just has to be done. We'll find a way of doing it. And there are ways of doing it, which we'll be talking about today. we'll also be exploring why big cloud providers' carbon reporting isn't really telling us the full story.

Or, well, we are not interpreting it in the way that it was designed to be interpreted. And we need to be more careful about that. We're reading more into it than is true. And we need to be much, much more explicit about what the carbon reports from people like AWS and Azure actually mean.

We'll also be talking about data centers and AI. And that's something that my guest today is an expert in. And talking about my guest, joining me today is my co-author on Building Green Software, the book on what we need to do to make the tech industry green, and also Environment Variables regular, the lovely Sara Bergman.

So Sara, do you want to introduce yourself?

Sara Bergman: Yeah, hi. Thanks for having me on again. Always lovely to be here. I'm so excited to have a chat with you, Anne. Yeah, my name is Sara Bergman. I am a senior software engineer at Watttime. Microsoft, author of Building Green Software. And like here, I'm always asked, what have you been up to recently? Nowadays, I'm like, what have I been up to recently?

I did a fun thing, though. I had the talk for the Norwegian, because I live in Norway, and the Norwegian tax authorities about green software. And that was really fun. I love it when Because they are actually very far ahead in their journey, they're one of the most innovative companies when it comes to IT, so it was really fun to come out and have a chat with them.

Anne Currie: That's great. And I should introduce myself as well. My name is Anne Currie and I am co-author of O'Reilly's new Building Green Software along with Sara and our other co-author, Sara Hsu. And I also do a lot of training. So I've been busy at the moment doing loads of training courses. So, workshops on building green software and also an experts training course, which is all quite good.

So, if you want to get involved in any of that stuff, you can follow me on LinkedIn. So, as usual, today, we will be talking about a couple of interesting articles, publications that have come out over the past week around green software, all things green, and as usual, all the links to the articles will be in the show notes, so you'll be able to read them yourselves afterwards.

But I'll give you a little bit of a summary about what they say. So the first article we'll be talking about today is from the Green Web Foundation. And it was written by our normal Environment Variables host, Chris Adams. So that's where he is, or that's what he's been up to today in his work at the Green Web Foundation.

And he wrote it with his colleague, Hannah Smith. And the report is all around AI's environmental impacts. And it's got some interesting figures in there. Basically, AI uses a load of electricity and at the moment, as we don't yet have a completely green grid, that means that a lot more CO2 is being emitted into the atmosphere as a result of the fact that we're training a lot of models, doing a lot of inference.

So it is an interesting report and it's, I'm going to kind of summarize, they have some actionable things, some questions for you to ask yourself at the end of the report. So I will go over those now and then Sara and I can discuss them. The first is that you should always question your use of AI.

That's kind of part of using the right tool for the job. Is AI the right tool for what you're doing? Is it overkill? Could you use a spreadsheet? That's If you are using AI and you decide it is the right tool for the job, are you using it properly? Are you using it well? Are you using the right AI tools for the job?

And the third is to try and get ideas of your footprint, of the footprints of the work that you're doing with the AI, so you actually have an idea about what impact it is having and what you're going to need to do about it in the future. So Sara, this is kind of your area. Did you enjoy the report? What did you think?

Sara Bergman: I did. It's a very long report. It's very well written. Obviously, I mean, with Chris and Hannah, you're going to get something that's well written, of course. So no surprises there. No, it's good. And I think also for people who are maybe newer to the field of green software and green AI in general, there was a lot of good background to like really help understand the intricacies of this area. And something that I particularly find interesting in the shift we are now is that they talk about different phases where your emissions kind of stem from, it's like manufacturing, training and inference. And now, like you said, we talk a lot about inferencing, like using AI, that the use phase is what we talk about.

But back when I started, sounds like I'm really old, only four years ago, not that long ago, but when I first started talking about green AI. Yeah, a lot of, a few people, not so many were talking about green AI. A lot of people were researching, but not so many people were like discussing it. And then it was a lot of focus on the training.

