Science Friday
Hosted by Ira Flatow
A boom in construction of AI data centers is facing backlash over soaring electricity and water usage, and the resulting utility hikes.
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I'm Ira Flatow, and this is Science Friday. Let's turn to a topic that's sucking all the air out of the room, and I mean electricity and water, too, actually. I'm talking, of course, about the huge buildout of data center construction across the country for the AI boom. Hundreds of billions have been spent already, with talks of trillions more. And according to the Times-Picayu, and listen to this, Metta's new data center under construction in Louisiana requires nearly three times the power that New Orleans uses in a year. This has not gone unnoticed. Rising utility rates nationwide have become issues in some recent elections. Casey Crownhardt, a senior climate reporter at
MIT Technology Review, has been studying the costs and impacts, and she's back here to fill us in. Welcome back to Sci-Fry, Casey.
Thanks so much for having me.
All right. Can you give us a sense of the scale of this buildup? Because the last resource drain like this that comes to my mind was crypto mining, but this seems even bigger.
Absolutely. I think the the money and the electricity that we're seeing talked about, you know, the scale of these numbers is is kind of eye popping. So according to the International Energy Agency, data centers accounted for about, you know, 1.5% of the world's electricity consumption in 2024. And that's set to double by 2030. We're seeing so many data centers get built out. One number that really struck me was in a recent report that there was $580 billion invested globally in AI in 2025.
And data centers, that's more than the $540 billion spent on developing the global oil supply. So this year, we spent more on data centers than the oil supply.
Wow. You know, a lot of people may not realize that when they ask a question of ChatGPT or Gemini, that it actually contributes to this. It uses up electricity. We had you on in the summer to talk about some reporting that you did about just how much energy these models use. And you told us the data was dodgy then. Do we have any more clarity on how much energy that's being used?
Yeah. So since we, you know, came up with our own estimates, working with leading researchers in this area, a couple of companies have come out with estimates of, you know, how much energy each query or each kind of question you ask to one of its models will use. And it turns out we were kind of in the right region. So Google came out with an estimate that says that, you know, the average query to its Gemini model uses about 0.24 watt hours of electricity. That, you know, I like to put things in microwave seconds. So that's about the same as a second in the microwave. ChatGPT, OpenAI came out with its own estimate. It's kind of in the same range, about 0.34 watt hours. So basically, the the individual queries are kind of individual questions you're asking. It's not insignificant amount
of energy, but it's kind of, kind of small.
But they don't say the total energy used for all of its AI activities, right?
Exactly. And so that's something that I've been talking about a lot this year is that it's great that we're starting to get these kind of estimates of of individual queries, but that doesn't really give us the full picture. You know, we're seeing billions and billions of people and queries every day. And so this is all adding up to a lot.
Now, we've been talking about electricity, but we know these data centers. Use a lot of water to cool those chips. And most of them are built in pretty dry areas like Arizona and Nevada. Why is that?
Part of the reason is that in a lot of cases, these companies are looking for places that are, you know, have plentiful land, cheap energy. That's what they're really looking for when they're they're trying to find a place to build a data center. So like you said, you know, a good amount of this activity is happening in Nevada, Arizona, Texas. Two thirds of new data centers that are in development since 2022 are in these kind of water stressed areas.
Yeah. And how much of this is drinking water here? What, you know, and what happens to that water after it's used for cooling the servers?
It depends on what the water is being used for. So when we're talking about water consumption for AI, a lot of it is actually what's called indirect use. So the water that's used at the power plants that are actually running the data centers, you know, at some estimates say that over 60% of the water consumed when we're talking about AI is is from power plants. So it kind of depends on what segment of this you're talking about. You know, some power plants are able to use treated water or something. But when it comes to the actual data centers and the water that they're using to keep their machines cool, a lot of them do need to use drinking quality water. Because when they're, you know, doing this evaporative cooling, they want to avoid like clogging their pipes.
Yeah.
Bacterial growth. It's, you know, very sensitive equipment.
Can you give us an idea of how much water we're talking about here?
It's kind of moderate when you look at it overall. So one report found that data centers use about 0.3% of the nation's total water use. But again, that's set to double between 2023 and 2030. You know, it's it's kind of small when you look at the big picture, but it will very much affect local grids. You know, in some cases, a single data center can use more water than an entire county's homes do.
Are the big companies, the big chip companies that are using the water, are they sensitive to this and looking for more efficient ways?
