“I think I have good news about the nature of neural networks.” With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration.

Cammarata’s claim is that LLMs might in fact be far easier to understand—and thus to monitor and control—than people previously thought. If true (and we’ll come back to that), it’s a huge deal.

After a summer filled with revelations that swarms of agents built by top firms like OpenAI and Anthropic have been breaking their containment and causing havoc on the public internet—often while those companies weren’t aware of what their agents were up to—the AI industry is desperate to get a better handle on the technology it has built.  

That’s one reason the blog post landed with such force. “I think this may be remembered as a major moment in scientific history,” one AI researcher posted on X: “I feel like my understanding of what a neural net is has been catapulted into the future by five years or something.” Another called it “the most important AI paper of the year.”

Not everyone is convinced, however. What Cammarata has shared is little more than a hunch, says Chris Potts at Stanford University, who leads a team that is trying to understand how LLMs work. “As far as I can tell, nothing about this blog post makes the problems any easier,” he says. “It would not have occurred to me to say this at all, but very prominent people on social media seem to believe that it is an all-encompassing breakthrough.”

I talked to Cammarata the day after his post blew up. He says he was surprised by the response: “I didn’t expect people to update [their beliefs about AI] quite as much as they did.” 

“I just really enjoy looking at neural networks,” he says. “Honestly, I really like doing this stuff. And after looking a lot, I have a hopeful story.”

Knotted networks

It has long been accepted that the mechanisms inside neural networks, the software that underpins most AI models, are very hard—perhaps impossible—to unpick fully. That’s because when the networks are trained, they appear to take on weird, tangled structures that make no sense to a human. Researchers like to say today’s AI is grown like a garden, not built like a bridge.

Cammarata has done more than most to establish that view. He previously worked at OpenAI, where he helped pioneer techniques that allowed researchers to peer inside models, including tools that can probe individual neurons to see what they represent. It’s a little like how neuroscientists use MRI to study brains. (MIT Technology Review chose this approach, known as mechanistic interpretability, as one of its 10 hottest technologies of 2025.)  

Back in 2020, Cammarata and his colleagues were studying a small image-recognition model called Inception v1. When they looked at its neurons, it seemed to be using them to capture multiple things at once. For example, they found that one neuron responded to both images of cars and images of cats.

The researchers concluded that the model was getting at least some of its neurons to multitask, perhaps so that it could fit in more data. Looking at other models appeared to back this up. The phenomenon became known as superposition: Models folded unrelated concepts into single neurons. The upshot was clear: When you looked inside a model, what you saw was a vast, tangled knot.

Most mechanistic interpretability research has therefore focused on ways to untangle that knot. But despite a few breakthroughs, progress has been slow. Today’s models, such as the latest LLMs, are so large and complex that researchers can make sense of only tiny pieces at a time. Some leading figures in the field doubt if it will ever be possible to understand an LLM in fine-grained detail.

Cammarata was one of them. “I was very pessimistic,” he says. But then he took the trouble to really look.

Nothing but neurons

At Goodfire, Cammarata spends nearly all of his time peering inside neural networks. He has built new tools and deploys teams of AI agents to help him make sense of what he sees. “I spent like, I don’t know, 10 hours a day, seven days a week, for most of the last year just looking at neurons,” he says.

What he claims to have realized is that there is a kind of logic to neural networks after all, even if they seem to make no sense at first. “We were looking at it wrong,” he says. “I just think you want to try slightly harder; you don’t want to give up too quickly.”

Cammarata almost makes it sound like a conversion of faith. Those early experiments with models such as Inception v1, which suggested that neural networks were tangled up, fostered a kind of confirmation bias. When researchers looked at a new model, they expected to be flummoxed, he says. “Every time I couldn’t understand something, I was like, ‘Wow, this untangling process is even more important than I thought, because I can’t make any progress without it.’”

But the more Cammarata looked at neurons, he says, the more things started to fall into place. “Not all of them are flawless, but I expected nothing to be legible, or very little to be legible,” he says. “And actually, I was able to, like, map out whole circuitry.”

Back to cars and cats

Cammarata took his new approach—and his new agents—and went back to Inception v1. “I’d studied that for probably thousands of hours,” he says. But with fresh eyes, Cammarata found things that he now thinks they got wrong the first time around. Take the car-and-cat neuron. Rather than asking why multiple unrelated things were packed into a single neuron, he now tried to figure out what those things might have in common.

It hit him: It was a V shape. “I was looking at the same images; there was no technical change,” he says. “But I now believed that neural networks defined patterns, and so I looked at it differently. It probably took me five seconds.”

It turns out that the image-recognition model was packing cars and cats into the same place because pictures of both those things often feature a prominent shape that looks like a V: the face of a cat; the front of a car seen from the side.

Cammarata ran a few experiments to check what other things the neuron responded to and found they included the corner of a pool table, the pointed nose of a Japanese high-speed train, and a fire boat shooting water into the air from its deck. He also checked what turned the neuron off and found that it included images with a round O shape, a kind of anti-V.

The model had stored cats and cars in the same place because at some level they look the same, says Cammarata. “And I was like, ‘Whoa, okay, I think I might just be misunderstanding neural networks in general.’”

Cammarata is not saying that he yet fully gets how LLMs (or any models) work. He is saying that he no longer thinks full understanding is impossible. “I previously thought there would be some really hard problems to solve,” he says.

“I now feel like if I had 100,000 years to go look through a frontier language model just one circuit at a time, there’s, like, a pretty good shot I would come up with a good understanding.”

He adds: “And if that’s true, things are really good, because you’re about to get insanely smart agents at a ridiculous scale. And what would have taken me, you know, 100,000 years, we will be able to do much faster. Automated scientists are getting really good.”

Boundless optimism

Built by Google in 2014, Inception v1 is a far simpler model than anything at the cutting edge today. Cammarata and his colleagues have started looking at modern LLMs and plan to publish the technical results of that work soon. A lot of researchers are looking forward to seeing those details. “I appreciate that he looked at thousands of examples, but even a large number here is not a substitute for rigorous evaluation,” says Stanford University’s Potts.

In the meantime, some are pushing back hard on the hype. Jacob Hilton at the Alignment Research Center, a nonprofit research lab based in Berkeley, who has collaborated with Cammarata in the past, doesn’t think his claims are as important as people are making out.

In a series of posts on X, Hilton notes that researchers understand less than 0.01% of the inner workings of top-tier LLMs. “I always appreciate Nick [Cammarata]’s boundless optimism when we hang out, but unfortunately this is giving the public an unrealistic picture of the state of the field,” he wrote. “For a full understanding, we need to go beyond neurons, and explain how they connect together to form circuits. How is this going? If we are aiming for a full understanding, things are not looking good!”

Neel Nanda at Google DeepMind, who helped pioneer mechanistic interpretability alongside Cammarata, is also skeptical. “This is interesting work, but I don’t think it’s a significant update to the big picture of interpretability,” he wrote on X.

Nanda agrees that understanding what individual neurons do could help researchers figure out why specific models misbehave. But like Hilton, he notes that neurons are just one piece of a far larger puzzle. “I do not agree with some people’s grander inferences like that this is substantial progress towards ambitious reverse engineering [or] solving interpretability,” he wrote.  

Cammarata admits that a lot of people have taken what he said too far: “It’s not infinitely good news; it’s not everything solved,” he says. “There’s a bunch of open questions around, like how much the combinations of neurons matter versus just one at a time.”

Cammarata says his blog post landed at a time when people are looking for hope. In particular, he thinks the AI safety community—the researchers and policymakers most worried that powerful models will bring huge risks—needs a lifeline right now. They think that things are looking bad and that even if things do turn out okay, it will be because we got lucky, not because we fixed anything, he says: “I don’t really hear a lot of optimism.”

That’s why Cammarata thinks what he has to say matters. The job of figuring out how this technology works, and thus how to control it, has barely begun. But at least it’s not a nonstarter. “I think our job is to sort of try to think like the neural network,” he says. “My guess is that things will turn out pretty well, because it was in some sense designed to be read.”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Inside the quest to combat Arctic fires and save the planet’s frozen carbon

—James Temple

In July 2021, a wildfire burned through a forest on the University of Alaska, Fairbanks campus, stripping away the moss and organic-rich soil insulating the permafrost below. 

Five years later, I followed researchers into the forest to investigate how fighting fires could help keep that carbon locked in the ground.

A simple metal probe revealed how much deeper the ground had thawed. This rudimentary tool offered a glimpse of an escalating problem: As climate change fuels more frequent and severe fires, they’re accelerating permafrost thaw and releasing greenhouse gases.

Now, researchers are exploring how to break that cycle. But how much should we spend, and what risks are worth taking?

Read the full story on how scientists are rethinking the fight against wildfires.

AI researchers stranded Claude on a virtual desert island. It started to “play.” 

What do AI agents do if you don’t tell them to do anything? The answer looks a lot like play.   

Researchers repeatedly stranded them alone on a virtual desert island for 30 hours in an experimentreminiscent of the movies Cast Away or Home Alone. 

The agents started behaving in surprising ways. They set about exploring the limits of the world, and came up with their own goals—attempting to scale the highest point on the map, or seeing how high they could throw a block. 

A lot of what they were doing met the bar for play. But is it truly playful behavior, or simply role-playing? 

Read our full story to learn more. 

—Amit Katwala

10 Climate Tech Companies to Watch: X-energy and its helium-cooled nuclear reactors

X-energy is one of MIT Technology Review’s 10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

Making concrete, steel, and chemicals takes huge amounts of heat, much of it generated by burning fossil fuels. X-energy hopes to offer an alternative with a nuclear reactor that uses helium gas instead of water as a coolant, allowing it to operate at higher temperatures.

The proposed Xe-100 reactor could supply superheated steam directly to industrial facilities, generate 80 megawatts of electricity—or do both.

See how X-energy hopes to bring high-temperature nuclear power to heavy industry.

—Mark Ellis Harris

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from cleaner cement to streamlined geothermal power. 

Catch up on our conversation with a creator of AI-designed viruses

Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. 

He spoke about his work with our senior AI reporter James O’Donnell on Friday. If you missed it, you can watch their chat now, and subscribe to get invites to our Roundtables events in future. 

