AI and the Environment

Summary

Consider This from NPR: AI and the Environment

Release Date: March 30, 2025
Host: Emily Kwong

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Introduction

In the episode titled "AI and the Environment," NPR's Emily Kwong delves into the burgeoning relationship between artificial intelligence (AI) and its environmental footprint. As AI technologies advance at an unprecedented pace, concerns over energy and water consumption, as well as carbon emissions, have come to the forefront. The episode explores the challenges and solutions associated with making AI more sustainable, featuring insights from experts and industry leaders.

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The Personal Impact: Sasha Luccioni's Journey

Emily Kwong opens the discussion with the story of Sasha Luccioni, an AI researcher who initially joined Morgan Stanley in 2018. Despite her excitement about working in AI, Luccioni grappled with "climate anxiety" due to the disconnect between her professional role and personal values.

Sasha Luccioni (00:10): "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."

Facing this internal conflict, Luccioni contemplated leaving her job to engage in more environmentally impactful work, such as tree planting. However, encouraged by her partner, she decided to leverage her AI expertise to contribute positively to environmental sustainability.

Sasha Luccioni (00:36): "Maybe you can use that to make a difference in the world."

Ultimately, Luccioni transitioned to a movement focused on making AI more sustainable, highlighting a growing trend among professionals seeking to align their careers with environmental stewardship.

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AI's Growing Environmental Footprint

Since 2022, the AI sector has experienced exponential growth, leading to a significant surge in energy consumption. The construction of data centers—vast infrastructures housing hundreds of thousands of computers—has intensified this impact.

Lawrence Berkeley National Laboratory projects that by 2028, data centers could consume up to 12% of the United States' electricity.

Benjamim Lee (01:36): "Under current infrastructure investment plans, you could possibly achieve those net zero goals."

Additionally, AI advancements have exacerbated water consumption concerns. As pointed out by David Craig, the water usage associated with AI operations is "astonishing."

David Craig (01:08): "The amount of water that AI uses is astonishing."

This heightened resource demand has sparked widespread discussions on social media and among environmental advocates about the sustainability of AI technologies.

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Big Tech's Climate Commitments and Challenges

The episode examines the climate goals set by major tech companies—Google, Microsoft, Amazon, and Meta—all of whom aim to reach net-zero carbon emissions by as early as 2030, with Amazon targeting 2040. These companies are exploring various strategies to achieve these targets.

One prominent approach is investing in nuclear energy. In a significant move, Amazon, Meta, and Alphabet (Google's parent company) have signed agreements to support the tripling of the global nuclear supply by 2050. Microsoft has committed to purchasing power from the historic Three Mile Island nuclear plant in Pennsylvania, symbolizing a commitment to revitalize nuclear energy sources.

Emily Kwong (04:37): "Microsoft has committed to buying power from an old nuclear plant on Three Mile Island in Pennsylvania."

However, integrating nuclear energy with AI infrastructure presents unique challenges. Benjamin Lee from the University of Pennsylvania highlights the difficulty in reconciling the rapid innovation pace of Silicon Valley with the conservative and risk-averse nature of the nuclear industry.

Benjamin Lee (02:58): "Generative AI refers to the AI that uses large language models."
Benjamin Lee (06:09): "As you can imagine, it's much more efficient because now you're just cooling the surface of whatever the cold plate is covering rather than just blowing air through the entire machine."

The lengthy and costly process of developing nuclear energy infrastructure contrasts sharply with the faster, more cost-effective deployment of renewable energy sources like solar and wind. This discrepancy poses a significant hurdle for big tech companies striving to meet their sustainability goals amidst a rapidly expanding AI landscape.

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Innovative Solutions: Enhancing Data Center Efficiency

To mitigate the environmental impact, tech companies are investing in making data centers more energy-efficient. One such innovation is liquid cooling—a method that uses synthetic fluids to absorb and dissipate heat from servers more effectively than traditional air cooling systems.

David Craig, the retired CEO of Isotope, emphasizes the benefits of liquid cooling:

David Craig (06:25): "With liquid cooling, a lot of the heat stays in the system and computers don't have these massive swings in temperature."

