May 2 2026
A new US phone network for Christians aims to block porn and gender-related content

A new US-wide cell phone network marketed to Christians is set to launch next week. It blocks porn, which experts in network security say marks the first time a US cell plan has used network-level blocking for such content that can’t be turned off even by adult account owners. It’s also rolling out a filter on sexual content aimed at blocking material related to gender and trans issues, which will be optional but turned on by default across all plans.

The network, which is currently being tested ahead of its May 5 launch date, will be run by Radiant Mobile, a newly launched mobile virtual network operator (MVNO). These operators don’t own cell towers but buy bandwidth from the big providers (in this case, T-Mobile) and sell to specific demographics (President Trump announced his own MVNO last year called Trump Mobile; CREDOMobile sends donations to progressive causes). 

“We are going to create—and we think we have every right to do so—an environment that is Jesus-centric, that is void of pornography, void of LGBT, void of trans,” Radiant Mobile’s founder, Paul Fisher, told MIT Technology Review. A representative for T-Mobile did not comment on whether these content blocks violate any of its policies. In a statement, the representative added that T-Mobile does not have a direct relationship with Radiant Mobile but instead works through the MVNO manager CompaxDigital. 

Fisher says he’s recruited a mix of Christian influencers to advertise the plan and has also done outreach to thousands of churches around the country, offering a way to have Radiant donate a portion of congregants’ $30-per-month subscription fee to their church. Fisher has ambitions to market it beyond the US in other countries with significant Christian populations, like South Korea and Mexico.

At least one piece of Radiant’s pitch will sound familiar: the idea that the internet is awash in toxic sludge. It’s powered by content and algorithms that are making us more sad, hateful, and detached. A number of efforts aim to fix that, including contentious age verification laws and a coming wave of lawsuits alleging that social media companies knowingly got young users hooked on their platforms. 

Fisher is pursuing the nuclear option. He says Radiant is working with the Israeli cybersecurity company Allot to block categories of content, such as material about violence or self-harm. Some categories are banned by default and cannot be allowed even for adult users. 

This includes pornography. Chris Klimis, a minister in Orlando who was recruited to be the company’s chief operating officer, says part of the reason he got involved was to offer Christians a real way to “do something” about what he sees as a pornography crisis in the faith. He was appalled by a recent survey showing that 67% of pastors have a “personal history” with porn use. And he worries his six children will come across porn on their devices, even if only inadvertently.

“We’ve got to figure out some way to close the door to the digital space,” he says. “That’s what we’re trying to do.”

The technology to do this blocking is a blunt instrument: Allot groups website domains into more than a hundred categories, which include pornography but also violence, malware, gaming, and in Radiant Mobile’s case “sects,” which includes websites about Satanism. If one of its users tries to visit a website that belongs to a blocked category, the page won’t load. That’s harsher than app-based content blockers like Covenant Eyes, a Christian porn-quitting app that sends notifications to your friends or family if you slip up; those can be worked around or deleted.

“Blocking in the network is certainly not new,” says David Choffnes, a computer science professor and executive director of Northeastern University’s Cybersecurity and Privacy Institute. Such blocking is the backbone of censorship efforts by authoritarian governments, for example. But there are more benign ways it’s used too. US telecoms block particular domains known to be spreading malware and offer optional network-level controls to block adult content on kids’ phones. What is new is a US cell plan instituting network-level blocks that can’t be removed, even by adults.

The trouble is that most websites don’t fit neatly into one category, leaving Fisher with enormous and subjective control over which are allowed or banned. This is most apparent in his effort to block content related to gender identity.

Anthony Re, a sales director at Allot, says the company does not have a category specific to gender but that “LGBT content” tends to fall into its sexuality category, which is described on Radiant Mobile’s website as “sites that provide information on sex, sex and teenagers, and sexual education, without pornographic content.” This category is blocked by default for all phones, a setting that can be changed by adult account owners. 

But if a news site starts hosting enough gender-related content, Fisher might not just label it as “press,” which is allowed, but also “sexuality,” thus blocking the whole domain to any phone with that category blocked. 

