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Congress just passed perhaps its biggest support for zero-carbon energy since the Inflation Reduction Act. The ADVANCE Act, which the Senate adopted overwhelmingly last week, aims to keep America at the cutting edge of the global nuclear industry by cutting regulatory fees, making it easier for U.S. companies to build nuclear power plants abroad, and reforming the agency that oversees it all, the Nuclear Regulatory Commission.
On this week’s episode of Shift Key, Rob and Jesse talk with Ryan Norman, a senior policy advisor at Third Way’s climate and energy program, about how America got here. We talk about why nuclear is such a bipartisan issue, what the ADVANCE Act will actually do, and how soon new nuclear power plants could actually get built. Shift Key is hosted by Robinson Meyer, the founding executive editor of Heatmap, and Jesse Jenkins, a professor of energy systems engineering at Princeton University.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
You can also add the show’s RSS feed to your podcast app to follow us directly.
Here is an excerpt from our conversation:
Ryan Norman: The U. S. Nuclear Regulatory Commission has a very well-regarded reputation, around the world partially because of the way it thinks about layers of different issues.
Stepping back for a brief second, when we talk about these relationships with other countries — I had mentioned that it’s an interagency option, but it’s also much deeper than financial. There’s a market piece, but there’s also a long-term relationship that you end up building with the country because your regulators understand each other. You’ve built a relationship with the international regulators and the monitoring agencies. You’re more or less introduced into that relationship by your partner, so by the U.S,. or by the French, or the Koreans, or whoever it is. So there’s a long-term relationship of trust that needs to be built there between those two poles.
So it’s really important that you work with a country that has experience mitigating some of these social issues and working that into the process effectively. Because when those disputes happen in a partner country, they want to be able to replicate the discourse process of transparency and all the different things that the NRC does.
When you think about how that translates to some of our competitors, countries like Russia and China, the dynamic of those countries’ regulators in the industry is very opaque. It’s much closer to the way the NRC’s precursor, the Atomic Energy Commission, used to operate in the United States, right? There’s just a lot of issues that those industries in Russia and China aren’t concerned with. Practically speaking, there’s no such thing as environmental or energy justice in China, right? Like there’s no community benefits plan process that they have to go through to build a new reactor. They have a lot of space. The density is very different. The authority and the permitting process is so different that they basically just make a decision and that’s how it goes.
So then that means that when you’re basing — when a country, you know, like a partner like Ghana, for example, is trying to base, okay, how do I want my regulator to look? Well, if I take the structure they have in another country that is not used to incorporating social engagement and understanding around some of these issues, and really mitigating social backlash, you’re really just replicating a system that is not going to be as equitable as what you could do if you were a partner with the U.S. So it’s another reason that U.S. leadership is really an imperative.
Robinson Meyer: And this is what makes nuclear reactors so different from solar, or onshore wind, or really any kind of wind or other kinds of energy technologies, I suppose, is that you’re signing up … You alluded to, like, a 50-year agreement, basically, between two countries, and you’re pledging a very long-term integration between those two regulatory states. In between, for lack of better term, energy-planning elites in those two countries.
Norman: Yeah, they call it the 100-year relationship, and that’s a long time. But it’s super real, and it’s super important because there’s a lot of influence that comes with being an energy partner, and you have the ability and I would say even the responsibility to guide that energy partner to do things responsibly and do things equitably. And I think that if we want a clean energy future that is abundant but also just, we can’t just defer leadership in these spaces to folks who are not focused on these principles.
This episode of Shift Key is sponsored by …
Watershed’s climate data engine helps companies measure and reduce their emissions, turning the data they already have into an audit-ready carbon footprint backed by the latest climate science. Get the sustainability data you need in weeks, not months. Learn more at watershed.com.
As a global leader in PV and ESS solutions, Sungrow invests heavily in research and development, constantly pushing the boundaries of solar and battery inverter technology. Discover why Sungrow is the essential component of the clean energy transition by visiting sungrowpower.com.
