You’re out of free articles.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Sign In or Create an Account.
By continuing, you agree to the Terms of Service and acknowledge our Privacy Policy
Welcome to Heatmap
Thank you for registering with Heatmap. Climate change is one of the greatest challenges of our lives, a force reshaping our economy, our politics, and our culture. We hope to be your trusted, friendly, and insightful guide to that transformation. Please enjoy your free articles. You can check your profile here .
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Subscribe to get unlimited Access
Hey, you are out of free articles but you are only a few clicks away from full access. Subscribe below and take advantage of our introductory offer.
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Create Your Account
Please Enter Your Password
Forgot your password?
Please enter the email address you use for your account so we can send you a link to reset your password:
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.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
The spinoff of Lawrence Livermore National Lab has a new 10-point plan to get onto the grid by the 2030s.
One of fusion energy’s newest startups, Inertia Enterprises, is betting that the fastest route to commercial fusion runs through one of the field’s oldest ideas. The company, which raised a $450 million Series A earlier this year, plans to build a power plant based on the laser-driven fusion system pioneered at Lawrence Livermore National Laboratory’s — the only tech yet to have produced more energy from a fusion reaction than it took to initiate it. Now, Inertia has shared its commercialization roadmap exclusively with Heatmap, detailing the 10 near-term capabilities it must demonstrate before this landmark experiment can become a grid-scale power plant by the mid-2030s.
The roadmap offers a route from the national lab’s impressive but commercially impractical fusion demonstrations to an economical power plant capable of producing electricity for the grid. At its core are a set of milestones — mostly aimed at developing cheap, mass-manufacturable components — that Inertia says it must clear before those individual systems can be integrated into a working plant. This road is not necessarily linear, however, as various teams will likely be working on many of these goals simultaneously.
At least the physics of Inertia’s approach are already proven, the startup’s CEO Jeff Lawson told me, pointing to the fusion experiments at Lawrence Livermore’s National Ignition Facility as a proof-of-concept. The lab’s demonstration of net energy gain caps more than six decades and $30 billion (in 2026 dollars) of U.S. fusion research. The remaining challenges, he argued, are all engineering-related, requiring “elbow grease, hard work, and smart people” rather than breakthroughs in fusion science.
"It seems to us like a startup or a commercial company of any variety should be focused on commercializing a proven scientific result, as opposed to actually trying to demonstrate the basic science to begin with," Lawson told me. Basic science, he argues, is better left to national labs and universities, where researchers can pursue "unbounded problems" that don’t align with the expectations and timelines of venture-backed startups.
Indeed, no fusion startup has yet achieved scientific breakeven, the milestone Lawrence Livermore first hit in 2022, and has since repeated numerous times. But leading players such as Commonwealth Fusion Systems and Helion Energy maintain that it’s only a matter of time before they validate the physics behind their own reactor designs, which they claim will be highly cost-competitive.
Lawson, on the other hand, readily acknowledged that Lawrence Livermore’s tech is uneconomical in its current form. His bet is simply that the more predictable path to a commercial reactor is to drive down the cost of the lab’s validated fusion approach, known as inertial confinement. This system relies on high-powered lasers firing at a millimeter-scale pellet of fusion fuel, compressing it to extreme temperatures and pressures until the atoms fuse. Today, the National Ignition Facility makes each individual fusion target by hand, a workable solution given that it only uses about a dozen per year.
That production model, however, isn’t remotely plausible for a grid-scale power plant. Because each fusion reaction lasts just a fraction of a billionth of a second, a commercial facility must fire its lasers at a fresh target about 10 times per second to generate continuous electricity — requiring the production of hundreds of millions of targets each year.
Scaling production to roughly a million pellets per day and making them inexpensive enough for commercial operation without compromising the strength or precision required for fusion ignition is central to Inertia’s roadmap. That includes goals five, seven, eight and nine — industrializing the manufacturing of the carbon shells that hold the fusion fuel, making the thin films that hold those carbon shells both durable and cheap, scaling up and automating fusion target assembly, and speeding up how fast targets are filled with the requisite deuterium-tritium fuel.
The other central focus of the roadmap is the laser system, which will ultimately consist of 1,000 individual units operating in concert to compress and heat the fusion fuel. Key priorities include reducing the system’s cost (goal two), dramatically increasing its firing cadence (goal three), and bolstering its durability to withstand high-intensity operations (goal four). Goal six also complements these efforts, calling for the development of a control system capable of tracking moving fusion targets to precisely align each laser shot.
Goals one and 10 bookend the journey with some broader milestones. The first focuses on increasing the fusion target’s energy gain — the ratio of fusion energy produced to laser energy delivered — to more than 25 times ignition. Today, the National Ignition Facility’s best-performing laser shot has yielded a gain of just over four times what it took to start the reaction. Goal 10 then zooms out to the ultimate objective: integrating all these technologies into a commercially viable power plant that can deliver either electricity or industrial heat to end customers.
To reach that point, Inertia has embarked on an industrial engineering hiring spree, recruiting folks with experience taking complex hardware systems from prototype to mass production, “not unlike the processes that are used in the semiconductor or consumer electronics world,” Lawson explained. The company has been making progress on its component development goals since the beginning of the year, he told me, and expects to announce the successful demonstration of a few of these milestones in the coming months. Lawson ultimately expects Inertia to complete the core components of its laser and target manufacturing systems by the middle of next year.
The team will spend the next two to three years integrating these individual pieces into two fully operational subsystems, a prototype laser system and a target manufacturing line. Around 2030, the company will begin combining those subsystems into a first-of-a-kind fusion power plant, which will also serve as the proving ground for the target chamber, tritium fuel breeding system, and power conversion system that turns fusion heat into electricity. By the middle of the next decade, Inertia aims to be generating power from this first plant, setting the stage for the company to build and connect additional grid-scale commercial power plants.
There are plenty of engineering trade-offs that the company will have to solve for. Take the decision around how to size the target chamber, for example. “If you make it bigger, your walls have an easier time and survive longer, but it’s more expensive. If you make it smaller, your walls have a tougher time because they’re closer to all the heat and energy that the fusion reaction is creating, but now your power plant costs less to build.”
But to Lawson, this represents exactly the type of problem Inertia was built to solve: complex engineering issues that come to the fore once scientists have demonstrated the fundamental physics are sound. He thinks other fusion companies may someday reach this stage, as well — though he’s unwilling to hazard a guess on exactly what approach or startup is best positioned to do so.
“There have been generations of scientists who’ve made their predictions about fusion energy and gotten it wrong,” he told me. “I’m not going to pretend to be smarter than them. All I’m here to say is, just knowing that one did work, we can commercialize it.”