Apr 30 2026
It’s time to make a plan for nuclear waste

Today, nuclear energy enjoys a rare moment of support across the political spectrum in the US. Interest from tech companies that are scrambling to meet demand for massive data centers has sparked a resurgence of money and attention in the industry. That newfound interest is exactly why it’s time to talk about an old problem: nuclear waste. 

In the US alone, nuclear reactors produce about 2,000 metric tons of high-level waste each year. And there’s nowhere to put it.

Though newly popular, the nuclear program in the US is nothing new. The US hosts more reactors and production capacity than any other country in the world. And yet nearly seven decades after the first permanent nuclear facility in the US went online, there’s still not a long-term solution for nuclear waste. 

Used fuel is largely stored onsite at operating and shut-down reactors, in pools and casks made of steel and concrete. Experts generally agree that these methods are safe, but they’re not designed to be permanent.

The leading strategy around the world for long-term storage of this high-level radioactive waste is to house it in a deep geological repository—dig a hole, put radioactive material down there, and fill it up with concrete. These holes, hundreds of meters underground, are designed to be a permanent home.

There aren’t any operating geological repositories for spent fuel yet, but some countries are well on their way. Finland is the furthest along; as of 2026, the country is testing its facility. Final approvals are expected soon, and operations could start later this year. Some other countries aren’t far behind.

France is home to over 50 nuclear reactors, and its grid gets more of its power from nuclear than any other. The country also has the world’s most established program for reprocessing spent fuel. The process separates out the plutonium and uranium to create a type of fuel known as mixed oxide (MOX) fuel. But reprocessing isn’t a perfect recycling loop, so the leftovers from this process still need somewhere to go. The country currently stores waste onsite at the La Hague reprocessing plant, but it plans to build a repository. Initial approvals could come later this decade, and pilot operations could start up by 2035.

Technically, the US also has a destination for its spent fuel: Yucca Mountain in Nevada. The site, which is on federal land, was designated by Congress in 1987. However, progress has entirely stalled out because of political opposition. In 2011, the federal government stopped providing funding for the site, and for roughly a decade, there’s been no activity to speak of.

In the meantime, waste continues to pile up.

The nuclear industry is kicking into a new gear around the world. China is home to the world’s fastest–growing nuclear energy program, and countries including Bangladesh and Turkey are building their first reactors.

Even the long-established US program is seeing growth: Interest in and approval for nuclear energy have spiked, and Big Tech is throwing money around to meet rising electricity demand. Companies are proposing (and beginning to receive regulatory approval for) next-generation reactors, which employ different coolants, fuels, and designs.

Given all this new interest, and the impending arrival of new types of nuclear waste, it’s time for nuclear companies, as well as their powerful customers, to push for progress on building geological storage facilities. As the richest country on the planet and home to a large chunk of the activity in next-generation reactors, the US should aim to join the leaders rather than continue to lag behind. 

Directing even a small fraction of the recent surge in funding and attention to progress on waste could make a difference. Some experts are calling for a new organization in the US to manage nuclear waste rather than leaving it to the Department of Energy. This organization would mirror programs in Finland, Canada, and France.

The process of planning, building, and commissioning a permanent solution for nuclear waste is a long one. Finland started planning in the 1980s and selected its site in the early 2000s, and it’s nearly ready to start accepting waste. For countries that don’t have a permanent storage solution sorted, the best time to start was decades ago. But the second-best time is now. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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Apr 28 2026
The missing step between hype and profit

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

In February, I picked up a flyer at an anti-AI march in London. I can’t say for sure whether or not its writers meant to riff on South Park’s underpants gnomes. But if they did, they nailed it: “Step 1: Grow a digital super mind,” it read. “Step 2: ? Step 3: ?”

Produced by Pause AI, an international activist group that co-organized the protest, it ended with this plea to the reader: “Pause AI until we know what the hell Step 2 is.” 

In the South Park episode “Gnomes,” which first aired in 1998, Kenny, Kyle, Cartman, and Stan discover a community of gnomes that sneak out at night to steal underpants from dressers. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.”

The gnomes’ business plan has since become one of the greats among internet memes, used to satirize everything from startup strategies to policy proposals. Memelord in chief Elon Musk once invoked it in a talk about how he planned to fund a mission to Mars. Right now, it captures the state of AI. Companies have built the tech (Step 1) and promised transformation (Step 3). How they get there is still a big question mark.

As far as Pause AI is concerned, Step 2 must involve some kind of regulation. But exactly what it will call for and who will enforce it are up for debate.

