AI: Is It Ruining the Environment?

The internet is abuzz with accusations that artificial intelligence is using up tons of energy and water. People are even protesting the building of new AI data centers, saying they’ll put a huge strain on local resources. But some AI defenders say that this fear is overblown and that AI isn’t actually that bad for the environment. So who’s right? We talk to science and tech reporters Casey Crownhart and James O’Donnell, and computer scientist Prof. Shaolei Ren. UPDATE, 11/13/25: This episode has been updated to note that some AI companies advertise on the show. Find our transcript here: https://tinyurl.com/ScienceVsAIEnvironment Read James and Casey's article here: https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/ Check out the Mythbusters GPU/CPU demonstration here: https://www.youtube.com/watch?v=WmW6SD-EHVY In this episode, we cover: (0:00) Chapter One: No More AI For Dank Memes?! (3:34) Chapter Two: How Much Energy Does Your AI Query Use? (15:37) Chapter Three: How Much Energy Does AI Use Total? (21:18) Chapter Four: Is AI Drinking All Our Water? (29:29) Chapter Five: Should You Quit Using AI? This episode was produced by Rose Rimler and Blythe Terrell, with help from Meryl Horn and Michelle Dang. We’re edited by Blythe Terrell. Fact checking by Diane Kelly. Mix and sound design by Bobby Lord. Music written by Emma Munger, So Wylie, Peter Leonard, Bumi Hidaka and Bobby Lord. Thanks to all the researchers we reached out to, including Prof. Melissa Scanlan, and special thanks to Andrew Pouliot and Jesse Rimler. Science Vs is a Spotify Studios Original. Listen for free on Spotify or wherever you get your podcasts. Follow us and tap the bell for new episode notifications. Learn more about your ad choices. Visit podcastchoices.com/adchoices

• 0:00Speaker 00

  Hi, I'm Rose Rimler. I'm filling in for Wendy Zukerman, and this is Science vs.

• 0:06Speaker 00

  This is the show that pits facts against filling the world with AI data centers.

• 0:15Speaker 00

  Today on the show, AI and the Environment.

• 0:20Speaker 00

  Lately, we've been hearing a lot about how power-hungry AI is.

• 0:24Speaker 01

  AI uses a [BLEEP] ton of electricity. Yeah.

• 0:27Speaker 02

  Training the nation's aging power grid and creating more planet-warming emissions.

• 0:31Speaker 00

  And how thirsty it is.

• 0:33Speaker 03

  The amount of water that AI uses is astonishing.

• 0:37Speaker 04

  Asking ChatGPT to write one email is the equivalent of pouring out an entire water bottle.

• 0:42Speaker 05

  One bottle of water. Let that sink in. Let that sink in.

• 0:46Speaker 00

  And the major culprit here is the data centers, warehouses full of computer servers that AI needs to function. And tech companies are trying to build more of these data centers, but people who live nearby are protesting them, often saying that they're going to compete for their electricity and use up their water.

• 1:06Speaker 06

  We do not want a data center built in St. Charles City.

• 1:13Speaker 00

  Because of all this, around some corners of the internet, using AI has become kind of a faux pas, especially if you use it for something silly.

• 1:22Speaker 07

  You are actively contributing to global warming and climate change all because you want to Photoshop Chris Brown into your picture.

• 1:27Speaker 04

  So next time you use AI to generate an image for a meme, think about the impact on the environment first.

• 1:33Speaker 00

  Stop ruining your planet for a [BLEEP] Instagram post.

• 1:40Speaker 00

  But on the flip side, we've got people pushing back against this idea. They say that these reports are skewed or misleading, and that the impact of AI on the environment isn't nearly as bad as a bunch of other stuff we're already doing, like eating meat or taking international flights. In fact, recently, some of the big AI companies have said that their products only use a tiny bit of power and a few drops of water for each prompt. So what's really going on here? Is AI actually ruining the planet? Or have the bots been framed? Because when it comes to AI and the environment, there's a lot of... Stop ruining your planet for a [BLEEP] Instagram post. But then, there's science.

• 2:22Speaker 00

  And that's coming up after the break. And full disclosure, some AI companies do advertise on Science Versus.

• 2:37Speaker 08

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• 4:47Speaker 00

  Welcome back. I'm Rose Rimler. I'm a senior producer at Science Versus, and I'm here with our editor, Blythe Terrell. Hi, Blythe.

• 4:54Speaker 10

  Hey, Rose.