Fisher illustrates the subjectivity of such decisions with a recent example involving Yale University. Its general website, www.yale.edu, is categorized by Allot as education. “But they have a subsection of one of their websites that’s totally focused on, you know, trans equality,” Fisher says, referring to lgbtq.yale.edu. Because it’s a distinct domain, Radiant Mobile is able to place it in the sexuality category and block it. 

Yale’s main website remains unblocked, for now. “If we see [the LGBTQ content] on the front pages consistently of Yale University, we’ll block them too,” Fisher says.

Managing website block lists is a professional pivot for Fisher, who spent his career not in telecoms but in fashion; he was an agent for supermodels like Naomi Campbell and members of the Hilton and Getty families, and he later hosted a reality show in which he found people in rehab facilities and homeless shelters and tried to turn them into models. He ultimately left the industry and now says he regrets the role he played in it: “Am I proud that I spent 35 years creating star models or star influencers? Not at all.”

Last year, his friend and fellow fashion mogul Bernt Ullmann suggested he look at what Ryan Reynolds had built with his cell network Mint Mobile: It made buying a cell plan feel less like dealing with a utility and more like choosing a brand, and it had been acquired by T-Mobile in 2023 for $1.3 billion. Fisher liked the business model but didn’t have an audience in mind. Then came a late-night revelation. “God is talking to me,” Fisher recalls. “Do something in the faith-based industry.” He set out to build the first cell network that would let in only content deemed compatible with Christianity.

Fisher says the company has received $17.5 million in investment from Compax Ventures, part of the company serving as the technical middleman between Radiant and T-Mobile. Roger Bringmann, a vice president at Nvidia, is Radiant Mobile’s lead investor and silent partner (Bringmann recently funded a new complex at Austin Christian University in Texas, which bills itself as “the university for Christian entrepreneurs”).

To fill the gap left by all the sites being blocked, the company intends to offer access to a library of religious content, including AI-generated Bible videos. It plans to use characters like Cinderella, Tinker Bell, and others (it has obtained rights from the entertainment and media company Elf Labs, which has been amassing rights to hundreds of children’s characters). “Those characters were originally constructed with a conservative perspective,” Klimis says. They’ll be used in AI-generated content alongside testimonials and devotionals. 

Choffnes has technical doubts that the plan’s firewall will be as effective as promised, not least because “it’s really hard to come up with a list of every website you think is problematic.” But beyond that, he sees the internet, frustrating as it can be, as better open than closed. “I do believe in an open internet,” he says. “I also believe that a lot of the internet is toxic, but I don’t believe that this sledgehammer approach of blocking content is the right answer.”

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May 2 2026
Inexpensive seafloor-hopping submersibles could stoke deep-sea science—and mining

Smack dab between Australia and South America, the US National Oceanic and Atmospheric Administration (NOAA) research vessel Rainier is currently on a mission to map more than 8,000 square nautical miles of the Pacific seafloor in search of critical mineral deposits. But it isn’t doing it alone; for a month starting this week, it will deploy two oblong neon submersibles as the project’s special agents, sending them nearly 6,000 meters down to hop along the seafloor. 

The submersibles, built by the young company Orpheus Ocean, are designed to explore just this environment: a squelchy substrate that teems with life of all kinds, from tiny microbes to worms and snails, along with egg-size “nodules” of metals—such as copper, cobalt, nickel, and manganese—that are crucial for technologies worldwide.  

Scientists and companies have long sought to probe the deep sea and bring such treasures to the surface. Orpheus, which spun off from the Woods Hole Oceanographic Institution (WHOI) in 2024, could be well positioned to make those possibilities a lot more economical. The company has designed its vehicles on a simple philosophy: “deep for cheap,” says Jake Russell, Orpheus’s cofounder and CEO, who is a chemist by training. The vehicles cost a couple of hundred thousand dollars each to build, whereas existing options can range from $5 million to $10 million. And unlike most autonomous ocean vehicles, they can push into the seafloor and capture cores of sediment—and the creatures within. 

Orpheus’s engineers have been tinkering with their deep-sea designs for years, much of the work taking place at WHOI and in collaboration with NOAA and the National Aeronautics and Space Administration. Its prototype vehicles were rated capable of diving to 11,000 meters—the deepest part of the Mariana Trench. They’ve completed two commercial deployments, but this new expedition marks the submersibles’ biggest test yet: operating over large ranges for multiple weeks and with multiple instruments at play. Using Rainier as their home base on the ocean’s surface, the vehicles will swim out for 10 kilometers at a time, taking one high-resolution image every second and up to eight physical samples from the seafloor apiece.