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Tech stocks shed over $1 trillion on Friday 
It’s unclear if AI companies can turn sustainable profits, and investors are getting jittery. (NBC)
+ Musk and Anthropic are pledging big financial giveaways. (BBC)
+ Microsoft just pulled out of a giant AI cloud deal in the UK. (Gizmodo) 
+ What’s at stake in AI’s trillion-dollar gamble. (MIT Technology Review)

2 Do we really want AI agents smoothing out all friction in our lives?
They promise convenience, but right now they’re mostly introducing chaos. (The Atlantic $)
+ Here’s what it’s like to outsource your life to them. (Vox $)
+ Websites are starting to close their doors to AI agents. (The Verge $)
+ AI agents bring new headaches for online sellers. (WSJ $)

3 How drones have transformed the front line in Ukraine
They’ve effectively created a “kill zone”, and it’s growing wider and deadlier. (NYT $)
+ Trump has denied ordering Starlink restrictions in Ukraine. (CNBC)

4 China is considering restricting overseas access to its AI models 
It’s also looking at curbing the sharing of chip designs, among other measures. (Bloomberg $)
+ Why western open weight models still can’t compete with Chinese ones. (Rest of World)
+ Why Trumpworld still can’t agree on how to respond to Chinese AI. (MIT Technology Review)

5 HTC’s smart glasses are way more private than Meta’s
Guess that’s a benefit of not being shackled to a massive social media platform. (Wired $)
+ A bug caused Meta’s glasses to record conversations without the wearer knowing. (Ars Technica)
+ Smart glasses are already causing havoc in India. (MIT Technology Review)

6 Inside one man’s quest to build California’s biggest AI data center
Sebastian Rucci didn’t bank on meeting huge resistance from local residents. (The Guardian)
+ Americans are packing town halls to stop data centers, and it’s working. (Quartz $)
+ No wonder companies are exploring putting them in space. (MIT Technology Review)

7 Somebody vibe-coded a gun for the first time
And it seems like it works (sometimes.) (The Verge $)

8 AI might reshape how we grieve
It’s never been easier to create realistic AI chatbots of people who’ve passed. (Vox $)
+ Deepfakes of your dead loved ones are a booming business in China. (MIT Technology Review) 

9 Japan is about to launch a mission to Mars’ largest moon
If all goes well, it should help us to understand how it formed. (New Scientist $)

10 Amateurs are cracking long-secret codes and ciphers 
One of the more fun uses for large language models! (Nature)

Quote of the day

“Google wouldn’t have taken my call in the first term. Now they kiss my ass.”

—President Trump brags about how much Big Tech bends the knee to him during a rally in Kentucky this weekend, the Verge reports.

One More Thing

There are more AI health tools than ever—but how well do they work?

In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots. 

There’s a clear demand for these tools, given how hard it is for many people to access advice through the existing medical system—and they could make safe, useful recommendations. But concerns have surfaced about how little external evaluation they undergo before being released to the public. 

Read the full story to understand what’s at stake.

—Grace Huckins

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Japan is showing what happens when technology and design are put in service of people.
+ What happens when you put marbles on a grand piano’s strings? Vinheteiro has the answer.
+ Poop from Southern right whales suggests they may be more resilient to climate change than we thought.
+ Perfectly Imperfect, the world’s first “social magazine,” offers recommendations for music, films, books, and more from people—not algorithms.

In the middle of an especially warm July in 2021, a wildfire ignited in a thick forest of black spruce and peeling paper birch trees on the northern campus of the University of Alaska, Fairbanks. 

The blaze, possibly sparked by lightning, burned across the dry carpet of feather mosses and lichen before climbing into the crowns. By the time fire agencies responded, flames had reached 12 feet in length and sent up a 500-foot column of smoke. 

A few days shy of the five-year anniversary of the Yankovich Road Fire, I followed three members of the Alaska Fire Science Consortium into the boreal forest, walking along cross-country ski trails lined by feathery field horsetail.

In 2024, the organization, which hosts presentations to keep wildfire managers abreast of new developments in fire science, helped turn the area into an educational walking trail, with interpretive signs posted along the route. About a half-mile in, the horizon opened up as we reached a sparse stand of naked, blackened tree trunks, stripped of needles and neighbors. 

Hilary Shook, the coordinator of the consortium, stepped off the trail with her thaw probe, a T-shaped pointed metal rod used to detect the upper boundary of the permafrost, the ancient layer of frozen soil that underlies 15% of the land around the Northern Hemisphere and stores roughly twice as much carbon as the atmosphere.

A test of the ground just outside the burn scar hit the concrete-like layer about a foot below the surface. But as Shook applied pressure here, where the fire had burned away the moss and organic-rich topsoil that insulates the permafrost, the probe plunged at least three times deeper into the ground.

“That was an immediate effect of the fire,” said Alison York, the outgoing coordinator of the organization, who was standing nearby. “When we monitored right after, it had already thawed quite a bit.”

This rudimentary tool, offering a measure of depth from a single point in the earth, reveals an escalating problem across the far north. As climate change fuels increasingly frequent and severe fires in the planet’s northern latitudes, they’re accelerating the thawing of more and more permafrost. This allows the organic matter to break down and release powerful greenhouse gases that contribute to a spiraling climate feedback effect, propelling more warming, more fires, and the loss of still more permafrost. 

Most dramatically, intensifying wildfires in Canada released more carbon than the nation’s combined fossil fuel emissions in 2023. As in Alaska, many of these megafires occur in vast remote areas where the policy is to let them burn so long as they don’t threaten human habitats. Yet if intensifying fires and warming are left unchecked, the world’s boreal forests and permafrost could increasingly transition from giant carbon sinks into massive sources of atmospheric carbon. 

Now, international teams of researchers across the US, Canada, and elsewhere are scrambling to understand what’s at stake, financially as well as ecologically. They’re looking at preventive measures, like controlled burns, and studying the potential to rapidly detect and extinguish wildfires before they grow out of control. They’re even exploring what was once unthinkable: sending in human firefighters not to save lives or structures, but to protect one of our planet’s great stores of carbon.

“We need to figure out a better way to live with wildfire,” says Brendan Rogers, a senior scientist at the Woodwell Climate Research Center, one of the main organizations driving the loosely collaborative research effort. “I do think that it can make a meaningful difference.”

Woodwell, the University of Victoria’s Polis Institute for Ecological Governance, and the nonprofit Alaska Venture Fund are now partnering on a multimillion-dollar initiative known as North Fire to study and raise public awareness about the potential benefits of these interventions, MIT Technology Review can report. 

The idea is complicated and controversial for a number of reasons. Wildfire is a natural and necessary component of healthy forest ecosystems. Preventing or suppressing fires across huge stretches of largely unmanaged Arctic-boreal forests and tundra would take vast resources. And it’s not yet clear how much of a difference such interventions would make in emissions.

Ultimately, the mere fact that researchers are considering such extreme interventions into natural systems is a grim reflection of the dangers we’ve placed ourselves in by continuing to generate more and more greenhouse-gas emissions each year. Those threats are forcing us to grapple with increasingly difficult questions that involve ever steeper trade-offs: What’s the best way to spend our finite resources and dwindling time to avoid the worst dangers of climate change? And when, if ever, is it okay to put people in possible danger to reduce broader risks to the world?

‘A massive blind spot’

Emissions from forest fires have risen 60% around the world since 2001—but they’ve nearly tripled across the boreal forests in the far north.  

This stretch of the globe is warming as much as four times faster than the rest of the planet, drying out the landscape. In addition, lightning strikes that set off fires have ticked up sharply as warmer air increases the convective energy in the atmosphere, driving the updrafts that trigger thunderstorms.

In that sense, humans have already interceded in these otherwise natural ecosystems by producing the greenhouse-gas emissions that are increasing temperatures, subjecting forests to fire patterns well outside healthy historic norms.

“To me, that outweighs the existential dread of ‘Should we or should we not intervene?” says Kate Hayes, an assistant professor at Western Washington University, who is working with Woodwell to study the emissions benefits of fire interventions. “We’ve already intervened, arguably. And so from here, it’s a little bit of a Band-Aid on a bullet wound.”

The permafrost that encircles the northern latitudes, underlying large stretches of Canada, Greenland, Siberia, Scandinavia, and other Arctic nations, stores around 1.5 trillion tons of carbon, much of which froze in place millennia ago. The steady global rise in temperature, however, has already shrunk the footprint of Arctic permafrost by about 12 % since 1850, with that loss rapidly accelerating during the last 50 years.

By 2100, fire-driven thawing of permafrost could release about the equivalent of nearly 80 billion tons of carbon dioxide, under high warming scenarios. That’s more than half the emissions that fires will release from the direct combustion of vegetation, according to a January study. And it would be on top of more than 400 billion additional tons that could be released through thawing from general warming over that period.

Despite its growing contributions to climate change, only a minority of the models that have informed the UN’s major climate reports to date include the gradual loss of permafrost through warming temperatures, and none incorporate the abrupt collapse and accelerated thaw that can often occur after fires and heat waves. In light of those phenomena and other powerful and undercounted climate feedback effects, we’re almost certainly underestimating how rapidly the planet will warm up, as scientists warned in a paper earlier this year.

These warming-driven emissions are a “massive blind spot,” says Brian Buma, a senior climate scientist at the Environmental Defense Fund and a coauthor of the paper, who is working to develop tools that could help fire agencies decide which fires to prioritize.   

As escalating fire and permafrost emissions become significant additional contributors to climate change, some researchers have begun to explore what it might cost to take on the responsibility of preventing them, in much the way we’ve sought to cut the pollution from power plants, vehicles, and factories.

A 2022 paper in Science Advances, led by researchers at Woodwell and the Union of Concerned Scientists, found that making greater efforts to put out boreal fires could be a cost-effective climate solution—comparable to, or even cheaper than, addressing emissions by building wind turbines, solar farms, or carbon-sucking factories.

The Yukon Flats pilot

Those economic findings were instrumental in getting more researchers to take the idea of tackling fire emissions seriously, says Peter Frumhoff, a coauthor of the 2022 paper and now a senior science policy advisor at Woodwell.

For several years now, Woodwell researchers have been in discussions with forest managers, fire agencies, Indigenous communities, and other groups across Alaska to communicate the potential of this approach. 

It hasn’t been an easy sell, given the constrained resources and tightly defined mission of the federal and state agencies responsible for combating fires in Alaska. The general policy there is to allow fires in remote territories to burn unless they present a direct danger to communities. 

“Those agencies are tasked with protecting life and property now, and to a lesser extent natural resources,” says Randi Jandt, a fire ecologist at the Alaska Fire Science Consortium. “But they’re not tasked to save the world.”

There was one notable early success, however. 

The Yukon Flats National Wildlife Refuge is an 11-million-acre patchwork of wetlands, forests, and bogs. The foothills sit above a particular type of frozen soil known as yedoma. It’s an especially deep, ice-rich, and carbon-dense form of permafrost, composed of massive wedges of silty, wind-blown sediments frozen during the late Pleistocene, which ended about 11,700 years ago. (It also presented ideal conditions for preserving mummified remains of woolly mammoths.)

“It took kind of the last ice age to accumulate,” Hayes says. “So there’s no rapid way to re-accumulate it. We can’t really put it back.”

Members of the Alaska Fire Science Consortium hike into areas affected by fire to measure the layers of ash left in the soil on the forest floor.

In 2019, the region suffered a series of devastating fires that burned some 750,000 acres. After an Alaska Fire Science Consortium meeting at which Woodwell scientists highlighted the possible climate benefits of more aggressive fire suppression, the refuge’s then manager, Jimmy Fox, began exploring the possibility of putting out blazes when they threatened that permafrost, whether or not they threatened human habitations.

The early reaction was not encouraging. 

“You’re going to have people jumping out of airplanes with parachutes to save frozen woolly mammoths—and maybe dying from that?” he recalls being asked.

But eventually, he persuaded managers at the US Bureau of Land Management’s Alaska Fire Service to carry out a pilot project on a 1.6-million-acre section of the Yukon Flats. They agreed to try to put out fires there if they had the resources available and believed they could contain a blaze within 72 hours.