AI boosters, on the other hand, are convinced that Step 3 is salvation and tend to glaze over the middle bit. They see us racing toward sunny uplands on the back of an “economically transformative technology,” as OpenAI’s chief scientist, Jakub Pachocki, put it to me a few weeks ago. They know where they want to go—more or less: It’s hazy up there and still some way off. But everyone’s taking a different route. Will they all make it? Will anyone?

For every big claim about the future, there is a more sober assessment of how the rubber meets the road—one that quells the hype. Consider two recent studies. One, from Anthropic, predicted what types of jobs are going to be most affected by LLMs. (A takeaway: Managers, architects, and people in the media should prepare for change; groundskeepers, construction workers, and those in hospitality, not so much.) But their predictions are really just guesses, based on what kinds of tasks LLMs seem to be good at rather than how they really perform in the workplace.   

Another study, put out in February by researchers at Mercor, an AI hiring startup, tested several AI agents powered by top-tier models from OpenAI, Anthropic, and Google DeepMind on 480 workplace tasks frequently carried out by human bankers, consultants, and lawyers. Every agent they tested failed to complete most of its duties.   

Why is there such wide disagreement? There are a number of factors. For a start, it’s crucial to consider who is making the claims (and why). Anthropic has skin in the game. What’s more, most of the people telling us that something big is about to happen have reached that conclusion largely on the basis of how fast AI coding tools are getting. But not all tasks can be hacked with coding. Other studies have found that LLMs are bad at making strategic judgment calls, for example.

What’s more, when they’re deployed, the tools aren’t just dropped into a cleanroom. They need to work in places contaminated with people and existing workflows. And sometimes adding AI will make things worse. Sure, maybe those workflows need to be torn up and refashioned around the new technology for it to achieve transformative status, but that will take time (and guts).  

That big hole? It’s right where Step 2 should be. The lack of agreement on exactly what’s about to happen—and how—creates an information vacuum that gets filled by the latest wild claim of the week, evidence be damned. We’re so unmoored from any real understanding of what’s coming and how it will be deployed that a single social media post can (and does) shake markets.

We need fewer guesses and more evidence. But that’s going to require transparency from the model makers, coordination between researchers and businesses, and new ways to evaluate this technology that tell us what really happens when it’s rolled out in the real world.

The tech industry (and with it the world’s economy) rests on the held-out promise that AI really will be transformative. But that is not yet a sure bet. Next time you hear bold claims about the future, remember that most businesses are still figuring out what to do with their underpants.

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Apr 28 2026
Elon Musk and Sam Altman are going to court over OpenAI’s future

After a yearslong legal feud, Elon Musk and OpenAI CEO Sam Altman are heading to trial this week in Northern California in a case that could have sweeping consequences. Ahead of OpenAI’s highly anticipated IPO, the court could rule on whether the company is allowed to exist as a for-profit enterprise and might even oust its current executive leadership, including Altman.

Musk is suing OpenAI, alleging that Altman and OpenAI president Greg Brockman deceived him into bankrolling the company in its early days by promising to maintain it as a nonprofit dedicated to developing AI that benefits humanity, only to later restructure the company to operate a for-profit subsidiary. Musk cofounded OpenAI with Altman and others in 2015, but he left in 2018 after a bitter power struggle. 

Musk is seeking as much as $134 billion in damages from OpenAI and Microsoft, one of OpenAI’s biggest financial backers. He is also asking the court to remove Altman and Brockman from their roles and to restore OpenAI as a nonprofit. Musk has asked the court to award any damages to OpenAI’s nonprofit rather than to him personally. 

Nine jurors will deliver an advisory verdict, a non-binding recommendation, to guide the judge in deciding Musk’s claims against Altman. Musk, Altman, and Brockman will take the stand. Former OpenAI chief scientist Ilya Sutskever, former OpenAI CTO Mira Murati, and Microsoft CEO Satya Nadella are also expected to testify. Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light.

In an industry enveloped in secrecy, the trial will be a rare opportunity for the public to look behind the curtain and find out what’s going on in the companies creating the most transformative technology ever built. 

What are they fighting about?

When OpenAI was originally founded as a nonprofit, backed by a $38 million donation from Musk, the company vowed to create open-source technology for the public’s benefit, unconstrained by a need to generate financial returns. But over the years, the company began to claim that intensifying competition could make it dangerous to share how it develops its AI models and that a nonprofit structure could not raise enough money to keep building AI. (MIT Technology Review was first to report on OpenAI’s internal conflicts around its mission.)

The court has already found that in 2017 Altman and Brockman wanted to establish a for-profit arm, while Musk proposed merging OpenAI with his electric-car company, Tesla. When Musk threatened to stop funding, Altman and Brockman told him that they were committed to keeping the company a nonprofit. Musk alleges that they pursued plans to pivot to a for-profit without informing him. According to OpenAI, Musk agreed that the company needed a for-profit entity and even wanted to be its CEO. 