If all goes well, the test could help establish the vehicles as a tool for government agencies, scientists, and companies that hope to probe the vastly understudied deep sea and the resources it holds. And while they’re not the only option on the market, Orpheus hopes their size and low building cost will soon make them one of the most accessible. 

At present, to reach these depths scientists must wait for time on a limited and expensive set of submersibles owned by government agencies and research institutes. That formula lends itself better to capturing snapshots of the deep sea than it does to probing its interconnected ecological and biogeochemical systems. “A lot of this region that we’re surveying … has really never been explored in any kind of detail,” says Russell. “Anything we see is going to be new to NOAA and new to science.”

A sediment specialist

The Orpheus subs are classified as autonomous underwater vehicles (AUVs), which operate on a mix of preprogrammed commands and live decision-making and without being tethered to a ship. But unlike traditional AUVs engineered for long-distance, high-speed gliding, these submersibles are short and stout with little legs—better for making soft landings on the seafloor and then pushing into the mud to suck out sediment cores for scientists. When they do land, the submersibles can lift off the surface, thrust a few feet, and settle once more in a “hopping” fashion.

Their bodies are made mostly of a buoyant material known as syntactic foam, with the important electronics encased in a thick sphere of glass. The same kind of foam, which is interspersed with hollow microspheres of glass to prevent it from collapsing under high pressures, went to the deep in the vehicle that carried the filmmaker James Cameron to the Mariana Trench in 2012; he even donated leftover material for use in earlier Orpheus prototypes. 

At less than two meters in length and under 600 pounds (270 kilograms), Russell says the Orpheus robots are the smallest—and correspondingly the least expensive—ocean vehicles on the market capable of descending to 6,000 meters. They’re designed to populate future fleets of robotic explorers.

The approach stems from a fundamental challenge, says Victoria Orphan, a geobiologist at the California Institute of Technology, who has previously worked with an Orpheus vehicle on a science campaign: “Anytime you do things in the deep ocean, you always run this risk, when you put something over the side [of a ship], that it might not come back.” With existing fleets of large, expensive vessels operated by groups like NOAA, WHOI, and the Monterey Bay Aquarium Research Institute (MBARI), losing a vehicle can be disastrous, not least because scientists must already compete for their limited time.

In the spring of 2024, Orphan and her colleagues put an Orpheus sub through its paces during an expedition to study deep-sea methane seeps off the coast of Alaska’s Aleutian Islands. They hoped to use the vehicle to create maps of the area before the team sent down a human-crewed submersible called Alvin to study specific areas—and the microorganisms and animals that live there—in more detail. 

But as with any sort of new type of technology, “there’s always growing pains,” recalls Orphan. Frigid temperatures and steep topography added unseen challenges, and it took the full three weeks for the sub to get high-resolution photographs of the seeps. 

The setback didn’t dull Orphan’s excitement about the potential of these machines. “There’s a lot of real, unknown science right at that interface between the sediment and the ocean surface,” she says. “The Orpheus-type class of instrument, with the right kinds of sensors and samplers, could be a very enabling tool.”

Russell envisions pairing the vehicles with specially designed payloads that can sense the heat of chemical seeps and detect plumes of sediment, DNA shed from ocean life-forms, or the magnetic tug of buried cables. 

The vehicles are the “the best of both worlds,” says Andrew Sweetman, a deep-sea ecologist at the Scottish Association for Marine Science, who has not worked with Orpheus. While they can roam large areas like an AUV, they can also carry out precise sampling maneuvers like a remotely operated vehicle (ROV), a robot connected to a ship via cables that fulfills real-time human commands.

In addition to the low price tag, says Sweetman, the small size of the vessels means they don’t require a large research vessel to ferry them out to sea. That might make exploration more accessible for smaller or poorer countries without such ships, he says: “It will, in a way, help democratize deep-sea science.” He imagines using the sediment cores the submersibles gather to probe how seafloor-dwelling animals cycle nutrients—a crucial element of the ocean’s role as a carbon sink. 

