Could AI Go Green?

Google https://sustainability.google/operating-sustainably/net-zero-carbon/, Microsoft https://blogs.microsoft.com/blog/2020/01/16/microsoft-will-be-carbon-negative-by-2030/ and Meta https://sustainability.atmeta.com/climate/have all pledged to reach at least net-zero carbon emissions by 2030. Amazon https://sustainability.aboutamazon.com/climate-solutionsset their net-zero deadline for 2040. To understand how these four tech companies could possibly meet their climate goals amid an artificial intelligence renaissance, Short Wave co-host Emily Kwong https://www.npr.org/people/767284140/emily-kwong discusses the green AI movement. Speaking with scientists, CEOs and tech insiders, she explores three possible pathways: nuclear energy, small language models (SLMs) and back-to-the-future ways of keeping data centers cool. Listen to Part 1 of Short Wave's reporting on the environmental cost of AI here https://www.npr.org/2025/05/07/1249592906/energy-water-ai-climate-tech. Have a question about AI 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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• 0:16Regina Barber?

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• 1:16Emily Kwong?

  You're listening to Short Wave from NPR.

• 1:21Regina Barber?

  Hey, Short Wavers. Regina Barber here with my co-host Emily Kwong with the second half of a mini-series she reported on the environmental footprint of AI. Hey, Em.

• 1:30Emily Kwong?

  Hi, Gina. So today, I am bringing you a story of a personal crisis.

• 1:35Regina Barber?

  It's very relatable. Go on.

• 1:37Emily Kwong?

  OK, so in 2018, computer scientist Sasha Lucioni took a new job, AI researcher for Morgan Stanley. She was excited to learn something new in the field of AI, but she couldn't shake this worry.

• 1:52Speaker 03

  I essentially was getting more and more climate anxiety. I was really feeling this profound disconnect between my job and my values and the things that I cared about. And so essentially, I was like, oh, I should quit my job and go plant trees. I should, you know, I should do something that's really making a difference in the world. And then my partner was like, well, you have a PhD in AI, maybe you can use that.

• 2:15Emily Kwong?

  So Sasha quit her job. Wow. And she joined this growing movement to make AI more sustainable.

• 2:22Regina Barber?

  Yeah, you were saying that like AI innovation was causing this like surge in energy and water use like to cool data centers.

• 2:28Emily Kwong?

  That's

• 2:29Regina Barber?

  And the construction of those data centers was only going to increase.

• 2:31Emily Kwong?

  Yes. Some think exponentially. Gina, by 2028, Lawrence Berkeley National Laboratory forecasts that data centers could consume as much as 12% of the nation's electricity. That's 580 terawatt hours.

• 2:46Regina Barber?

  Okay, can you give me like a different way to kind of think about how much that actually is?

• 2:50Emily Kwong?

  It's like the amount of energy that Canada consumed two years ago.

• 2:53Regina Barber?

  Okay, so US data centers alone could someday use a Canada-sized amount of energy.

• 2:59Emily Kwong?

  They could.

• 3:00Regina Barber?

  Wow.

• 3:01Emily Kwong?

  So Sasha is on a quest to find AI models that are smaller and use less energy. She is now the climate lead at Hugging Face, which is an online community for AI developers to share models and data sets.

• 3:14Regina Barber?

  And a model is just like an AI program that's trained to take in data and like output data?

• 3:19Emily Kwong?

  Yes, so virtual assistants such as ChatGPT, Microsoft Copilot, Google Gemini, they are all powered by what's known as large language models. And Sasha, as she made quite plain in her 2023 TED Talk, is not a fan.

• 3:34Speaker 03

  In recent years, we've seen AI models balloon in size because the current trend in AI is bigger is better. But please don't get me started on why that's the case.

• 3:44Regina Barber?

  Wait, so I actually do want her to get started. Like, why are these like big players all using these huge models?

• 3:50Emily Kwong?

  I'm glad you asked.

• 3:51Regina Barber?

  Thank you.

• 3:52Emily Kwong?