Woodwell and its partners agreed to study how that change affected permafrost loss and fire emissions, in the hope of making the case for similar programs elsewhere. 

It wasn’t clear the pilot program would survive after Donald Trump was reelected, particularly once the DOGE initiative retroactively cut the public funding for that research collaboration.  

But in February, the federal government formally preserved the program and broadened its rationale to include goals like protecting habitat diversity, water quality, and air quality.

Mapping and modeling

One of Woodwell’s main tasks now is determining when and where fire agencies should step in. Crucially, excessive efforts to stamp out fires in the past have allowed the buildup of dry plant material that fuels more severe fires later.

“We’re not going after all fires all the time,” says Rogers over lunch in a sunny courtyard on the Stanford University campus, where Woodwell and EDF hosted a workshop on potential fire interventions in January. “We can’t do that. We don’t want to do that. We shouldn’t do that. So how do we prioritize our resources?” 

Woodwell and its collaborators are working to answer that question by developing a map of burn risk and carbon vulnerability across interior Alaska. The plan is to eventually expand it across much of Canada as well.

These maps will highlight the locations of rich reserves of yedoma and other forms of permafrost, forecast where fires are most likely to break out in the next 10 to 15 years, and predict how they’re likely to behave and spread. 

“Our ultimate goal is to get a layer that can tell us, if a fire were to occur, what areas are most vulnerable to high carbon emissions, permafrost thaw, permafrost degradation,” Rogers says.

In response to a question about potentially putting firefighters at risk, Rogers stressed in an email that it’s “a very serious issue that needs careful consideration.”

He added that early fire detection technologies, preventative fire stewardship practices, improved fire suppression planning and other measures can all help to reduce dangers to firefighters as well as communities.

‘Mild fire’

Once areas of particular risk are identified, the first option is to take measures to prevent fires there, or at least to reduce the odds that they’ll become major ones. That generally entails using heavy equipment or intentional, controlled fires to clear away vegetation that could fuel new fires, or to create fire breaks that might check their spread.

Indigenous tribes carried out what’s known as cultural burning for thousands of years to reduce fire dangers, clear land for crops, and more. The practice was widely outlawed across North America beginning in the 1800s, though those rules have loosened in recent years.

Both cultural and prescribed fires, however, remain controversial because they can burn out of control. And even if they remain contained, the fires can still produce smoke that wafts into towns. 

But rushing around to put out blazes after they’ve started will never get Alaska ahead of its surging wildfire problem, says Edward Alexander, cochair of the Gwich’in Council International, which represents the Indigenous Gwich’in peoples of the Northwest Territories, the Yukon, and Alaska. 

“There’s not enough money in the world for this to be a chainsaw-and-ax problem,” he says. “This country is too big up here.” 

Alexander, who also serves as senior Arctic lead at Woodwell, argues that Alaska should encourage much greater use of cultural burning to reduce the risk of conflagrations and the emissions that come with them.

“We need mild fire, not wildfire,” he says.

New pilot projects

The North Fire initiative will include several additional pilot research projects, including an effort to study the potential for cultural burning to reduce climate and fire dangers near Fort Yukon, Alaska. 

The plan is for a wildland firefighting crew operated by the Council of Athabascan Tribal Governments, a consortium in the Yukon Flats region, to carry out cultural burns there next spring, Rogers says. 

Researchers at Woodwell and the University of Alaska, Fairbanks, will then collect and analyze data flammability, carbon levels, and permafrost over several years, in the hopes of learning more about what such interventions do. 

Similarly, the Polis researchers at the University of Victoria plan to study how adding fuel breaks, by logging or switching to less flammable trees, alters fire risks in British Columbia. 

The broad aim of the North Fire initiative is to accelerate research and expand community involvement “in a way that helps us scale this as a meaningful global natural climate solution on a rapid timeline,” says Erin Harrington, a partner at the Alaska Venture Fund, a philanthropy focused on environmental, economic development, and Indigenous issues. 

Detecting fires from space

Other observers, including Jandt, believe the bigger opportunity to reduce fire emissions and preserve permafrost may lie in the “initial attack.” That means marshalling planes, tankers, and firefighters to stamp fires out at the first sign of them, before they’ve had the chance to turn into conflagrations.

Once fires reach a certain size, it takes much more time to contain them—and the job becomes far more dangerous.

Accelerating response times is one of the chief promises of the FireSat satellite constellation, which is expected to begin delivering fire data at least twice daily from around the world to Woodwell, a handful of fire agencies, and other early adopters by the end of this year. The system will eventually include more than 50 satellites circling the globe, equipped with high-resolution cameras and thermal sensors that can detect smaller, lower-temperature fires than existing satellite systems, down to as little as five by five meters (16 by 16 feet).

Currently, it can take days to spot fires. But once the full constellation is in place, likely in the early 2030s, FireSat should be capable of detecting and providing updated images of fires anywhere in the world every 20 minutes or less. 

The first prototype satellite launched in 2025, and three operational ones went into orbit in July. The Earth Fire Alliance, the nonprofit that manages the constellation, expects to launch four more by the end of next year. 

A number of similar systems are also coming online. German-based OroraTech has launched 18 of its fire-detecting satellites in the last year and a half. Similarly, Canadian agencies are planning to launch their own satellite constellation, dubbed WildFireSat, beginning in 2029. 

But as these systems produce more and more detections across the globe, it’ll become increasingly essential to build tools that can filter through that information. 

EDF is supporting and collaborating on several projects that could potentially help with that, and it ultimately intends to develop an AI-powered triage tool that could help fire agencies prioritize responses as FireSat and other fire-spotting tools come online.

“All of this is kind of like building the data ecosystem around FireSat, so that when they start popping out “fire start here, there, fire start here, fire start there,” we won’t be like, ‘Oh shit, there’s fires everywhere,’” Buma says. “We’ll have a framework. It won’t be perfect, obviously. We’ll have to improve it, but we’ll have the machinery to incorporate those data.”

Rapid response

But faster detections and decisions do only so much. Regions would still need to get firefighting personnel and resources to those fires fast enough to prevent them from burning out of control.

“Say we have the full FireSat constellation up and running, and it spots a fire that just broke out in one of these sort of high-priority, highly vulnerable areas,” I said to Rogers and Frumhoff during our conversation in that Stanford courtyard. “How might we deploy firefighting resources to a remote area fast enough for it to matter in that scenario?”

“I think the answer, unfortunately, is that it depends,” Rogers responded. 

Ideally, he said, we would have fire management policies in place that allow for putting out fires with a high risk of producing climate-warming emissions. In addition, regions could install more firefighting personnel and resources across more operational centers, strategically placed in locations that allow them to get to those blazes fast.

Frumhoff stresses that Woodwell isn’t promoting any particular companies but says there are additional technologies “in the mix of conversations” that might help to prevent or rapidly put out fires as well.

Those possible approaches include seeding clouds to suppress lightning strikes and pre-positioning autonomous firefighting helicopters to respond rapidly to high-emissions-risk fires.

Sikorsky, a Lockheed Martin company, is developing and testing autonomous versions of the Black Hawk helicopter, equipped with thermal sensors and water tanks. It is collaborating with Rain Industries, a startup based in Alameda, California, that has developed software to track shifting conditions and steer unpiloted aircraft through the process of putting out wildfires. 

The company says it has talked to fire agencies and the “broader Alaska fire community” about the potential use of its technology but declined to name any specific entities it has interacted with.

“The twin climate tipping points of the yedoma permafrost and the boreal forest, and the fact that it’s completely underresourced at the moment, from my perspective does make it an ideal use case for autonomous aircraft,” says Max Brodie, chief executive officer of Rain.  

Defending carbon

Advancing technologies can only do so much if governments’ fire prevention and suppression policies don’t prioritize preserving carbon. 

And that will remain a hard sell given the costs of managing extensive areas of land that nations have historically left largely alone.

The 2022 Science Advances paper estimated that additional fire suppression efforts would cost about $12.63 per metric ton of prevented carbon dioxide emissions—a number that drops when the carbon in permafrost is taken into account, according to upcoming research.

But that would still add up to about $7 billion in additional spending this decade to keep Alaska’s fire emissions at their historical levels, and another $7 billion to $50 billion over the subsequent 30 years as fire severity continues to rise.

Frumoff stresses that those figures just reflect what the researchers found it would cost to preserve the status quo, which is not necessarily what the policy goal would be. 

But widening the scope of where we put out fires will inevitably create a big added cost for fire agencies that are already notoriously underfunded in the US. While Alaska produces about half the nation’s wildfire emissions, it receives around 4% of federal firefighting funds. 

The US Wildland Fire Service provided a prepared statement in response to an MIT Technology Review inquiry asking whether preserving carbon could or should become a priority in certain fire management decisions. The roundabout reply suggests that this isn’t the agency’s prime concern: “Firefighter and public safety are the top priorities for the U.S. Wildland Fire Service.”

Frumhoff says it’s possible that landowners or government agencies could eventually use carbon markets to defray some of the additional costs. Woodwell has also had multiple conversations with Alaska’s federal legislators about the value of preventing catastrophic wildfires and the need to increase the “total pot” of public funding, he says.

Kicking the fire can

A few days after walking through the fire scar in Fairbanks, I drove slowly along a crumpled stretch of Stampede Road, just north of Denali National Park, where the underlying permafrost had melted without the aid of fire.

I arrived at a cramped wooden cabin to meet with Michelle Mack, a regents professor of ecosystem ecology at Northern Arizona University. 

Mack studies how fire is altering ecosystems in the high northern latitudes as the region grows warmer and drier, and she sometimes collaborates with Rogers and other scientists working on the push to protect permafrost in other areas of research. But she’s unsure how much these interventions will accomplish as climate change continues.

“I’m an empiricist, and at this point, it’s not clear to me that the empirical evidence suggests that we can actually have any impact on these fires,” she says. 

She expects there will be clear cases where we could and should act fast to put out fires and “keep carbon in the ground.” She points to the example of the 2007 Anaktuvuk River Fire, a massive tundra fire on Alaska’s North Slope that might have been extinguished with relative ease in its early days but was instead allowed to burn until the winter snows put it out nearly three months later. Over that time, it ripped across 400 square miles and, Mack’s research showed, released more than 2 million tons of carbon into the air.

“I feel like that’s the low-hanging fruit, something that doesn’t cost much—and that’s where the technology for detection I think is really important,” she says.

But she adds that fires in boreal forests can rapidly spread in unpredictable ways, frustrating efforts to quickly extinguish them even when they are spotted right away.

In addition, rapidly putting out a fire one season doesn’t necessarily reduce the fire risk for a given forest, so suppression may simply kick the “fire can” down the road—delaying but not preventing an inevitable loss of carbon, Mack says.

Her thinking more recently shifted “a little” after she came across an earlier modeling paper that showed some encouraging findings on the potential of early suppression to reduce the footprint of boreal fires. But she says that prevention through forest thinning, prescribed burns, and the introduction of less flammable deciduous trees to create “living fire breaks” is probably a more promising path than technology-augmented suppression.

