We Need to Talk About AI

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Commons Team

May 20, 2025

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When you ask ChatGPT to write a cover letter, make your grocery list, or edit an image, what's actually happening in the real world? As AI gets bigger by the day, it's requiring more and more energy, water, and land in communities around the world.

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Tech companies are investing billions of dollars in data centers and technologies to power AI, but are they also investing in sustainable and equitable resources to keep it going?

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Today, we’re going to take a step back from the chatbots to understand the true impact of AI, how we’re tracking and regulating that impact, and we’ll find out what it will take to build a sustainable future for AI.

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Here are some of the people you'll hear from in this episode:

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Episode Credits

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Listener contributions: Elizabeth, Stella, Joao Vilca Soto, Lin Diaz Maceo, Airlea Rasul, Jessika
Editing and engineer: Evan Goodchild‍
Hosting and production: Katelan Cunningham

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Citations and Further Reading

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Full Transcript

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AI (00:00):

<Silence> Hey there, how's your day going?

Katelan (00:03):

Hi. Welcome back to Second Nature, a podcast from Commons. Commons is the app that over 100,000 people use to live sustainably by buying less and buying better. And on this show we talk to people about how they're living sustainably in an unsustainable world.

Speaker 3 (00:22):

Hey, my day's going well. Thanks for asking. How about yours?

Katelan (00:28):

Right here on this show, we talk about a lot of sustainability efforts like ditching convenience culture or eating plant-based or boycotting fast fashion. And all of these things require us to think about how all of our stuff is being made. But when I type a question or a prompt into Chat GPT, I just have to wait a few seconds for a response to pop up on my screen in that few seconds. While AI is generating your grocery list or making your vacation photo look like a Ghibli movie, it's hard to imagine that anything really consequential is happening in the real world.

Katelan (01:08):

That's mostly due to the fact that you can't hold your Chat GPT response in your hand. It didn't get shipped to you on a truck. You didn't go buy it at a store. The content and information you get from Chat GPT is intangible, but it's not inconsequential. Behind the scenes of that typing robot are miles and miles of data centers using a lot of energy, a lot of water, a lot of metals, materials, and a lot of land. In the past few years, companies have invested billions of dollars into creating, perfecting and deploying AI to make their tech smarter and more efficient. Even if you don't use AI tools outright, you've probably seen it in your Google searches or your inbox or other online tools that you use. It's popping up everywhere

Katelan (01:59):

And at the rate AI is growing, it's going to need twice as much energy by 2030. But some AI technology is accelerating progress in climate science. On the other hand, it can also be used for fleeting social media trends. From the frivolous to the fundamental AI in all its forms is getting big fast. I'm your host, Katelan Cunningham, and on today's episode, we are going to take a step back from the chat bot to understand best we can the true climate impact of ai, how we're tracking and regulating that impact, and we're gonna find out what it takes to build a sustainable future for ai. Here we go,

AI (02:50):

Chat GPT, show me an apple,

Katelan (02:52):

Apple. It

AI (02:53):

Looks plastic Imperfections. The leaf is dead.

Katelan (02:56):

One of the main ways that everyday people are using AI is through a software called Chat GPT. If you haven't used it, Chat GPT is a Chat GPT and it's like having an actual conversation with a search engine. You can ask Chat GPT something super practical and objective like the leaves of my tomato plant looks splotchy, what's wrong with it? Or something very personal or nuanced, like what are my strengths and weaknesses? It can also write for you emails, cover letters, podcast scripts. Don't worry, every word of this was written by me and my brain. I swear. Chat GPT can also be a personal assistant helping you plan meals or vacations. Some people even use it as a therapist, but when you type a prompt or a question into Chat GPT, what's actually happening behind the scenes or the screen as it were to answer that, let's zoom out from your home and your computer and take a visit to the city of Ashburn. In Northern Virginia, it's been dubbed data center alley with over 40 million square feet of data centers and there are plans to double that in the coming years.

Katelan (04:08):

There are over 300 facilities across the state of Virginia with hundreds more on the way. Within these data centers, there are thousands of physical servers. Let me try and paint a picture for you. The inside of a data center has aisles like in a grocery store, but instead of shelves stocked with food, they're like shelves stocked with servers. The servers have blinking lights and cords, and I'm sure a lot of other technical things that I know nothing about, but these servers are computers that communicate with each other and with your computer. If you live in the United States and you use Chat GPT to draft an email for example, there's a super high chance that your computer communicates with one of those servers in Virginia. And you might be wondering, is chat GPT really that different than using Google? Well, the difference between asking chat GPT to write an email and asking Google how to write an email is that Chat GPT has to do a lot more work.

Katelan (05:13):

Google search would use its servers to search the internet and give you a list of the most popular webpages about writing emails. But Chat GPT, its servers are going to read those articles for you. They're going to gather and learn from the most relevant information. Then they're going to coalesce the information and write the email for you. It's basically the difference between finding a book on tomatoes at the library and reading every book on tomatoes than writing a report on it. AI's ability to learn and make decisions goes way beyond Chat GPT. It has the potential to revolutionize a lot of technology and in some cases it's already started that process, but

Katelan (05:57):

It comes with a huge cost to the environment because AI uses a lot of electricity and a lot of water. For example, a chat DPT prompt uses over 10 times more energy than a Google search. Without ai, one AI-focused data center uses as much electricity as 100,000 households, but some bigger ones under construction now they'll use 20 times that much. That's enough energy to power nearly all the homes in Dallas with so many people using ai, not just for chat GPT, but all kinds of technology. Our demand for data centers in Virginia and beyond is multiplying every year, every month. Here in the us almost half of energy demand growth from now to 2030 is predicted to come from data centers and to cool these data centers. It takes a lot of water. You may have seen that stack that using AI to draft a 100 word email is like pouring out a bottle of water. By 2027, AI is predicted to take as much water from the environment as all of New Zealand.

Katelan (07:07):

We will dive more into the energy and water usage in just a little bit, but first we have to acknowledge the elephant in the room, the reason we're in this predicament. Sure, in the beginning, AI was kind of rough, often inaccurate and more trouble than it was worth, but the technology and the people building the technology are getting better and AI is getting more useful. Yeah, it can save you time making a grocery list or spare you from reading through Google search results, but there are other use cases beyond our daily conveniences. AI can be used to make public transportation more efficient, help with wildfire risk identification, make farming more resilient, or even improve cancer research and treatment. So one of the big questions here is, is there a way to take the bad with the good? Is it okay to use AI to help us with our small everyday tasks if it's also helping to make big life-changing shifts to entire industries to get a pulse check? We of course ask you our community. How do you feel about AI these days?