• 4:55Speaker 00

  Blythe, it seems like you're my AI buddy. I invite you to talk to me about controversies when it comes to AI.

• 5:03Speaker 10

  It's because I'm part robot.

• 5:06Speaker 00

  I would say that of the team, you're the person who is like most tuned in to this idea about AI using up all the energy, using up all the water. You've been into this for a while.

• 5:16Speaker 10

  Yes, this is actually, I am one of those people who probably shared a meme, Rose, without knowing if it was true on the water use or the whatever. Like, I do remember seeing those memes and being like, is this true? And then, and then honestly, for me, it did, it did make me take a step back from AI and be like, before I get involved in this, I do want to know the truth. Like, is this actually terrible for the environment? Is this actually terrible for the water? Because why would I want to integrate it into my life? If it is.

• 5:46Speaker 00

  Right, so the first question is, why do we think AI would use so much more energy than all the other stuff that we do in our digital lives? Just like messing around on the computer, posting on Instagram, watching Netflix? Like, this is all stuff that we do pretty routinely and don't think a lot about the like footprint of that behavior.

• 6:06Speaker 10

  Right, like looking at pictures of Jeff Goldblum. How much energy is that using? Just as an example hypothetically.

• 6:13Speaker 00

  Photoshoping Jeff Goldblum as your prom date?

• 6:15Speaker 10

  You know, I don't know who you might be talking about, Rose, but that sounds like a pretty good use of electricity and energy, and water, no matter how much it takes.

• 6:24Speaker 00

  So, that kind of stuff also requires data centers and energy to run them. But the thing that's different about AI is that their servers are using a different kind of computer chip. So, normal computing uses a CPU, but AI uses a GPU. And if you're familiar with video games, you might think of this as like a graphics card. But it's actually become the powerhouse behind machine learning. This is an extremely visual and potentially copyrighted.

• 6:55Speaker 00

  Analogy, but I was looking around on YouTube. I love a copyrighted analogy. The Mythbusters guys, they did a demonstration of a CPU versus a GPU. And in their demonstration, they used paintball guns. OK. The CPU was like programming a paint, one paintball gun to, to draw a happy face with like one paintball pellet at a time, like firing at a piece of paper on a wall. OK. That's a CPU. A GPU was like 200 paintball guns all bound together. Making it like a mega, a mega gun. Paintball gun and like one switch is hit and they all fire at once. And the image that they create is the Mona Lisa.

• 7:38Speaker 00

  Oh, man, those guys are good. Mythbusters. So the, the point is that while CPUs are good at doing one task after another, the GPUs are good at doing a bunch of tasks at once. And that requires a lot more energy. OK. How much energy? Are you ready for that? Yeah, be ready.

• 7:58Speaker 00

  And I got a bit of an assist here. I talked to some journalists who have covered this stuff for years.

• 8:02Speaker 11

  My name is James O'Donnell. I'm a senior reporter for AI at MIT Technology Review.

• 8:08Speaker 12

  I'm Casey Crownhart. I'm a senior climate reporter at MIT Technology Review.

• 8:13Speaker 00

  So James and Casey both report a lot on AI and energy use. Mm hmm. OK. And about a year ago, they started a project trying to figure out like, how much does an average query or prompt to say ChatGPT, how much energy does that use? And they were inspired to do that because they were seeing all these numbers out there floating around that just didn't seem all that reliable. Here's Casey.

• 8:35Speaker 12

  These kind of wild estimates of, you know, oh, a query to, to something like ChatGPT uses this much water and this much energy and, and isn't that so much? And so I think that, that started to kind of get our gears turning and wondering, you know, is that right? How can we add all of this up? What does it, what does it all add up to?

• 8:54Speaker 00

  So Casey and James looked around for the real number, but.

• 8:59Speaker 11

  We learned very quickly that it's not going to be so easy to know that number. Companies, they're not particularly willing to share the details of how much energy their AI models require to answer one question. And so, you know, we weren't going to get it from them. And so.

• 9:16Speaker 00

  What did they say when you reached out and asked?

• 9:19Speaker 11

  They said in so many words, no.

• 9:23Speaker 00

  So James and Casey went a different route. There are AI models that are not proprietary. Anybody can use them, even download them, host them on their own computer, as long as they have the power to do that. These are open source models. And you can kind of open the hood, poke and prod them. And so James and Casey teamed up with experts, including academics at the University of Michigan, to measure it themselves. So they ran a bunch of different prompts through an open source large language model called Llama. Hmm, OK. And then they were able to actually measure how much energy those requests required. And so they got some answers. Are you curious? I, I, yes, I would love to know. Give me the answers.