There was a lot of great research being done on how to minimize the impact on training. I think in the research community, that's maybe the easiest, not the easiest thing, but a good first thing to research, right? And now we're seeing more focus on. on the production side, not like inferencing. And I think that shift has been very interesting to follow.

Anne Currie: But yeah, it is fascinating, isn't it? Cause there's loads, and we've had this conversation offline, because obviously we a book together and therefore we talk together quite a lot. But we've had this conversation a lot in that it feels like there's a load of stuff to be learned about inference.

So how you actually get the answers back as a user for models from the world of things like CDNs, how do you get fast answers and easy answers to things all over the world from data that is not necessarily by default, wasn't created close to the user who's querying it?

So yeah, there's, loads of prior art there to learn from it. It's a really interesting field.

Sara Bergman: It is a very interesting field and I think an additional like spiciness of this is that now a lot of people equate AI to large language models. That's not all of the AI that exists in the world, it's just the right now the most popular and what we think maybe will be the most interesting use case, or for like productivity and stuff.

And for that we require a lot of specialized hardware, like GPUs or TPUs on CPUs, et cetera, which is another thing to kind of reason over. And, maybe if you're very good at handling your production workloads on a CPU, like this might be a new area for you where you, we need to learn as an industry. I thought that was an interesting part of the article.

Anne Currie: Yes. Yeah, it is. It is all very interesting, isn't it? it's like something that all three of us say in the book a lot, is that there's a load of clever stuff going on in the tech industry, and it's usually, there's a desire to make things efficient because it's cheaper. Otherwise, everything gets very expensive.

So if you use things as they were intended to be used and use the right tools for the job, usually that's significantly more energy efficient and therefore greener. And the same is very much true of AI, isn't it? If you use the right tools, the right hardware, the right models,

Sara Bergman: Yeah, exactly. Exactly. So that and then I think that ties into right, that was it number two on their like, use AI responsibly, which was interesting. I also like the first question that they had. Now I'm scrolling and losing it was it think about when you should use AI? Was that the phrasing?

Yeah, question your use, know when to use it. Like, do you have a favorite use case?

Anne Currie: Well, I have to say, I do love generative AI. 

Sara Bergman: Yeah. 

Anne Currie: it's, an interesting one, cause quite often... well, it's amazing how often it comes up where people think that it's kind of like you're either doing AI or you're green. That's it. Those are the two, two options. And they cannot be the two options.

It can't be either don't do AI and therefore it's green or do AI and therefore it's not green. They have be brought together because if there's one thing that we know, two things we know are coming in the future for humanity. One is climate change. The other is AI and use of AI and AI systems.

They cannot be mutually exclusive. They have to be brought together. I mean, and AI is just computing. It's the same kind of things that we do when we talk about in Building Green Software for all computing just needs to be applied to AI. It's not a, it's not a new thing on its own, but

Sara Bergman: and I agree. And I think that's also so interesting with this, like, larger discourse, as you say, I think not only that it exists in other areas of life, where there are some people who would be like, "no, we should just stop doing this, like, stop advancing technology, and then everything will solve itself," but that doesn't work. We cannot and does not want to go back to like a farmer society where everyone grows your own food. Like if you're into growing your own food, that's fantastic. I'm happy for you. I grow radishes on my balcony and I enjoy that thoroughly. But there's so much advantage and good for humanity and our planet that comes from technology as well.

So we need to do both. We must do both. Like, it's non negotiable. So it's more like how and when? And 'when' is hopefully starting now.

Anne Currie: Cause the trouble is, if you have that conversation, if you say, "Oh, well, I want to do AI, therefore I can't do green." People will choose AI because the businesses, if they don't, if they don't try these new tools and services as they come available, they will go out of business. So, if you say, "Oh, well, if you're going to be green, you can't use AI," then what you're telling people is don't be green.

And it's crazy. You're just making the wrong arguments. But anyway.