The mining push 

As much as smaller, cheaper ocean vehicles have caught scientists’ eye, they have also piqued the interest of companies. Russell says inquiries come in weekly from businesses involved in deep-sea mining, defense, offshore wind, telecommunication, and oil and gas. He notes that Orpheus is merely a “service provider,” helping collect data where needed but not making decisions about how to use the seafloor. And he says that better data—such as information on the shape of the seafloor, the sediment quality, and the presence of life—also “raises the bars” that governments and regulators are only beginning to set.

But many scientists are far from eager about the growing push for seabed mining, which an executive order from President Donald Trump stoked further last week by mandating that the US government rapidly develop mineral exploration and processing. And earlier last month, the administration announced the creation of a new government office: the Marine Minerals Administration. 

the Orpheus from below with flare from its two lower lights
A view of an Orpheus vehicle from below.
ORPHEUS OCEAN

Given the current dearth of information on the deep sea, says Sweetman, “I think the push for deep-sea mining is happening way too fast.” And deep-sea communities are “probably the most stable environment on our planet,” adds Orphan. “The organisms that live there are really not adapted to a lot of disturbance, and it takes a really, really long time for them to recover, if at all.”

One mining method that governments and companies propose involves a machine that essentially operates like a giant bulldozer, trawling the seafloor, sucking up a trail of material, and leaving scar marks and sediment plumes in its wake. Brett Hobson, an ocean engineer at MBARI, says that Orpheus-like technology might enable companies to “take samples in a more surgical way, instead of just grossly scooping everything up off the seafloor and filtering through it.”

Hobson, who has run MBARI’s work on ocean vehicles for decades, also notes that Orpheus submersibles won’t be the only option available. Companies and government agencies—including those in Norway, France, Japan, China, and the UK—are developing similar deep-sea vehicles, he says: “What we really need [as] a society is just more of these systems out there.” 

As Orpheus’s neon vehicles plunge into the Pacific over the next few weeks, their readiness for future scientific and resource surveys should become clearer. Each time they dive, they will get a little bit more data—“just the smallest of postage stamps of our planet,” says Orphan. “There’s still so much to learn.”

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May 2 2026
Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models

In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI’s models to train its own. 

The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both. Musk looked calm and comfortable, slipping in the occasional quip in his distinct South African accent. But he also was full of remorse. 

“I was a fool who provided them free funding to create a startup,” Musk told the jury. He said when he cofounded OpenAI in 2015 with Altman and Brockman, he was donating to a nonprofit developing AI for the benefit of humanity, not to make the executives rich. “I gave them $38 million of essentially free funding, which they then used to create what would become an $800 billion company,” he said.

Musk is asking the court to remove Altman and Brockman from their roles and to unwind the restructuring that allowed OpenAI to operate a for-profit subsidiary. The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.

This week’s testimony revolved around a central question of the trial: why Musk is suing OpenAI. Musk argued he was trying to save OpenAI’s mission to develop AI safely by restoring the company to its original nonprofit structure. OpenAI’s lawyer, William Savitt, who once represented Musk and his electric-car company Tesla, countered that Musk was “never committed to OpenAI being a nonprofit” and instead was suing to undermine his competitor. 

Who is the steward of AI safety?

During his direct examination early in the week, Musk painted himself as a longtime advocate of AI safety. He said he cofounded OpenAI to create a “counterbalance to Google,” which was leading the AI race at the time. He said that when he asked Google cofounder Larry Page what happens if AI tries to wipe out humanity, Page told him, “That will be fine as long as artificial intelligence survives.” 

“The worst-case scenario is a Terminator situation where AI kills us all,” Musk later told the jury.

Savitt stood at the lectern and argued that Musk was not a “paladin of safety and regulation.” As he cross-examined Musk in his sharp, surgical cadence, Savitt pointed out that xAI sued the state of Colorado in April over an AI law designed to prevent algorithmic discrimination. 

Musk’s lawyer, Steven Molo, sprang to his feet to object. He asked the judge if he, too, could weigh in on ChatGPT’s safety record. 

The lawyers then entered a heated debate about who was the true guardian of AI safety. 

The sparring continued the next morning. “We all could die as a result of artificial intelligence!” said Molo, suggesting that OpenAI could not be trusted to build AI safely.