Artie (08:15):

So I am an art student vehemently against AI usage altogether. The ethics of it, both in image generation and information generation just upset and scare me. I really don't use it whenever I have the chance. I encourage the people around me not to use it because I feel so strongly about this technology being a net negative in the way that we've used it so far.

Brian (08:42):

I do rely heavily on chat GPT, and I've been using it to streamline some of my business processes. I've also created my own scripts so that whenever I'm using ai, I'm not just fussing around on the program and wasting energy and time. I get in, I'm precise with what I need and I get out. Even though I am creating an implication on nature and the environment, it's minimized by my usage as opposed to me just rambling on and having a conversation with this computer. I try to look at moments where my energy on my grid is clean and I try to pull during those times.

Kylie (09:23):

I kind of am freaked out by AI every time I use it. I say thank you in case like the robots come back, they can't attack me, which sounds like really crazy, but I say thank you. So my bases are covered. I use it to either get like a template or like a title because I don't really have time to brainstorm titles and to be super, super clever,

Nicole (09:48):

I personally don't really use AI that much on a regular basis because I'm so aware of the impacts it has on the climate. The only times I've really used it are during school I'll be assigned a reading that is pretty difficult to read because of all the jargon or it's just really technical, so I'll use it to help break it down or to help get me started on an essay.

Parisa (10:16):

I've been in the AI field pretty early on and I've firsthand seen the impact of AI and what it's capable of. I've worked as an engineer in the field for years and I've actually been an AI developer, many other startups and my own startup, we have been using AI to solve more complex problems that we haven't been able to solve prior to this. When you are facing a problem as complex as the environment, honestly, you kind of need to have tools that are able to analyze and provide solutions that can deal with those complex situations. And AI is perfect for that. Ai just like any tool is not either evil or good, it is a tool.

Artie (11:01):

I really hate how energy demanding it is. It's just kind of like woven its way into everything because the technology industry, you know, they're constantly searching for the new thing to like put them ahead of their competitor and I think that you know, really comes to bite us in the when we don't actually think about what we're doing before we actually do it. I don't know, it just feels like it SAPs the life out of everything. If it was doing something that humans genuinely could not do without it, I think that would be great. But at the moment AI seems to be contributing to convenience culture. We've gone so accustomed to just like getting things when we want them. Either it's click on Amazon and get this item within the next day or ask AI for the answer to this question and it just spits out something. Who cares what the quality of it is? I just want

Kylie (11:52):

Something now.

Brian (11:55):

I think it can be worth it because I'm using it to come up with solutions for climate issues. So it's helping me and it understands the climate situation more so than some humans that I speak to. Where I try to grapple with using it and not using it is I don't use it as a crutch.

Kylie (12:15):

I am a big water conservation gal, like I love the species in the water. I love learning about watersheds. The water thing definitely stuck with me because I do try and monitor like my water footprint. So I guess minimizing my use of AI will help minimize my water footprint. And it's kind of concerning because I feel like everyone is coming out with a different version of ai. We started out with chat GPT, we probably had something before that and now we have Microsoft Co-pilot and then there's the Apple intelligence that just came out. So all these phones are being loaded already with ai. I don't think you can really escape it. Maybe if someone can come out with something that doesn't use a lot of water, that'd be really great.

Nicole (13:00):

In my area, in the western part of Virginia, there have been a bunch of data centers built and they're like taking away habitat and they're real eyesore when you drive by them. It's just really upsetting to see that what was once a forest be a data center, the impact on climate is so severe that I morally just cannot agree with using it so often. And I think it's also taking away our abilities to critically think and have empathy, which is really detrimental in the long run and the short run honestly for climate, for our communities, just for the entire planet. But I know that there are positive ways to use AI like climate modeling or disaster relief things, but to use it to do your homework, to use it, to read an article, to write a paper, I think using it on day-to-day things is just not the right move.

Parisa (14:04):

AI could be used for solving a lot of healthcare struggles. We've been seeing AI be able to detect cancer very early on many environmental problems as well. That's it. Just like any tool, it has its own downsides and there needs to be more discretion when it comes to using the findings, but also the data in general. I also hope that with the technology enhancements we will be seeing more efficient server runs and just hopefully less heat production when it comes to the AI servers.

Katelan (14:46):

If you've heard any stats about how much water AI uses, they probably came from this 2023 paper called Making AI Less Thirsty, uncovering and addressing the Secret Water Footprint of AI Models. This paper was a big deal because not many people had studied AI's water usage before that and one of the people who worked on that paper was Shale Ren. He's an associate professor of electrical and computer engineering at the University of California Riverside, and a lot of his research focuses on AI for good and I was so lucky that he was able to answer some of our burning questions about ai. Hi Shaolei, thanks for coming on the show.

Shaolei (15:32):

Good to be here.

Katelan (15:33):

I have to ask you are an electrical and a computer engineer by trade. So what made you start caring so much about water specifically?

Shaolei (15:42):

Well, I think I spent a couple of years in a small town back in China where we only had water access for about half an hour each day. So we just had to use water very wisely and water is a finance resource. Even though here in the US water doesn't seem to be a big problem for many people, but still it's a finance resource and we have to allocate the resource wisely. So actually my research, a lot of my work is about resource allocation and I find that water resource allocation is a very interesting and also challenging problem.

Katelan (16:15):

All right, so where is all this water coming from that we're using in data centers? And is the water that's being used typically recycled in the center or elsewhere?

Shaolei (16:26):

Most data center will get their water from municipal water facilities. So those are basically clean drinking water and tech technically we also call it through water. And it's different from the gray water or green water. Green water is the rain water in the soil to grow the grasses or basically used by plants and a portion of the water will get recycled several times before discharge to the wastewater processing plants and a small portion of water will also get evaporated. Okay. but overall, actually, if you look at some of the companies sustainability reports, they will be evaporating about 80% of their water withdrawal.

Katelan (17:05):

Gotcha. And which of these are, and and so you're saying that most of the water that's used at data centers, they're using the blue water, is that right?

Shaolei (17:12):

Yeah, they're using blue water and actually blue water is also includes the water in rivers and lakes. Basically those are the fresh water that humans can directly tap into for industrial, for residential usages.