Sara Bergman: I fully agree for sure. And I think it was something we were in a different, or like this podcast together, but another episode and you said something that I've been carrying with me for so long. Sometimes people say something and it just get like something clicks. And what you said, and I'm going to maybe paraphrase, but things that are limited are sort of less concerning. Like, for example, if you build a super efficient washing machine, I still have only so much washing up to do. There is an end to how much I will wash it. Like this is in response to Jevons paradox, of course. And the same goes for this. Like, yeah, AI will make us more efficient, more productive.

Okay. But we'll then just do more. It's like, yeah, but the working day is sort of eight hours and we are only so many people on the planet. There is a limit to when we'll be done, whereas for crypto, for example, which always comes up when we talk about this, of course, there's no limit. I will never say I have enough money, I'm done mining. But there is a limit where I say, thank you, co-pilot for GitHub, I don't need more code suggestions.

I'm happy with my feature now, sort of.

Anne Currie: Oh, yeah. That's an issue, because, I always tend to think that one of the, one of the things about AI is that, that we're not,, we haven't yet reached the limits of what we want to do with it. But, you're right. There's, it's nowhere near. I mean, we've already come up with the thing that is the most limitless.

You've heard the use of CPU and it's CPU use that's really the problem that's, that uses a lot of electricity, is crypto. It is literally boundless how much we might want to do with it. So we've kind of already created the worst 30.

Sara Bergman: yes.

Anne Currie: AI is so

Sara Bergman: not it.

Anne Currie: it's a comparison. At least there's some people, 

I don't know, we shouldn't be controversial because there are a whole load of people who say, well, there's loads of benefits of cryptos, but I personally do not, I'm not a crypto bro.

Sara Bergman: Same. No, I did write my master's thesis on blockchain. I think blockchain is a cool invention. Like, it's a cool technology. And there are use cases that are, of course, bounded. And then there are use cases that are unbounded. And I think people can use their own head to figure out what's what.

Anne Currie: We've now got ourselves into enough trouble talking about good and bad uses of technology. And to a certain extent, it's very hard to say what's a good or bad use of technology. I tend in my head to think it was a bad use of technology, I, not everyone agrees with me, but we will move on to the next article, which is one, now...

So this one, cloud provider Greenhouse Gas reporting isn't enough. The case for product level accountability. Which is, again, the link is included in the show notes. Now I know, because this talks about Azure, Sara, you, might be in a position where I'm saying things to you and you have to say, you may say that "Anne, but I cannot possibly comment."

So in which case you'll just have to leave me to talk, but that's fine. Cause I can talk endlessly about this stuff because I find it really, I think this is absolutely fascinating and there's loads of stuff to learn from this article. So the article is all about, it's highlighting the limits of cloud providers and data, the carbon footprint reports.

And it emphasizes the need for companies to track emissions at product, at data product level. And it argues that these broader reports mask the true environmental costs of specific processes like data and that things would be better if you broke the emissions down to product level. And I think that's great, but I don't think that's actually the problem.

I think it's well worth reading the article, but I think it skips over, I think, what is the more significant problem that we're having, that we're seeing, because I'm out there talking to people a lot and I know this is a significant problem at the moment, that the cloud providers' reports are doing something, a lot of the new cloud providers' reports, particularly the recent AWS releases are doing something which I can't really shout at them for because I demanded it myself some years ago and I am somewhat hoist by my own petard for this.

So many years ago, me and a fellow, a collaborator called Paul Johnston ran a, back in 2018, we ran a campaign called Sustainable Servers by 2024. And what we were campaigning for was that all of the cloud providers would commit, and this was some time ago, would commit to being carbon neutral by 2024.

And it was really aimed entirely at AWS because Google and Azure were already carbon neutral at that point and AWS was not. So, we ran this big campaign and we had petitions and all kinds of things for saying that they should be carbon neutral, collateral and tradeful. And of course, carbon neutrality is quite a limited demand.