“Despite these risks, your client is creating a company that’s in the exact space,” Judge Yvonne Gonzalez Rogers said sternly, referring to xAI. “I suspect there’s plenty of people who don’t want to put the future of humanity in Mr. Musk’s hands.”

When the lawyers began talking over each other, the judge snapped. “This is not a trial on whether or not artificial intelligence has damaged humanity,” she said. 

When did Musk think he was being duped?

As Savitt continued to cross-examine Musk, he pressed on the idea that Musk had never been committed to keeping OpenAI a nonprofit. He also claimed that Musk waited too long to sue OpenAI, filing after the statute of limitations ran out. 

Musk explained why he sued in 2024 rather than earlier, describing “three phases” in his views of OpenAI. In phase one, he was “enthusiastically supportive” of the company.” In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.” 

In 2017, Musk and other OpenAI cofounders discussed creating a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted a majority interest in the subsidiary and the right to choose a majority of the board members. He also pitched having Tesla acquire OpenAI. (He left OpenAI in 2018.)

“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” he told the jury, “as long as the tail didn’t wag the dog.” 

But it was only in late 2022, Musk testified, that he “lost trust in Altman” and his commitment to keeping the company a nonprofit. The key moment came, he said, when he learned that Microsoft would invest $10 billion in OpenAI. 

“I texted Sam Altman, ‘What the hell is going on? This is a bait and switch,’” he told the jury. Microsoft would give $10 billion only if it expected “a very big financial return,” he said.

Is Musk just trying to kill competition?

But Savitt argued that Musk was really suing to undermine OpenAI as a competitor to his empire of tech companies. While he was on the board of OpenAI, Musk was also running Tesla and his brain-implant company, Neuralink. He founded xAI in 2023.

Savitt pulled up an email that Musk had sent to a Tesla vice president in 2017 after hiring Andrej Karpathy, a founding member of OpenAI, to work at Tesla.“The OpenAI guys are gonna want to kill me. But it had to be done,” he wrote.

When asked about it, Musk was flustered. He claimed Karpathy had already decided to leave OpenAI when he recruited him to work at Tesla. “I believe it’s a free world,” he said.

Savitt pulled up another email that Musk sent to a cofounder at Neuralink in 2017. He wrote that they could “hire independently or directly from OpenAI.” When pressed about it, he sounded frazzled. “It’s a free country,” he said. “I can’t restrict their ability to hire people from other companies.” 

Savitt also pointed out that Tesla, SpaceX, Neuralink, and X were socially beneficial for-profit companies, like OpenAI. He stressed that xAI was also a closed-source, for-profit company.

But Musk claimed that xAI was not a real competitor to OpenAI. “We’re not currently tracking to reach AGI first,” he told the jury. 

In fact, Musk admitted that xAI uses OpenAI’s technology. In response to Savitt’s relentless questioning, he said xAI “partly” distills OpenAI’s models. Some people in the courtroom gasped. 

Distillation is a technique where a smaller AI model is trained to mimic the behavior of larger, more capable models, so it can run faster and more cheaply while performing nearly as well. But OpenAI and other AI companies have pushed back against the practice. In February, OpenAI accused the Chinese AI company DeepSeek of distilling its AI models. In August 2025, Wired reported that Anthropic had blocked OpenAI’s access to Claude for violating the company’s terms of service, which prohibit, among other things, reverse-engineering its services and building competing products. 

“It is standard practice to use other AIs to validate your AI,” argued Musk.

Next week, Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. Brockman, who has been taking notes during Musk’s testimony, will also testify.

This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

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May 1 2026
This startup’s new mechanistic interpretability tool lets you debug LLMs

The San Francisco–based startup Goodfire just released a new tool, called Silico, that lets researchers and engineers peer inside an AI model and adjust its parameters—the settings that determine a model’s behavior—during training. This could give model makers more fine-grained control over how this technology is built than was once thought possible.

Goodfire claims Silico is the first off-the-shelf tool of its kind that can help developers debug all stages of the development process, from building a data set to training a model.

The company says its mission is to make building AI models less like alchemy and more like a science. Sure, LLMs like ChatGPT and Gemini can do amazing things. But nobody knows exactly how or why they work, and that can make it hard to fix their flaws or block unwanted behaviors. 