Katelan (17:27):

Gotcha. And while some data centers recycle their water, most of it's being evaporated as it's being used. So you can only recycle like about 20% of the water anyway because the rest of it's just blowing off as like steam.

Shaolei (17:42):

Yes. So if you look at some sustainability reports by those tech companies, overall they will be vapor about 70 to 80% of the water that we saw this percentage changes by different facilities. Some facility will be evaporating more and others will be evaporating less depending on the coding technology they use and also the water quality.

Katelan (18:02):

When we talk about the water that AI uses, you know, we focus a lot on the data centers as we've been talking about, but AI uses water in other phases right, of the lifecycle. So I just wanted to better understand what goes into AI's water footprint and how does water usage further up the supply chain compare to water usage at the sort of end of the supply chain? At the data centers,

Shaolei (18:25):

The concept of AI's water footprint is very broad and includes the water used to mine the rare earth to manufacture the AI servers and also to produce the electricity. And if you follow the carbon emission scoping definition, we have scope one scope and scope three. Scope One is the direct water consumption by the data center facilities, and scope two is for generating electricity and scope three is ev everything else like recycling and manufacturing. So typically the scope three is the largest amount of water, but there's not much information available in the public. And if you only focus on scope one, scope two, this is the water. Sometimes we call it operational water footprint. And scope two is also generally higher portion compared to scope one direct water consumption.

Katelan (19:13):

That's so interesting. I've never thought about, I know the scoping with carbon emissions, but not necessarily the scoping with water. That's fascinating. Do you know of any other AI companies that are using water or energy more efficiently than others?

Shaolei (19:28):

Well, based on the public disclosure of the water efficiency some companies like Microsoft is are doing pretty good job in terms of water efficiency

Katelan (19:39):

As far as like consumer tools that we can use, I think one of the most popular ones is chat DPT. Is there a version of chat GPT that's better for water or electricity that you know of? Is there, is there some other tool like that that we can use that maybe doesn't use quite as much resources?

Shaolei (19:56):

I think they offer many different versions. Typically, the free version will be more efficient in terms of resource consumption than the paid version. So, and also non reasoning model will be more efficient than reasoning model.

Katelan (20:10):

Why would the free version be more efficient than the paid one?

Shaolei (20:13):

Because it's a closed source model, so we don't really know exactly what is going on, but you know we can reasonably expect that the paid version will have more model parameters and more resource consumption as well.

Katelan (20:26):

In the research that you've done and your experience, like living in a place where there was water allocation and water scarcity, have you noticed that these data centers have put a strain on water resources in communities, whether it's in the US or elsewhere?

Shaolei (20:41):

We've heard quite a few conflicts in places like Spain, Chile, Uruguay, and also Arizona. So some companies are building data centers in those places and that are obviously putting some strain on the, on the local water resources.

Katelan (20:58):

We know that AI consumes a ton of water in a ton of energy, but do you believe that it can be optimized to basically reduce more than it's using not only for its own use, but maybe even it could offset energy use or water use and other systems, basically. Do you think that AI can do more <laugh> good than, than bad when it comes to the environmental impact?

Shaolei (21:22):

I think in the long run, yes. Even for now, despite the huge energy consumption, water consumption it's not at the point where we should panic yet. I think we still have the resources to deal with the energy to deal with the water demand to support this technology development. And in the long run, we're gonna be expecting to use this technology to help the energy sector become greener, become cleaner detect the water leaks. So we, we should expect more benefits out of this AI technology.

Katelan (21:56):

I know that you've been doing some work on water equity yourself. Could you just share anything about the work that you're, you're doing?

Shaolei (22:03):

Yeah, we work on the resource allocation problem. We try to allocate the workload and computer resources in a way that are not particularly straining any regions disproportionately. So that's one of the problem we've been working on. And also we are working on other aspects of the equity and environmental protection.

Katelan (22:26):

Well, that is really, really exciting. This has been really optimistic <laugh> and it's been really helpful to kind of contextualize the sort of water piece of ai, so I really appreciate it. Thank

Shaolei (22:37):

You.

Katelan (22:38):

Thank you.

Katelan (22:47):

Earlier when we heard from our community, I really loved parisa's reminder that AI is a tool. It's not inherently bad or good, it's all in how we build it and how we use it. On one hand, AI is already helping to better track and tackle climate change. It's being used to do things like accelerate the transition to renewable energy with more reliable forecasting. It's measuring deforestation across more than 1 million hectares of land around the world. It's tracking the speed at which icebergs are melting and doing it 10,000 times faster than humans can. It's helping people be better prepared for climate disaster earlier. It's even mapping litter in the oceans, but is also being used for a lot of small tasks and curiosities. And besides the climate impact of these small scale uses of ai, there are studies that show increased use of AI can decrease our individual ability for critical thinking, and we will need critical thinking to fight the climate crisis. Plus AI works off of all that we've already created. So it's not great at generating truly novel ideas, and I think we're gonna need those to fight climate change too.

Katelan (24:05):

I'm starting to think of AI like with great power comes great responsibility. AI is a powerful tool and we can use it to do great things, but the way it's built, who builds it and how it's regulated, those things can make or break how much good we can do with it. And right now we're way behind on tracking AI's. Big picture impact. Governments don't even capture comprehensive global statistics on data centers like it does for other sectors like transportation or construction, and tech companies are not going out of their way to fill in those information gaps. So I called up commons founders, Sanchali Seth Pal to chat about what we know and what we don't know about the energy use and regulation of ai. Welcome back, char.

Sanchali (24:55):

Thanks, Katelan. Glad to be back.

Katelan (24:57):

So we heard from Shaolei Ren about AI and water usage, and now we're gonna dive into energy usage.

Sanchali (25:05):

Data centers are hungry for energy. Just to give you a sense, in 2022, it's estimated that data centers, many of which are used to power ai, were the 11th largest electricity consumer in the world, bigger than the electricity consumption of Saudi Arabia. But by next year, 2026 MIT research shows that AI could become the world's fifth largest electricity consumer, putting it between Japan and Russia.

Katelan (25:31):

Okay, wait, so you're saying that AI usage requires a country's worth of energy?

Sanchali (25:37):

Yeah, and it's becoming a bigger and bigger country. A lot of this is coming from demand from big companies like Microsoft and Google. These companies are increasing their energy use and for the first time actually missed their net zero targets this year because of it. So from 2019 to 2023, Google's greenhouse gas emissions increased by a shocking 48% thanks to its uptick in data center energy use. And Microsoft's emissions rose by 29% from 2020 to 2023.