  Because today on the show, we're going to talk about why bigger isn't always better when it comes to generative AI. In part two of our series, we'll talk about how this big sprawling industry is looking to shrink its environmental footprint with everything from small models, clean energy, and a Back to the Future way of keeping data centers cool. I'm Emily Kwong.

• 4:12Regina Barber?

  And I'm Regina Barber. You're listening to Short Wave, the science podcast from NPR. Don't worry, you won't be lost if you haven't heard part one.

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• 5:27Regina Barber?

  OK, Em, you've been talking with like four of the biggest tech companies, Google, Meta, Microsoft, and Amazon, which I should say are like all financial supporters of NPR.

• 5:36Emily Kwong?

  It's true. Amazon also pays to distribute some of NPR's content.

• 5:41Regina Barber?

  Right. And these four companies like all have ambitious goals for hitting net zero carbon emissions, most by 2030, Amazon by 2040. How are they going to get there?

• 5:50Emily Kwong?

  There are three paths, as far as I can tell. But before we talk about small AI models, you know, what Sasha's describing, let's talk about two solutions to make large language model computing more green. And that is more efficient data centers and nuclear power. What do you want to start with, Gina?

• 6:08Regina Barber?

  I'm a physicist.

• 6:09Emily Kwong?

  Nuclear, obviously. Of course, of course. Nuclear because Amazon, Meta and Alphabet, which runs Google, made a big announcement in March, as reported by Straight Arrow News.

• 6:20Speaker 05

  Three of the world's largest tech companies are promising to help triple global nuclear power supply by 2050.

• 6:27Emily Kwong?

  They're going to build new nuclear power plants and along with Microsoft, purchase nuclear energy.

• 6:32Regina Barber?

  OK.

• 6:33Emily Kwong?

  And Microsoft plans to get its nuclear energy by reviving a plant in Pennsylvania.

• 6:38Regina Barber?

  Yeah, our colleague Jeff Brumfield, he came on the show in December to talk about how Microsoft purchased Three Mile Island, like the site of a partial nuclear meltdown in 1979.

• 6:48Emily Kwong?

  Yes, only one of the reactors melted down. By the way, the whole site was shut down in 2019. And now Microsoft wants to bring it back.

• 6:55Regina Barber?

  OK, so are AI companies turning into energy companies?

• 6:59Emily Kwong?

  They are turning into energy movers and shakers, for sure. But Jeff sees a discrepancy in this, you know, between the AI people and the nuclear energy people.

• 7:09Regina Barber?

  Yeah.

• 7:09Speaker 06

  Silicon Valley loves to go fast and break things. The nuclear industry has to move very, very, very slowly because nothing can ever break.

• 7:19Emily Kwong?

  Nuclear is also extremely expensive.

• 7:21Regina Barber?

  Yes.

• 7:21Emily Kwong?

  And while solar and wind energy, combined with batteries, is quicker to build and more inexpensive than nuclear or gas power plants, it still takes time.

• 7:32Regina Barber?

  I mean, like, do we need to move that quickly to grow AI?

• 7:36Emily Kwong?

  Well, it depends on who you ask. Kevin Miller, who runs global infrastructure at Amazon Web Services, says yes.

• 7:42Speaker 07

  I think you have to look at the world around us and say, we're moving towards a more digital economy overall. And that is ultimately kind of the biggest driver for the need for data centers and cloud computing.

• 7:54Emily Kwong?

  But Sasha Lucioni, the computer scientist who we met earlier, feels this rush for AI is coming from industry, not from consumers.

• 8:02Speaker 03

  It's unfair to say that users want more because users aren't given the choice.

• 8:06Regina Barber?

  Yeah, I mean, like, I hear Sasha here because, like, I'm a big fan of AI's benefits. It's totally changed science and medicine and business and banking, all these things that affect our lives. But it does feel like opting out of AI is like becoming more and more difficult.

• 8:21Emily Kwong?