• 10:07Speaker 00

  Well, there's a range here.

• 10:10Speaker 12

  I was really struck throughout this project of, I think we went in and I was looking for kind of one definitive answer. You know, like, what is AI's energy burden? And I think that one of my biggest takeaways was just how much it depends. It depends on the model. It depends what you're asking. And so there's just this really big range. That, that was one of my biggest takeaways.

• 10:36Speaker 00

  Hmm. So, OK. You know, basically, when, when people like us say, I asked AI, you know, we kind of act like AI is this one thing. And it's totally not. There are all these different models. And these models come in different sizes. So a model, sorry, a model is like a ChatGPT or a Gemini or a Claude or whatever. Yeah. And, and they're within ChatGPT, Gemini, Claude. There are multiple models. Within Llama, there are multiple. Ah. And there are, some are bigger, some are smaller. If you imagine that this AI model, like, imagine it's the, like, the command of a spaceship or actually, my favorite is like a, a switchboard with tons of knobs and dials. That, you can kind of imagine that's what these parameters are. James says.

• 11:21Speaker 11

  Each of those knobs is helping the AI come up with a better answer. But also, each of those knobs requires energy to operate.

• 11:32Speaker 00

  So the smallest model that the team looked at for this analysis had 8 billion parameters. So 8 billion knobs.

• 11:40Speaker 10

  That sounds big.

• 11:41Speaker 00

  The biggest one they looked at had 400 billion parameters.

• 11:44Speaker 10

  400 billion?

• 11:45Speaker 00

  And when it comes to the big players here, we actually don't know how many parameters they have. But James said if he had to guess, it's...

• 11:56Speaker 11

  You know, in the order of trillions.

• 11:58Speaker 00

  Whoa.

• 11:59Speaker 11

  Really big. A lot of knobs.

• 12:02Speaker 00

  A lot of knobs. I mean, I'm imagining basically like a switchboard, but now I have to completely change it because it's like a switchboard that goes on for miles.

• 12:11Speaker 11

  Yes, that's right.

• 12:12Speaker 10

  So these parameters you're talking about, which is like sort of what underpins the model, I guess.

• 12:19Speaker 00

  They are, yeah, they're numbers, values. And the more of them that there are, the better the model is at learning patterns and making predictions, which is how large language models work.

• 12:32Speaker 10

  OK. OK, so say I have like one request and I pop it into a model with 8 billion parameters. And then I pop that same request into a model with like 400 billion parameters. That same request is going to use different amounts of energy based on the model that I'm using.

• 12:49Speaker 00

  Yes, and actually, we can move into... We don't even have to hypotheticalize here. I have real numbers for you.

• 12:56Speaker 10

  Oh, nice. OK.

• 12:58Speaker 00

  So the smallest Llama model that the team used, they fed in some prompts like, "Teach me about quantum computing" or "suggest some travel tips." And then they measured how much energy that used. The smallest model, when it spit out an answer, used on average 114 joules.

• 13:21Speaker 10

  Oh, great, great. That's very helpful. Thank you.

• 13:25Speaker 00

  Are you being sarcastic? Do you want some other way to think about this?

• 13:29Speaker 10

  Yes, please give me something more concrete.

• 13:31Speaker 00

  I, well, I didn't, you know, it was James and Casey. So they came up with something for some context. They, one thing they converted these energy units into is a fun new type of unit called microwave seconds.

• 13:43Speaker 12

  I love the microwave seconds unit. It's so much more relatable than joules or watt hours.

• 13:50Speaker 10

  Casey gets me.

• 13:51Speaker 00

  All right, so 114 joules is roughly a 10th of a second in a microwave.

• 13:55Speaker 10

  Okay, so that's one query, small model, a 10th of a second in the microwave.

• 14:01Speaker 00

  Yeah.

• 14:01Speaker 10

  Okay.

• 14:02Speaker 00

  The biggest model, which was 50 times bigger, that was like zapping something in a microwave for eight seconds.

• 14:08Speaker 10

  Oh, so that's the biggest model was still only, for one query, the biggest model was still only eight seconds. Okay, that doesn't even get my rice remotely hot.

• 14:19Speaker 00

  Right. I mean, and after James and Casey published their article, frustratingly for them, OpenAI and Google did release a little bit of information on how much energy their text prompts use on average. And what they said suggests that a text query is equivalent to one or two seconds in the microwave.

