What's The Environmental Cost Of AI?

By 2028, Lawrence Berkeley National Laboratory forecasts that U. S. data centers could use as much as 12% of the nation's electricity. The reason: generative AI. Since 2022, AI innovation by four leading tech companies — Google, Microsoft, Meta and Amazon — has led to annual increases in both energy and water consumption. So, in this episode, Short Wave co-host Emily Kwong https://www.npr.org/people/767284140/emily-kwong probes huge water footprint of AI. We begin with the rise of data centers, then look at how computers came to need so much water and, finally, what tech companies are doing to try to turn the ship around. P. S. Part 2 talks about the leading solutions in the green AI movement. So don't miss our Friday episode! Curious about tech and the environment? Email us at [email protected] mailto:[email protected] — we'd love to hear from you! Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave http://plus.npr.org/shortwave See pcm.adswizz.com https://pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences. NPR Privacy Policy https://www.npr.org/about-npr/179878450/privacy-policy

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• 0:12Regina Barber?

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• 1:10Emily Kwong?

  You're listening to Short Wave from NPR.

• 1:15Regina Barber?

  Hey, Short Wavers. It's Regina Barber with my co-host, Emily Kwong. Hey, Em.

• 1:19Emily Kwong?

  Hi, Gina. So today, our episode starts with water.

• 1:25Emily Kwong?

  And someone who's been thinking about water for a long time. He says maybe that's because of where he grew up.

• 1:32Speaker 03

  The official name is Kang'ar Town. And the town only had like 50,000 people at that time.

• 1:38Emily Kwong?

  This is Xiaole Yan. He's from a coal mining town in northern China, where, growing up, water was really scarce. So he learned how to make every drop count.

• 1:47Speaker 03

  We only had water access for like half an hour each day. So we just had to use water very wisely.

• 1:54Emily Kwong?

  So he grew up very water-conscious. And now at UC Riverside, Xiaole studies the water footprint of the tech industry. Because, as you know, Gina, as the tech industry has grown, so too have data centers.

• 2:06Regina Barber?

  Right, these data centers that are those huge buildings filled with hundreds of thousands of computers that store cloud data and do a lot of computing for AI, those computers can get really hot.

• 2:17Emily Kwong?

  Right, which is why water, you know, chilled H2O, has become an ally in keeping those computers cool. And Shale wanted to know exactly how much water was being used. But his early research, some of the first ever studies on water efficiency in data centers, was kind of met with crickets.

• 2:37Speaker 03

  Back in 2013, there was no attention at all. Zero.

• 2:42Emily Kwong?

  But then, in 2022, OpenAI's ChatGPT took the internet by storm, and people started to look at Shale's work.

• 2:50Speaker 04

  The amount of water that AI uses is astonishing. AI needs water. People are saying that every time you use ChatGPT--

• 2:57Emily Kwong?

  ChatGPT uses this much water for 130--

• 2:59Speaker 04

  And where did that water come from?

• 3:01Emily Kwong?

  Just to train a large language AI model and keep a data center cool can consume hundreds of thousands of liters of fresh water. And by consume, I mean that the water evaporates and doesn't necessarily return to the local watershed.

• 3:17Regina Barber?

  Like, the water turns into vapor, goes up in the air, and does not come down to that location.

• 3:21Emily Kwong?

  Not necessarily. That's water consumption.

• 3:23Regina Barber?

  Wow.

• 3:24Emily Kwong?

  Yeah, it's where the water is no longer available for reuse. In 2023, for example, Google's data center in Council Bluffs, Iowa, consumed nearly 1 billion gallons of potable water.

• 3:36Regina Barber?

  Wow. Okay, so I know data centers also use a lot of energy, primarily like fossil fuels, but I guess they're also using like a ton of water.

• 3:44Emily Kwong?

  Yes, and it's because of AI infrastructure. Now, unless you live near a data center or a power plant, AI infrastructure is mostly invisible. And my goal with this reporting was just to pull back the curtain and ask what toll this is all taking on the environment.