But even if Musk proves he was duped by Altman and Brockman, he may not have standing in the first place to sue them for restructuring the company to operate a for-profit subsidiary. Some legal scholars are puzzled over why the judge allowed him to bring this claim. “The idea that Elon Musk can sue because he was a donor or used to be on the board is pretty puzzling,” says Jill Horwitz, a law professor who studies nonprofit law at Northwestern University. “Typically, it’s up to the attorneys general to bring such a claim to enforce the charitable purposes. And that’s already happened.” 

In October 2025, state attorneys general of California, where OpenAI is headquartered, and Delaware, where OpenAI is incorporated, struck a deal with OpenAI to approve its new corporate structure on a series of conditions. For example, a safety and security committee at the nonprofit would review safety-related decisions made by the for-profit subsidiary. Critics of the restructuring, including Musk, AI safety advocates, and civil society groups, have tried to stop it. 

California’s attorney general has declined to join Musk’s lawsuit, saying that the office did not see how his action serves the public interest.

Still, whether the deal holds OpenAI to its nonprofit mission is an open question. “Elon Musk should have to show … what the deficiencies are in what’s been agreed to by OpenAI with the attorneys general,” says Rose Chan Loui, the director of the UCLA School of Law’s philanthropy and nonprofit program. Even with the terms in place, holding OpenAI to them depends on “how much they can enforce it and how much transparency they get into OpenAI’s work.”

More importantly, legal experts say the case is being considered under the wrong body of law. Musk argues that Altman and Brockman breached OpenAI’s charitable trust by creating a closed-source, for-profit subsidiary. As a result, the court has been analyzing the claim under the law of trusts. “But OpenAI is not a trust. OpenAI is a corporation. And so really they should be looking at … the law of charitable nonprofit organizations,” says Chan Loui.

What’s on the line?

Despite all the legal muddiness, the outcome of the trial could upend the AI race. Any one of the remedies that Musk seeks could cripple OpenAI as it races to go public by the end of the year. OpenAI, which is valued at over $850 billion, has described the litigation with Musk as a potential risk to its business. Musk’s rival company xAI, which makes the chatbot Grok, is expected to go public as a part of his rocket company SpaceX as early as June. If Musk prevails, xAI, which in combination with SpaceX is valued at $1.25 trillion, could get a big advantage in the AI race. 

And the trial has helped expose the bitter schism between Musk and the company he once helped to found. An OpenAI spokesperson referred MIT Technology Review to a post on X: “This lawsuit has always been a baseless and jealous bid to derail a competitor.” Although Musk’s lawyers did not immediately respond to a request for comment, he has posted on X that “Scam Altman lies as easily as he breathes.”  

MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting. 

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Apr 25 2026
Health-care AI is here. We don’t know if it actually helps patients.

I don’t need to tell you that AI is everywhere.

Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.

A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients?

We don’t yet have a good answer.

That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto, argue in a paper published in the journal Nature Medicine this week.

Wiens tells me she has spent years investigating how AI might benefit health care. For the first decade of her career she tried to pitch the technology to clinicians. Over the last few years, she says, it’s as though “a switch flipped.” Health-care providers not only appear much more interested in the promise of these technologies, they have also begun rapidly deploying them.

The problem is that many providers aren’t rigorously assessing how well they actually work.

Take “ambient AI” tools, for example. Also known as AI scribes, they “listen” to conversations between doctors and patients, then transcribe and summarize them. Multiple tools are available, and they are already being widely adopted by health-care providers.

A few months ago, a staffer at a major New York medical center who develops AI tools for doctors told me that, anecdotally, medics are “overjoyed” by the technology—it allows them to focus all their attention on their patients during appointments, and it saves them from a lot of time-consuming paperwork. Early studies support these anecdotes and suggest that the tools can reduce clinician burnout.

That’s all well and good. But what about patient health outcomes? “[Researchers] have evaluated provider or clinician and patient satisfaction, but not really how these tools are affecting clinical decision-making,” says Wiens. “We just don’t know.”

The same holds true for other AI-based technologies used in health-care settings. Some are used to predict patients’ health trajectories, others to recommend treatments. They are designed to make health care more effective and efficient.

But even a tool that is “accurate” won’t necessarily improve health outcomes. AI might speed up the interpretation of a chest X-ray, for example. But how much will a doctor rely on its analysis? How will that tool affect the way a doctor interacts with patients or recommends treatment? And ultimately: What will this mean for those patients?

The answers to those questions might vary between hospitals or departments and could depend on clinical workflows, says Wiens. They might also differ between doctors at various stages of their careers.

