What's The Environmental Cost Of AI?

Summary

Short Wave: What's The Environmental Cost Of AI? Podcast Episode Released on May 7, 2025 by NPR

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Introduction

In the May 7, 2025 episode of NPR's Short Wave, hosts Emily Kwong and Regina Barber delve into the environmental ramifications of artificial intelligence (AI), focusing particularly on the substantial water usage associated with data centers. This episode marks the first installment in a two-part series exploring the elusive true environmental footprint of AI.

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The Rise of Data Centers and AI Infrastructure

Emily Kwong introduces the topic by highlighting the exponential growth of data centers fueled by the burgeoning AI industry. She explains that these vast facilities house hundreds of thousands of computers essential for cloud data storage and AI computations. The increasing demand for AI capabilities, such as those provided by OpenAI's ChatGPT, has significantly amplified the operational requirements of these data centers.

Notable Quote:

"We only had water access for like half an hour each day, so we just had to use water very wisely."
— Xiao Lei Ren, UC Riverside, 01:52

Benjamin Lee, a computer architecture professor at the University of Pennsylvania, elaborates on the evolution of data centers from single rooms during the dot-com boom of the 1990s and 2000s to expansive buildings today. He explains the critical need for effective cooling systems to prevent server overheating, which is central to understanding the environmental impact of AI.

Notable Quote:

"The amount of water that AI uses is astonishing. AI needs water."
— Benjamin Lee, 02:55

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Water Consumption in Data Centers

The discussion pivots to the substantial water consumption required to cool data centers, especially those running intensive AI computations. Regina Barber and Emily Kwong outline how water is used not just for cooling but also how its consumption affects local water supplies.

Key Points:

• Cooling Technology: Data centers typically use either air cooling, which is electricity-intensive, or water-based cooling systems that are more efficient at heat transfer.
• Water Consumption Metrics: In 2023, Google's data centers in Council Bluffs, Iowa, consumed nearly 1 billion gallons of potable water, enough to irrigate 43 golf courses in the southwestern United States.

Notable Quote:

"Google's data center in Council Bluffs, Iowa consumed nearly 1 billion gallons of potable water."
— Regina Barber, 03:41

Impact Example: In Dalles, Oregon, Google's first data center caused a significant drop in local well water levels by 15 feet, as reported by resident Don Rasmussen. This stark example underscores the environmental stress caused by large-scale water consumption.

Notable Quote:

"The water level in our wells dropped 15ft. When you have dry conditions, it's stressful on the plants, the animals and the people and the community."
— Don Rasmussen, 09:36

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Corporate Pledges and Sustainability Efforts

Emily and Regina explore the commitments made by major tech companies to mitigate their environmental impact. Google, Microsoft, Meta, and Amazon have pledged to become water positive by 2030, aiming to return more water to the environment than they consume. Additionally, these companies have set ambitious goals to achieve net-zero carbon emissions by 2030, with Amazon extending its target to 2040.

Key Initiatives:

• Water Positive Goals: Partnerships with local watersheds and infrastructure projects like Google's aquifer pumping system.
• Net-Zero Commitments: Investments in renewable energy and sustainable technologies.

Notable Quote:

"All four have pledged to be water positive by 2030, which means they'd put more water back into the environment than they use."
— Emily Kwong, 11:12

Skepticism and Challenges: Despite these pledges, experts like Benjamin Lee express skepticism about the feasibility of these goals, given the rapid increase in energy and water usage driven by AI advancements. Computer scientist Sasha Luccioni emphasizes the lack of mandatory reporting, making it challenging to assess the true progress of these initiatives.

Notable 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."
— Jesse Dodge, Senior Research Scientist at the Allen Institute for AI, 13:57

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Regulatory Landscape and Future Directions

The episode highlights the absence of comprehensive regulations governing AI and data center operations. While some states like California and Connecticut have introduced bills to regulate these sectors, there is no overarching federal framework. Senator Edward Markey of Massachusetts has proposed a bipartisan bill to establish federal standards and voluntary reporting guidelines for measuring AI's environmental footprint.