It's basically saying, "I want you to be carbon neutral. I want you to know how much your carbon emissions are. And then buy offsets that match the carbon emissions." Now, we all know that offsets are good in the past, but they're not time matched. They don't mean that the carbon savings that you've created are time matched with the carbon emissions of your systems.

So your systems can still be throwing off carbon dioxide into the atmosphere whilst you are carbon neutral. So it was a useful, a useful measure in 2018, it was still useful. Now in 2024, it's actually not as good as it could, it's not enough. it's the bare bones. It's the least we can ask for.

But AWS have done it on time, 2024, and they are producing these reports that they're giving to AWS customers that say, "you're carbon neutral, your systems are net, not producing any carbon." But the problem is that a report that says, and that's great, but it is nowhere near enough.

It's not enough. It's great information for your finance team because your finance team don't want that data. They need that data, particularly if you're going to be, if you're in the EU or you're selling into the EU. Because the EU is now demanding carbon taxes, and carbon taxes are another form of offsetting, really.

You pay for the carbon you're emitting into the atmosphere. So if AWS produced a report saying, look, there's no net carbon you're producing into the atmosphere at the moment, which they are generally at the moment for most AWS systems, it seems. That is really a report for your finance team, so they don't have to, so they can go, "oh, that's great.

The offsetting has already been done for me." That is not a report for your tech team because they're not saying that no carbon is actually being produced by your systems. Your systems are still producing loads of carbon and you still need to adjust your systems. to reduce carbon, which you can do, but the report is kind of giving you the impression that you don't need to.

So is that something you can comment on or?

Sara Bergman: Yeah, this gets me going, right? Cause I'm all about enabling like people with feet on the ground, hands on the code is what I say. So I do agree a lot of the reporting that comes out, it's much better than it used to be. This difference, and maybe now we're slipping into the next paper a little bit, but there, of course, difference between market-based and location-based reporting in the Greenhouse Gas Protocol.

That is a feature, a fundamental feature of the Greenhouse Gas Protocol, which every single industry makes use of. Now, not only, 

Anne Currie: I'm going to interrupt you at this point, just because this is something that I realized when talking to people. One of the issues is that people don't understand the difference between market-based and location-based. What does that mean? So market-based, I think that the word that people really understand is offset.

If you're market-based, it's saying you're still putting carbon into the atmosphere, but we are offsetting it, which was great 10 years ago, nowhere near good enough nowadays. location-based means actually, it's about reducing, it's about not putting carbon into the atmosphere. So that's what we want. Market offsets are a step to getting there, but they are not there.

So sorry, go on.

Sara Bergman: yeah, no, but that's, good. I also like it's like the electrons you pay for versus the electrons you use, if you want to get really down to the socket level. So yeah, I think that's something to talk about. Also, something that I kept thinking about after reading this article, and I just want to read the comment from it because I think the comment was amazing.

And the comment is, "conceptually, I agree. More data is better. However, I've never met anyone advocating for product level data who has actual operational experiences of running shared service platforms and therefore would understand the complexity of delivering these metrics. Because yes, if we're talking about, we could have one report for the finance team, that's fine, but should there be an additional one for, the people who write the code, who maintain the service? And then the question becomes, okay, but what data do we need to take in order to take meaningful action? Like, what is the level that, of course, yeah, if I could get minute by minute, like, there's tons of stuff we could do, correlations we could draw, but what is the level of data that We would need to start taking meaningful action? And I think that could unlock a lot of good things.

Maybe we don't need the world. Do you know what I mean?

Anne Currie: Yeah. I know what you mean. I totally know what you mean. I think we should step down our emissions. Well, having said that, within the Green Software Foundation, there is a project, the Real Time Metrics Project. And that is looking forward to when we can have second, millisecond by millisecond data about, so we can tune applications to, to get there.