Take the AI scribes, as another example. Some research on AI use in education suggests that such tools can impact the way people cognitively process information. Could they affect the way a doctor processes a patient’s information? Will the tools affect the way medical students think about patient data in a way that impacts care? These questions need to be explored, says Wiens. “We like things that save us time, but we have to think about the unintended consequences of this,” she says.

In a study published in January 2025, Paige Nong at the University of Minnesota and her colleagues found that around 65% of US hospitals used AI-assisted predictive tools. Only two-thirds of those hospitals evaluated their accuracy. Even fewer assessed them for bias.

The number of hospitals using these tools has probably increased since then, says Wiens. Those hospitals, or entities other than the companies developing the tools, need to evaluate how much they help in specific settings. There’s a possibility that they could leave patients worse off, although it’s more likely that AI tools just aren’t as beneficial as health-care providers might assume they are, says Wiens.

“I do believe in the potential of AI to really improve clinical care,” says Wiens, who stresses that she doesn’t want to stop the adoption of AI tools in health care. She just wants more information about how they are affecting people. “I have to believe that in the future it’s not all AI or no AI,” she says. “It’s somewhere in between.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
 

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Apr 25 2026
Three reasons why DeepSeek’s new model matters

On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that helps it handle large amounts of text more efficiently. Like DeepSeek’s previous models, V4 is open source, meaning it is available for anyone to download, use, and modify.

V4 marks DeepSeek’s most significant release since R1, the reasoning model it launched in January 2025. R1, which was trained on limited computing resources, stunned the global AI industry with its strong performance and efficiency, turning DeepSeek from a little-known research team into China’s best-known AI company almost overnight. It also helped set off a wave of open-weight model releases from other Chinese AI firms. 

DeepSeek has kept a relatively low profile since then—but earlier this month, it effectively teased V4’s release when it added “expert” and “flash” modes to the online version of its model, prompting speculation that the updates were tied to a bigger upcoming release.

While the company has become a powerful symbol of China’s AI ambitions, its big return to cutting-edge frontier models comes after months of scrutiny—including major personnel departures, delays to previous model launches, and growing scrutiny from both the US and Chinese governments. 

So, will V4 shake the AI field the way R1 did? Almost certainly not, but here are three big reasons why this release matters.

1. It breaks new ground for an open-source model.

As with R1 before it, DeepSeek claims that V4’s performance rivals the best models available at a fraction of the price. This is great news for developers and for companies using the tech, because it means they can access frontier AI capabilities on their own terms, and without worrying about skyrocketing costs.

The new model comes in two versions, both of which are available on DeepSeek’s website and in its app, with API access also open to developers. V4-Pro is a larger model built for coding and complex agent tasks, and V4-Flash is a smaller version designed to be faster and cheaper to run. Both versions offer reasoning modes, in which the model can carefully parse a user’s prompt and show each step as it works through the problem.

For V4-Pro, DeepSeek charges $1.74 per million input tokens and $3.48 per million output tokens, a fraction of the cost of comparable models from OpenAI and Anthropic. V4-Flash is even cheaper, at about $0.14 per million input tokens and about $0.28 per million output tokens, making it one of the cheapest top-tier models available. This would make it a very appealing model to build applications on.

In terms of performance, V4 is, perhaps unsurprisingly, a huge jump from R1—and it seems to be a strong alternative to just about all the latest big AI models. On the major benchmarks, according to results shared by the company, DeepSeek V4-Pro competes with leading closed-source models, matching the performance of Anthropic’s Claude-Opus-4.6, OpenAI’s GPT-5.4, and Google’s Gemini-3.1. And compared to other open-source models, such as Alibaba’s Qwen-3.5 or Z.ai’s GLM-5.1, DeepSeek V4 exceeds them all on coding, math, and STEM problems, making it one of the strongest open-source models ever released. 

DeepSeek also says that V4-Pro now ranks among the strongest open-source models on benchmarks for agentic coding tasks and performs well on other tests that measure ability to carry out multistep problems. Its writing ability and world knowledge also lead the field, according to benchmarking results shared by the company. 

In a technical report released alongside the model, DeepSeek shared results from an internal survey of 85 experienced developers: More than 90% included V4-Pro among their top model choices for coding tasks.

DeepSeek says it has specifically optimized V4 for popular agent frameworks such as Claude Code, OpenClaw, and CodeBuddy.

2. It delivers on a new approach to memory efficiency.

One of the key innovations of V4 is its long context window—the amount of text the model can process at once. Both versions can handle 1 million tokens, which is large enough to fit all three volumes of The Lord of the Rings and The Hobbit combined. The company says this context window size is now the default across all DeepSeek services and it matches what is offered by cutting-edge versions of models like Gemini and Claude. 