Expansion of Data Centers: President Trump announced the Stargate project, a private joint venture to build 20 large data centers across the United States, projecting a consumption of 15 gigawatts of power—comparable to the energy use of 15 Philadelphia-sized cities. This expansion underscores the urgent need for regulatory measures.

Notable Quote:

"There aren't any state or federal regulations for AI or data centers."
— Regina Barber, 14:52

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Conclusion and Looking Ahead

The episode concludes by setting the stage for the second part of the series, which will explore the Green AI movement and initiatives aimed at creating more sustainable AI models. The hosts emphasize the critical need for transparency, standardized reporting, and effective regulations to accurately assess and mitigate the environmental costs of AI.

Notable Quote:

"Next time on Short Wave: The Green AI Movement."
— Regina Barber, 15:37

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Key Takeaways

• AI's Environmental Footprint: The rise of AI has significantly increased the water and energy consumption of data centers.
• Corporate Accountability: Major tech companies have made ambitious sustainability pledges, but skepticism remains regarding their ability to meet these goals.
• Regulatory Needs: There is an urgent need for federal and state regulations to ensure transparent reporting and sustainable practices in the AI and data center industries.
• Future Directions: The upcoming episode will delve into efforts and movements striving to make AI more environmentally sustainable.

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This comprehensive exploration by Short Wave sheds light on the often-overlooked environmental consequences of advancing AI technologies, urging both corporate accountability and regulatory intervention to foster a sustainable technological future.

Transcript

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[Regina Barber]

We start today's show. You may have heard that President Trump has issued an executive order seeking to block all federal funding to npr. This is the latest in a series of threats to media organizations across the country. Whatever changes this action brings, NPR's commitment to reporting the news without fear or favor will never change. Even as paywalls rise elsewhere, we offer this vital resource to everyone, regardless of their ability to pay. This is a pivotal moment. It's more important than ever that every supporter who can contribute comes together to pitch in as much as they are able. Support the news and programming you and millions rely on by visiting donate.NPR.org and if you already support us via NPR or another means, thank you. Your support means so much to us. Now more than ever, you help us make NPR shows freely available to everyone. We are proud to do this work for you and with you.

[Emily Kwong]

You're listening to Short Wave from npr.

[Regina Barber]

Hey, shortwavers, it's Regina Barber with my co host Emily Kwong.

[Emily Kwong]

Hey, Em. Hi, Jeanna. So. So today our episode starts with water. And someone who's been thinking about water for a long time, he says maybe that's because of where he grew up.

[Benjamin Lee]

The official name is Kangar Chong, and the town only had like 50,000 people at that time.

[Emily Kwong]

This is Xiao Lei Ren. 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.

[Benjamin Lee]

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

[Emily Kwong]

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

[Regina Barber]

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, right?

[Emily Kwong]

Which is why water, you know, chilled H2O has become an ally in keeping those computers cool. And Chalet 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.

[Benjamin Lee]

Crickets back in 2013. There was no attention at all. Zero.

[Emily Kwong]

But then in 2022, OpenAI's Chat GPT took the Internet by storm and people started to look at Shalet's work.

[Benjamin Lee]

The amount of water that AI uses is astonishing. AI needs water.

[Emily Kwong]

People are saying that every time you use ChatGPT, ChatGPT uses this much water for 100.

[Regina Barber]

Where would that water come from?

[Emily 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 alpha, I mean that the water evaporates and doesn't necessarily return to the local watershed.

[Regina Barber]

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

[Emily Kwong]

Not necessarily. That's water consumption. 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.

[Regina 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 to of water.

[Emily 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.

[Regina Barber]

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

[Emily 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.

[Regina Barber]

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

[Emily Kwong]

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

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[Regina 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?

[Emily Kwong]

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

[Regina Barber]

Right.

[Emily 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.

[Regina Barber]

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

[Emily 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.