• 14:39Speaker 10

  Okay.

• 14:39Speaker 00

  So basically, what we can tell you is like a text prompt to a large language model is probably on the order of zapping something in the microwave for less than 10 seconds.

• 14:48Speaker 10

  Mm hmm, okay.

• 14:49Speaker 00

  And then for images, this is a different kind of machine learning, but it also uses a fair amount of energy. And I would have assumed that this image making thing is inherently more energy sucking than text making, but as it turns out, that is not necessarily the case. Here's James.

• 15:08Speaker 11

  If you have a really big, large language model that's generating text and answers, it may actually use more energy than generating an image. And that was kind of counterintuitive for me because, you know, you think about like these AI models that come up with fantastical images that we've all seen over the past few years, and it just seems like such an intense process to kind of create that from scratch.

• 15:34Speaker 00

  Yeah, because it always takes longer too than getting your text back.

• 15:37Speaker 11

  Yeah, exactly. But what we found was that if you have a really large text model, it has so many parameters, so it has so many knobs and dials, that it actually can use up more energy than generating, you know, certain types of images.

• 15:52Speaker 00

  They found that making an image was like running a microwave for five and a half seconds.

• 15:56Speaker 10

  So like a big language model can be like eight seconds of microwave time. So that's a little less, okay.

• 16:02Speaker 00

  Right.

• 16:02Speaker 10

  I'm with you.

• 16:03Speaker 00

  And you know, for all this stuff, if you don't like microwave time, you could also think about it in lightbulb time. So it's like running an LED lightbulb for somewhere between 10 seconds and two minutes.

• 16:16Speaker 10

  So I guess, Rose, what this maybe tells me is that if I wanted to make my Jeff Goldblum prom picture with AI, that is a slightly less energy-intensive process than perhaps using AI to write romantic Jeff Goldblum fanfiction.

• 16:34Speaker 00

  Possibly if you use a really big model to write your Jeff Goldblum fanfiction.

• 16:38Speaker 10

  Obviously, I would need a very large model for this work, Rose. Okay, got it.

• 16:45Speaker 00

  And then there's video generation. This might not surprise you to hear that that used the most energy. So the team, what they did was they looked at an open-source video generation model, and they just made like a crap video. It was 16 frames a second, five seconds long. They compared it to like the quality of a silent film era-type film.

• 17:05Speaker 10

  Hmm, okay.

• 17:06Speaker 00

  And that one...

• 17:07Speaker 10

  That would be the equivalent of over an hour in the microwave.

• 17:11Speaker 00

  I don't think I've ever microwaved anything for an hour.

• 17:14Speaker 10

  I don't think I have either. A long time in the microwave, for sure.

• 17:18Speaker 00

  That one scares me more because I'm seeing a lot of AI-generated videos out there.

• 17:22Speaker 10

  Yeah, that's what's huge right now and getting bigger, right? There's a ton of, what is it, Sora?

• 17:27Speaker 00

  Yeah, Sora is big right now.

• 17:29Speaker 10

  Yeah.

• 17:30Speaker 00

  And they can look incredibly realistic, right?

• 17:32Speaker 10

  Right.

• 17:32Speaker 00

  We don't know if that's also using up as much energy. We asked OpenAI, which makes Sora, and they didn't give us any information on Sora's energy use. And James and Casey didn't want to speculate, it's a different model. But it's probably using a fair amount of electricity. I think that's safe to assume.

• 17:51Speaker 10

  So I mean, but, OK, so what we have so far is all about like individual use.

• 17:56Speaker 00

  Yeah.

• 17:57Speaker 10

  But I want to, but what I want to know, though, is like, obviously, lots of us are doing this, lots of us are using this. Like, what is the impact if you like add it all up? If you like scoop up all the AI use that we're doing? Like, what do we know about that?

• 18:09Speaker 00

  Right. It's interesting. Like, on an individual level, certainly like the texting and image generation stuff, they're not that crazy energy intensive. But that doesn't leave AI off the hook because when you do zoom out, and to answer your question, it, it really adds up fast because OpenAI says it receives 2.5 billion prompts per day

• 18:31Speaker 10

  Jeez.

• 18:32Speaker 00

  from people around the world.

• 18:33Speaker 10

  Wow.

• 18:34Speaker 00

  And AI in general is getting integrated into all these institutions, which I think a lot of us are noticing. In fact, one survey of a variety of organizations around the world found that 78% of them are now using AI to some extent.