Katelan (26:07):

And the data centers that a lot of these companies are using, they're right here in the United States, right?

Sanchali (26:11):

They are. The US has nearly 5,500 data centers, which is way more than any other country. China, Germany, and the UK come in a distant second with about a 10th of the total data centers. As the US has

Katelan (26:25):

This energy that the data centers are using, is it coming from fossil fuels or renewable energy?

Sanchali (26:31):

So today it's kind of both. We're building so many data centers that we can't keep up with demand with just one source of energy. Data centers are requiring us to really fire on all cylinders. Wherever we have energy sources. It means even coal plants, which were meant to be shut down, are now being kept online because they have to stay open.

Katelan (26:51):

Oh my God, we're keeping coal plants open in order to power ai and coal creates so many emissions. It

Sanchali (26:57):

Does. Coal is really high emissions. It's not even the cheapest source of energy, and it creates a ton of toxic pollution for people living around coal-fired power plants.

Katelan (27:08):

I'm sure just at large, these huge data centers are changing communities

Sanchali (27:13):

For sure. What makes it really dramatic is that they require a ton of physical infrastructure and data centers are often built in clusters so they can share power grids, cooling centers, and information. So Virginia is an example where the government has incentivized data center clusters by giving tax breaks. That means that any adverse effects from the development of those data centers, including local energy sources like coal-fired power plants, are negatively affecting those local communities too.

Katelan (27:41):

You would think that AI is a perfect reason to prioritize investment in cheaper, cleaner sources of energy. Are we seeing any uptick in progress there?

Sanchali (27:50):

It's really fascinating. The need for more cheap electricity is causing a bit of a revival and renewable energy investment and clean energy investment. Specifically for the nuclear industry, the demand for electricity to power data centers is skyrocketing and it can't be met by existing energy sources. So experts are looking for new sources because estimates suggest we'll need three times the electricity supply from our data centers over the next five years.

Katelan (28:16):

Wow, that's that's a lot.

Sanchali (28:18):

And the reason people are excited about nuclear is because at scale it's cheaper than coal and it can be used reliably at high energy density, which is really important for keeping AI online. It's not variable like solar or wind and doesn't require batteries to sustain. Big companies and CEOs for this reason, are making significant investments in renewable energy. It includes folks like Microsoft, Google, Amazon, Tesla Meta, and even OpenAI. But there are some health and safety concerns with nuclear. Historically nuclear power plants that were decommissioned in the past are now being brought back online. So it's an area that's gonna need some real regulation to come up alongside the new investments.

Katelan (28:58):

So it sounds like there's an important role for government to play here,

Sanchali (29:03):

Ensuring safety is definitely gonna be critical, especially given the concerns that the public has with past nuclear disasters. And it's not just about safety, it's about information. Enforcing transparency will be really critical too, but as the AI space becomes more and more competitive, companies are increasingly becoming tight-lipped about their operations and energy usage. So this might be challenging.

Katelan (29:24):

Are they allowed to do that? I know that in Virginia data centers are using more than a quarter of the state's energy, and yet these companies are not being transparent about it. That seems kind of messy

Sanchali (29:34):

Because AI is still really new and it's blowing up so quickly. There's really very little regulation and we do need to set some parameters sooner rather than later.

Katelan (29:44):

Are there any regulations right now on ai?

Sanchali (29:47):

There's some federal laws proposed around the ethics of ai, and there's definitely no AI specific federal regulator in the us.

Katelan (29:54):

It feels like AI is kind of like in its wild west phase.

Sanchali (29:58):

It definitely is. It's still really early and the technology is changing so fast, and the US is really setting a lot of the new innovation, and it's so, it's important that there's new regulation to emerge here. We're still early enough that it could happen, but it's really important that we push for it. There is a path where AI isn't so scary. Let's imagine a world where things work out really well. I hope there's a world where it's net positive. In theory, the tools could create efficiencies that are good for the environment. It could allow us to do the things we do today, but just more efficiently it could help us unlock new climate solutions and maybe even speed up a clean energy transition. But it could also not turn out that way. And it's incredibly critical that these concerns get built in now. So I'm really glad that we're starting to ask these questions, and I hope that everyone listening to this podcast feels like they know how to keep asking good questions about AI and its energy use going forward.

Katelan (30:56):

I learned a lot working on this episode, and I learned a lot from people sharing how it's affecting them and their community. So I hope we pick the right path forward and we will keep folks updated here and on Instagram as we navigate this wild west of ai.

Sanchali (31:10):

I have also learned so much. This has been really illuminating. Thanks, Caitlyn.

Katelan (31:14):

Thanks so much.

Katelan (31:23):

AI is a huge industry, and there are many environmental, ethical, and moral considerations that have to be at the forefront as it continues to boom. Because this is a sustainability show and there's so much to talk about when we talk about AI and sustainability. We focus today on the resource use of ai, but we didn't even get into the issues of privacy and surveillance bias, art theft. While it increases our efficiency and capacity, this gargantuan new technology is going to continue to stretch and strain our natural resources and call into question, what is ethical? When it seems that all of the internet is up for grabs, we have to stay grounded in its real world implications. Every day, people are making decisions about AI technologies that could eventually raise virgin's, electricity bills, or strain the water use of Texans during a heat wave. The upside is that this technology is still new, and now is the time to set the precedent.

Katelan (32:24):

So many climate change drivers like fossil fuels or the meat industry, are grounded in decades of systems and business deals, but AI is still relatively new. So now is the time to pay attention and to share. Share what you know about the impacts of AI and ask questions. Pay attention to how AI is affecting your community and pay attention to legislation for AI companies and data center developers. Share how you expect companies to stay accountable to the communities where they're using all this energy, all this land and all this water, and pay attention to your own AI usage too. Thanks to our community who shared their takes on AI Today, you heard from [names]

Katelan (33:21):

This is an ongoing conversation and if you wanna keep chatting, pop over to our Instagram at Second Nature Earth. This episode was edited and engineered, not by robot, but by the very real Mr. Evan Goodchild. It was written and produced by me, a real human being as well. Katelan Cunningham, thank you so much for listening. Thank you for caring, and we'll see you back here soon. Bye.