But it’s important to know not just that DeepSeek has made this leap, but how it did so. V4 makes significant architectural changes to the company’s former models—especially in the attention mechanism, which is the feature of AI models that helps them understand each part of a prompt in relation to the rest. As the prompt text gets longer, these comparisons become much more costly, making attention one of the main bottlenecks for long-context models.

DeepSeek’s innovation was to make the model more selective about what it pays attention to. Instead of treating all earlier text as equally important, V4 compresses older information and focuses on the parts most likely to matter in the present moment, while still keeping nearby text in full so it does not miss important details. 

DeepSeek says this sharply reduces the cost of using long context. In a 1-million-token context, V4-Pro uses only 27% of the computing power required by its previous model, V3.2, while cutting memory use to 10%. The reduction in V4-Flash is even larger, using just 10% of the computing power and 7% of the memory. In practice, this could make it cheaper to build tools that need to work across huge amounts of material, such as an AI coding assistant that can read an entire codebase or a research agent that can analyze a long archive of documents without constantly forgetting what came before.

DeepSeek’s interest in long context windows didn’t start with V4. Over the past year and a half, the company has quietly published a series of papers on how AI models “remember” information, experimenting with compression and mathematical techniques to extend what AI models could realistically handle.

3. It marks the first steps on the hard road away from Nvidia.

V4 is DeepSeek’s first model optimized for domestic Chinese chips, such as Huawei’s Ascend—a move that has turned the launch into something of a test of whether China’s homegrown AI industry can begin to loosen its dependence on US chip giant Nvidia. 

This was largely expected, since The Information reported earlier this month that DeepSeek did not give American chipmakers like Nvidia and AMD early access to V4, though prerelease access is common to allow chipmakers to optimize support of the new model ahead of a launch. Instead, the company reportedly gave early access only to Chinese chipmakers. 

On Friday, Huawei said its Ascend supernode products, based on the Ascend 950 series, would support DeepSeek V4. This means that companies and individuals who want to run their own modified version of Deepseek V4 will be able to use Huawei chips easily.

Reuters previously reported that Chinese government officials recommended that DeepSeek integrate Huawei chips in its training process. And this pressure fits a broader pattern in China’s industrial policy: Strategic sectors are often pushed, and sometimes effectively required, to align with national self-reliance goals. But there’s a particular urgency when it comes to AI. Since 2022, US export controls have cut Chinese firms off from Nvidia’s most powerful chips, and they later also restricted access to downgraded China-market versions. Beijing’s response has been to accelerate the push for a domestic AI stack, from chips to software frameworks to data centers.

Chinese authorities have reportedly been pushing data centers and public computing projects to use more domestic chips, including through reported bans on foreign-made chips, sourcing quotas, and requirements to pair Nvidia chips with Chinese alternatives from companies such as Huawei and Cambricon. 

Still, replacing Nvidia is not as simple as swapping one chip for another. Nvidia’s advantage lies not only in its chips, but in the software ecosystem developers have spent years building around them. Moving to Huawei’s Ascend chips means adapting model code, rebuilding tools, and proving that systems built around those chips are stable enough for serious use.

To be clear, DeepSeek does not appear to have fully moved beyond Nvidia. The company’s technical report reveals that it is using Chinese chips to run the model for inference, or when someone asks the model to complete a task. But Liu Zhiyuan, a computer science professor at Tsinghua University, told MIT Technology Review that DeepSeek appears to have adapted only part of V4’s training process for Chinese chips. The report does not say whether some key long-context features were adapted to domestic chips, so Liu says V4 may still have been trained mainly on Nvidia chips. Multiple sources who spoke on the condition of anonymity, due to political sensitivity around these issues, told MIT Technology Review that Chinese chips still don’t perform as well as Nvidia chips but are better suited for inference than training.

DeepSeek is also tying the future costs of V4 to this hardware shift. The company says V4-Pro prices could fall significantly after Huawei’s Ascend 950 supernodes begin shipping at scale in the second half of this year. 

If that works, V4 could be an early sign that China is successfully building a parallel AI infrastructure.

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Apr 24 2026
Will fusion power get cheap? Don’t count on it.

Fusion power could provide a steady, zero-emissions source of electricity in the future—if companies can get plants built and running. But a new study suggests that even if that future arrives, it might not come cheap.

Technologies tend to get less expensive over time. Lithium-ion batteries are now about 90% cheaper than they were in 2013. But historically, different technologies tend to go through this curve at different rates. And the cost of fusion might not sink as quickly as the prices of batteries or solar.

It’s tricky to make any predictions about the cost of a technology that doesn’t exist yet. But when there’s billions of dollars of public and private funding on the line, it’s worth considering what assumptions we’re making about our future energy mix and its cost.