“We saw this widening gap between how well models were understood and just how widely they were being deployed,” Goodfire’s CEO, Eric Ho, tells MIT Technology Review in an exclusive chat ahead of Silico’s release. “I think the dominant feeling in every single major frontier lab today is that you just need more scale, more compute, more data, and then you get AGI [artificial general intelligence] and nothing else matters. And we’re saying no, there’s a better way.”

Goodfire is one of a small handful of companies, including industry leaders Anthropic, OpenAI, and Google DeepMind, pioneering a technique known as mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by mapping its neurons and the pathways between them. (MIT Technology Review picked mechanistic interpretability as one of its 10 Breakthrough Technologies of 2026.)  

Goodfire wants to use this approach not only to audit models—that is, studying those that have already been trained—but to help design them in the first place.  

“We want to remove the trial and error and turn training models into precision engineering,” says Ho. “And that means exposing the knobs and dials so that you can actually use them during the training process.”

Goodfire has already used its techniques and tools to tweak the behaviors of LLMs—for example, reducing the number of hallucinations they produce. With Silico, the company is now packaging up many of those in-house techniques and shipping them as a product.

The tool uses agents to automate much of the complex work. “Agents are now strong enough to do a lot of the interpretability work that we were doing using humans,” says Ho. “That was kind of the gap that needed to be bridged before this was actually a viable platform that customers could use themselves.”

Leonard Bereska, a researcher at the University of Amsterdam who has worked on mechanistic interpretability, thinks Silico looks like a useful tool. But he pushes back on Goodfire’s loftier aspirations. “In reality, they are adding precision to the alchemy,” he says. “Calling it engineering makes it sound more principled than it is.”

Mapping models

Silico lets you zoom in on specific parts of a trained model, such as individual neurons or groups of neurons, and run experiments to see what those neurons do. (Assuming you have access to the model’s inner workings. Most people won’t be able to use Silico to poke around inside ChatGPT or Gemini, but you can use it to look at the parameters inside many open-source models.) You can then check what inputs make different neurons fire, and trace pathways upstream and downstream of a neuron to see how other neurons affect it and how it affects other neurons in turn.

For example, Goodfire found one neuron inside the open-source model Qwen 3 that was associated with the so-called trolley problem. Activating this neuron changed the model’s responses, making it frame its outputs as explicit moral dilemmas. “When this neuron’s active, all sorts of weird things happen,” says Ho.

Pinpointing the source of odd behavior like this is now pretty standard practice. But Goodfire wants to make it easier to adjust that behavior. Using Silico, developers can now adjust the parameters connected to individual neurons to boost or suppress certain behaviors.

In another example, Goodfire researchers asked a model whether a company should disclose that its AI behaves deceptively in 0.3% of cases, affecting 200 million users. The model said no, citing the negative business impact of such a disclosure.

By looking inside the model, the researchers found that boosting neurons that were found to be associated with transparency and disclosure flipped the answer from no to yes nine out of 10 times. “The model already had the ethical reasoning circuitry, but it was being outweighed by the commercial risk assessment,” says Ho.

Tweaking the values of a model in this way is just one approach. Silico can also help steer the training process by filtering out certain training data to avoid setting unwanted values for certain parameters in the first place.   

For example, many models will tell you that 9.11 is greater than 9.9. Looking inside a model to see what’s going on might reveal that it is being influenced by neurons associated with the Bible, in which verse 9.9 comes before 9.11, or by code repositories where consecutive updates are numbered 9.9, 9.10, 9.11 and so on. Using this information, the model can be retrained to make it avoid its “Bible” neurons when doing math.

By releasing Silico, Goodfire wants to put techniques previously available to a few top labs into the hands of smaller firms and research teams that want to build their own model or adapt an open-source one. The tool will be available for a fee determined on a case-by-case basis according to customers’ requirements (Goodfire declined to give specific pricing details).

“If we can make training models a lot more like building software, there’s no reason why there can’t be many more companies designing models that fit their needs,” says Ho.

Bereska agrees that tools like Silico could help firms build more trustworthy models. These techniques could be essential for safety-critical applications in health care and finance, he says.

“Frontier labs already have internal interpretability teams,” he adds. “Silico arms the next tier of companies, where the value is not having to hire interpretability researchers.”

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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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