  Absolutely, yes. And until nuclear power catches up with AI's energy demand, data centers will... For the foreseeable future, continue to use fossil fuel sources. Yeah. So the question becomes, you know, is there a way to make data centers themselves more efficient? And the tech giants are trying through better hardware, better chips, and this really captured my attention, more efficient cooling systems. So that's solution number two. I love a tech solution to a tech problem. What are some of these strategies? Well, one method that's become quite popular is to design a data center to bring in cool air from outside the facility. No chilling required. So they just like pull in this cold air? Yeah, this is what's known as a free air cooling system.

• 9:04Emily Kwong?

  And then there's a design paradigm that's getting a bit of buzz. Folks in the industry call it liquid cooling. OK, and this is a different kind of liquid cooling evaporation we talked about in the first episode. Yes, this does not use water. Liquid cooling uses a special synthetic fluid that runs through the hottest parts of the server to take the heat away. OK. Or whole servers are immersed in this cool liquid bath. OK, so the idea of like running coolant through like a car engine? The very same. You can think of this like coolant, but for computers. OK. Benjamin Lee, who studies computer architecture at the University of Pennsylvania, said this is just a much more efficient way to cool off a hot computer.

• 9:46Speaker 08

  Because now you're just cooling the surface of whatever the cold plate is covering rather than just blowing air through the entire machine.

• 9:54Emily Kwong?

  So I wanted to talk to someone who's trying to bring liquid cooling to the market, and I found this company called Isotope. David Craig is their recently retired CEO.

• 10:03Speaker 09

  I definitely come from the point of view that, you know, we literally have just one planet, and I cannot understand why anybody would want to do anything other than care for it.

• 10:13Emily Kwong?

  David says the older way of cooling data centers, that daisy chain of moving heat with air and water, is just completely consumptive. Yeah. And while he couldn't tell me which tech companies have struck agreements with Isotope.

• 10:26Speaker 09

  So there are a number of confidentiality clauses that sit around kind of customers.

• 10:33Emily Kwong?

  Isotope has announced public partnerships with Hewlett Packard and Intel. And Ashley Saddle, a spokesperson at Meta, told me that Meta anticipates some of its liquid cooling-enabled data centers will be up and running by 2026. Wow, OK. But because I'm a numbers person, like, how much energy is being saved by liquid cooling versus like air or water cooling? Depends on the data center. You can say that the very best liquid cooling system uses about 40% less energy than a traditional air cooling system. Okay. And it uses no water.

• 11:07Speaker 09

  And then what we're doing is we're capturing that heat in a closed water loop. So it's a bit like a domestic central heating system at that point. And in huge parts of the planet, particularly the north, we can return that heat and do useful things with it in much more intelligent ways.

• 11:23Emily Kwong?

  David is talking about something called district heating. And that's where the heat from a data center, any data center, it doesn't have to be liquid cooled, is then diverted to a local neighborhood. And that is starting to happen at some data centers in Europe. Google has a data center in Finland that is providing heat to 2,000 people. That's so cool. I think I've actually read about this. I think it's called Hamina Data Center? That's the one, yeah. Now, Hamina does not use liquid cooling, but it is kind of a poster child for a green data center. Hamina runs on 97% renewable energy and pumps in seawater from the Bay of Finland to keep cool. Wow, that's really cool. Literally. Mm-hmm. Okay. So Hamina is just like one of these data centers like out of thousands, right?

• 12:07Emily Kwong?

  Yes, and this is the challenge. Most data centers are not situated by bodies of water in northern Europe. Right. So I want to talk about a third and final innovation. And it's the one that the tech companies I spoke to were kind of quiet about. Oh, okay. But the one that scientists and engineers outside the industry could not stop mentioning, and that is smaller AI models. I mean, of course. Right? One's good enough to complete a lot of the tasks we care about, but in a much less energy-intensive way. So a third and final solution to AI's climate problem is just to use less AI. Hmm. One kind of disruptor in this space is DeepSeek. Right. That's the chatbot out of a company in China, and it is claiming to use less energy.

• 12:52Emily Kwong?

  Yes, I did reach out to DeepSeek for comment. I didn't hear back. But here's the thing, Gina. Large language models like ChatGPT are often trained with really large data sets. DeepSeek, on the other hand, appears to have been trained with fewer chips and consists of smaller models that run fewer parameters. Benjamin Lee at UPenn says this is called a mixture of experts.