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APPLE PODCASTS

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← BACK

We Need to Talk About AI

APPLE PODCASTS

SPOTIFY

‍

When you ask ChatGPT to write a cover letter, make your grocery list, or edit an image, what's actually happening in the real world? As AI gets bigger by the day, it's requiring more and more energy, water, and land in communities around the world.

‍

Tech companies are investing billions of dollars in data centers and technologies to power AI, but are they also investing in sustainable and equitable resources to keep it going?

‍

Today, we’re going to take a step back from the chatbots to understand the true impact of AI, how we’re tracking and regulating that impact, and we’ll find out what it will take to build a sustainable future for AI.

‍

Here are some of the people you'll hear from in this episode:

‍

‍

Episode Credits

‍

Listener contributions: Elizabeth, Stella, Joao Vilca Soto, Lin Diaz Maceo, Airlea Rasul, Jessika
Editing and engineer: Evan Goodchild‍
Hosting and production: Katelan Cunningham

‍

Citations and Further Reading

‍

Full Transcript

‍

AI (00:00):

<Silence> Hey there, how's your day going?

Katelan (00:03):

Hi. Welcome back to Second Nature, a podcast from Commons. Commons is the app that over 100,000 people use to live sustainably by buying less and buying better. And on this show we talk to people about how they're living sustainably in an unsustainable world.

Speaker 3 (00:22):

Hey, my day's going well. Thanks for asking. How about yours?

Katelan (00:28):

Right here on this show, we talk about a lot of sustainability efforts like ditching convenience culture or eating plant-based or boycotting fast fashion. And all of these things require us to think about how all of our stuff is being made. But when I type a question or a prompt into Chat GPT, I just have to wait a few seconds for a response to pop up on my screen in that few seconds. While AI is generating your grocery list or making your vacation photo look like a Ghibli movie, it's hard to imagine that anything really consequential is happening in the real world.

Katelan (01:08):

That's mostly due to the fact that you can't hold your Chat GPT response in your hand. It didn't get shipped to you on a truck. You didn't go buy it at a store. The content and information you get from Chat GPT is intangible, but it's not inconsequential. Behind the scenes of that typing robot are miles and miles of data centers using a lot of energy, a lot of water, a lot of metals, materials, and a lot of land. In the past few years, companies have invested billions of dollars into creating, perfecting and deploying AI to make their tech smarter and more efficient. Even if you don't use AI tools outright, you've probably seen it in your Google searches or your inbox or other online tools that you use. It's popping up everywhere

Katelan (01:59):

And at the rate AI is growing, it's going to need twice as much energy by 2030. But some AI technology is accelerating progress in climate science. On the other hand, it can also be used for fleeting social media trends. From the frivolous to the fundamental AI in all its forms is getting big fast. I'm your host, Katelan Cunningham, and on today's episode, we are going to take a step back from the chat bot to understand best we can the true climate impact of ai, how we're tracking and regulating that impact, and we're gonna find out what it takes to build a sustainable future for ai. Here we go,

AI (02:50):

Chat GPT, show me an apple,

Katelan (02:52):

Apple. It

AI (02:53):

Looks plastic Imperfections. The leaf is dead.

Katelan (02:56):

One of the main ways that everyday people are using AI is through a software called Chat GPT. If you haven't used it, Chat GPT is a Chat GPT and it's like having an actual conversation with a search engine. You can ask Chat GPT something super practical and objective like the leaves of my tomato plant looks splotchy, what's wrong with it? Or something very personal or nuanced, like what are my strengths and weaknesses? It can also write for you emails, cover letters, podcast scripts. Don't worry, every word of this was written by me and my brain. I swear. Chat GPT can also be a personal assistant helping you plan meals or vacations. Some people even use it as a therapist, but when you type a prompt or a question into Chat GPT, what's actually happening behind the scenes or the screen as it were to answer that, let's zoom out from your home and your computer and take a visit to the city of Ashburn. In Northern Virginia, it's been dubbed data center alley with over 40 million square feet of data centers and there are plans to double that in the coming years.

Katelan (04:08):

There are over 300 facilities across the state of Virginia with hundreds more on the way. Within these data centers, there are thousands of physical servers. Let me try and paint a picture for you. The inside of a data center has aisles like in a grocery store, but instead of shelves stocked with food, they're like shelves stocked with servers. The servers have blinking lights and cords, and I'm sure a lot of other technical things that I know nothing about, but these servers are computers that communicate with each other and with your computer. If you live in the United States and you use Chat GPT to draft an email for example, there's a super high chance that your computer communicates with one of those servers in Virginia. And you might be wondering, is chat GPT really that different than using Google? Well, the difference between asking chat GPT to write an email and asking Google how to write an email is that Chat GPT has to do a lot more work.

Katelan (05:13):

Google search would use its servers to search the internet and give you a list of the most popular webpages about writing emails. But Chat GPT, its servers are going to read those articles for you. They're going to gather and learn from the most relevant information. Then they're going to coalesce the information and write the email for you. It's basically the difference between finding a book on tomatoes at the library and reading every book on tomatoes than writing a report on it. AI's ability to learn and make decisions goes way beyond Chat GPT. It has the potential to revolutionize a lot of technology and in some cases it's already started that process, but

Katelan (05:57):

It comes with a huge cost to the environment because AI uses a lot of electricity and a lot of water. For example, a chat DPT prompt uses over 10 times more energy than a Google search. Without ai, one AI-focused data center uses as much electricity as 100,000 households, but some bigger ones under construction now they'll use 20 times that much. That's enough energy to power nearly all the homes in Dallas with so many people using ai, not just for chat GPT, but all kinds of technology. Our demand for data centers in Virginia and beyond is multiplying every year, every month. Here in the us almost half of energy demand growth from now to 2030 is predicted to come from data centers and to cool these data centers. It takes a lot of water. You may have seen that stack that using AI to draft a 100 word email is like pouring out a bottle of water. By 2027, AI is predicted to take as much water from the environment as all of New Zealand.

Katelan (07:07):

We will dive more into the energy and water usage in just a little bit, but first we have to acknowledge the elephant in the room, the reason we're in this predicament. Sure, in the beginning, AI was kind of rough, often inaccurate and more trouble than it was worth, but the technology and the people building the technology are getting better and AI is getting more useful. Yeah, it can save you time making a grocery list or spare you from reading through Google search results, but there are other use cases beyond our daily conveniences. AI can be used to make public transportation more efficient, help with wildfire risk identification, make farming more resilient, or even improve cancer research and treatment. So one of the big questions here is, is there a way to take the bad with the good? Is it okay to use AI to help us with our small everyday tasks if it's also helping to make big life-changing shifts to entire industries to get a pulse check? We of course ask you our community. How do you feel about AI these days?