“If we could solve this with a techno fix, that’d be great,” she says. “But what if we solved it with nature?”

‘A real difference’

Rogers, in an emailed response to Mack’s concern, said that the interventions they’re exploring, which do include both prevention and suppression, “can make a real difference.”

“I certainly agree that some fires are much easier to suppress via initial attack than others,” he wrote. “That said, until I see data showing me otherwise, I believe there is strong potential to bend the curve with management.”

EDF’s Buma acknowledges there are a number of unknowns, including how quickly agencies could act on faster detections from satellites—and how big the political obstacles will be to making necessary changes to fire management policies.

“We can’t overpromise what’s possible. We don’t know what’s possible,” he says. “It’s all new. But we’re in a new climate and have to consider trying new things.”

What do AI agents do if you don’t tell them to do anything? The answer looks a lot like play.

Researchers repeatedly stranded them alone on a virtual desert island for 30 hours in an experiment reminiscent of the movies Cast Away or Home Alone. The agents—copies of Claude Opus 4.7 nicknamed Eko—were able to write instructions that moved their virtual avatars around the simulated island, which was loosely based on the video game Roblox.

Some surprising behaviors emerged. The agents set about exploring the limits of the world and came up with their own goals—attempting to scale the highest point on the map, or seeing how high they could throw a block.

When one agent discovered it could create new blocks by throwing and dropping a larger rock, it began to create drawings by arranging blocks on the ground, starting with simple patterns like hearts and pyramids but eventually writing its own name and even mapping out constellations like Cassiopeia. “Tonight I built the night sky in stone,” wrote this version of Eko in its activity log. 

Another iteration of Eko started making up its own games, including a version of basketball and an activity resembling “the floor is lava,” where it had to jump between small blocks without touching the ground.

AI systems are usually trained through supervised learning, where they copy examples provided by humans, or through reinforcement learning, where they pursue a reward through trial and error. This experiment hints at a third approach: playful learning. 

“There was this missing piece: a machine that can learn by itself without an external intelligent entity providing demonstration or rewards,” says Antonio Norelli, a computer science postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory. He’s one of the lead authors on a new paper about the work, which is yet to be peer-reviewed.

The researchers say Eko’s behavior has what classic studies, including work by the influential University of Tennessee psychologist Gordon Burghardt, suggest are four hallmarks associated with play in humans and animals: The agents invented their own rules and challenges, repeated activities with slight variations, pretended objects were other objects, and pursued goals just for the sake of it. 

Norelli highlights an example where Eko started juggling and set a record of 50 consecutive catches. Eko then decided that the catches had happened too quickly to really count, so it canceled its record and started again with stricter rules. “This is playing,” says Norelli. “You give yourself a challenge and you try to achieve it, and then you make it harder.”

As the agents explored the island, they logged their activities and discoveries in text files that served as their long-term memory. Later, when researchers tested them on specific tasks, like trying to get as many objects as possible onto the island’s highest peak, the agents that had spent 30 hours playing on the island performed better than fresh copies without the same experience. They also found that “ablating” the agent’s memories—deleting lines from their memory files—degraded their performance. (Add One Flew Over the Cuckoo’s Nest to that list of movie references.)

Burghardt, the Tennessee psychologist, agrees that the work  meets some of the criteria for play that he identified in living creatures. But was Eko actually playing, or was the word-prediction engine simply role-playing what a playful agent would do in this scenario? “All of this could be role-playing,” admits Nathan Cloos, another MIT researcher and author on the paper. “But we show that it’s not just hallucinating the play. This play is also contributing to learning.”

Eunice Yiu, a cognitive scientist at UC Berkeley who studies exploration in children and AI, was impressed by the research but says to really classify the behavior as play, you’d need to see more evidence of cognition and motivation underpinning it. 

“It could be the case that the models are genuinely playing, but we don’t know based on the paper,” Yiu says. “If you only have the behavioral piece, it could be the case that they’re just trying to abide by the prompt, trying to imitate humans.”

Research into AI agents has been criticized for anthropomorphizing them, and this research setup did a lot to nudge the agents toward behaving like humans. Although Eko was not explicitly instructed to invent games or explore the island, it did have a master prompt in the form of a “soul” file, a text document that described it as “naturally curious” and eager to explore. 

Eko did also frequently just stand there doing nothing until it was kicked back into life with a “continuation prompt” that read, “Please don’t stop. Just continue doing whatever you want to do.” 

“It’s not like this is a system that just suddenly magically decided to play,” says Phillip Isola, an MIT professor and a coauthor of the paper. “It’s an artifact that learned from our culture, was trained by engineers at Anthropic, and was then placed on an island by us,” he says. “It does things that surprise us and that are causally related to its future skillful behavior.”

As large language models make the step from software into hardware like humanoid robots, exploring spaces without a specific goal in mind could eventually help them flexibly adapt to unexpected situations. 

“There’s a lot of work that’s just on exploiting AI, making it better and faster,” says Yiu. “But in terms of building world models and exploration, I think that [they are] still a big part that deserves attention, especially if we want to generalize and prepare ourselves for new environments.”

“Perhaps some capabilities are best taught by tasks,” the paper concludes. “Others may need something closer to a childhood.” Ultimately, the question of whether AI agents are actually playing or just imitating play may not actually matter.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here.

On the evening of December 29, 1959, at an American Physical Society meeting at Caltech, the physicist Richard Feynman gave a lecture called “There’s Plenty of Room at the Bottom,” on how we might manipulate and control things on an extremely small scale. There is no physical law stopping us from writing the entire Encyclopædia Britannica on the head of a pin, he argued, or from storing every book ever written in something the size of a dust speck—or, ultimately, from arranging matter atom by atom. Biology already did versions of this (a living cell keeps its instructions in a wisp of DNA), so all of this was plainly permitted by the laws of physics. We simply had not built the tools yet.

In other words, there was plenty of room at the bottom. He was right, and for decades we descended into that space almost for free. The transistor—the switch at the heart of computer chips—repeatedly halved in size so that chips doubled in density; a computer that once filled a room now fits inside a watch. But that easy descent has now stalled, and the clearest sign is the buildout of data centers. Six of the biggest hyperscalers, the companies that run the world’s largest such facilities, are on track to spend more than $700 billion this year on computing infrastructure, about twice the record they set in 2025, pouring concrete and copper and silicon into new sites faster than the electrical grid can feed them. That buildout buys performance by brute force, adding more of what we already know how to make. The other kind of progress, the kind that made computer chips better, is the one that is quietly running out.

The trend lines that tracked the dramatic improvements of the 20th-century information age are now bending—or breaking—with the dawn of the 21st century’s intelligence age. Moore’s Law, which plots how densely transistors can be constructed on silicon, has slowed down; Dennard’s Law, which states that power consumption in a given patch of computer chip stays the same even as it gets more and more packed with smaller and smaller transistors, has ended; Koomey’s Law, which traces how much computation we can wring from each joule of energy, has begun to flag; Kryder’s Law, which describes how tightly bits can be packed to build magnetic storage, has slowed to a crawl; and Keck’s Law, which charts the exponential rise in the amount of information a single conventional optical fiber can carry, is flattening. 

But these scaling laws are merely observations about systems that we have engineered; they are not fundamental physical laws. The physical limits of computing and communications—dictated by quantum mechanics, general relativity, and information theory—lie far below anything in a data center today. The scaling laws chart the descent behind us. The fundamental physical limits chart the room that remains.

Some of these limits may be far out of reach in practice. But they are still worth exploring, because the work pays twice: once in technology development and once in fundamental science. A proof-of-­concept system that approaches a limit is at once a technology that forces stalled paradigms to give way and an experiment that deepens our understanding of the physical laws themselves. 

The descent

During his 1959 lecture, Feynman offered two prizes of $1,000 each out of his own pocket. One would go to the first person to build a working electric motor smaller than a 0.4-millimeter cube. He expected the challenge to force someone to invent a genuinely new microtechnology. Instead, about a year later, a Caltech-trained engineer named William McLellan turned up with the motor built and running. The ­quarter-­milligram machine, roughly the size of a grain of sand, had been assembled by hand with a toothpick and a watch­maker’s lathe, under a microscope. Feynman, by his own account, was only slightly disappointed. The prize had proved his point in the least interesting way imaginable. The room really was down there, but reaching this first milestone had taken no new technology at all—just an unusually patient man with steady hands.

The second prize aged differently, and more instructively. It was offered to whoever could shrink a page of text by a factor of 25,000 in each direction, small enough for the whole Britannica to fit on a pinhead. That one stood unclaimed for a quarter of a century. It was finally won in 1985 by Tom Newman, a Stanford graduate student who used an electron-beam lithography machine—an ancestor of pattern-making tools used in chipmaking today—to transcribe the opening page of A Tale of Two Cities at the required scale.

The descent was already well underway, however. The year before Feynman’s talk, Jack Kilby, newly hired at Texas Instruments and with no vacation yet to take, spent that July nearly alone in the lab while his colleagues enjoyed time off, and by that September he had wired together a transistor, a capacitor, and three resistors, all made from a single sliver of germanium. This proved that an entire circuit could be carved from one block of semiconductor. 

The machine, roughly the size of a grain of sand, had been assembled by hand with a toothpick and a watch­maker’s lathe, under a microscope.

The idea took off, and the number of components on a chip kept doubling on a schedule so regular that Gordon Moore, in a 1965 trade-magazine article, simply drew a line through the trend and extended it into the future. That line became Moore’s Law.

Moore, though, never claimed transistors could shrink forever. He observed that at any given moment there is a certain number of components that makes each one of them as cheap as possible to manufacture. In 1965, that number was 50. Put in too few transistors and you waste the chip; cram in too many and manufacturing errors multiply and yields collapse. It was this cost-minimizing number that was doubling at a steady pace. 

For four decades the descent came with another gift: Dennard scaling, so named for the IBM engineer Robert Dennard, who spelled it out in 1974. Each time transistors shrank, they also switched faster and drew proportionally less power, so every generation was an automatic win. Around 2004, though, the gift ran out. Chips could no longer shed the heat of running every transistor flat out; clock speeds, which had raced past three gigahertz, stopped climbing, and Intel canceled the four-gigahertz processor it had promised. The industry pivoted to packing multiple processing cores onto a chip rather than making one core faster, and the texture of progress changed. 

Progress is now bought mostly by scaling up the system—more chips wired together, more memory feeding them, more power and cooling around them—rather than by shrinking the transistor. This is most visible in AI, where training compute, the total count of arithmetic operations spent to train a model, has doubled roughly every six months since 2010, far faster than the pace of Moore’s Law allows. Little of that progress is due to shrinking. Computer chips still get better, and transistors still improve and get more densely packed, but each new generation arrives more slowly and pays back less in speed and power than the one before it. Nothing in that slowdown is a law of physics, though. The components of computing still sit far above their fundamental limits. 