• 18:49Speaker 10

  That is a lot.

• 18:50Speaker 00

  And so nerds have looked at how much electricity is going to data centers to see if AI has made an impact. And they saw that from 2014 to 2023, the electricity consumption of data centers tripled. That's according to a report from the Lawrence Berkeley National Laboratories.

• 19:08Speaker 10

  Oh, wow. So like in this, and that's the AI period? Like, that's like sort of-

• 19:12Speaker 00

  That is like the, yeah, basically the age of AI, like, taking off.

• 19:15Speaker 10

  Taking off.

• 19:16Speaker 00

  And the energy suck is expected to keep sucking. More and more. One analysis predicts that by 2028, AI data centers will use as much electricity as a quarter of US households use per year.

• 19:33Speaker 10

  Wow.

• 19:35Speaker 00

  Imagine adding 25% more households to the US in 2028. That's what the prediction is, that these AI data centers are gonna use up.

• 19:44Speaker 10

  That doesn't sound good.

• 19:45Speaker 00

  Well, I mean, Casey, who's a climate reporter, she was like, you know, it's not the electricity per se that's the problem here.

• 19:53Speaker 12

  That's kind of the crucial thing that I like to bring up and really harp on is that, you know, if we had abundant solar and wind power and batteries, you know, we might be less concerned about some of this, this energy demand. But the reality is that grids around the world are still largely reliant on fossil fuels. So, it's not good.

• 20:13Speaker 00

  Uh, right now in the US, only 9% of the country's power comes from renewable sources. It's still mostly fossil fuels that we use to power our electric grid.

• 20:23Speaker 10

  Right.

• 20:23Speaker 00

  A third of our energy comes from petroleum, a third comes from natural gas, which is another fossil fuel. Both are greenhouse gas emitters.

• 20:31Speaker 10

  But, and coal?

• 20:32Speaker 00

  Uh, coal is in the mix too. It's 8%.

• 20:35Speaker 10

  So just a lot of this energy is, is dirty.

• 20:37Speaker 00

  Mm-hmm. And of course, there are other countries with cleaner energy grids than the US. But more than half of the data centers for the world are here in the US.

• 20:47Speaker 10

  Well, and you know, I feel like the headlines, some of the headlines I've seen around this, Rose, have been like, related to nuclear energy. Because there were headlines a while back that one of these companies was gonna reopen Three Mile Island, which is this nuclear plant that was shut down because of an accident. And so there was talk of like that being reopened and like, you know, really a lot of these companies being very interested in what's going on with nuclear. So it, it does make me wonder, could nuclear help if we can get that ramped up?

• 21:16Speaker 00

  I asked Casey about that and she was like, the thing about nuclear reopening or building a new nuclear plant, it takes so long.

• 21:23Speaker 10

  Mmm.

• 21:24Speaker 00

  The last nuclear plant that we built in the US took 15 years to complete.

• 21:30Speaker 10

  Yeah.

• 21:30Speaker 00

  And companies are just not gonna wait for that to happen. And they're not, they're not waiting for it. I mean, look at XAI. Uh, they brought in gas burning generators to run their data center in Tennessee.

• 21:43Speaker 10

  Right. Okay. Uh, okay, well, that sucks.

• 21:52Speaker 00

  Yeah, so I reached out to XAI and I didn't hear back. I also contacted Google and Anthropic just to ask about all this stuff that we've been talking about. I didn't get answers from them by our deadline.

• 22:03Speaker 10

  Mm-hmm.

• 22:04Speaker 00

  OpenAI did get back to me. They mostly pointed me to stuff that's already publicly available, open letters and blog posts, that kind of thing. Um, talking about their energy use and how they see that, uh, in the future. And basically what OpenAI is saying is that they wanna work with the government to add capacity to the grid. Uh, and they say that they want that energy to come from all kinds of sources, including renewables.

• 22:29Speaker 10

  Uh huh, okay.

• 22:31Speaker 00

  And just overall, I will say, there might be some changes coming for the positive. So the energy that AI requires to answer your query, or make your image, or your video, that could be going down, because a lot of the tech companies are trying to make their models more efficient. One way they're doing that is by turning off some of the parameters that we talked about earlier, when they don't necessarily need them to answer a particular question or do a task. So that's like shrinking the switchboard essentially, as needed.

• 22:59Speaker 10

  So there is some evidence that like the tech companies are like trying to adjust to make this thing.

