Google, Microsoft and Meta have all pledged to reach at least net-zero carbon emissions by 2030. Amazon set 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 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. Have a question about AI and the environment? Email us at [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.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy
Chapter 1: What is the focus of 'Could AI Go Green'?
Hey, short wavers. Regina Barber here with my co-host, Emily Kwong, with the second half of a miniseries she reported on the environmental footprint of AI.
Hey, Em. Hi, Gina. So today I am bringing you a story of a personal crisis. It's very relatable. Go on. Okay. So in 2018, computer scientist Sasha Luciani 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.
Chapter 2: Why did Sasha Luciani leave Morgan Stanley for sustainable AI?
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 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.
So Sasha quit her job. Wow.
And she joined this growing movement to make AI more sustainable.
Yeah, you were saying that AI innovation was causing this surge in energy and water use to cool data centers. And the construction of those data centers was only going to increase.
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. Okay, can you give me like a different way to kind of think about how much that actually is?
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Chapter 3: How much energy do data centers consume?
It's like the amount of energy that Canada consumed two years ago. Okay, so U.S. data centers alone could someday use a Canada-sized amount of energy. They could. Wow.
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.
And a model is just like an AI program that's trained to take in data and like output data.
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.
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.
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Chapter 4: How can AI models be made more sustainable?
Wait, so I actually do want her to get started. Like, why are these like big players all using these huge models? I'm glad you asked.
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.
And I'm Regina Barber. You're listening to Shortwave, the science podcast from NPR. Don't worry, you won't be lost if you haven't heard part one.
Support for NPR and the following message come from Jarl and Pamela Moan, thanking the people who make public radio great every day and also those who listen.
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. It's true.
Amazon also pays to distribute some of NPR's content.
Right. And these four companies all have ambitious goals for hitting net zero carbon emissions, most by 2030, Amazon by 2040. How are they going to get there?
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?
I'm a physicist.
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Chapter 5: What role does nuclear power play in sustainable AI?
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.
Three of the world's largest tech companies are promising to help triple global nuclear power supply by 2050.
They're going to build new nuclear power plants and along with Microsoft, purchase nuclear energy. And Microsoft plans to get its nuclear energy by reviving a plant in Pennsylvania.
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.
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.
Okay, so are AI companies turning into energy companies?
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. Yeah.
Silicon Valley loves to go fast and break things. The nuclear industry has to move very, very, very slowly because nothing can ever break.
Nuclear is also extremely expensive. Yes. 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. I mean, like, do we need to move that quickly to grow AI? Well, it depends on who you ask. Kevin Miller, who runs global infrastructure at Amazon Web Services, says yes.
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Chapter 6: Are tech companies becoming energy companies?
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.
But Sasha Luciani, the computer scientist who we met earlier, feels this rush for AI is coming from industry, not from consumers.
It's unfair to say that users want more because users aren't given the choice.
Yeah, I mean, like I hear Sasha here because like I'm a big fan of like 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.
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.
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Chapter 7: Who is driving the demand for large AI models?
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. And then there's a design paradigm that's getting a bit of buzz. Folks in the industry call it liquid cooling.
Okay, 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. Okay. Or whole servers are immersed in this cool liquid bath.
Okay. 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. Okay. 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.
Because now you're just cooling the surface of whatever the cold plate is covering rather than just blowing air through the entire machine.
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.
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.
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.
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