But then we also have another project, which I run, so their real time metrics project is run by Adrian Cockcroft and I run a project called the Maturity Matrix Project, which, comes out of our book. So it's, the penultimate chapter in our book. And the Maturity Matrix Project is start, is, it says that all that real time stuff is really quite advanced.

It's way further advanced than pretty much anybody in the world currently is. What we actually need at the moment is quite simple stuff, like "just turn off machines." Turn off machines when you're not using them. And you can, you don't need real time metrics for that. The other thing that we can do that doesn't require real time metrics is, so one of the things that kind of annoys me about the new AWS Cloud Footprint report, which is, it's fine, it's not a lie, it's just giving you offsets.

It's just telling you what your offsets are. It's totally fine. But people are misusing it and misreading it as thinking it's about carbon emissions. There's another thing that AWS have said, which I really like, which is that, "look, we all do a load of work."

It's called the shared responsibility model. "We will take responsibility for the sustainability of the cloud, if you take responsibility for the sustainability in the cloud. Now" I like it, except that it's also very confusing. What they are saying is that "we will build tools that can be aligned with actual location-based zero carbon operations.

We will build those tools." Those tools in the book, we call them green platforms. They're things like serverless, spot instances, really clever instance types. You see, it kind of aligned with their whole modernization strategy, how you get into the cloud and use it well, as it was intended to be used.

So they're saying that, "but it's your responsibility to use those tools. We can build those tools, but if you don't use them, you won't be green. If you just sit in dedicated instances, you will, there's nothing we can do about it. You will never be green. We'll offset you," which is why, the reports say, "well, we'll pay for your bad behavior.

But it is still bad behavior." You'll get to carbon neutral, but you'll never get to carbon zero. And I think that those messages, which are quite complicated, can both exist, but they need to be quite clearly communicated. And at the moment, I don't think we are being so clearly communicated. What's your thoughts?

Sara Bergman: Yeah, this is also, I think this is also something I thought a lot about reading this article. Like, where is the line? Because many cloud providers, they do have clearly green ambition. They are financially incentivized in many ways to be more energy efficient, use less resources of course, because all of those things cost money for any cloud provider, even if that cloud provider is your local on-prem.

So that is one thing, they wanna make cheaper stuff that are often greener, almost always greener, but it is your responsibility to get on them. And how do you do that? Now, all three of the cloud providers do have architected frameworks that have a dedicated sustainability section.

And there for some scenarios, it's actually incredibly good. Like it's very detailed. So you can go and just like, "Oh, my scenario fits into this." I would like those sections to be longer for all of the big three and for the smaller ones, maybe to include more. But I also now maybe skipping randomly ahead, back and forth, something, a mental image that I think helps because sometimes I think the key message is getting lost.

"Okay. But the cloud is green. Why do I need to take action?" and that is a tricky conversation to have because Yeah, if you're selling the cloud, of course, you don't want to say "no, but it's not green." It's like, it just becomes a bit messy. So a mental model I like to use, which I heard from another wonderful woman here in Norway, which is if you have a car and you have a ski box, because right, we have lots of skis here.

So they can think of the car as the cloud and it's the responsibility of the car manufacturer to make that as efficient and green as possible. And they do, right? And but you, as a user, you choose how do I transport my skis in the car? Do I transport them inside the car? Yeah, maybe they that would be greener right because then there's no additional wind catch of the ski box on top, but maybe they don't fit, so you put them on the ski box on the roof. Fine, you accept that additional wind cost and thus increase the energy. But, once ski season is over, and you don't need the skis anymore, what does eeveryone do? You've remove the ski box. I don't see anyone riding around with the ski box in May, just because, right?

And the same goes for the cloud. So yeah, the cloud has lots of work, a lot of stuff in it to make it greener. But if you choose to use a ski box, that's fine. But once the ski season is over, remove the ski box. So once you're not using your test environment, remove it, shut it down.

Anne Currie: Yeah. Yeah. I like the analogy. It's very Scandinavian.