• 23:05Speaker 00

  Yes, it might get better.

• 23:07Speaker 10

  Okay, so that's energy, Rose, but, but I know there's another piece to this. Yeah.

• 23:13Speaker 00

  Mm hmm.

• 23:13Speaker 10

  What about water? What is going on with water?

• 23:15Speaker 00

  Right, so we're gonna talk about that after the break.

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• 25:32Speaker 00

  Welcome back. I'm Rose Rimler. I'm here with Blad Terrell. Hello. So let's talk about water and AI. And if you want to talk about water and AI, you call up Shaolei Ren. He is a professor at UC Riverside. He's actually in the computer engineering department, but he focuses on sustainability. Most people in his field look at, you know, energy, greenhouse gases, like we were just talking about, but Shaolei has kind of forged his own path because he's thought about conserving water for just a lot of his life.

• 26:01Speaker 17

  I spent my first few years in a small town back in China. We just had access to fresh water, drinking water for half an hour each day. During those half an hour, we had to use a big bucket to collect the water and use it for the rest of the day. So in my memory, I just, I never thought water is something unlimited. It's just, it's a finite resource.

• 26:28Speaker 00

  Hmm, you've never taken it for granted. Right. And so one reason AI uses a lot of water is something that you've probably heard before. The data centers get really hot because they're running all these fancy chips, doing all this computation, like we were talking about earlier. And so these buildings, they often use a cooling tower that uses water to cool everything down.

• 26:49Speaker 17

  Just like our human bodies, we sweat and we feel cooler. For data center, if you use water evaporation, you can take away the heat very naturally, very efficiently.

• 26:58Speaker 00

  And where do they get that water from?

• 26:59Speaker 17

  Most typically, it's from the municipal water infrastructure system.

• 27:05Speaker 00

  So the same as where, if I lived there, if I were to turn my turn on?

• 27:08Speaker 17

  Yeah.

• 27:09Speaker 10

  So they get water where everyone gets water? From the faucet, basically?

• 27:13Speaker 00

  And the reason for that is they want like clean filtered water because if there was salts or minerals or like gunk in it, then it could gum up this system, basically.

• 27:22Speaker 10

  Hmm, okay.

• 27:23Speaker 00

  So as the water cools, the data centers, it, you know, it evaporates away. It evaporates and if I remember my like kindergarten, you know, the water cycle, when water evaporates, it eventually comes back as rain, right? So why do we need to worry about this?

• 27:39Speaker 17

  So the evaporated water, yeah, it still stays within our global water cycle system. It doesn't go away from the earth. But still, when the water will be coming back and where it will be coming back, that's highly uncertain. And it's very unevenly disputed across the globe. So due to the long-term climate change, we're seeing more and more uneven distribution of the water resources. So essentially, the wetter regions are getting wetter and drier regions are getting drier.

• 28:15Speaker 00

  So even if the water is evaporated in, say, Arizona, that doesn't mean it'll come back as rain in Arizona? At least not anytime soon?

• 28:22Speaker 17

  Correct.

• 28:23Speaker 10

  Okay, oh, okay. So the argument is it's using a bunch of water, it's drawing it out from where everyone else is getting their water. And it's not, that's not necessary, and it's not necessarily going to be replenished that easily.

• 28:36Speaker 00

  Well, yeah, yeah, it's going to evaporate. The drinking water in Tucson, and that water might next show up as a flood in Shanghai.

• 28:44Speaker 10

  Hmm, right, okay.

• 28:45Speaker 00

  So let's talk about how much water is actually getting used here. Xiaolei and his team, they went down this rabbit hole fairly recently, and they published a paper that kind of went viral. In fact, a lot of people turned their results into a meme, basically saying that every time you use AI, they'll say it in different ways. Like every time you chat with ChatGPT, every time you write an email with AI, you're consuming a bottle of water. Have you seen this, Blyth?

• 29:14Speaker 10

  Yes, yes. This was one of the memes I first saw and shared without evidence.

• 29:20Speaker 00

  I've seen videos of people filming themselves like with a nice, beautiful, fresh bottle of water from the store, opening it up and like pouring it down the drain and saying like, this is what you're doing when you use AI or someone will like dressed up and pretending to be AI, like dressed as a robot and they're just like guzzling water. But that's not quite accurate.

• 29:41Speaker 17

  Yeah, so that's, that's, yeah, that's a distortion of the message that we're showing the paper.

• 29:47Speaker 10

  A distortion.