• 3:58Regina Barber?

  Today on the show, the first in a two-part series on why the true environmental footprint of AI is so elusive.

• 4:05Emily Kwong?

  Starting with the rise of data centers and how computer architecture got to the point of needing gallons of water in the first place.

• 4:12Regina Barber?

  Then, we'll talk about how big tech is trying to turn that ship around. I'm Regina Barber.

• 4:18Emily Kwong?

  And I'm Emily Kwong, and you're listening to Short Wave from NPR.

• 4:24Regina Barber?

  All right, Em. So all of these headlines about how AI is using water, it's because it takes a lot of energy to compute and solve really big problems, right?

• 4:33Emily Kwong?

  Right. So data centers, they grew from these like single rooms to whole buildings.

• 4:39Regina Barber?

  Right.

• 4:40Emily Kwong?

  During the dot com boom of the 90s and aughts. And now, these big buildings contain hundreds of thousands of computers. If they get too hot, the servers can shut down or suffer damage.

• 4:50Regina Barber?

  So what is the method of like cooling down these computers?

• 4:54Emily Kwong?

  Well, every data center is different, but I'll describe the basic principles of a mechanical cooling system. Okay, picture a room with rows and rows of computers on racks.

• 5:04Regina Barber?

  Yeah, I've seen them before. It makes me think of like a library.

• 5:08Emily Kwong?

  Yes, yes, it's like a computer library, except the floor is raised. So there's this void below that allows cool air to flow up through a bunch of grills and chill the computers. Benjamin Lee is a professor who studies computer architecture at UPenn, and he explained to me how air cooling basically works.

• 5:27Speaker 05

  You push the cool air through the front of the machines, and all the warm air gets pushed out the back.

• 5:33Emily Kwong?

  And then what happens is a refrigerant takes the heat outside the building, where it gets dissipated into the air.

• 5:39Regina Barber?

  Yeah.

• 5:40Emily Kwong?

  But the thing about an air cooling system like this is it requires a lot of electricity. So some systems also use water to help pull heat away from the data center.

• 5:49Regina Barber?

  Yeah, which is smart, because like water is so much better at transferring heat than air.

• 5:53Emily Kwong?

  Yeah, your physics degree really pays off at a time like this.

• 5:56Regina Barber?

  Just in these moments. But like, where does this warm water go?

• 6:00Emily Kwong?

  Well, a lot of it gets sent to a cooling tower and is evaporated. You can think of it like sweat. The data center is the brain. It needs to be cooled down because it's getting hotter and hotter in this era of AI.

• 6:12Speaker 05

  I think the difficulty has been that the air conditioning infrastructure is having trouble keeping up with the latest in GPUs and how closely packed GPUs are.

• 6:23Emily Kwong?

  Benjamin is talking about microprocessors. And a certain type of microprocessor known as a GPU is widely favored for running AI.

• 6:31Speaker 05

  They are delivering more performance, but they also may be drawing more power, which is why we are now taking unprecedented steps to cool them.

• 6:39Emily Kwong?

  Now, the thing about data centers, Gina, is that some are more energy efficient than others. There's even free air cooling systems which pull in air from the outside and use no water. But the point I really want you to remember is that in order to reduce the electricity demands of data centers, some have turned to water. And that has meant the overall water consumption, like the number of gallons getting evaporated away, has gone up. Because of AI? Because of AI, getting integrated into products from the four biggest data center operators: Google, Microsoft, Meta, and Amazon. Which, quick sidebar, we should note that they're all financial supporters of NPR. Like, Amazon also pays to distribute some of NPR's content. Yes.

• 7:22Emily Kwong?