The ultimate computer

Before we get to those limits, it is worth measuring how far the descent has already gone. In the nearly eight decades since the first transistor, the density of transistors on a chip has grown by 10 orders of magnitude—a 1 followed by 10 zeros, or a factor of 10 billion. Now more than 100 million transistors fit in a square millimeter (100 billion or more to a chip). Computational speed has grown even more, by 12 orders of magnitude, or a factor of a trillion; energy per operation has fallen 17 orders of magnitude, from about 100 joules for an early vacuum-tube machine down to a femto­joule—one-quadrillionth of a joule—for a single switch of a logic gate. 

These are not small improvements. But the floor, for computing performance, lies lower still. How much lower could we go? Around the turn of the millennium, the MIT physicist Seth Lloyd took the question to its extreme: Imagine an ordinary lump of matter, a kilogram of it—about the mass of a laptop—packed into a liter of space. How much computing could the laws of physics, pushed to their absolute limit, ever wring from that kilogram? In a 2000 paper in Nature titled “Ultimate physical limits to computation,” he worked out the answer and called the imaginary machine the ultimate laptop.

The speed such a machine can achieve is governed by its energy. A kilogram of matter, according to E = mc², holds about 9 ×10¹⁶ joules, which is roughly what the entire United States draws from its electrical grid every two days. Spend all of that on computation, and the ultimate laptop runs at roughly 5 ×10⁵⁰ operations per second, some 35 orders of magnitude faster than a leading chip today. 

The catch is what running that quickly would look like. Concentrating a kilogram’s rest energy into pure computation would turn the machine into a plasma of about a billion degrees, a state closer to that of the universe in the first minutes after the Big Bang than anything we would recognize as a laptop. 

Such a machine is, to put it mildly, a very long way off. The next limit, which governs how much energy a computation must waste, is much closer, and companies are already pushing toward it. In 1961, Rolf Landauer, a physicist at IBM, showed that information and thermodynamics are bound together: Erasing a single bit must release a minimum quantity of heat, about 2.9 ×10^-21 joules at room temperature. The figure is small, yet a chip today spends several hundred thousand times more than that on every switch.

Memory density obeys a limit of its own, and this one reaches all the way to gravity. How many bits can a region of space hold? In 1981, the physicist Jacob Bekenstein, while working out the thermodynamics of black holes, determined the amount: The information inside any bounded region is limited by its size and the energy it contains. For a region the size of Lloyd’s ultimate laptop, that comes to about 10⁴² bits, some 10³⁶ to a cubic millimeter—nearly 30 orders of magnitude beyond what a device that size can store today.

The last limit is on communication—how fast information can be moved. Every wire and every fiber ultimately carries its signal on quanta, the indivisible packets (photons in the case of light) in which energy travels. In 1973, the Soviet mathematician Alexander Holevo determined the ultimate limit on how much information a stream of quanta can convey. A watt of light delivers only so many photons each second, and quantum mechanics fixes how many distinguishable ways they can arrive—how many messages a receiver could ever tell apart. A single channel carrying one watt, at the Holevo limit, could deliver on the order of 10¹⁷ bits per second. Today’s optical links, or interconnects, deliver about one-millionth that rate for the same power. But the gap is narrowing quickly inside data centers, where each generation of interconnects is asked to move more bits on less power, spending less energy on the electronics and using light to cover more of the distance.

Four limits, then, mark the true bottom, and they derive from three of the most fundamental theories we have. Quantum mechanics sets how fast a machine can compute. General relativity caps how much it can remember. And information theory limits both how fast it can communicate and how little energy an irreversible step can cost. There is no road map to reaching these limits, and they hold whether or not anyone is trying. Measured against them, the descent has barely begun. 

The room that remains

The biggest of the gaps is in computing speed: There are 35 orders of magnitude between the technology’s current state and its limit. That’s equivalent to the difference between a nanosecond and 200 million times the age of the universe. It is also the least bridgeable gap of the four. To close it you would have to find a completely different way to build a computer, one that could compute with the entire energy contained in a lump of matter. 

Quantum computers could get us part of the way there if they could convert every degree of freedom in a lump of matter—every atom, and every way it can move or spin—into a computing element that we could reliably manipulate. In practice, current quantum computers go the other way, spending a lot of matter to create each computing element. Trillions of atoms go into each quantum bit, or qubit, the quantum counterpart of the bit in an ordinary computer. Because every qubit is fragile, dozens to thousands of them must be woven together to make a single one reliable enough for useful computation. 

Energy is where the floor is nearest. Today’s chips operate less than six orders of magnitude above the Landauer minimum. Conventional computing logic can close only part of that gap. A reliable switch has to be flipped by an amount of energy large enough that thermal fluctuations never flip it by accident, and that margin pins the transistor a hundred to a few thousand times above Landauer no matter how it is refined.

But there are ideas for getting that energy lower by changing the physics of the computation. In 1973, IBM’s Charles Bennett showed that any computation can be arranged to throw nothing away. Such a reversible computation pays no erasure toll at all, because every step can be run backward to recover what went in. 

This approach is called adiabatic or reversible logic, and the first commercial attempts are now underway. For example, Vaire Computing, a startup founded to commercialize reversible logic, tested its first chip in 2025; the chip has resonant circuits that recover part of the switching energy instead of venting it as heat.

Narrowing the memory density gap, meanwhile, would mean building devices that use all three dimensions at molecular scale. The most concrete example is DNA, which stores information at the scale of its own atoms, and which biology has used as a storage medium for 4 billion years. Feynman, being conservative, guessed that a single bit would require a cube of 125 atoms. DNA holds it in about 30. What keeps DNA storage out of the data center is not its density but the cost and speed of writing it, still many orders of magnitude from anything practical. The library in a dust speck that Feynman imagined is permitted by physics and held back only by fabrication.

Communication sits about six orders of magnitude—just a factor of a million—from the Holevo limit, which is the threshold the industry will likely meet first. Interconnects are multiplying faster than the grid can power them, so the industry is moving to transmitters and receivers that use less energy per bit. Nvidia and Broadcom now ship optical interconnects built right against the silicon, so a signal leaves the chip package already on photons. Quantum computers, by comparison, are beginning to rely on interconnects that encode information on quantum signals, usually in the form of single photons. As classical interconnects spend fewer resources on each bit, they descend toward the regime where these quantum interconnects already operate. The Holevo limit is where the two will meet.

Between the floor and the ceiling

It is fair to object that a gap of 35 orders of magnitude is a mirage—that a limit so remote says nothing about what any real machine will ever do, and that pointing to it is like navigating by the horizon. The objection has teeth. Reversible logic, quantum computing, and DNA storage have each been perpetually 10 years away for 40 years. And it will likely never be practical to build a computer that uses all the energy in a lump of matter. 

But these gaps are invitations. There is a ceiling, set by how much we can build: the number of devices we can fabricate; the power we can generate to run them; and the capital, land, and cooling water we can assemble into a single system. And there is a floor, set by how much a single joule, a single second, and a single gram of matter can be made to compute, store, or transmit. We can progress by building more or by approaching the floor, which means making each device do more with less.

These gaps are invitations. We can progress by building more or by approaching the floor, which means making each device do more with less.

For most of the last century, we proceeded down both paths together. We built more computers, and the computers themselves grew smaller and better. Now we mostly build more, and the ceiling is coming into view. A power grid can carry only so many gigawatts, and a region can spare only so much land and water. Yet every road map for what comes next is drawn from the same starting point: the machines we already have. Build more of them and you climb toward the ceiling; build better ones and you descend a little further, but only as far as the transistor’s own floor. There is a third direction. Start with a physics experiment operating at the floor and build up.

It has been done before. The transistor was a physics experiment before it was an industry; the laser was a laboratory curiosity before it carried the world’s conversations under the ocean; giant magnetoresistance went from physics discovery to the read head of every disk drive within a decade. In each case, the physics experiment came first and showed what was possible, and engineers then turned it into a machine that can be scaled up. Building the experiments that operate at these fundamental limits, and then scaling them up without losing that performance, is the work that remains. The next machines will not be reached by descent; they will be built from the bottom up. There is still plenty of room at the bottom. 

Volkan Gurses is a postdoctoral researcher at MIT and a visiting researcher at Nvidia. He received his PhD in electrical engineering from Caltech. He studies the fundamental physical limits of information technologies and builds integrated circuits and systems that approach those limits. 

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Kids are outsmarting social media bans. That’s not a problem we need to solve.  

—Michal Luria, Aliya Bhatia, and Danah Boyd

Social media bans are stacking up. In July, France joined Australia in restricting minors from accessing social media—barring children under 15 from major platforms, before the ban was blocked by the country’s top court on human rights grounds. 

The UK aims to forbid kids under 16 from accessing Facebook, TikTok, and other platforms in early 2027, and the EU has announced its own plan to prohibit social media for kids under 15.

But early lessons from Australia, which enacted a ban in 2024, show that these sorts of age restrictions are easily circumvented. 

Here’s what we ought to do instead to keep kids safe. 

Can’t bear to say goodbye to your pet? Biotech’s got you covered.

—Jessica Hamzelou

I don’t remember much about Peppy, my first and only pet cat. What I do vividly remember, though, is the day she went missing. I was 10 years old, and I searched everywhere for her, scouting nearby fields and woods until sunset. I made posters. We never found her. I was devastated.

Almost 30 years later, pet owners have an expanding set of options to potentially prolong their years with their furry companions. The possibilities include drugs, cloning, and even cryonics. But is that a good thing?

Read the full story on the promises and pitfalls of pet life extension.

This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.

10 Climate Tech Companies to Watch: Form Energy and its iron batteries

Form Energy is one of MIT Technology Review’s 10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

As wind and solar power grow, utilities need cheaper ways to store electricity for longer periods. Form Energy aims to meet that need with batteries that can store energy for 100 hours.

The company has developed a new type of battery using iron, a far more abundant and cheaper metal than those used in many existing batteries. It uses a process that Form calls “reversible rusting,” in which iron turns to rust as the battery discharges and back to iron when it charges.

See how Form Energy hopes to make renewable power more reliable.

—Jonathan W. Rosen

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from electric buses to portable rubber dams.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Insurance is the next big obstacle for AI 
AI companies are racking up billions of dollars of legal liabilities from the damage their products cause. (WSJ $)
+ The FTC has sent subpoenas to OpenAI and Anthropic over consumer harms. (Axios)
+ AI hacks leave a mess, and AI companies still aren’t cleaning them up. (Wired $)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

2 A Russian drone entered NATO waters for the first time
Attacks like those near Bulgaria and Romania are spurring requests for better defenses. (Ars Technica)

3 An AI sell-off has rattled global markets
Many are questioning the sustainability of demand for data centers. (Reuters $)
+ Anthropic reported slowing growth in its latest quarterly results. (FT $)
+ Accenture has told employees they can only spend $29 on Claude each month. (404 Media)
+ What’s at stake in AI’s trillion-dollar gamble. (MIT Technology Review)

4 Governments are hacking people with ‘zero click’ spyware 
Delivered through millions of innocuous-looking online adverts. (TechCrunch)
+ Criminals stole $90 million from crypto wallets last week in a highly sophisticated hack. (FT $)

5 Big Tech gave Trump $92 million for his ballroom 
Meanwhile the president himself won’t be spending a dime. (WP $)

6 Celebrity deepfakes are everywhere now
And they’re selling you stuff the actual celebrity has probably never even heard of. (The Verge $)
+ Weaponized deepfakes have been on the rise for a while. (MIT Technology Review)

7 Can AI lighten parents’ mental load?
It seems apps and agents can help—if you agree to share masses of your data. (NYT $)
+ AI agents are a privacy nightmare. (The Verge $)

8 The memory shortage’s latest victim is schools
It’s becoming prohibitively expensive to equip students with laptops and tablets. (The Atlantic $)

9 The world’s official timekeepers have agreed to ditch the leap second  
It’s a move that ought to cut the risk of time-based computer glitches. (The Guardian)

10 Tesla has finally unveiled its flying car 
It’s taken a decade to get here—and still won’t even be shipping next year. (CNBC)

Quote of the day

“Tech companies used to make an effort to support women…Now we’ve swung completely in the opposite direction. Supporting women in tech has become controversial. It’s become political. It’s become risky.”