One crucial measure is a metric called experience rate—the percentage by which an energy technology’s cost declines every time capacity doubles. A higher figure means a quicker price drop and better economic gains with scaling.

Historically, the experience rate is 12% for onshore wind power, 20% for lithium-ion batteries, and 23% for solar modules. Other energy technologies haven’t gotten cheap quite as quickly—fission is at just 2%.

In the new study, published in Nature Energy, researchers aimed to improve predictions of fusion’s future price by estimating the technology’s experience rate. The team looked at three key characteristics that can correlate with experience rate: unit size, design complexity, and the need for customization. The larger and more complex a technology is, and/or the more it needs to be customized for different use cases, the lower the experience rate.

The researchers interviewed fusion experts, including public-sector researchers and those working at companies in the private sector. They had the experts evaluate fusion power plants on those characteristics and used that info to predict the experience rate. (One note here: The study focused only on magnetic confinement and laser inertial confinement, two of the leading fusion approaches, which together receive the vast majority of funding today. Other approaches could come with different cost benefits.)

Fusion plants will likely be relatively large, similar to other types of facilities (like coal and fission power plants) that rely on generating heat. They will probably need less customization than fission plants—largely because regulations and safety considerations should be simpler—but more than technologies like solar panels. And as for complexity, “there was almost unanimous agreement that fusion is incredibly complex,” says Lingxi Tang, a PhD candidate in the energy and technology policy group at ETH Zurich in Switzerland and one of the authors of the study. (Some experts said it was literally off the scale the researchers gave them.)

The final figure the researchers suggest for fusion’s experience rate is between 2% and 8%, meaning it will see a faster price reduction than nuclear power but not as dramatic an improvement as many common energy technologies being deployed today.

That means that it would take a lot of deployment—and likely quite a long time—for the price of building a fusion reactor to drop significantly, so electricity produced by fusion plants could be expensive for a while. And it’s a much slower rate than the 8% to 20% that many modeling studies assume today.

“On the whole, I think questions should be raised about current investment levels in fusion,” Tang says. (The US allocated over $1 billion to fusion in the 2024 fiscal year, and private-sector funding totaled $2.2 billion between July 2024 and July 2025.) “If you’re talking about decarbonization of the energy system, is this really the best use of public money?”

But some experts say that looking to the past to understand the future of energy prices might be misleading.“It’s a good exercise, but we have to be humble about how much we don’t know,” says Egemen Kolemen, a professor at the Princeton Plasma Physics Laboratory.

In 2000, many analysts predicted that solar power would remain expensive—but then production exploded and prices came crashing down, largely because China went all in, he says. “People weren’t exactly wrong then,” he adds. “They were just extrapolating what they saw into the future.”

How fast prices drop depends on regulations, geopolitical dynamics, and labor cost, he says: “We haven’t built the thing yet, so we don’t know.”

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

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Apr 23 2026
3 things Michelle Kim is into right now

Isegye Idol

If you thought K-pop was weird, virtual idols—humans who perform as anime-style digital characters via motion capture—will blow your mind. My favorite is a girl group called Isegye Idol, created by Woowakgood, a Korean VTuber (a streamer who likewise performs as a digital persona). Isegye Idol’s six members are anonymous, which seems to let them deploy a rare breed of honesty and humor. They play games (League of Legends, Go, Minecraft), chitchat, and perform kitschy music that’s somewhere between anime soundtrack and video-­game score. It’s very DIY—and very intimate. And the group’s wild popularity speaks to the mood of Gen Z South Koreans, famously lonely and culturally adrift—struggling to find work, giving up on dating, trying to find friendships online. Isegye Idol shows what a magical online universe people can build when reality stops working for them.

Mr. Nobody Against Putin

Pavel Talankin didn’t have the easiest life as a schoolteacher in the copper-­smelting town of Karabash, Russia; UNESCO once called it the most toxic place on Earth. But video he shot, partially in secret, makes it clear he loved it—the smokestacks, the cold, the ice mustache he’d get walking around outside, and, most of all, his bright-eyed students. That makes it all the more painful when a distant, grinding war and state propaganda change the town. An antiwar progressive with a democracy flag in his classroom, Talankin had to deal with a new patriotic curriculum, mandatory parades, visits from mercenaries—and the loss of the creative space he’d built with his students. Talankin’s footage tells his story in this Oscar-winning documentary from director David Borenstein, and what struck me most is how strange it is being an adult around kids. We shape them in profound ways we might not even recognize.