  And Amazon does not disclose how many gallons of water they consume. They only report their water usage effectiveness, or WUE. So we don't know how much water they consume? We do not. Oh, wow. Okay. We have a better sense from Google, Microsoft, and Meta. Since 2021, all three have reported a bigger water footprint, meaning they are consuming more and more water lost to evaporation every year. So who's consuming the most? Google. Okay. So in 2023, and this is according to their own report, consumption across all their data centers totaled 6.4 billion gallons. That's enough to irrigate 43 golf courses in the southwestern US. Wow.

• 8:04Emily Kwong?

  Although, keep in mind, that is nothing compared to how much water is used by agriculture. I mean, 43 golf courses sound like still a lot of water to me. It's a lot of water, yeah. And the concern, of course, is that, once the water is evaporated, it's not available for reuse. Right. So just to give you an example of how this can play out badly, the Dalles, that's a city 80 miles east of Portland, Oregon, is where Google built its first data center, and residents noticed a change to the local water supply.

• 8:33Speaker 06

  The water level in our wells dropped 15 feet.

• 8:36Emily Kwong?

  This is Dalles resident Dawn Rasmussen, talking to the AP in 2021.

• 8:41Speaker 06

  When you have dry conditions, you know, it's stressful on the plants, the animals, and the people in the community.

• 8:49Emily Kwong?

  So the Oregonian, the local paper, asked Google, hey, what are your water numbers? And Google said, no way, we're not going to tell you, it's a trade secret. And after a year-long legal battle, it came to light that Google was using a quarter of all the water available in town.

• 9:04Regina Barber?

  That is so much.

• 9:06Emily Kwong?

  Now, this surge of water use, I was like, why? Why so much water? It can be directly traced to the AI Renaissance. And that's because tech companies are searching for what Benjamin Lee at UPenn calls the next killer app.

• 9:20Speaker 05

  The search engine was a killer app. Another example of that would be a recommendation system that social media feeds use to recommend ads and content. That was a killer app. But we don't have that for generative AI.

• 9:32Emily Kwong?

  Ben says that's why you're seeing things like AI overviews in Google Search, or AI chatbots on Instagram, or AI product summary reviews on Amazon.

• 9:41Speaker 05

  There's a lot of generative AI being invoked on your behalf as these companies try to figure out what it's good for.

• 9:47Emily Kwong?

  Which is, you know, they're prerogative. But in the meantime, there doesn't seem to be a standard for these companies to report the details of their water use.

• 9:55Regina Barber?

  So that golf course number that you mentioned earlier, we only know that because Google freely reported it in like a progress report on their own climate pledges. Can you tell me more about those pledges? Like, what has each company promised to do for the climate?

• 10:08Emily Kwong?

  Well, all four have pledged to be water positive by 2030, which means they'd put more water back into the environment than they use. And they're trying to do this through partnerships with local watersheds. In the Dalles, that city in Oregon I mentioned earlier, Google is now building a system to pump excess surface water into an existing aquifer for later use in drier months.

• 10:33Regina Barber?

  It sounds like they're trying to be water positive.

• 10:36Emily Kwong?

  Yeah, water positive and clean energy. Google, Microsoft, and Meta have all pledged to reach at least net zero carbon emissions by 2030. Amazon has set their deadline for 2040. But again, Gina, because all of their energy and water data is shared voluntarily, the public has no way to wrap its arms around the scope of AI's environmental footprint. And computer scientist Sasha Lucioni, climate lead at Hugging Face, thinks that is a problem.

• 11:04Speaker 07

  We don't have any mandatory reporting mechanisms for companies, for compute providers. So they tend to give kind of very high level numbers on a company level, sometimes, not even all the time.

• 11:15Emily Kwong?

  So after realizing just the scope of AI, I had to ask these four tech companies, are your climate and water goals even realistic?

• 11:23Regina Barber?

  So what did they say?

• 11:25Emily Kwong?

  Well, Meta said they, quote, "remain committed." Google said they are "fully committed." Microsoft said they remain resolute and, quote, "are proactively working to address resource challenges associated with the energy needs of AI." And Amazon, Amazon actually sat down with me. Can Amazon meet its climate and energy goals as stated?