[Regina Barber]

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

[Emily 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.

[Benjamin Lee]

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

[Emily Kwong]

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

[Regina Barber]

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

[Emily Kwong]

Yeah, your physics degree really pays off.

[Regina Barber]

At a time like this, just in these moments. But, like, where does this warm water go?

[Emily 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, I think.

[Benjamin Lee]

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

[Emily Kwong]

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

[Benjamin Lee]

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.

[Emily 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.

[Regina Barber]

Because of AI.

[Emily Kwong]

Because of AI getting integrated into products from the four biggest data center operators, Google, Microsoft, Meta, and Amazon.

[Regina Barber]

Which, quick sidebar. We should note that, like, they're all financial supporters of NPR. Like, Amazon also pays to distribute some of NPR's content.

[Emily Kwong]

Yes. And Amazon does not disclose how many gallons of water they consume. They only report their water usage, effectiveness or wue.

[Regina Barber]

So we don't know how much water they consume.

[Emily Kwong]

We do not.

[Regina Barber]

Oh, wow. Okay.

[Emily Kwong]

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.

[Regina Barber]

So who's consuming the most?

[Emily Kwong]

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 U.S. wow. Although keep in mind, that is nothing compared to how much water is used by agriculture.

[Regina Barber]

I mean, 43 golf courses sound like still a lot of water to me.

[Emily Kwong]

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.

[Regina Barber]

Right.

[Emily Kwong]

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.

[Regina Barber]

The water level in our wells dropped 15ft.

[Emily Kwong]

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

[Regina Barber]

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

[Emily 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. That is so much 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.

[Benjamin Lee]

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.

[Emily 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.

[Benjamin Lee]

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

[Emily Kwong]

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

[Regina 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?

[Emily 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 drier months.

[Regina Barber]

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

[Emily 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 Luccioni, climate lead at Hugging Face, thinks that is a problem.

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We don't have any mandatory reporting mechanisms for companies for compute providers, so they tend to give kind of very high.

[Emily Kwong]

Level numbers on a company level. Sometimes not even all the time. So after realizing just the scope of AI, I had to ask, these four tech companies, are your climate and water goals even realistic?

[Regina Barber]

So what, what did they say?

[Emily 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?

[Benjamin Lee]

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

[Emily Kwong]

And he told me all the ways Amazon is investing in green energy infrastructure and all the tech companies are right.

[Regina Barber]

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.

[Emily Kwong]

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

[Benjamin Lee]

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.

[Emily Kwong]

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

[Benjamin Lee]

I don't see how you can undercurrent infrastructure investment plans. You could possibly achieve those net zero goals.

[Emily Kwong]

Sasha @ Huggingface agrees. I mean, for what it's worth, Microsoft and Google already failed to meet their.

[Sponsor Announcement]

Own goals last year.

[Emily Kwong]

So I think that the tendency is going towards no. 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.

[Regina Barber]

Yeah? Where?

[Emily Kwong]

All over the country. Jeffersonville, Indiana, Rosemount, Minnesota and Abilene, Texas. On January 21, 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 New American company that will invest $500 billion at.

[Regina Barber]

Least in AI infrastructure in the United States. And very, very quickly, moving very rapidly.

[Emily 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 in 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.

[Regina 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.

[Emily Kwong]

That is why there is a part two of this series. Next time on shortwave. The Green AI Movement.

[Regina Barber]

I can't wait. This episode was produced by Hannah Chin, edited by our showrunner Rebecca Ramirez, and fact checked by Tyler Jones. Jimmy Keeley was the audio engineer. Special thanks to Brent Baughman, Johannes Durgi and our incredible standards team.

[Emily Kwong]

The ChatGPT commentary you heard at the beginning of this episode came from TikTokers Dylan Page, Carter Smith and Nikita Redcar. You also heard tape from Morning Brew. And now this. Beth Donovan is our senior director and Colin Campbell is our senior vice president of podcasting strategy. I'm Emily Kwong.

[Regina Barber]

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

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