Artie (08:15):

So I am an art student vehemently against AI usage altogether. The ethics of it, both in image generation and information generation just upset and scare me. I really don't use it whenever I have the chance. I encourage the people around me not to use it because I feel so strongly about this technology being a net negative in the way that we've used it so far.

Brian (08:42):

I do rely heavily on chat GPT, and I've been using it to streamline some of my business processes. I've also created my own scripts so that whenever I'm using ai, I'm not just fussing around on the program and wasting energy and time. I get in, I'm precise with what I need and I get out. Even though I am creating an implication on nature and the environment, it's minimized by my usage as opposed to me just rambling on and having a conversation with this computer. I try to look at moments where my energy on my grid is clean and I try to pull during those times.

Kylie (09:23):

I kind of am freaked out by AI every time I use it. I say thank you in case like the robots come back, they can't attack me, which sounds like really crazy, but I say thank you. So my bases are covered. I use it to either get like a template or like a title because I don't really have time to brainstorm titles and to be super, super clever,

Nicole (09:48):

I personally don't really use AI that much on a regular basis because I'm so aware of the impacts it has on the climate. The only times I've really used it are during school I'll be assigned a reading that is pretty difficult to read because of all the jargon or it's just really technical, so I'll use it to help break it down or to help get me started on an essay.

Parisa (10:16):

I've been in the AI field pretty early on and I've firsthand seen the impact of AI and what it's capable of. I've worked as an engineer in the field for years and I've actually been an AI developer, many other startups and my own startup, we have been using AI to solve more complex problems that we haven't been able to solve prior to this. When you are facing a problem as complex as the environment, honestly, you kind of need to have tools that are able to analyze and provide solutions that can deal with those complex situations. And AI is perfect for that. Ai just like any tool is not either evil or good, it is a tool.

Artie (11:01):

I really hate how energy demanding it is. It's just kind of like woven its way into everything because the technology industry, you know, they're constantly searching for the new thing to like put them ahead of their competitor and I think that you know, really comes to bite us in the when we don't actually think about what we're doing before we actually do it. I don't know, it just feels like it SAPs the life out of everything. If it was doing something that humans genuinely could not do without it, I think that would be great. But at the moment AI seems to be contributing to convenience culture. We've gone so accustomed to just like getting things when we want them. Either it's click on Amazon and get this item within the next day or ask AI for the answer to this question and it just spits out something. Who cares what the quality of it is? I just want

Kylie (11:52):

Something now.

Brian (11:55):

I think it can be worth it because I'm using it to come up with solutions for climate issues. So it's helping me and it understands the climate situation more so than some humans that I speak to. Where I try to grapple with using it and not using it is I don't use it as a crutch.

Kylie (12:15):

I am a big water conservation gal, like I love the species in the water. I love learning about watersheds. The water thing definitely stuck with me because I do try and monitor like my water footprint. So I guess minimizing my use of AI will help minimize my water footprint. And it's kind of concerning because I feel like everyone is coming out with a different version of ai. We started out with chat GPT, we probably had something before that and now we have Microsoft Co-pilot and then there's the Apple intelligence that just came out. So all these phones are being loaded already with ai. I don't think you can really escape it. Maybe if someone can come out with something that doesn't use a lot of water, that'd be really great.

Nicole (13:00):

In my area, in the western part of Virginia, there have been a bunch of data centers built and they're like taking away habitat and they're real eyesore when you drive by them. It's just really upsetting to see that what was once a forest be a data center, the impact on climate is so severe that I morally just cannot agree with using it so often. And I think it's also taking away our abilities to critically think and have empathy, which is really detrimental in the long run and the short run honestly for climate, for our communities, just for the entire planet. But I know that there are positive ways to use AI like climate modeling or disaster relief things, but to use it to do your homework, to use it, to read an article, to write a paper, I think using it on day-to-day things is just not the right move.

Parisa (14:04):

AI could be used for solving a lot of healthcare struggles. We've been seeing AI be able to detect cancer very early on many environmental problems as well. That's it. Just like any tool, it has its own downsides and there needs to be more discretion when it comes to using the findings, but also the data in general. I also hope that with the technology enhancements we will be seeing more efficient server runs and just hopefully less heat production when it comes to the AI servers.

Katelan (14:46):

If you've heard any stats about how much water AI uses, they probably came from this 2023 paper called Making AI Less Thirsty, uncovering and addressing the Secret Water Footprint of AI Models. This paper was a big deal because not many people had studied AI's water usage before that and one of the people who worked on that paper was Shale Ren. He's an associate professor of electrical and computer engineering at the University of California Riverside, and a lot of his research focuses on AI for good and I was so lucky that he was able to answer some of our burning questions about ai. Hi Shaolei, thanks for coming on the show.

Shaolei (15:32):

Good to be here.

Katelan (15:33):

I have to ask you are an electrical and a computer engineer by trade. So what made you start caring so much about water specifically?

Shaolei (15:42):

Well, I think I spent a couple of years in a small town back in China where we only had water access for about half an hour each day. So we just had to use water very wisely and water is a finance resource. Even though here in the US water doesn't seem to be a big problem for many people, but still it's a finance resource and we have to allocate the resource wisely. So actually my research, a lot of my work is about resource allocation and I find that water resource allocation is a very interesting and also challenging problem.

Katelan (16:15):

All right, so where is all this water coming from that we're using in data centers? And is the water that's being used typically recycled in the center or elsewhere?

Shaolei (16:26):

Most data center will get their water from municipal water facilities. So those are basically clean drinking water and tech technically we also call it through water. And it's different from the gray water or green water. Green water is the rain water in the soil to grow the grasses or basically used by plants and a portion of the water will get recycled several times before discharge to the wastewater processing plants and a small portion of water will also get evaporated. Okay. but overall, actually, if you look at some of the companies sustainability reports, they will be evaporating about 80% of their water withdrawal.

Katelan (17:05):

Gotcha. And which of these are, and and so you're saying that most of the water that's used at data centers, they're using the blue water, is that right?

Shaolei (17:12):

Yeah, they're using blue water and actually blue water is also includes the water in rivers and lakes. Basically those are the fresh water that humans can directly tap into for industrial, for residential usages.