Music for Shift Key is by Adam Kromelow.
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At the end of the day, there will always be politics.
Today’s internet is inundated with “AI slop,” and mocking the often bizarre outputs produced of language models (recall the Google AI that recommended making pizza with glue) has become an online pastime. Yet artificial intelligence advocates have not been deterred from their claims of a utopian future made possible by AI. Whatever problems we face — including climate change — one day, we are told, they will be solved by the magical power of computing. The breathless headlines have been around for years: “How artificial intelligence can tackle climate change”; “How AI could power the climate breakthrough the world needs”; “Here are 10 ways AI could help fight climate change”; “9 ways AI is helping tackle climate change.”
Like much of the hype around AI, the specifics aren’t necessarily wrong. AI could help us understand the impacts of climate change more comprehensively, and can be used to locate solutions to particular challenges in technology and manufacturing. But as the extraordinary energy demands AI will impose on our system are coming into focus, and as some of the most important corporate AI leaders join hands with what could be the most anti-environment administration in history, the big picture problem becomes even clearer. Artificial intelligence can’t solve climate change because doing so will always require passing through the bottleneck of politics.
For those hoping to bring us to a glorious future guided by superintelligent computers, claiming that AI will solve climate change has become more urgent as the energy demands of the technology increase. Google reported last summer that since 2019, its emissions have increased by 48% because of its use of AI. The International Energy Agency projects that by 2026, AI will consume 1,000 terawatt-hours of electricity, as much as the entire nation of Japan, the world’s fourth-largest economy. Countries around the world are rushing to develop their own AI systems (the surprising capabilities of a new Chinese system called DeepSeek just sent the stock market tumbling), any of which could entail the same scale of energy demand as the ones created by American tech giants.
But imagine if we could snap our fingers and make that problem disappear? That’s what OpenAI CEO Sam Altman suggested in a recent interview with Bloomberg. “Fusion’s going to work,” he said when asked about AI’s energy demands, going on to say that “quickly permitting fusion reactors” is the answer — particularly those made by Helion Energy, a company whose executive chairman is, you guessed it, Sam Altman.
Of course, Helion has no fusion reactors to permit yet because no one does. Fusion’s promise of essentially limitless clean energy at low cost is tantalizing, which is why billions of public and private dollars have been invested in fusion research. But while technological gains are being made, there is still a great deal of uncertainty about how long it will take until fusion can reach commercial scale. It might be 10 years, or 20, or 50 or 100 — no one knows for sure.
But blithely insisting that incredibly complex problems will be solved easily and quickly is a specialty of tech barons. And if AI itself finds the solution to our energy problems? Even better.
There’s no question that AI is improving at a rapid pace, even if there are some things it’s still terrible at. And when it comes to climate, over time it will probably help produce incremental gains across a wide number of areas, from manufacturing efficiency to urban planning. But the more dramatic and consequential any idea is — whether it comes from AI or not — the more likely it is that it will have to move through the political process in order to be implemented.
And that’s where AI can’t help. A machine learning system can’t tell you the precise formula to please a recalcitrant senator or navigate a hundred city councils with different ideas about what kinds of clean energy projects they’ll allow in their towns. Politics is about people — their goals, their incentives, their fears, their prejudices — and it’s far too messy to be solved with numeric calculation, even by the most powerful AI system imaginable.
Let’s say that a year from now, an AI came up with both an entirely new way to design a fusion reactor and a revolutionary battery design that offered longer and denser storage, together solving so many of the problems scientists and engineers struggle with today. How would the fossil fuel industry react to this development? Would it say, “Oh well, oil and gas had a pretty good run, but now the world can move on”?
Of course not. It would use its extraordinary resources to battle against their competition, just as they always have. That’s what it did in the last election cycle, when it spent $450 million on campaigns and lobbying to preserve the industry and the riches it generates.