Sara Bergman: It's very Scandinavian, I'm sorry, but I am very Scandinavian, so that's what you get. I guess the same applies if you have a surfing board, I suppose you put them on the roof as well, 

or a canoe,

Anne Currie: A canoe!

Sara Bergman: yeah, or a kayak, yeah. They have longer seasons though, but, so yeah, to try to translate, I feel like I was very, poor Chris who has to edit this, all my ramblings.

But yeah, as a cloud user, you have a responsibility and I think there could be two reports of showing this is what we as a cloud provider, I think all cloud providers are pretty good at this. This is what we took responsibility for. That's awesome. So much better than it was like 10, 5 years ago.

But then also how do we enable people using the cloud to take green actions? Because they want to take green actions, we want them to take green actions, like how do we enable that at the same time? Yeah.

Anne Currie: Yeah. I mean, it's, kind of like "is the cloud green?" The trouble is the, answer is as always in tech, it depends. Oh yeah. It's like, it depends. Are you using it as it was intended to be used? And if you, and the, one of the reasons why you sometimes see Gartner reports and things saying that the cloud is really poorly used,

it's because there's two stages in moving into the cloud. One, is you just kind of lift and shift. For most people, they lift and shift. Although you might argue that's actually never a really good way to go into the cloud. I've written books about this. It's not, but it's, it is a way, a common way for people to go into the cloud.

But once you've lifted and shifted, you have to then actually use the cloud, as it were, you see, you have to go beyond that. If you stop at lift and shift, it's really ungreen. It's worse than being on prem', because in the cloud, it's so easy to over provision. So you have to actually, you have to adopt the cloud, it's called cloud native and it's not really cloud native, but then you have to adopt the cloud way of doing things because otherwise you will over provision and you will then make more carbon than you did on prem', because it's harder to over provision on prem'.

Sara Bergman: Yeah, And that's a journey. And that's something I think all cloud providers are pretty good at supporting, right? That's their bread and butter in many ways.

Anne Currie: It is, they just want people to do it, but still it's hard because it's really so hard to lift and shift into the cloud that once people get there, they go, "thank God for that." And they don't want to go and look at what happens next, which is kind of one of the issues of lift and shift as opposed to kind of just slicing up and moving bits and bits one by one into the cloud, is that

these big projects, they're so painful that once you've done them, you never want to, I've done so many big projects in the past, and you always think you're going to go on and do the next stage, but you're so destroyed by that project. You just think, "Oh my God, we're all burned out. We don't even want to think about it again."

It's, yeah. Yes.

Sara Bergman: We need to glamorize DevOps or operations. It needs to be something there, more and more afterworks, I don't know. Something to make them more glamorous.

Anne Currie: These big projects are awful. I never want to do one again. But anyway. Yeah. And they're, ungreen because you then go on to the next bit where you start to actually refine and improve. So yeah, it's, we need to step back and think about how we're doing that. So, which is, I think a lot, I was signing books at a conference about kind of, DevOps and, CICD and team topologies and moving faster releases, last week.

And I think that's really green because if you can't move, you can't adopt these better tools and services that remain.

Sara Bergman: Yeah, exactly. Yeah, I also said someone, or heard someone who said like, what we need now is like performance engineering, we need to go back to basics in many senses, right? And because a lot of these learnings are not new. It's the same when people are like, "ooh, if I," now I'm going on a tangent here, but if people want to be greener, they're like, "oh, I should just rewrite, like, make my code more efficient."

And like, by all means do. But if you haven't had like a reason to do that yet, I don't think you're going to convince your management chain that sustainability is going to be the reason why you go implement it because high performance computing is not new at all. Like we've known about all these things for a very long time.

So if you haven't done it yet, I mean, kudos to you if sustainability is the thing that makes you implement it. Kudos to you. I just think it's going to be a tough sell.