Liquid cooling not only reduces energy consumption but also extends the lifespan of hardware by minimizing thermal stress and mechanical wear from fans. Companies like Isotope are partnering with industry giants such as Hewlett Packard and Intel to scale this technology, with some Meta data centers expected to implement liquid cooling by 2026.

Despite its advantages, liquid cooling remains an expensive solution, limiting its widespread adoption. Nonetheless, it represents a critical step toward more sustainable AI operations.

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Smaller AI Models: A Path to Reduced Energy Consumption

Another promising avenue for reducing AI's environmental footprint is the development of smaller, task-specific language models. Unlike large language models (LLMs) like ChatGPT, which require immense computational resources, smaller models are designed to handle specific tasks with significantly lower energy demands.

Sasha Luccioni (08:57): "Nowadays more and more I think companies especially are like, well actually for our intents and purposes we want to do X, like whatever, summarize PDFs."

Benjamin Lee describes this approach as a "mixture of experts," where a collection of smaller, specialized models collectively perform tasks more efficiently than a single, large model.

Benjamin Lee (08:23): "You don't need a single huge model with a trillion parameters to answer every possible question under the sun."
Benjamin Lee (08:46): "Because each expert is so much smaller, it's going to cost less energy to invoke."

Companies like Meta, Microsoft, and Amazon are experimenting with smaller models to balance AI capabilities with sustainability. Innovations such as Deepseek's chatbot in China exemplify efforts to develop energy-efficient AI solutions, though some skepticism remains regarding their scalability and true energy savings.

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Towards Industry-Wide Sustainability Standards

Despite these advancements, there is a pressing need for standardized measures to evaluate the energy efficiency of AI models. Sasha Luccioni advocates for an industry-wide score for AI models, akin to the Energy Star ratings for household appliances, to promote transparency and accountability.

Sasha Luccioni (10:26): "We're having a lot of trouble getting buy-in from companies. 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."

However, resistance from tech companies, concerned about reputational risks and proprietary information, has hindered progress toward such standardized evaluations.

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The Future of AI and Environmental Sustainability

As the AI revolution continues to unfold, the episode poses a critical question: Do we truly need the vast computing power that AI demands, given its potential to undermine climate goals? While some, like David Craig, express skepticism about humanity's willingness to change behaviors for sustainability, others remain hopeful.

David Craig (11:00): "We're always that kid who does touch the very hot ring on the cooker when her mum said, don't you know we are always the people who touch the wet paint sign and stuff, right?"

Google CEO Sundar Pichai draws a parallel between AI and the discovery of electricity, emphasizing that unlike past technological revolutions, we are now aware of AI's environmental costs and have the opportunity to steer its development responsibly.

Emily Kwong (11:23): "Google CEO Sundar Pichai compared AI to the discovery of electricity. Except unlike the people during the Industrial Revolution, we know AI has a big climate cost, and there's still time to adjust how and how much of it we use."

The episode concludes with a call to action for both the tech industry and consumers to prioritize sustainability in the ongoing AI expansion, underscoring the delicate balance between technological advancement and environmental preservation.

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Produced by: Avery Keatley and Megan Lim
Audio Engineering: Ted Mebane
Edited by: Adam Raney, Sarah Robbins, and Rebecca Ramirez
Executive Producer: Sammy Yanigun

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For more science reporting, tune into Emily Kwong's co-hosted podcast, Shore Wave.

Transcript

[Emily Kwong]

In 2018, Sasha Luccioni started 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.

[Sasha Luccioni]

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 to make a difference in the world.

[Emily Kwong]

So Luccione quit her job and joined a growing movement to make AI more sustainable. Since 2022, AI has boomed and it's caused a surge in energy consumption. Tech companies are racing to build data centers to keep up. These huge buildings filled with hundreds of thousands of computers that require a lot of Energy. By 2028, Lawrence Berkeley National Laboratory forecasts the data centers could consume as much as 12% of the nation' electricity. And AI is also leading a surge in water consumption. It's a concern echoed all over social media.

[David Craig]

The amount of water that AI uses is astonishing.