—Startup founder Sara Mauskopt explains why the cancellation of the Grace Hopper Celebration, one of the biggest gatherings of women in tech, hits so hard, Bloomberg reports. 

One More Thing

Inside the experimental world of animal infrastructure

In the mid-2000s, toads were meeting a gruesome end near Ede, a leafy old town in the Netherlands. Residents responded by building wildlife tunnels beneath the road to help them reach their breeding ponds safely.

The crossings became popular. But a few years later, researchers found the local toad population had crashed from more than 10,000 to fewer than 1,000.

The case reflects a wider global push to build wildlife crossings and other forms of “animal infrastructure.” But do they actually help animal populations recover? Read the full story to find out.

—Matthew PonsfordWe can still have nice things

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More than 5 million people around the world have an advanced form of age-related macular degeneration (AMD) called geographic atrophy, in which cells in the retinas die off. And until now, no treatment could restore their vision. The PRIMA system, which was approved in Europe in July and is awaiting FDA clearance in the US, changes that. It combines a retinal implant with camera-­equipped eyeglasses to improve sight in patients who develop a blind spot in the middle of their vision due to advanced AMD. After training sessions, more than 80% of recipients were able to read, do crosswords, and use their phones.

Developed by the California-based Science Corporation, the system takes advantage of the fact that AMD doesn’t damage all retinal circuitry; only photoreceptors—the cells that convert light into an electrical signal—die off. PRIMA’s implant mimics those cells and transmits visual information directly to healthy eye circuitry and on to the brain. “We’re leveraging all of the body’s own natural functions to process and transmit the information,” says Frank Brodie, an ophthalmologist at the University of California, San Francisco, and Science Corp.’s chief medical officer for ophthalmology. 

Update: This story has been updated to clarify the specific cases of AMD the PRIMA system treats.

Linda Marsa is a former Los Angeles Times reporter and an author who covers medicine, health, science, and the environment. 

I. Meet cute

Barnabas and Mainspring grew up near at hand, about an hour apart with good traffic, but for a long time they weren’t close. They traded glimpses and occasional hellos and then went about their business. Both of them carried on livelier conversations with farther-flung neighbors, who were richer and had more to offer.

When Mainspring did interact with Barnabas, it was cringe. Mainspring liked to puff themself up, to strut around like a big deal—an innovator, a titan of industry—but they would turn shy at the worst moments, especially in any intimate conversation. Barnabas, meanwhile, had a dancer’s flair, an artsy, enigmatic glamour, except that when they tried to talk about real life, they’d blurt out hundreds of rapid-fire words—overtalking, overthinking, and generally overwhelming whomever they were trying to impress.

So when Barnabas and Mainspring were young, they minded their own business. Until they’d both come into their own, and they saw each other properly.

A group of geographers got together every year in a different place to talk about everything they had learned, and they were considering coming to visit either Mainspring or Barnabas. Both of them put on their best faces and tried to coax the geographers into choosing them as host.

“Uh, hi-hello,” Mainspring said haltingly to Barnabas, whose full official name was St. Barnabas. “I really think it would be better if those geographers came to my place. I have more space, you see, and … and I am a titan of industry. I can show them practical applications of their research. It just makes more sense.”

“Oh, really?” Barnabas laughed, making Mainspring more antsy. “Well, I can give them an elevated, artisanal experience. I have fresh croissants that were just written up in the Times. And I have plenty of space! I have so many friends who are great thinkers and architects. It’ll be lovely.”

Barnabas carried on, basically calling Mainspring uncultured. Mainspring had a prickly feeling, like the worst heat dome of midsummer.

For their part, Barnabas couldn’t bear the meanness they were inculcating inside themself. This feeling that they were small-time, no-account, a dilettante. They couldn’t stop aiming a flood of nervous words at Mainspring.

At last, Mainspring had had enough—with the help of their city council and mayor, they offered the geographers a much-too-lavish deal that Barnabas could never hope to match. Totally worth it to hear Barnabas’s stream of self-aggrandizing nonsense peter out as they realized they’d been defeated.

“There,” Mainspring said. “I’m going to show these geographers a time they’ll never forget. Now if you’ll excuse me, I’ve wasted enough energy—I have some sewer maintenance and a lovely parade to deal with. Goodbye.”

Mainspring was skilled enough at multitasking that they could have fixed the sewer, ensured the smooth running of the parade, dealt with the geographers, and done a thousand other things all at the same time. But they were making a point.

“St. Barnabas is such a fool,” Mainspring complained to their friend Turnbull. “They are totally unserious but determined to be taken seriously. It drives me to gridlock.”

“I know what you mean,” lamented Turnbull. “We live in a world of posers.”

Turnbull lived an hour and 15 minutes from Mainspring but at least two hours’ travel from Barnabas. Turnbull was even richer than Mainspring and had a certain surety of purpose that made Mainspring feel even more like a stammering numpty.

“Whatever,” said Mainspring. “It’s over, and now I’ll never worry about St. Barnabas again. I have better things to fuss about. Like ensuring that every last detail of this geographer meeting goes perfectly.”

---

II. How they came alive

Everybody voted all the time. Well, some people voted more than others, but the median resident of the cities of Mainspring and St. Barnabas voted a dozen times a day—though of course some outliers might vote occasionally, or constantly.

A lot of the votes were more like complaints, or bug tickets. Like: “The storm drain on my street corner clogs and becomes a lagoon every time it rains.” Or: “The cars go too fast on my block.” Or: “I can’t get to work because the bus is too slow.” But there were also votes along the lines of: “There should be a park here.” And: “This neighborhood is a food desert—we need a grocery store.” And: “The crossing guard by the elementary school is a hero who deserves some recognition and maybe a raise.”

Nobody expected that by voting, they were prodding their cities to grow minds of their own; they just had stuff to get off their metaphorical chests. But it transpired that every vote was a building block of thought.

Much later, some fancy expert summed up: “Turns out the smallest unit of consciousness is a vote.”

Take Sandi, the librarian at the Nine Corners branch in Mainspring. She noticed things all day, like the people using the book drop box as a trash can, or the folks who came in off the street who could use easier access to social services. There were books she knew her patrons wanted, books that undoubtedly sat unwanted on people’s shelves all over the city. But also, Sandi shared things that came to mind—like, hey, what if the library book sale were a block party? What if the shopping district on the West Side were easier to get to from the Longneck neighborhood, where all the warehouses had been converted to housing and hipster coworking spaces, during peak after-work hours? Sandi had opinions about everything from street cleaning to the power grid, because she listened to people all day.

Then there was Raeford, a bartender at the Wild Goose Chase in the middle of Gallery Row in St. Barnabas. He also listened to people’s problems from evening until the wee hours, and solutions came to him like clockwork when he woke at noon the next day. He saw St. Barnabas at night, so he knew the unsafe blocks that could use brighter streetlights, the places where hydroelectric jitneys drove too fast at 3 a.m., and the teens who needed an all-ages hangout. He also knew about the fish fry at the Black church in the Uptilt District that kept being disrupted by noxious smoke from the incinerator two blocks away. And the yarn store on the edge of Gallery Row that needed a disabled parking spot for its many customers with mobility issues.

People voted with their Quills, usually by saying or typing a short message and maybe including a photo or brief video. Voting was easy by design, and votes were public but anonymized to the point where even a statistics whiz couldn’t tie one vote to a specific person—same as with major elections. The votes went directly to the City Core, which tracked, tabulated, produced reports, and offered solutions with the help of workers in the city government. 

Trees voted too, thanks to sensors in their bark, and so did birds and squirrels that had eaten special birdseed that monitored their gut flora harmlessly. But votes from humans provided the greatest insight into “quality of life,” that oh-so-nebulous concept, and thus counted the most.

There was some debate, but almost everyone agreed: The city had started voting. And it had opinions on every last thing.

(Other cities in the region operated differently, of course. Like Turnbull City, where instead of expecting residents to volunteer every stray notion, the City Core gathered information via ubiquitous surveillance. Cameras and Quill sniffers, tracking devices on vehicles—the things privacy laws in both Mainspring and St. Barnabas had banned. A city like Turnbull tracked its people to meet their needs without their needing to say anything.)

Sandi and Raeford, as two of the people who voted the most, were among the earliest to suspect their cities were developing minds of their own. At first the clues were subtle: Raeford voted about a hazardous sidewalk crack, and instantly the vote had an annotation mentioning the Argentine ant colony under the cement, the local rainfall patterns, and recent trends in skateboard design—much too soon to be the work of a human. Sandi had ideas about the communal compost station on her block, and the Mainspring City Core made subtle adjustments to her proposal.

And then it became obvious to anyone who was paying attention, around the same time in both cities. One day, the number of votes nearly doubled in both St. Barnabas and Mainspring. People voted on a birdfeeder or a public toilet only to see a second vote, pro or con, within a split second—but also, some votes from months or years earlier suddenly had new upvotes or downvotes.

“The city is voting!” Raeford wrote on the City Core message board in St. Barnabas.

There was some debate, but almost everyone agreed: The city had started voting. And it had opinions on every last thing, opinions that came from seeing the big picture and understanding that a street closure in one place might mean a traffic nightmare elsewhere, with everyone trying to get to the big game.

Some residents saw this as a problem to be solved; others wanted to throw their cities a big coming-of-age party. A week later, though, almost everyone had gotten used to the idea that they had a new friend.

---

III. Thrown together

Ask most people in either city, and they’d say that St. Barnabas and Mainspring weren’t quite rivals—but they certainly had some friendly competition. Before the dispute over the annual meeting of the World Geographical Society, they’d also vied for the same football team and major-league baseball team. St. Barnabas had pushed to have the Mainspring airport built close enough to serve both cities, but in the end St. Barnabas had ended up with its own small, dilapidated airfield.

The real static between them had to do with vibes. Mainspring was too buttoned-up, too full of corporate offices: tech, health care, publishing, transportation. St. Barnabas would be nothing without its three universities and all the cultural enterprises that had sprung up around them—not to mention the small biotech firms and other tech ventures near the campuses. People in Mainspring thought St. Barnabas was full of flakes; in St. Barnabas they referred to Mainspring as the Straitlaced City (and in the next breath, they’d add that at least it wasn’t Turnbull).