Katelan (17:27):

Gotcha. And while some data centers recycle their water, most of it's being evaporated as it's being used. So you can only recycle like about 20% of the water anyway because the rest of it's just blowing off as like steam.

Shaolei (17:42):

Yes. So if you look at some sustainability reports by those tech companies, overall they will be vapor about 70 to 80% of the water that we saw this percentage changes by different facilities. Some facility will be evaporating more and others will be evaporating less depending on the coding technology they use and also the water quality.

Katelan (18:02):

When we talk about the water that AI uses, you know, we focus a lot on the data centers as we've been talking about, but AI uses water in other phases right, of the lifecycle. So I just wanted to better understand what goes into AI's water footprint and how does water usage further up the supply chain compare to water usage at the sort of end of the supply chain? At the data centers,

Shaolei (18:25):

The concept of AI's water footprint is very broad and includes the water used to mine the rare earth to manufacture the AI servers and also to produce the electricity. And if you follow the carbon emission scoping definition, we have scope one scope and scope three. Scope One is the direct water consumption by the data center facilities, and scope two is for generating electricity and scope three is ev everything else like recycling and manufacturing. So typically the scope three is the largest amount of water, but there's not much information available in the public. And if you only focus on scope one, scope two, this is the water. Sometimes we call it operational water footprint. And scope two is also generally higher portion compared to scope one direct water consumption.

Katelan (19:13):

That's so interesting. I've never thought about, I know the scoping with carbon emissions, but not necessarily the scoping with water. That's fascinating. Do you know of any other AI companies that are using water or energy more efficiently than others?

Shaolei (19:28):

Well, based on the public disclosure of the water efficiency some companies like Microsoft is are doing pretty good job in terms of water efficiency

Katelan (19:39):

As far as like consumer tools that we can use, I think one of the most popular ones is chat DPT. Is there a version of chat GPT that's better for water or electricity that you know of? Is there, is there some other tool like that that we can use that maybe doesn't use quite as much resources?

Shaolei (19:56):

I think they offer many different versions. Typically, the free version will be more efficient in terms of resource consumption than the paid version. So, and also non reasoning model will be more efficient than reasoning model.

Katelan (20:10):

Why would the free version be more efficient than the paid one?

Shaolei (20:13):

Because it's a closed source model, so we don't really know exactly what is going on, but you know we can reasonably expect that the paid version will have more model parameters and more resource consumption as well.

Katelan (20:26):

In the research that you've done and your experience, like living in a place where there was water allocation and water scarcity, have you noticed that these data centers have put a strain on water resources in communities, whether it's in the US or elsewhere?

Shaolei (20:41):

We've heard quite a few conflicts in places like Spain, Chile, Uruguay, and also Arizona. So some companies are building data centers in those places and that are obviously putting some strain on the, on the local water resources.

Katelan (20:58):

We know that AI consumes a ton of water in a ton of energy, but do you believe that it can be optimized to basically reduce more than it's using not only for its own use, but maybe even it could offset energy use or water use and other systems, basically. Do you think that AI can do more <laugh> good than, than bad when it comes to the environmental impact?

Shaolei (21:22):

I think in the long run, yes. Even for now, despite the huge energy consumption, water consumption it's not at the point where we should panic yet. I think we still have the resources to deal with the energy to deal with the water demand to support this technology development. And in the long run, we're gonna be expecting to use this technology to help the energy sector become greener, become cleaner detect the water leaks. So we, we should expect more benefits out of this AI technology.

Katelan (21:56):

I know that you've been doing some work on water equity yourself. Could you just share anything about the work that you're, you're doing?

Shaolei (22:03):

Yeah, we work on the resource allocation problem. We try to allocate the workload and computer resources in a way that are not particularly straining any regions disproportionately. So that's one of the problem we've been working on. And also we are working on other aspects of the equity and environmental protection.

Katelan (22:26):

Well, that is really, really exciting. This has been really optimistic <laugh> and it's been really helpful to kind of contextualize the sort of water piece of ai, so I really appreciate it. Thank

Shaolei (22:37):

You.

Katelan (22:38):

Thank you.

Katelan (22:47):

Earlier when we heard from our community, I really loved parisa's reminder that AI is a tool. It's not inherently bad or good, it's all in how we build it and how we use it. On one hand, AI is already helping to better track and tackle climate change. It's being used to do things like accelerate the transition to renewable energy with more reliable forecasting. It's measuring deforestation across more than 1 million hectares of land around the world. It's tracking the speed at which icebergs are melting and doing it 10,000 times faster than humans can. It's helping people be better prepared for climate disaster earlier. It's even mapping litter in the oceans, but is also being used for a lot of small tasks and curiosities. And besides the climate impact of these small scale uses of ai, there are studies that show increased use of AI can decrease our individual ability for critical thinking, and we will need critical thinking to fight the climate crisis. Plus AI works off of all that we've already created. So it's not great at generating truly novel ideas, and I think we're gonna need those to fight climate change too.

Katelan (24:05):

I'm starting to think of AI like with great power comes great responsibility. AI is a powerful tool and we can use it to do great things, but the way it's built, who builds it and how it's regulated, those things can make or break how much good we can do with it. And right now we're way behind on tracking AI's. Big picture impact. Governments don't even capture comprehensive global statistics on data centers like it does for other sectors like transportation or construction, and tech companies are not going out of their way to fill in those information gaps. So I called up commons founders, Sanchali Seth Pal to chat about what we know and what we don't know about the energy use and regulation of ai. Welcome back, char.

Sanchali (24:55):

Thanks, Katelan. Glad to be back.

Katelan (24:57):

So we heard from Shaolei Ren about AI and water usage, and now we're gonna dive into energy usage.

Sanchali (25:05):

Data centers are hungry for energy. Just to give you a sense, in 2022, it's estimated that data centers, many of which are used to power ai, were the 11th largest electricity consumer in the world, bigger than the electricity consumption of Saudi Arabia. But by next year, 2026 MIT research shows that AI could become the world's fifth largest electricity consumer, putting it between Japan and Russia.

Katelan (25:31):

Okay, wait, so you're saying that AI usage requires a country's worth of energy?