• 29:47Speaker 00

  Distortion. Here's what they actually found. So they found that if you have a back and forth conversation with, in this case, they looked, the model they looked at was ChatGPT-3. It's a slightly older model, but if you have a back and forth with ChatGPT-3, medium-length messages, if you go back and forth for on average about 30 times, that uses up essentially the volume of a bottle of water, a half liter of water.

• 30:14Speaker 10

  Hmm, okay. So it's like a, it's a decent conversation that gets you to that half liter?

• 30:19Speaker 00

  Yeah, and that's where the meme comes from. So it's not super duper wrong, but what they're getting wrong or misunderstanding is that the fresh drinking water that's used to cool the data center, that's actually only a small part of this calculation. So out of this half liter of water that we're talking about, only about 12% of it is drinking water that's used directly by the data center for cooling.

• 30:45Speaker 10

  Oh.

• 30:46Speaker 00

  And the rest of it is non-potable water from elsewhere. It's drawn out of rivers, lakes, whatever. It's used in the process of making electricity. So that brings us back again to the power plants, you know, that old chestnut.

• 31:00Speaker 10

  Okay, wait, wait. So it's talking, so some of this is drinking water, but some of this is like.

• 31:05Speaker 00

  But most of it is not.

• 31:06Speaker 10

  But most of it's not. But, I mean, but still, like, that's water in the environment could eventually become drinking water, right? So like, why does, so why does that distinction actually really matter?

• 31:18Speaker 00

  If you think the data center moving into your town is a threat because it's going to turn on a bigger tap than yours, that's not quite right. And I asked Chalet about that. Do you think it's possible that a town will accept a data center and it uses up all the town's water, essentially? Like, you live next door to a data center, you turn your tap and no water comes out?

• 31:41Speaker 17

  I think in certain towns, it could be possible. But in most towns, I think the US infrastructure tends to be, at least for the water infrastructure, they should be able to have the capacity available for data centers.

• 31:58Speaker 00

  He said that the biggest problems here might be likely to happen in really small towns with really old or limited water infrastructure.

• 32:06Speaker 10

  Okay.

• 32:07Speaker 00

  So when I see people talking about how data centers are using up water, I think like, we might be ignoring the bigger issue here, which is the water used by power plants. And by the way, if we had more wind and solar on the grid, the water use would go down.

• 32:23Speaker 10

  Uh huh.

• 32:23Speaker 00

  But anyway, as of right now, overall, taking into account the water used by power plants and the water used for cooling, we know that data centers consume 0.3% of the nation's water supply. I asked Chalet about this.

• 32:39Speaker 10

  I don't know what to make of that. Is that a lot or is that a little? 0.3%.

• 32:42Speaker 17

  So it's roughly the same amount of total public water supply in Rhode Island? So whether this 0.3% is high or not, I would say it's modest. It's not that much.

• 32:56Speaker 00

  Brings up the question, should we be letting Rhode Island use all that water? I mean, what has Rhode Island done for anyone else lately, you know?

• 33:04Speaker 10

  It's, you know, finally, the podcast is getting around to that question, which I've also had for years.

• 33:09Speaker 00

  What is the point of Rhode Island? Yeah, I mean, and the water used for the data centers, for the power generation and cooling, it's projected to go up. It's actually expected to double in the next few years. But ultimately, Chalet and another expert I spoke to said that whether or not this becomes a problem is a regional question. It makes more sense to be granular about this. Like, is the water being taken from an area that doesn't have the capacity? You just can't paint with a broad brush here.

• 33:43Speaker 10

  So complicated, I guess.

• 33:49Speaker 10

  Is where we so often land. Okay, so taking all this together, Rose, where do you land? Like, how evil is AI when it comes to the environment?

• 34:04Speaker 00

  I asked all of our guests basically that same question. I kind of put it in terms of like, well, do you personally use AI, knowing about all these environmental impacts? Because these are people, all these people care a lot about the environment and these issues. And all of them, Casey, Chalet, James, they all said that, yes, they do still use AI.

• 34:23Speaker 12

  I'm awful at planning trips. So asking for an itinerary for going on a road trip or something, that's, I found that that's really helpful.

• 34:34Speaker 17

  I use it to polish my text writing, to help me answer some questions. And also, my students use AI to generate paper summaries.

• 34:42Speaker 11

  So I've used AI for technical things, like how to do certain repairs on my bike. But I've also used it for seeing what people have said on a certain topic, like hikes in New England with the best views.