Repertoire by James Acaster

I am the kind of person who will pay $150 to watch a comedian in a smelly theater in San Francisco that charges $20 for a can of water—because I am crazy enough to hope that standup will not die. In February, I saw the British comedian James Acaster perform live … and it was a mediocre show. But Repertoire, his 2018 miniseries on Netflix, is gold. Shot shortly after Acaster went through a breakup, the four-part show features him portraying, among other characters, a cop who goes undercover as a standup comedian, forgets who he is, and gets divorced. And then things get weird. “What if every relationship you’ve ever been in,” Acaster asks, “is somebody slowly figuring out they didn’t like you as much as they hoped they would?” If the best comedy comes from paying attention to the hellhole that you’re in, I wish Acaster many more pitfalls.

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Apr 23 2026
One town’s scheme to get rid of its geese

“Pull over!” I order my brother one sunny February afternoon. Our target is in sight: a gaggle of Canada geese, pecking at grass near the dog park. As I approach, tiptoeing over their grayish-white poop, I notice that one bird wears a white cuff around its slender black neck. It’s a GPS tracker—part of a new tech-centered campaign to drive the geese out of my hometown of Foster City, California. 

the United States with a dot on the California coast line
__________________________
THE PLACE
Foster City, CA
USA

About 300 geese live in this sleepy Bay Area suburb, equal to nearly 1% of our human population—and some say this town isn’t big enough for the both of us. Goose poop notoriously blanketed our middle school’s lawn, and the birds have hassled residents for generations. My own grandmother remembers when geese took over her garage for five whole minutes before waddling out. She says, “I wanted to kill them, but I thought I’d get in trouble.”

Indeed, that idea doesn’t fly here. City officials backed out of a previous plan to kill 100 geese following uproar from local environmentalists. Still, the poop creates a public health hazard; the birds need to go. 

So the city paid nearly $400,000—roughly $1,300 per goose—to Wildlife Innovations, a company that resolves conflicts between humans and wildlife, to haze the geese with gadgets. The company’s approach is “basically, making the geese less comfortable,” Dan Biteman, head of the goose management plan and senior wildlife biologist at Wildlife Innovations, tells me.

The need for such conflict resolution is on the rise as land development collides with changes in animal behavior. Though overpopulation of Canada geese is a national nuisance in the US, such tensions also surface with other species in this country and elsewhere, including grizzlies on the Montana prairies, coyotes on San Francisco streets, and savanna elephants in Tanzania parks. 

So the people whose job it is to deal with recalcitrant critters are bringing on the gadgets.

Back in Foster City, I spot a black camera mounted to a tree trunk at Gull Park by the lagoon. They’re in seven parks around town, programmed to snap photos every 15 minutes and transmit them back to Wildlife Innovations HQ. If they detect geese, a biologist immediately drives over to disperse the birds. One team member uses devices like lasers or drones; another brings along a goose-hating border collie named Rocky. 

An orange foam pontoon boat with yellow eyes and sharp-looking jagged teeth
Belligerent birds must grapple with the Goosinator.
ANNIKA HOM

As a special measure, staff deploy the “Goosinator,” a small, remote-controlled neon-orange pontoon boat with a fearsome dog-like mouth painted on its bow, meant to evoke geese’s fear of coyotes and bright colors. It comes with attachable wheels and can zoom around on land or water to chase birds away. Biteman tells me the company is thinking about mounting speakers on trees and flying drones that will screech the calls of goose predators like red-tailed hawks or golden eagles. 

The company received federal permits required by the Migratory Bird Treaty Act to stick GPS trackers on 10 geese, too. This way, staff can surveil the geese and research their behavior and movements. 

At local goose hangouts, signs that look like “Wanted” posters alert the public to the new plan. As I watch some culprits graze (and defecate) on a church lawn, I think to myself: Enjoy it while it lasts. 

Annika Hom is an award-winning independent journalist. She’s written for National Geographic, Wired, and more.

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Apr 23 2026
There is no nature anymore

When people talk about “nature,” they’re generally talking about things that aren’t made by human beings. Rocks. Reefs. Red wolves. But while there is plenty of God’s creation to go around, it is hard to think of anything on Earth that human hands haven’t affected.

Mat Honan

In the Brazilian rainforest, scientists have found microplastics in the bellies of animals ranging from red howler monkeys to manatees. In remotest Yakutia, where much of the earth remains untrodden by human feet, the carbon in the sky above melts the permafrost below. In the Arctic Ocean, artificial light from ship traffic—on the rise as the polar ice cap melts away—now disrupts the nightly journey of zooplankton to the ocean surface, one of the largest animal migrations on the planet. The remote mountain lakes of the Alps are contaminated with all kinds of synthetic chemicals. Polar bears are full of flame retardants. Cesium-137, fallout from nuclear bomb explosions, lightly rimes the entire planet. 