• 13:15Speaker 09

  The idea behind a mixture of experts is

• 13:17Speaker 08

  You don't need a single huge model with a trillion parameters to answer every possible question under the sun. Rather, you would like to have a collection of experts, smaller models, and then you just sort of route the request to the right expert. And because each expert is so much smaller, it's going to cost less energy to invoke.

• 13:37Emily Kwong?

  But DeepSeek, here's the thing about it. It's still a big, general-purpose model. And Sasha Lucioni at Hugging Face wants to walk away from large models entirely.

• 13:44Speaker 03

  Nowadays, more and more, I think, companies, especially like for our intents and purposes, we want to do X, like, whatever, summarize PDFs. But you don't need a general-purpose model for that. You can use a model that's task-specific and a lot smaller and a lot cheaper.

• 13:59Emily Kwong?

  Basically, Sasha wants to see companies develop small language models, models that have far fewer parameters and are trained for a specific task.

• 14:10Regina Barber?

  So like, are tech companies experimenting with this?

• 14:13Emily Kwong?

  A few. Uh, OK, here's what I found. Last year, Meta announced a smaller quantized version of some of their models, and Microsoft announced a family of small models called phi-3. But honestly, in all of my conversations and emails with big tech, it's clear to me that large language models are here to stay. So if that's the case, Sasha has one last idea: an industry-wide score for AI models.

• 14:38Regina Barber?

  OK, so like, EnergyStar was created to like rank the energy efficiencies of appliances. You see that little star on a lot of those appliances.

• 14:45Emily Kwong?

  Yes, something like that for AI models. But at least, according to Sasha, tech companies are not embracing a rating system.

• 14:53Speaker 03

  There's like such a blanket ban on any kind of transparency because it could either like make you look bad, open you up for whatever legal action, or just kind of give people a sneak peek behind the curtain.

• 15:06Emily Kwong?

  And as a science reporter for NPR, my question was just...

• 15:12Regina Barber?

  Do we really need all of this computing power when we don't know how much it's costing us environmentally and when it could imperil our climate goals? And David Craig, the recently retired CEO of Isotope, he chuckled when I said this, and he's like, Emily, you know, human nature is against us.

• 15:31Speaker 09

  We are always that kid who does touch the very hot ring on the cooker. When our mom said, don't, we are always the people who touch the wet paint. We're not good at learning until bad things happen to us. The truth is with data, you know, this stuff has just grown up in the background. People just haven't known about it.

• 15:51Regina Barber?

  I mean, yeah, I mean, I certainly had no idea about all of this while I was growing up. All this was happening in the background. But, Em, you can't like, disinvent the internet, right?

• 16:02Emily Kwong?

  Nor should we. Where would we get our cat videos?

• 16:04Regina Barber?

  In the mail.

• 16:06Emily Kwong?

  There's something I think that we as consumers can think about. The AI revolution is fairly new. Google CEO Sundar Pichai compared it to the discovery of electricity. Except unlike the people during the Industrial Revolution, we know that this has a climate cost.

• 16:25Regina Barber?

  Wow, yep.

• 16:26Emily Kwong?

  And there's still time to adjust how and how much we use AI.

• 16:31Regina Barber?

  Em, thank you so much for bringing us this reporting.

• 16:33Emily Kwong?

  Thanks, Gina.

• 16:35Regina Barber?

  If you liked this episode, you can check out part one of our series on AI and the Environment. It's already out. This episode was produced by Hannah Chen, edited by showrunner Rebecca Ramirez, and fact-checked by Tyler Jones. Robert Rodriguez was the audio engineer. Special thanks to Brent Baughman, Johannes Sturgy, and our incredible Standards Team, and special thanks to TED Conferences LLC.

• 16:57Emily Kwong?

  Special thanks also to Julia Simon on NPR's Climate Desk. Beth Donovan is our senior director, and Colin Campbell is our senior vice president of podcasting strategy. I'm Emily Kwong.

• 17:07Regina Barber?

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

• 17:17Speaker 00

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