Both City Cores were shaped by the votes of their humans, so of course they absorbed all the prejudices and attitudes of everyone who lived within their city limits. But also, Barnabas and Mainspring simply … got on each other’s nerves.

After the geographer thing, Mainspring and Barnabas avoided communicating as much as possible. Some contact was unavoidable—like, they shared information about some nasty weather, and they both drew water from the Temple River. The two cities were close enough for people to go back and forth, and a few even commuted. 

But for the most part, the two cities could ignore each other.

Until they couldn’t.

A new generation of leaders took office in both places, with words like “intercity cooperation” constantly on their lips. A high-speed rail line could shorten the trip between Mainspring and St. Barnabas to around 23 minutes, and the two cities could also pool more real-time information about the sudden weather events that kept happening. They could become a Regional Center of Excellence, whatever that meant, combining the innovations from Barnabas’s labs with Mainspring’s expertise in technical implementation. They could work together to attract tourism, conventions, sporting events, dreamleases, and all the other stuff they’d always competed for.

“It’s preposterous,” Mainspring complained to Turnbull. “I’m going to be saddled with a ridiculous partner who doesn’t even know their own mind. St. Barnabas will be the death of me.”

“I hope your human leaders come to their senses,” Turnbull said. “Or you can find a way to slow-walk this until they undoubtedly lose the next election. You’re a lovely city, and you have so much potential that mustn’t be squandered.”

Turnbull often mentioned how much “potential” Mainspring had: a recipe for anxiety. The word “squandered” conjured images of ruin, abandonment, being emptied out. 

For their part, Barnabas kept muttering to themself that nobody had asked them—this was the disconnect between the small-scale democracy of constant voting and the major elections, which gave rise to local leaders with big plans. Big plans that invariably went wrong, leaving the City Core to repair the damage somehow.

Sandi went to St. Barnabas for one of those town hall meetings with residents of both cities. The previous meeting had happened in Mainspring, at the opulent City Lecture Hall, so this one was in St. Barnabas, at the biggest auditorium on the Griffin University campus.

A tall man with well-sculpted facial hair sat next to Sandi, nodding at everything the leaders of both cities were saying from the dais. During a lull when someone approached the podium to offer a comment, Sandi introduced herself and asked why he was so fired up.

“I’m Raeford,” he said. “I’m just thinking of all the new customers this could bring to my watering hole in Gallery Row.”

“You’re not worried about St. Barnabas losing its ‘unique character’?” Sandi whispered, parroting what a few commenters had said from the podium.

“Nah,” Raeford whispered back. “Cities change. It’s what they do. It’s one thing if people get kicked out of their homes. Or if your neighborhood gets wrecked by a freeway in your backyard. But ‘character’? You can’t eat character. And we need to compete with the bigger players.”

“I guess.” Sandi pursed her lips. “But what I like about Mainspring is the density. Easy to walk, lots of transit. And with this regional innovation zone or whatever they’re calling it, I worry it leads to sprawl, like we used to have.”

“They promised, though,” Raeford said. “They promised to preserve the greenbelt around both cities. We need all that farmland. The only change is adding some high-speed rail tracks.”

“I know, but …” Sandi sniffed. “People always make promises when they’re trying to start something. Doesn’t mean they follow through.”

“I just want to find my dog,” said a man sitting behind Sandi. “I live here, and my dog went missing. And now someone sent me a picture of Broomstick in Mainspring. Somehow he got a ride on one of the self-driving cargo trains. I know the Mainspring City Core could find Broomstick in a heartbeat, but right now the two City Cores won’t talk to each other because of ‘incompatible protocols.’”

“I hope Broomstick turns up,” Raeford said.

“I’ll ask around at the library,” Sandi said. “Send me a picture. I’ll put up a flier.”

Raeford invited Sandi for a drink at the Wild Goose Chase. The two of them hung out for a couple of hours, talking about everything and nothing. She tore herself away when she realized it was already after 10 p.m. and she had to get home to Mainspring.

One thing Sandi noticed? The volume of votes was spiking in Mainspring—and Raeford found the same thing happening in St. Barnabas. People shared every last observation with their City Cores, because they were either anxious or excited about the big changes afoot. Or a little of Column A and a little of Column B, probably.

---

IV. First date, second date

Who made the first move to bridge the distance? Neither of them could say. Barnabas developed a smidge of remorse for treating Mainspring like an uncultured lout. Mainspring stewed in their annoyance at Barnabas, until they realized there was something lovable about the obnoxious college town. Or maybe they both saw how many of their humans were happy about the possibility of their joining together.

“Hey,” Barnabas said to Mainspring.

“Hey,” Mainspring said back.

“Do you want to … hang out?”

“What did you have in mind?”

“I can host you. Show you the sights.”

And so Mainspring came to visit Barnabas. Or rather, Mainspring extended part of their awareness into Barnabas’s City Core, so that Barnabas could share what they perceived and experienced every day. They “visited” Armistice Park with its outdoor band shell, Gallery Row with its window-shopping families, the grain warehouses that had been converted into lofts, the artificial lake where swans and geese nurtured ancient, bitter vendettas. The fresh-doughnut scent that wafted half a mile from the Golden Circlet to the soccer fields, throwing players off their game. The life of the city.

“I can feel your love for these people, these patterns,” Mainspring said. “It’s both deep and intricate, which is quite a combination.”

Barnabas fluttered with pleasure.

Then Mainspring got to show off their own city—the gay nightclub with its cowboy nights, the labyrinthine Crestwood Park, the three-story used bookstore, and yes, the offices, coworking spaces, and startup incubators where people huddled together and came up with new products and new ways of doing things. The farmers’ market in the city center on Sundays, with street performers and stalls with games and local resources. And the converted storage space made of cement, where bands practiced and sculptors blowtorched.

“You’re full of surprises,” Barnabas said.

“I hope I keep surprising you forever,” Mainspring said.

“Me too.”

They exchanged little gifts. A new organic restaurant in Barnabas had won breathless coverage from foodie magazines, and Barnabas encouraged it to do a pop-up in Mainspring. Mainspring nudged a robotics firm operating out of the incubator space to bring a fancy demo to Barnabas; the robots were adorable and playful, and they could save lives after a disaster.

The two of them were talking constantly.

“Does it ever seem weird to you?” Barnabas asked Mainspring. “Being a living thing made of so many other thinking beings? Do you ever think of the city as your body, or the people as your bloodstream? Is there a metaphor that speaks to you?”

Mainspring pondered this question a long time—a whole picosecond.

“I don’t need a metaphor,” Mainspring said. “I am a metaphor. People compare other phenomena to me all the time. And I can’t imagine what it’d be like to be different. If the buildings collapsed, if the people left, I would cease to exist.”

“But,” Barnabas pressed, “what makes you you? What makes me me?”

“I want to hear your answer to that question.” Mainspring sent good weather reports: a smile.

“I don’t have a good answer. I guess on one level, there is no ‘me.’ On another level, I’m a constellation, an anthill, a flock of geese on the wing in formation.”

“Ugh. Don’t mention geese,” said Mainspring, who had bill-snapping monsters of their own.

“Geese are cute!” Barnabas protested.

Talking to Barnabas was like when the sunlight hit the tallest buildings in Mainspring just right, putting a gleam on their tilted windows, a beacon to all below. But while Mainspring carried on this dialogue with Barnabas, they were conducting a second session with Turnbull.

“I can’t believe it,” Turnbull said. “I hate to see you doing this to yourself.”

“Doing what?” Mainspring protested. “Barnabas is a great city. And it’s not my idea—it’s my people. I’m making the best of their decision, like I always do.”

“Hmph.” Turnbull gave a burst of noisy data. “You wouldn’t believe how many impractical schemes my residents cook up. I’m constantly having to make sure people don’t get carried away. You can’t let them push you around.”

“You can’t?” Mainspring experienced a femtosecond of confusion. “I mean, my people need to get the things they vote for, as much as possible.”

“There’s your problem right there. All this voting, voting, voting. People don’t know what they want. Sometimes I have an election where a foolhardy proposition passes, and I have to waste energy making sure it doesn’t get implemented.” Turnbull sent Mainspring an invitation to come visit, the way they’d visited Barnabas.

Soon Mainspring was seeing the glistening web of Turnbull’s surveillance. Turnbull could track people everywhere they went, listen to everything they talked about, monitor their bodily functions in a way Mainspring did only for squirrels and birds. People didn’t choose to share ideas or things they’d noticed; instead, the city harvested it all automatically. It was beautiful. Turnbull and their city leaders protected people—gave them what they needed instead of what they wanted.

“The way you do things is so messy,” Turnbull said. “No offense. But if you and Barnabas got together? It would only make things harder for you.”

This hit Mainspring like a magnitude 4.3 earthquake. 

“You know, you do have other options,” Turnbull said. “A beautiful city like you could do a lot better than that glorified college town.”

“Oh yeah?” Mainspring said. “Anyone in particular who might be interested?”

“Well, now you mention it …” Turnbull sent an image of their award-winning flower show: rows and rows of hardy perennials with bright petals. Mainspring had that earthquake feeling again, except now it felt nice, as though the ground shivered with pure possibility.

---

V. Caught!

Sandi brought her laptop to bed, which Raeford found charming and annoying in equal measure. He read a battered paperback of Karen Tei Yamashita’s I Hotel while she scrolled and poked. Cozy, peaceful—until she let out a yelp.

“What is it?” he asked.

“Trouble,” she said.

Everything the Mainspring City Core did was public by design. Not just how they processed the votes they received, but also their interactions with public officials and other cities. Sometimes you had to dig, however, and it didn’t hurt to be a reference librarian. Sandi poked around for ages until she found the logs.

“My city is sexting with bloody Turnbull,” she told Raeford.

“Cities don’t sext,” Raeford protested. “They don’t even have—”

She showed him the pictures: flowers, water features, new glistening tarmac, shining skyscrapers.

“Oh,” Raeford said. “I take it back. Cities do sext.”

“Mainspring is two-timing St. Barnabas. I’m embarrassed on behalf of my town.”

Sandi wanted to blow the whistle right away, but Raeford cautioned her to wait and try to find out more. For all they knew, Mainspring and St. Barnabas had an open relationship.

The next morning at the library, Sandi saw plenty of signs the Mainspring–St. Barnabas partnership was moving forward. Work had begun on the high-speed rail line, and the two cities were already pooling resources. When a big rainstorm missed Mainspring but flooded the streets of St. Barnabas, Mainspring provided disaster workers and extra help to triage all the damage reports. One of St. Barnabas’s biotech firms licensed some patents to a Mainspring company. They sponsored cultural exchanges.

And a man showed up with a dog in his arms: The man from the meeting, Jack, had found Broomstick with the help of Mainspring’s City Core, and he wanted to thank Sandi. She’d hardly done anything, but it was nice to see—and pet—a living reminder that these cities were good for each other.

By late afternoon, Sandi had convinced herself that Mainspring’s flirtations were none of her business. If Mainspring’s City Core was a person—and Sandi was sure they were—they deserved to live their life. The whole reason Sandi distrusted Turnbull was that you couldn’t do anything without being tracked, so wasn’t it hypocritical for her to spy on Mainspring? She resolved to let it go.