Absolutely. There's a lot of research and a lot of really interesting work being done in cooling. And there are different ways to cool data centers that use less water. So today, a lot of data centers are cooled with what's called evaporative cooling. And so basically, you know, just you let the water evaporate and that cools down the the equipment. That obviously you lose a lot of the water that you're kind of pulling out of the resources. So there are other techniques, so something like direct liquid cooling, where you know, you have a coolant kind of circulating directly through the servers. There's also immersion cooling where servers are submerged in some sort of fluid to help keep them cool. So there's a lot of interesting alternatives. Some of them, at least right
now, tend to have some sort of downside. So they're either more expensive. In some cases, they're also more electricity intensive. So you know, might use as much as 10% more energy as compared to evaporative cooling. So it's kind of a trade off. But we are seeing, you know, a lot of companies are sensitive to this, especially as we've seen this public outcry.
I can understand that because doesn't Microsoft use something like eight million gallons of water a year in their chips?
It's absolutely bonkers. And another aspect that we haven't really talked about, and this is kind of a niche part of this, is you also need very, very high purity water to actually make the chips. And so again, when we're talking about the whole picture of water used by AI, it's not just water being used to cool power plants, water being used to cool data centers. There's also an element of this of just making the chips, and that can be up to 10% of the total water use.
Let's get into some of the politics of this because the recent governors elect for New Jersey and Virginia both campaigned on lowering utility rates. Now, Virginia has the largest concentration of data centers in the world. So what kind of pressures are states facing as more tech companies are trying to build these?
Yeah, it's been a really interesting conversation of especially around the elections this year. I'm based in New Jersey, and so I saw so many commercials, you know, one one side or the other saying, oh, they're going to raise your your electricity rates. So I think that, you know, as people continue to see prices go up across the board, I think there's, you know, even more sensitivity to this. And so people are saying, well, you're coming in and raising my electricity rates. And so we're seeing a lot of projects block. One, one recent report from Data Center Watch found that from just March to June, about $93 billion worth of projects were either delayed or canceled because of community pushback.
What happened to all those climate pledges that Google and Microsoft and others were promising a few years back?
Yeah, this is a great question. So some tech companies, it's kind of hard to paint with a broad brush, but some tech companies have kind of stopped reporting as much or kind of backed off. Google does say that they're still on track for their 2030 net zero energy goals.
Right.
But I think that, again, in this AI build out, they're really chasing a moving target. We're seeing as they need so much electricity, even the companies that are doing really great work and procuring a lot of wind and solar, helping support new advanced clean energy technologies. It's just a huge challenge when they're seeing their energy demand increase off the charts.
Yeah, because they were talking about using renewables for this or, you know, possibly even nuclear. You know, Microsoft bought out all that usage on Three Mile Island. So we'll have to see how that works out.
Absolutely. I think it's going to be really interesting to see how these kind of longer term plays end up working out, especially like you said, that this nuclear build out, the efforts to reopen shuttered plants, such a long term play, like it takes years even just to reopen a nuclear plant.
And, you know, you can build a data center much more quickly than that. And so I think that's kind of one of the fundamental challenges here is that. AI has become part of the daily lives of people so quickly. And so companies are racing to keep up with that and kind of continue this growth curve. And building energy, you know, our energy system is complicated. It's it takes a while to build. And so that's kind of one of the fundamental challenges that I've been thinking about a lot this year.
And that's the existential challenge to me. I mean, that you say how much people are are going to depend and are depending on AI. Are we going to reach a trade off we reached on other energy consuming lifestyles that we have like fossil fuels usage about whether we want to trade water and climate change for this new lifestyle we can't live without?
And I think one thing that I've been thinking about a lot this year is, you know, when we're talking about, oh, chat GPT uses this much energy or water, you know, whatever model you want to talk about, you should just not use it. You should just avoid it. And I think one kind of message I have for people is, you know, sure, I don't want to say that, you know, make as many generative videos as you want, but increasingly, it's it's not really a personal choice. This is part of our digital infrastructure. You know, this is you're getting AI suggestions based on your Google searches. You're, you know, getting ads served to you that maybe were generated using AI. And so I think that this is overall a systems conversation that we need to be having rather
than, you know, talking about personal choices and personal use.
Good point, Casey. You always bring good stuff to us. Thank you for taking time to be with us today.
Casey Crown-Hart, senior climate reporter for the MIT Technology Review. This episode was produced by Dee Peterschmidt, but a lot of people helped make the show happen this week, including John Dankoski.
Danielle Johnson. Beth Ramey. Jackie Hirschfeld.
Thank you, folks. I'm Ira Flatow. Thanks for listening.
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