• 34:57Speaker 00

  But everybody agreed that we should be thoughtful about how we use it, given this energy and water requirement as well.

• 35:05Speaker 10

  So it's annoying, because part of me is like, you know, the companies that make this and that are using this, like, they're, and that are like, using it for their products and services that I'm using, like, they're the ones who I want to think about their AI use, right? I want them to be thinking about whether they really need to use this or not. And I want them to be thinking about that in the context of energy use, water use, climate change, right? Like, that's my dream, Rose.

• 35:29Speaker 00

  Yes, it's on the companies, it's on the government. I mean, I think that the, my takeaway here is that, like, I'm not sure AI is the villain. I think the villain is our reprehensible and baffling inability to switch to renewable energy and to put any kind of real effort into getting off of [bleep] fossil fuels.

• 35:56Speaker 10

  Right.

• 35:56Speaker 00

  It's the same enemy we've been fighting for 50 years or whatever.

• 36:00Speaker 10

  Right, right.

• 36:02Speaker 00

  Also, I think that one reason AI is getting people riled up as opposed to like those old climate offenders flying, eating meat, you know, that kind of thing, is people see the value in the trade off of the environmental impact of something like taking a flight or eating a burger. There's an obvious benefit to those things. With AI, yes, some people have found it really useful, but a lot of people haven't and they just don't think it has much value at all. In fact, one survey found that 61% of people in the US think that AI has more drawbacks than it has benefits.

• 36:36Speaker 10

  OK, so more than half of us are just like, nah, overall.

• 36:39Speaker 00

  We hate this [bleep].

• 36:41Speaker 10

  No, thank you.

• 36:42Speaker 00

  Exactly.

• 36:42Speaker 10

  OK.

• 36:43Speaker 00

  I think that's one reason AI is our current villain, when in fact, I think the villain is, I think that's like a nostril on the larger villain, which is the evil monster that is keeping us glued to fossil fuels.

• 36:59Speaker 10

  Right, the nostril. OK, I appreciate that picture.

• 37:06Speaker 10

  OK, so I do want to know one last thing, though. Has learning this and digging into all of this AI and energy and water stuff, has it changed how you use AI?

• 37:19Speaker 00

  Yeah, a little bit. Also, I think the novelty is wearing off a bit. And I was never like using it a ton, but I don't know. I was asking people about like, what's some stupid stuff that you've seen generated by AI? And you're like, oh, my God, that wasn't worth the energy. And I thought of my own playing around with it. And I was like, remember that time I had AI generate an image of... My boyfriend cuddling with my cat because my cat doesn't like him. So I was like, oh, this is what it would be like if you guys got along, you know? And I sent it to him and I was like, I don't think I would do that again. I don't think that was worth the energy.

• 38:00Speaker 00

  So it has changed a little bit, how you would make that value assessment, kind of. Is this, is this a, using, is using AI for this thing going to like add value? Is it actually really useful for this? Or? Or could I just glue salmon to his fingers? And then the cat would actually maybe come over? Yeah, you know, yes, Rose, let's go back to the- Let's go back to basics. Let's go back to the basics of gluing salmon to our boyfriend's fingers to get our cats to like him. Yep. That's Science Versus.

• 38:30Speaker 00

  Thanks, Bligh. Thanks, Rose.

• 38:36Speaker 00

  Oh, and while we're here, how many citations are in this week's episode? There are 66 citations. Mm-hmm. Where can people find them? They can find them in our transcript. The link to the transcript is in our show notes. Also in our show notes, we'll put a link to the article that James and Casey wrote for MIT Technology Review. It's really good and people should go read it. And people should also check out our Instagram. We've got some interesting stuff there, maybe even a little Jeff Goldblum content for you. Give the people what they want, Rose. Exactly. Great, love it.

• 39:19Speaker 00

  This episode was produced by Rose Rimler and Bligh Terrell with help from Merrill Horn and Michelle Dang. We're edited by Bligh Terrell. Fact checking by Diane Kelly. Mix and sound design by Bobby Lord. Music written by Emma Munger, So Wiley, Peter Leonard, Bumi Hidaka, and Bobby Lord. Thanks to all the researchers we reached out to, including Professor Melissa Scanlon. And special thanks to Andrew Pouliot and Jesse Rimler. Science Versus is a Spotify Studios original. Listen for free on Spotify or wherever you get your podcasts. Follow us and tap the bell for new episode notifications. We'll be back soon.

• 40:01Speaker 15

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