[Emily Kwong]

AI needs water. People are saying that every time you use ChatGPT, ChatGPT uses this much water for 100 word email. Where will that water come from? And the four big data center operators with a growing water and carbon footprint are Google, Microsoft, Amazon and Meta. And to be clear, all four of those are among NPR's financial supporters and pay to distribute some of our content.

[Benjamin Lee]

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

[Emily Kwong]

Benjamin Lee studies computer architecture at the University of Pennsylvania. Generative AI refers to the AI that uses large language models.

[Benjamin Lee]

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

[Emily Kwong]

And data center construction is only going to increase. On January, 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, a new American company that will invest $500 billion.

[Benjamin Lee]

At 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. As much as big tech says they want to get to net zero, there are no regulations forcing them to do so. So how is the industry thinking about its future? And its environmental footprint. From npr, I'm Emily Kwong.

[Nina Totenberg]

Support for this podcast and the following message come from Indeed. You just realized your business needed to hire someone yesterday. Indeed Sponsored Jobs helps you stand out. According to Indeed data, Sponsored jobs posted directly on indeed have 45% more applications than non sponsored jobs. Speed up your hiring right now with Indeed and get a $75 sponsored job credit to get your job's more visibility@ Indeed.com NPR terms and conditions apply. Hiring Indeed is all you.

[Benjamin Lee]

Foreign.

[Emily Kwong]

Okay, so the four cloud giants, Google, Meta, Microsoft and Amazon all have climate goals. Goals for hitting net zero carbon emissions most by 2030, Amazon by 2040. And there's a few ways they can get there. Let's start with a very popular energy source for big tech, nuclear. Because Amazon, Meta and Alphabet, which runs Google, just signed an agreement along with other companies that supports tripling the global nuclear supply by 2050. And along with Microsoft, these four companies have signed agreements to purchase nuclear energy, an industry that has been stagnant for years. Microsoft has committed to buying power from an old nuclear plant on Three Mile island in Pennsylvania. You may remember that was the site of a partial nuclear meltdown in 1979. And N. Nina Totenberg talked to kids in the Harrisburg area right after you know what evacuation is that everybody has to go.

[Nina Totenberg]

Do you know why?

[Sasha Luccioni]

Because of radioactivity.

[Emily Kwong]

While some radioactive gas was released, thankfully it wasn't enough to cause serious health effects. And Microsoft now wants to build this nuclear site back. In a way, AI companies are turning into energy brokers. But my science desk colleague Jeff Brumfiel sees a discrepancy in this between the AI people and the nuclear energy people. These are just two super different engineering cultures, you know. And the way I've come to think about it is Silicon Valley loves to go fast and break things. The nuclear industry has to move very, very, very slowly because nothing can ever break because of accidents like Three Mile Island. Jeff says that nothing in the nuclear industry ever happens quickly. It's also extremely expensive. And while solar and wind energy combined with batteries is quicker to build and more inexpensive than nuclear or gas power power plants, it still takes time to build, and there are problems hooking up new energy sources to the grid. So in the meantime, many data centers will continue to use fossil fuels. But there's another solution here, and that's to make data centers themselves more efficient through better hardware, better chips, and more efficient cooling systems. One of the most innovative methods on the rise is liquid cooling. Basically running a synthetic fluid through the hottest parts of the server to take the heat away or immersing whole servers in a cool bath. It's the same idea as running coolant through your car engine and a much faster way to cool off a hot computer. Here's Benjamin lee again at UPenn and.

[Benjamin Lee]

As you can imagine, it's much more efficient because now you're just cooling the surface of whatever the cold plate is covering rather than just blowing air through the entire machine.

[Emily Kwong]

One of the biggest providers of liquid cooling is Isotope. David Craig is their recently retired CEO and based in the uk.

[David Craig]

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.

[Emily Kwong]

Craig says that the older way of cooling data centers, basically there's lots of methods, but it's a daisy chain of moving heat with air and water is consumptive. With liquid cooling, a lot of the heat stays in the system and computers don't have these massive swings in temperature.

[David Craig]

It's not got constant thermal shock, it's got less vibration from fans and stuff like that. So things last longer. And then what we're doing is we're capturing that heat in a closed water loop.