Anne Currie: I've mentioned a few times on environment variables, but it always interests me, is on that note of efficient code is expensive and we don't do it anymore, sara now works on Microsoft Exchange and 25 years ago, I also worked on Microsoft Exchange, but in Microsoft Exchange 25 years ago, we had to have everything written in C because the hardware was, it was just not possible to do it using anything other than the most optimized C code, which meant that it was really expensive to do things.

It took a long time. Yeah. The world has changed. we've got better hardware and we use it. We use it to go, to not have to write everything in C.

Sara Bergman: Exactly. And we see the same on the mobile industry, right? They have more apps that do more things now because their phones can handle it. They couldn't when the first smart modes came out and just, again, going back to what we talked about earlier, I don't think we can stop technology progressing in order to be greener.

We need it to progress and be green at the same time.

Anne Currie: Yeah. Yeah. We need it all. 

Sara Bergman: People are going to call us greedy.

Anne Currie: We want it all. So we're going to zip onto our final article link today, which is, 

Is Sustainable Data Storage a Paradox? So it's a piece in TechRadar by Jon Howes about environmental challenges posed by cloud storage and AI, and the rising energy demands of data centers that are associated with AI and storage. So thoughts? Is green data storage possible? Sarah?

Sara Bergman: To quote you, it has to be. No, but I, okay. So there are many things here, but I think also he said that cloud is the least wasteful storage solution because it is highly optimized, blah, blah, blah, blah. So I don't know if the paradox is. Could there be other ways? Or if the paradox is, I don't know, it was a catchy title, and I'm not sure I understood what the paradox was.

But also, what I really kept thinking about, is storage really what we should be concerned about here? I (naively, maybe) would guess that compute would be much more resource intensive than the actual, like, store at rest. Then, of course, training the model, you would need to access the storage.

But again, I would be, in these days, more concerned about inference. And then, again, naively maybe, I would expect the CPU and GPU usage to be what we're worried about, not our disks.

Anne Currie: Yeah, I tend to agree. I would say with data, the opportunity there is there's quite a lot of low hanging fruit. And it's a really, good example of the shared responsibility model as well, that it's up to you, to us as users to not be wasteful in our use of data. And by waste, I don't mean storing more than we need, because I think that telling people to throw the stuff away in the world of AI is just crazy talk.

No one's going to do it. You're going to keep everything, right? We just need to see if the general AI can make use of it. The wastage is having it in a medium that's where it can be accessed more quickly than it ever needs to be accessed. So that the longer, if you say, look, I'm going to access this once a year, you can stick it all on tape and it's practically free in terms of carbon.

You can save as long as you like, there's this, that kind of like never underestimate the bandwidth of a station wagon full of tapes driving down the freeway. Tapes are pretty good. You can put a load of stuff on tapes and most data, especially data for AI, it doesn't matter if it takes you a couple of hours or even a couple of days to get it back.

You can, so you don't need to keep it on sSD, where it's just really much more carbon intensive than tape. So it's just, it's quite easy. You just need to think about it and not store things in the wrong medium. Use the tools that are best suited to the job.

Sara Bergman: yeah, be diligent about hot, warm, and cold storage. Like, what do you need, when, and how much? And yeah, also people say, "should I delete all my photos and emails?" I'm like, "no, you want them, right?" You're going to look at your photos again, you're going to maybe search or read your emails again. So don't remove them.

Maybe you know, unsubscribe to the email you never look at. That's like an option if you always get an email from your build pipeline, but you never look at it. Yeah, maybe don't need to get those emails even. Again, low hanging fruits exists. But yeah, storage mediums are not energy intensive if they're cold.

Anne Currie: Yes. Yeah. Don't keep it hot when it can be cold. Cold is the new hot. It's the new hot.

Sara Bergman: and the new green.

Anne Currie: And the new green, yeah. Right, so yeah, we've talked through all of the things. So is there anything else you want to say, coming out of what we've talked about today?