Sanchali (25:37):

Yeah, and it's becoming a bigger and bigger country. A lot of this is coming from demand from big companies like Microsoft and Google. These companies are increasing their energy use and for the first time actually missed their net zero targets this year because of it. So from 2019 to 2023, Google's greenhouse gas emissions increased by a shocking 48% thanks to its uptick in data center energy use. And Microsoft's emissions rose by 29% from 2020 to 2023.

Katelan (26:07):

And the data centers that a lot of these companies are using, they're right here in the United States, right?

Sanchali (26:11):

They are. The US has nearly 5,500 data centers, which is way more than any other country. China, Germany, and the UK come in a distant second with about a 10th of the total data centers. As the US has

Katelan (26:25):

This energy that the data centers are using, is it coming from fossil fuels or renewable energy?

Sanchali (26:31):

So today it's kind of both. We're building so many data centers that we can't keep up with demand with just one source of energy. Data centers are requiring us to really fire on all cylinders. Wherever we have energy sources. It means even coal plants, which were meant to be shut down, are now being kept online because they have to stay open.

Katelan (26:51):

Oh my God, we're keeping coal plants open in order to power ai and coal creates so many emissions. It

Sanchali (26:57):

Does. Coal is really high emissions. It's not even the cheapest source of energy, and it creates a ton of toxic pollution for people living around coal-fired power plants.

Katelan (27:08):

I'm sure just at large, these huge data centers are changing communities

Sanchali (27:13):

For sure. What makes it really dramatic is that they require a ton of physical infrastructure and data centers are often built in clusters so they can share power grids, cooling centers, and information. So Virginia is an example where the government has incentivized data center clusters by giving tax breaks. That means that any adverse effects from the development of those data centers, including local energy sources like coal-fired power plants, are negatively affecting those local communities too.

Katelan (27:41):

You would think that AI is a perfect reason to prioritize investment in cheaper, cleaner sources of energy. Are we seeing any uptick in progress there?

Sanchali (27:50):

It's really fascinating. The need for more cheap electricity is causing a bit of a revival and renewable energy investment and clean energy investment. Specifically for the nuclear industry, the demand for electricity to power data centers is skyrocketing and it can't be met by existing energy sources. So experts are looking for new sources because estimates suggest we'll need three times the electricity supply from our data centers over the next five years.

Katelan (28:16):

Wow, that's that's a lot.

Sanchali (28:18):

And the reason people are excited about nuclear is because at scale it's cheaper than coal and it can be used reliably at high energy density, which is really important for keeping AI online. It's not variable like solar or wind and doesn't require batteries to sustain. Big companies and CEOs for this reason, are making significant investments in renewable energy. It includes folks like Microsoft, Google, Amazon, Tesla Meta, and even OpenAI. But there are some health and safety concerns with nuclear. Historically nuclear power plants that were decommissioned in the past are now being brought back online. So it's an area that's gonna need some real regulation to come up alongside the new investments.

Katelan (28:58):

So it sounds like there's an important role for government to play here,

Sanchali (29:03):

Ensuring safety is definitely gonna be critical, especially given the concerns that the public has with past nuclear disasters. And it's not just about safety, it's about information. Enforcing transparency will be really critical too, but as the AI space becomes more and more competitive, companies are increasingly becoming tight-lipped about their operations and energy usage. So this might be challenging.

Katelan (29:24):

Are they allowed to do that? I know that in Virginia data centers are using more than a quarter of the state's energy, and yet these companies are not being transparent about it. That seems kind of messy

Sanchali (29:34):

Because AI is still really new and it's blowing up so quickly. There's really very little regulation and we do need to set some parameters sooner rather than later.

Katelan (29:44):

Are there any regulations right now on ai?

Sanchali (29:47):

There's some federal laws proposed around the ethics of ai, and there's definitely no AI specific federal regulator in the us.

Katelan (29:54):

It feels like AI is kind of like in its wild west phase.

Sanchali (29:58):

It definitely is. It's still really early and the technology is changing so fast, and the US is really setting a lot of the new innovation, and it's so, it's important that there's new regulation to emerge here. We're still early enough that it could happen, but it's really important that we push for it. There is a path where AI isn't so scary. Let's imagine a world where things work out really well. I hope there's a world where it's net positive. In theory, the tools could create efficiencies that are good for the environment. It could allow us to do the things we do today, but just more efficiently it could help us unlock new climate solutions and maybe even speed up a clean energy transition. But it could also not turn out that way. And it's incredibly critical that these concerns get built in now. So I'm really glad that we're starting to ask these questions, and I hope that everyone listening to this podcast feels like they know how to keep asking good questions about AI and its energy use going forward.

Katelan (30:56):

I learned a lot working on this episode, and I learned a lot from people sharing how it's affecting them and their community. So I hope we pick the right path forward and we will keep folks updated here and on Instagram as we navigate this wild west of ai.

Sanchali (31:10):

I have also learned so much. This has been really illuminating. Thanks, Caitlyn.

Katelan (31:14):

Thanks so much.

Katelan (31:23):

AI is a huge industry, and there are many environmental, ethical, and moral considerations that have to be at the forefront as it continues to boom. Because this is a sustainability show and there's so much to talk about when we talk about AI and sustainability. We focus today on the resource use of ai, but we didn't even get into the issues of privacy and surveillance bias, art theft. While it increases our efficiency and capacity, this gargantuan new technology is going to continue to stretch and strain our natural resources and call into question, what is ethical? When it seems that all of the internet is up for grabs, we have to stay grounded in its real world implications. Every day, people are making decisions about AI technologies that could eventually raise virgin's, electricity bills, or strain the water use of Texans during a heat wave. The upside is that this technology is still new, and now is the time to set the precedent.

Katelan (32:24):

So many climate change drivers like fossil fuels or the meat industry, are grounded in decades of systems and business deals, but AI is still relatively new. So now is the time to pay attention and to share. Share what you know about the impacts of AI and ask questions. Pay attention to how AI is affecting your community and pay attention to legislation for AI companies and data center developers. Share how you expect companies to stay accountable to the communities where they're using all this energy, all this land and all this water, and pay attention to your own AI usage too. Thanks to our community who shared their takes on AI Today, you heard from [names]

Katelan (33:21):

This is an ongoing conversation and if you wanna keep chatting, pop over to our Instagram at Second Nature Earth. This episode was edited and engineered, not by robot, but by the very real Mr. Evan Goodchild. It was written and produced by me, a real human being as well. Katelan Cunningham, thank you so much for listening. Thank you for caring, and we'll see you back here soon. Bye.

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