[Emily Kwong]

Liquid cooling, however, is expensive, which makes it hard to scale. But Izotope has announced public partnerships with Hewlett Packard and Intel and a spokesperson at Meta told me they anticipate some of the company's liquid cooling enabled data centers will be up and running by 2026. Throughout my many emails and 7 hours of phone conversations with spokespersons at Amazon, Google and Microsoft too, there was one innovation they were kind of quiet about. And it's the one that scientists and engineers outside of big tech were most excited about. And that is smaller AI models, ones good enough to complete a lot of the tasks we care about, but in a much less energy intensive way. Basically, a third and final solution to AI's climate problem is using less AI. One major disruptor in this space is Deepseek, the chatbot. Out of a company in China claiming to use less energy. We reached out to them for comment, but they did not reply. You see, large language models like ChatGPT are often trained using large datasets, say by feeding the model over a million hours of YouTube content. But Deep Seq was trained by data from other language models. Benjamin Lee at UPENN says this is called a mixture of experts.

[Benjamin Lee]

The whole idea behind a mixture of experts is 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.

[Emily Kwong]

Even though Deepseek was trained more efficiently this way, other scientists I spoke to pointed out it's still a big model. And Sasha Luccioni at huggingface wants to walk away from those entirely.

[Sasha Luccioni]

Since JGPT came out, people were like, oh, we want general purpose models, we want models that can do everything at once, answer questions and write recipes and poetry and whatever. But nowadays more and more I think companies especially are like, well actually for our intents and purposes we want to do X, like whatever, summarize PDFs.

[Emily Kwong]

What Sasha is talking about are small language models which have far fewer parameters and are trained for a specific task. And some tech companies are experimenting with this. Last year, Meta announced a smaller quantized version of some of their models. Microsoft announced a family of small models called Phi 3. A spokesperson for Amazon said they're open to considering a number of models that can meet their customers needs. And a spokesperson for Google said they did not have a comment about small language models at this time. So meanwhile, the race to build infrastructure for large language models is very much underway. Here's Kevin Miller, who runs global infrastructure at Amazon Web Services. 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, the biggest driver for the need for data centers and cloud computing, if that is the level of computing we're headed for. Lucioni has one last idea. An industry wide score for AI models. Just like Energy Star became a widely recognized program for ranking the energy efficiency of appliances. She says that tech companies however, are far from embracing something similar.

[Sasha Luccioni]

So we're having a lot of trouble getting buy in from companies. 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.

[Emily Kwong]

So as a science reporter for npr, my main question is, do we really need all of this computing power when we know it could imperil climate goals? And David Craig, the recently retired CEO of Isotope, chuckled when I asked this. He said, emily, you know human nature is against us.

[David Craig]

We are always that kid who does touch the very hot ring on the cooker when her mum said, don't you know we are always the people who touch the wet paint sign and stuff, right? That's human beings. And the truth is with data, you know, this stuff has just grown up in the background. People just haven't known about it.

[Emily Kwong]

But here's something I think we can all think about. The AI revolution is still fairly new. Google CEO Sundar Pichai compared AI to the discovery of electricity. Except unlike the people during the Industrial Revolution, we know AI has a big climate cost, and there's still time to adjust how and how much of it we use. This episode was produced by Avery Keatley and Megan Lim with audio engineering by Ted Mebane. It was edited by Adam Raney, Sarah Robbins and Rebecca Ramirez. Our executive producer is Sammy Yanigun. It's Consider this from npr. I'm Emily Kwong. You can hear more science reporting like this on the science podcast I co host every week, Shore Wave. Check it out.

[Nina Totenberg]

This message comes from NPR sponsor Shopify. No idea where to sell? Shopify puts you in control of every sales channel. It is the commerce platform revolutionizing millions businesses worldwide. Whether you're a garage entrepreneur or IPO ready, Shopify is the only tool you need to start, run and grow your business without the struggle. Once you've reached your audience, Shopify has the Internet's best converting checkout to help you turn them from browsers to buyers. Go to Shopify. Com NPR to take your business to the next level.

[Emily Kwong]

Today.