Sara Bergman: Oh, I don't think so. I think it was an interesting discussion. We are, what I love about this industry is that we're constantly on our learning curve. New things always come up, we have to adapt and adjust, and I get to put on my engineering thinking hat. And I love that. I think, we should be excited about that.

Like we have opportunities to be green, "ooh, I have opportunities to learn, opportunities to explore," not like, "ooh, this dreadful thing I have to go do." It can be fun. I mean, yeah, make it glamorous.

Anne Currie: Well, on that incredibly positive note, I will, sign us off. That's fantastic. So before I sign off today, just a reminder, all the links are in the show notes, so have a read through the articles yourself. And to tell you that this week, it is the Green Software Foundation Global Summit event.

So there are live events in quite a few cities. There's one on the first in London. There are ones in Munich and Hamburg, and there's one in Dublin, there's Berlin and Singapore. And, do feel free, I can't actually make, it's a real shame because I do really love to meet people in person.

And I think flying is one of those things where we, flying is an incredibly valuable thing. I don't tell people not to fly, but for me, the reason to not fly all the way over to London if you're in the US, for example, to go and meet people in person is, that you should set up your own local meetups, which are in person, which people can get to and from.

And because having people locally that you can talk with is incredibly useful. So set up your own summits if you can't make any of the summits that are out there.

Sara Bergman: Or your own meetup, if a summit seems a bit much, then set up your own GSF meetup.

Anne Currie: Yeah, that is an excellent idea. You'll meet a load of lovely, like-minded people, and you can chat away about saving the world, which is always a good thing to chat about. So one of the nice things about the green community is we're all aligned on trying to make positive change without turning everything off and going back to the stage.

So we've come to the end of our podcast and oh, it looks like Chris, our editor, has given me a final fun question for you, which is, if you were to design an AI that had zero environmental mental impact for a totally non-serious purpose, what would it do?

Sara Bergman: I'm embarrassed to say that I thought so much about this, because my mind went, ooh, what would I do? And then I'm like, would I optimize my life? Would I make something silly? Would I make something useful? And then I just, there were so many options that I don't know. But some of the things that like, popped up was like, organize all my photos.

I have a small child. I take a lot of photos of him. I would like for there to be some nice organization going on there. That would be very nice. Also, an AI that designed cute nails that I could do at home with like the stuff I have. Because I am not artsy, but I like fun nails. So yeah, like, like, if I am bad at doing my nails and I have four colors, what can I do that's cute?

Anne Currie: And of course, all of those things could be done zero impact because the, key thing about them is that they are not latency insensitive. You could say, well, actually, I'll wait to run my models and do my inference till the sun's shining and the wind's blowing and there's excess electricity on the grid.

The, I don't, I would, to make it so it's there, I wouldn't say, well, it's in the middle of the night and it's still night, but I'm really desperate to get my fancy nails done.

Sara Bergman: Yeah. 

Anne Currie: Just wait. Just say, well, I'll, find out tomorrow morning what the AI is storming. It's all about making things less on demand, so we align with when the sun's shining and the wind's blowing. 

Sara Bergman: Thanks for taking me back to technical.

Anne Currie: So thank you very much for being on. It's been, as always, delightful to talk to you.

Sara Bergman: Likewise. I had a blast. Thank you so much.

Anne Currie: It's nice. And we don't say, obviously, while we're writing the book, we talked together all the time, but now that the book's finished, then we talk together less often. So for all listeners today, links are in the show notes. And if you haven't read Building Green Software from O'Reilly, you are missing a trick because that really, it's a fun read.

Everybody seems to be enjoying it.

Sara Bergman: Yeah. I think the top thing people are like, "Oh, it's fun." I'm like, "Yeah, it's funny. It's good."

Anne Currie: So if you haven't read it, crazy! Go out and read it, or join one of my training courses, which are also fun. So thank you very much. And I'm sure we will both be back on the Environment Variables sometime in the not-too-distant future. So goodbye from me. 

Sara Bergman: Bye. 

Anne Currie: Cheerio.

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

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