And while many hoped that the Republican Party would moderate its views on climate, at the moment it looks more like it is going backward — not just looking to undo every bit of progress made under the Biden administration, but also undermining renewable energy wherever it can. President Trump seems determined to destroy wind energy in America, which has been growing rapidly in recent years. Whether he succeeds will be up to the political system, not the inherent usefulness of a millennium-old technology.
In politics, good ideas don’t always win out. Who has power and what they are after will always matter a great deal, as Trump and the people he is bringing into the federal government are showing us right now.
AI can be a tool that helps us reduce emissions and mitigate the effects of climate change; the fact that its boosters regularly offer absurdly optimistic timelines for societal transformation doesn’t mean the underlying technology isn’t remarkable. But “solving” climate change isn’t merely a technological problem. It will always be a political one as well, and even the smartest piece of software won’t solve it for us.
It’s not just AI companies taking a beating today.
It’s not just tech stocks that are reeling after the release of Chinese artificial intelligence company DeepSeek’s open-source R1 model, which performs similarly to state-of-the-art models from American companies while using less expensive hardware far more efficiently. Energy and infrastructure companies — whose share prices had soared in the past year on the promise of powering a massive artificial intelligence buildout — have also seen their stock prices fall early Monday.
Shares in GE Vernova, which manufactures turbines for gas-fired power plants, were down 19% in early trading Monday. Since the company’s spinoff from GE last April, the share price had risen almost 200% through last Friday, largely based on optimism about its ability to supply higher electricity demand. Oklo, the advanced nuclear company backed by OpenAI chief executive Sam Altman, is down 25%, after rising almost 300% in the past year. Constellation Energy, the independent power producer that’s re-powering Three Mile Island in partnership with Microsoft, saw its shares fall almost 20% in early trading. It had risen almost 190% in the year prior to Monday.
“DeepSeek’s power implications for AI training punctures some of the capex euphoria which followed major commitments from Stargate and Meta last week,” Jefferies infrastructure analyst Graham Hunt and his colleagues wrote in a note to clients Monday. “With DeepSeek delivering performance comparable to GPT-4 for a fraction of the computing power, there are potential negative implications for the builders, as pressure on AI players to justify ever increasing capex plans could ultimately lead to a lower trajectory for data center revenue and profit growth.”
Investors fear that the proliferation of cheaper, more efficient models may hurt the prospects of technology companies — and their suppliers — that are spending tens if not hundreds of billions of dollars on artificial intelligence investments.
Just last week, both Altman and Mark Zuckerberg, the founder and chief executive of Meta, announced huge new investments in artificial intelligence infrastructure.
Altman’s OpenAI is part of Stargate, the joint venture with Microsoft and SoftBank that got a splashy White House-based announcement and promises to invest $500 billion in artificial intelligence infrastructure. There was already some skepticism of these numbers, with Altman-nemesis Elon Musk charging that certain members would be unable to fulfill their ends of the deal, Microsoft Chief Executive Satya Nadella told CNBC from Davos, “I’m good for my $80 billion.”
Zuckerberg, meanwhile, said late last week that his company was building a data center “so large it would cover a significant part of Manhattan,” which would require 2 gigawatts of electricity to power. (For scale, reactors 3 and 4 of the Vogtle nuclear plant in Georgia are a little over 1 gigawatt each.) He also said that Meta had planned up to $65 billion of capital expenditure this year.
These escalating announcements have been manna to investors in any company that provides the building blocks for large artificial intelligence systems — namely chips and energy, with companies like Nvidia, the chip designer, and power companies and energy infrastructure companies posting some of the best stock market performances last year.
But exactly how cheaper artificial intelligence plays out in terms of real investment remains to be seen. Late Sunday night Redmond, Washington-time, Nadella posted a link on X to the Wikipedia page for Jevons Paradox. The idea dates from 19th century Britain, and posits that increased efficiency in using a resource (in Jevons’ case, coal) could actually accelerate its depletion, as the resource becomes cheaper for the same economic output, encouraging more use of it (in Jevons’ case, iron).