These examples are mostly pollution—nuclear, carbon, chemical, light—but I raise them not to highlight the ways human industry and technology degrade the environment but to note how the things humans build change it. Nobody really knows what the exact effects of all that will be, but my point is that no part of the globe is free of human fingerprints. We have literally changed the world.  

We’ve changed ourselves as well. Humans are especially adept at bending human nature. Everything about us is up for grabs—appearance, health, our very thoughts. Pharmaceuticals, surgeries, vaccines, and hormones give us longer lives, take away our pain, ease our anxiety and depression, make us faster, stronger, more resilient. We’re getting glimpses of technologies that will let us change who our children will become before they’re even born. Electrodes implanted in people’s brains let them control computers and translate thoughts into speech. Prosthetics and exoskeletons straight out of comic books restore and enhance physical abilities, while gene-­editing technologies like CRISPR are rewriting our very DNA. And meanwhile, people have taken the sum total of all the information we have ever written down and poured it into vast calculating machines in an effort—at least by some—to build an intelligence greater than our own. 

So what even is nature, or natural, in this context? Is it “environmentalist,” in the conventional sense, to try to preserve what one could argue no longer exists? Should we employ technology to try to make the world more “natural”?  

Those questions led us to approach this Nature issue with humility. We try to grapple with them all the time—MIT Technology Review is, after all, a review of how people have altered and built upon nature.

And it’s a place to think about how we might repair it. Take solar geoengineering, for example—a subject we have covered with increasing frequency over the past few years. The basic idea of geoengineering is to find a technological fix for a problem technology caused: Burning ­petrochemicals to fuel the Industrial Revolution turned Earth’s atmosphere into a heat sink, fundamentally breaking the climate. Some geoengineers think that releasing particulate matter into the stratosphere would reflect sunlight back into space, thus reducing global temperatures. After years of theoretical discussions, some companies have begun to actively experiment with such technologies. This might seem like a great way to restore the world to a more natural state. It’s also fraught with controversy and peril. It could, for example, benefit some nations while harming others. It may give us license to continue burning fossil fuels and releasing greenhouse gases. The list goes on. 

Nature isn’t easy. 

In our May/June issue, we have attempted to take a hard look at nature in our unnatural world. We have stories about birds that can’t sing, wolves that aren’t wolves, and grass that isn’t grass. We look for the meaning of life under Arctic ice and within ourselves—and in the far future, on a distant world, courtesy of new fiction by the renowned author Jeff VanderMeer. I don’t know if any of that will answer the questions I’ve been asking here—but we can’t help but try. It’s in our nature. 

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Apr 23 2026
Los Angeles is finally going underground

Los Angeles deserves its reputation as the quintessential car city—the rhythms of its 2,200 square miles are dictated by wide boulevards and concrete arcs of freeways. But it once had a world-class rail transit system, and for the last three decades, the city has been rebuilding a network of trolleys and subways. In May, a new four-mile segment with three new subway stations will open along Wilshire Boulevard, a key east-west corridor that connects downtown LA to the Pacific Ocean. What today can be an hours-long drive through a busy, museum-­packed stretch of the city will be, if all goes well, a 25-minute train ride.

The existence of subway stops in this part of town—known as Miracle Mile—is a technological triumph over geography and geology. The ground underneath it is literally a disaster waiting to happen—it’s tarry and full of methane. One of those methane deposits actually exploded in 1985, destroying a department store in the neighborhood. In response, the city pushed its new train routes to other parts of town.

These days, dirt full of flammable goo is no longer a problem. “The technology finally caught up with the concerns,” says LA Metro’s James Cohen, a longtime manager of the engineering for this stretch of subway. The key was an earth-pressure-­balance tunnel-boring machine, an automated digger that is designed to chew through ground packed with explosive gas. It sends removed dirt topside via conveyor belts and slides precast concrete liner segments into the tunnel, which are joined together with gaskets to create a gas- and waterproof tube. All that let the machine dig about 50 feet every day. 

a car on the D line track
A Metro train pulls into La Cienega station
Fairfax Station
Art by Susan Silton at the Fairfax station
Eamon Ore-Giron - LA Metro D Line - La Brea Station
Art by Eamon Ore-Giron at the La Brea station

Meanwhile, engineers excavated the stations from the street level down. They worked mostly on weekends, digging out a space and then decking it with concrete so that work could go on underneath while LA drivers continued to exercise their God-given right to get around by car above.

Did the project finish on time? No. Did it come in under budget? Also no; this segment alone cost nearly $4 billion. Is the city now racing to build housing and walkable areas to take full advantage of the extension? Oh, please. Yet the new stations still manage to feel, in the end, transformative—as if Los Angeles’s train has finally come in.

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