• 11:43Speaker 08

  Yes, we are continuing on our path to meet our climate goals by 2040.

• 11:49Emily Kwong?

  Kevin Miller is the vice president of global data centers at Amazon Web Services. And he told me all the ways Amazon is investing in green energy infrastructure. And all the tech companies are.

• 11:59Regina Barber?

  Right. And speaking of like, you know, green energy and being more carbon neutral, I read that Amazon, Meta, and Alphabet, which like runs Google, just signed an agreement along with other companies that supports tripling the global nuclear supply by 2050.

• 12:13Emily Kwong?

  Yes, it's very ambitious.

• 12:15Regina Barber?

  Wow.

• 12:15Emily Kwong?

  And along with Microsoft, these four companies have signed agreements to purchase nuclear energy. But that industry has been stagnant for years. Takes a long time to get nuclear up and running. So computer scientists who study climate are doubtful. Here's Benjamin at UPenn.

• 12:31Speaker 05

  I think before generative AI came along in the late 2022, there was hope among these data center operators that they could go to net zero.

• 12:40Emily Kwong?

  But he's lost faith now as companies increase their energy use faster than they switch to renewables.

• 12:46Speaker 05

  I don't see how you can, under current infrastructure investment plans, you could possibly achieve those net zero goals.

• 12:53Emily Kwong?

  Sasha at Hugging Face agrees.

• 12:55Speaker 07

  I mean, for what it's worth, Microsoft and Google already failed to meet their own goal last year. So I think that the tendency is going towards no.

• 13:04Emily Kwong?

  I also asked Jesse Dodge, a senior research scientist at the Allen Institute for AI at MIT. And over email, he said to me, quote, "These companies are making non-binding pledges to get positive attention. And I expect that if or when they don't meet those pledges, they will simply move the goalpost." In the meantime, more data centers are being constructed.

• 13:24Regina Barber?

  Yeah, where?

• 13:25Emily Kwong?

  All over the country. Jeffersonville, Indiana, Rosemount, Minnesota, and Abilene, Texas. On January 21st, the day after his second inauguration, President Trump announced a private joint venture to build 20 large data centers across the country, as heard here on NBC.

• 13:42Speaker 09

  New American company that will invest $500 billion, at least, in AI infrastructure in the United States and very, very quickly, moving very rapidly.

• 13:53Emily Kwong?

  This new project, known as Stargate, would, together, consume 15 gigawatts of power. That would be like 15 new Philadelphia-sized cities consuming energy. There aren't any state or federal regulations for AI or data centers. Some legislators at the state level have introduced bills to regulate AI and data centers in California, in Connecticut. And at the federal level, Senator Edward Markey of Massachusetts introduced a bipartisan bill that would set federal standards and voluntary reporting guidelines to measure AI environmental footprint. But there really isn't a legal framework in place.

• 14:30Regina Barber?

  But like, until laws are in place, are tech companies like doing anything on their end to fix the problem? Like, to train or to create more sustainable AI models?

• 14:39Emily Kwong?

  That is why there is a part two of this series. Next time on Short Wave, the green AI movement.

• 14:45Regina Barber?

  I can't wait.

• 14:47Regina Barber?

  This episode was produced by Hannah Chen, edited by our showrunner, Rebecca Ramirez, and fact-checked by Tyler Jones. Jimmy Keely was the audio engineer. Special thanks to Brent Bachman, Johannes Durgey, and our incredible standards team. The ChatGPT commentary you heard at the beginning of this episode came from TikTokers Dylan Page, Carter Smith, and Nikita Redkar. You also heard tape from Morning Brew and now this.

• 15:12Emily Kwong?

  Beth Donovan is our senior director and Colin Campbell is our senior vice president of podcasting strategy. I'm Emily Kwong.

• 15:18Regina Barber?

  And I'm Regina Barber. Thank you for listening to Short Wave, the science podcast from NPR.

• 15:29Speaker 07

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