“Jevons paradox strikes again!,” Nadella wrote. “As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of.”
Investors in chips and energy companies are hoping that’s the case; at least so far, the market doesn’t appear to agree.
And it just raised a $20 million round of Series B funding.
A century ago, prospectors tromped through remote areas, hoping to spot valuable, mineralized rocks simply poking out of the ground. Eventually, after they found all of the obvious stuff, they progressed to doing airborne geophysical surveys that used tools such as electromagnetic sensing to identify minerals that were just below the surface or highly concentrated. But there’s always been a lot more out there than we had the mechanisms to find. So now, companies are training artificial intelligence models on heaps of historical data to help locate untouched reserves of minerals that are key to clean energy technologies such as electric vehicle batteries and wind turbines.
One of the biggest players in this space is Earth AI, a Sydney-based startup that today announced a $20 million Series B round, bringing the company’s total investment to over $38 million since its founding in 2017. While the company had initially sought to raise $15 million in this round, investor interest was so strong that it exceeded its target by $5 million. Lead investors were Tamarack Global and Cantos Ventures.
Earth AI has a two-stage business model. First, it uses its proprietary software to locate likely deposits and purchases the mineral rights to the land. Then, it sends in its drilling rig and in-house team of geologists to produce mineral samples. The team then shows these samples to mining companies to prove that the area warrants further development and — assuming the miners are interested — sells them the mineral rights. “Because of the scarcity of this, because of the deficit of where we are and where we’re going, these price tags are $500 million to $2.5 billion,” Monte Hackett, Earth AI’s chief financial officer, told me. Huge as that may sound, it’s much less than what a mining company would typically spend doing traditional exploration themselves. This latest funding will allow the startup to purchase additional drill rigs and increase its project pipeline to over 50 sites.
“The problem is obvious,” Hackett told me. “We need $10 trillion of critical metal production by 2050. We’re producing $320 billion, a $9.7 trillion shortfall.” To better locate the trillions of dollars worth of minerals necessary for the energy transition, the startup’s CEO, geoscientist Roman Teslyuk, digitized decades of old Australian geophysical survey data, then overlaid that with remote sensing data such as satellite imagery to train a model to recognize the Earth systems and geological processes that created minerals deposits millennia ago. “Another way I like to think about it is that our algorithm looks for the geological shadow that is cast by a dense mineral body,” Hackett explained in a follow-up email.
Hackett told me that Earth AI focuses specifically on “greenfield” applications — that is, areas where no mining activity or substantial minerals exploration has previously occurred. So far, the company’s discoveries include a significant deposit of palladium, which also contains platinum and nickel, as well as a gold deposit and a molybdenum deposit. “We’re finding things that go against what has been the common sense of the industry so far,” Hackett told me, referencing the company’s palladium discovery. “There was geological consensus that there was no platinum palladium on the East Coast of Australia, and our algorithm learned what it looked like on the West Coast and then identified it on the East Coast.”
While Hackett said Earth AI is open for business anywhere, right now all of its projects are in Australia, where the company has “600 ‘x’s on our treasure map” — that is, likely areas for deposits, Hackett wrote in a follow-up email. Outlining the advantages of doing business there, he explained, “We don’t have to go somewhere where there’s unsavory working conditions or there are issues where we have to put our principles into balance. Here, everything is very regulated and above board.” Plus, mining has long been an important component of the Australian economy. “They’re incredibly efficient at doing this well. So the timeline for development is the shortest compared to other places,” Hackett told me.
This tech could have important domestic implications too, though. As the newly-inaugurated President Trump prioritizes ramping up U.S. production of critical minerals, Earth AI could one day help locate deposits here, as well. And since Australia is a close American ally, the nation could play a key role in helping wean the U.S. off of Chinese imports, providing the U.S. with critical minerals that it can’t now, or perhaps ever, produce in sufficient quantities itself.