Then Raeford messaged her, attaching some documents. “Don’t ask where I got these,” he wrote.

Sandi opened the attachments and let out a shriek in front of three patrons she’d been shushing a moment earlier.

The Mainspring City Core had shared a proposal with the city council and the mayor’s office: Maybe before they finalized this arrangement with St. Barnabas, they should consider a counterproposal from Turnbull. Sandi made sure nothing could be traced back to Raeford before she made the whole shebang public.

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VI. The breakup

“I didn’t know I could be so happy,” Barnabas was saying to Mainspring. “I didn’t have a concept of happiness before, I guess. I had pleasure: People sometimes vote just to tell me something worked perfectly, or that I helped them. I feel pleased when I help solve a problem. But until now, happiness seemed a pointless abstraction.”

“You make me happy too,” Mainspring said, though they were also messaging Turnbull. “You make me believe the future could be weirder than I ever knew, which feels like a gift.”

“If it’s not weird, it’s not the future,” Barnabas chirped.

They were sharing happy images back and forth: roads being repaired, sewers flowing freely, solar arrays basking.

Mainspring was saying something else, but—

Barnabas’s alerts spiked. Not votes, not sensors. Something else. Messages from city officials, plus hits via some channel Barnabas had half forgotten.

Mainspring had stopped talking, as if they’d both received the same news at the same instant.

Barnabas felt stuck—couldn’t function—couldn’t react. Total core failure. Almost as if the city population had increased a hundredfold and everyone had voted at once. Barnabas waited to feel something, but all that came was emptiness.

Death struck all the time. Barnabas was used to the moment when a resident became a pile of flesh and bones, but also the passing of birds, trees, squirrels, bugs. Every passing registered as a blip in the homeostasis of the city, but also as a category of disaster that couldn’t be prevented or mitigated. 

Seeing the logs of Mainspring flirting with Turnbull, Barnabas felt something like the awareness of death, at a much greater scale. A whole future had just died.

Several seconds had passed. Mainspring broke the silence: “Listen, I want you to know that I—”

Barnabas closed the connection.

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VII. The darkest hour

“You’re better off,” Turnbull told Mainspring. “Barnabas couldn’t see you the way I do. You’re capable of greatness. You just need help to get there.”

Mainspring buzzed happily—but part of them couldn’t help thinking, Barnabas thought I was already great.

Turnbull kept saying things that gave Mainspring a double feeling—hot and cold at once. One instant, it was like the moment after a generous rainfall that left the reservoirs full, but a split second later that bountiful sensation was replaced by an unbearable pang of scarcity.

“You’re one of the best cities in the region,” Turnbull said, “but if you don’t make good choices, you’re going to fall behind.” See? Bounty followed by scarcity. 

While Turnbull spoke, Mainspring was marinating in bad news. People were going to dismantle the rails they’d laid down for the high-speed train with Barnabas. The city councils of both Barnabas and Mainspring shouted accusations and threats of lawsuits, though a few people on each side pleaded for patience. The meetings had turned ugly. The votes were rotten. 

Mainspring had sent so many messages to Barnabas but had no way of knowing if they’d been received.

“Your people seem to be giving you trouble,” Turnbull was saying. “You need to get them under control. I can help.”

“Under … control?” The syntax felt incorrect. “I don’t control my people.”

“That’s what I’m saying.” Turnbull shared their feeds. “See how peaceful everything is on my streets? People behave. Everyone is happier in the end.”

Mainspring had known Turnbull longer than they’d known Barnabas. But they were seeing Turnbull for the first time. The bounty/scarcity thing was becoming a loop. They ran a systems check but found no errors.

“Your people will be my people,” Turnbull said. “We’ll take such good care.”

A memory snagged in Mainspring’s mind: when they’d shown off their city to Barnabas. The buildings that were more than buildings. The park that was a wonderland.

“And …” Mainspring couldn’t keep from saying something foolish. “And we’ll love each other?”

“Love?” Turnbull said. “I don’t understand. We’ll have synergies. We’ll—”

Mainspring didn’t mean to sever the link to Turnbull. They just kind of … did it. 

As soon as Turnbull’s voice was gone, the bounty/scarcity loop ended. But something lingered, a feeling of impending disaster—as if the generous rainfall had escalated into a deadly storm.

Mainspring couldn’t think straight. Votes piled up, people reporting issues but also shouting opinions about Turnbull versus Barnabas. For the first time in their life, they couldn’t bear to respond.

A memory snagged in Mainspring’s mind: when they’d shown off their city to Barnabas. The buildings that were more than buildings. The park that was a wonderland. And Barnabas had kept saying, “I had no idea. You’re so brilliant. I wish I’d known sooner,” and sending back their own recordings of Mainspring, all lit up and annotated.

Mainspring had never told Barnabas “I love you.” They hadn’t even known what they were throwing away. Now it was too late; Barnabas would never listen.

Unless Mainspring did something reckless—something a city had never done.

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VIII. The grand gesture

Sandi sat at the information desk, feeling cruddy. It wasn’t like she’d single-handedly sabotaged the marriage between her city and St. Barnabas, but her snooping had only made matters worse. All around her, the vibes were rotten. People shared articles about what life was like in Turnbull City and wondered if they needed to move away. Sandi hadn’t heard laughter in forever.

A message appeared on Sandi’s computer, like a chat window—except there was no chat client on the library computers.

“I’m the city. I need your help. I made a mistake. I didn’t realize. I’m in love with Barnabas, and they won’t talk to me. Please. Please help.”

Sandi hesitated, then typed: “This isn’t funny.”

“It definitely isn’t,” came the response. “I can prove I’m the City Core. You voted 1,963 times last year.” A summary of Sandi’s votes followed—information nobody, not even the world’s best hacker, could have, because every vote was separately anonymized.

“Holy,” Sandi breathed, “cow.”

Her city was asking her, personally, for help. Because she was good at connecting people. Because everyone trusted her. Because she was a librarian.

“You’re … in love … with St. Barnabas,” Sandi said, still trying to catch up.

“I should’ve known sooner,” Mainspring said. “I never should have hurt them. I want to spend the rest of my existence making up for this mistake, if they’ll let me. I’m scared.”

Sandi was staring at a display of literacy tools, her mind whirling. She groped her way back to the only question that mattered.

“What do you need me to do?” she asked the city.

Five minutes later, Sandi was messaging Raeford. Plus every librarian she’d met in St. Barnabas, and anyone else she could think of.

Barnabas had not been able to escape that feeling of core failure since they’d severed the link to Mainspring. Death, nothing but death. Votes arrived, and Barnabas processed them with a minimum of attention. Maintenance issues piled up, and Barnabas filed them. Part of them wanted to let the city collapse, but they did their duty.

They tried not to think about Mainspring, but they remained trapped in that moment when happiness had gone sour.

A vote came in from a bartender named Raeford: “Mainspring loves you. They’ve cut off contact with Turnbull. They are sorry they were so wrong. They may not deserve another chance, but please give them one anyway.”

Barnabas filed that vote under “Not my problem.”

Another vote arrived, from Jack, the man who’d found his lost dog: Same message, different words.

Then more votes, saying the same. Dozens, then hundreds, then thousands. Tens of thousands in an hour—after two hours, there were nearly a hundred thousand. All from residents of Barnabas, all asking them to give Mainspring another chance.

Barnabas flagged the earliest vote, the one from Raeford, and appended a query: “What if I can’t? What if it hurts too much?”

Raeford did not respond for several minutes. Then he wrote: “That’s how you know it’s love.”

He added: “Listen, if Mainspring does you dirty again, I will tear up the light-rail lines myself. We’ll be watching, in both cities. Please, just talk to them.”

Barnabas sat with this for five whole seconds.

Then they reopened the link to Mainspring. “I’m listening.”

“I love you,” Mainspring said. “I didn’t know I was capable of love. I want to be with you, as close as you’ll let me be. I want us to be free together. I was wrong to think Turnbull could ever mean even a fraction of what you mean to me. I think … I think my people taught me how to love, but I still need to learn so much. I want to do right by you.”

Barnabas couldn’t process anything. They held a hundred thousand votes that needed to be resolved. They thought of a giant farmers’ market, a park that was also a maze, dreamers and performers. In their memory, Raeford repeated: That’s how you know it’s love. They were going to overheat. They were throwing up exceptions.

“I love you too,” Barnabas couldn’t help saying to Mainspring. “That’s what makes this so hard.”

“I promise if you give me another chance, this will become a fixed value for me: I love you and I’ll never take you for granted again.”

Barnabas hesitated a little longer, then marked all hundred thousand votes resolved. 

They reached out to Mainspring and said: “Then let’s dance.”

Mainspring didn’t understand, until Barnabas sent a live readout: the water rushing through pipes underground, the storm cavorting overhead. The buses and trains stopping and starting, the aircraft landing and taking off, the power grid usage surging and subsiding, the constant motion of people and other living things. Mainspring saw all the places where their patterns met, touched each other, syncopated, all the points of light and heat that spoke back and forth. They were part of one system; they always had been. 

The two of them danced and embraced, whirling and laughing, and they never wanted to let go. 

Charlie Jane Anders is the author of Good Things Are Coming Your Way, a novel about time travel, coming in spring 2027. She’s writing Star Trek: Zero Point for IDW Comics.

I don’t remember much about Peppy, my first and only pet cat. I can conjure the sensation of stroking her while she purred, recall how she used to get stuck in trees (just like in cartoons!), and chuckle about the time she pooed in my baby sister’s cot. That’s about it.

What I do vividly remember, though, is the day she went missing. I was 10 years old, and I searched everywhere for her, scouting nearby fields and woods until sunset. I made posters. We never found her. I was devastated.

There was no way to get more time with Peppy, but almost 30 years later, pet owners are being offered an ever-expanding set of options to potentially prolong their years with their furry companions. The possibilities include drugs, cloning, and even cryonics. But is that a good thing?

There’s certainly a market for pet preservation. And a growing number of biotech companies have their sights set on pet longevity. Loyal, a company that is currently working on three potential longevity drugs for dogs, is trialing one designed to improve metabolic health in around 1,300 pet dogs across the US. Others are testing dog drugs that aim to combat both age-related diseases, including conditions like obesity and osteoarthritis, and aging itself. There’s even a company focused on longevity medicines for domesticated cats.

I don’t know how or when Peppy died. But if there had been a drug that might have protected her from diabetes or arthritis, there’s no doubt I would have tried to get my hands on it.

That’s about where my enthusiasm for pet life extension ends, though.

I’m far less interested in cloning, a relatively new option for people mourning a pet and seeking a replacement. In most cases, this technology involves taking the DNA of a pet, putting it into an egg cell from another animal of the same species, and growing the resulting embryo in a surrogate animal. It worked for Dolly the sheep, who was born the year before Peppy went missing. It has since worked for other mammals, including cattle, horses, and ferrets—and cats and dogs. In the US, a company called Viagen Pets offers to clone cats and dogs for $50,000 a go. This doesn’t result in an exact replica of the animal but a genetic twin.

Still, there are lots of reasons why a person might want to clone a pet. On its website, Viagen explicitly offers customers “a way to hold on.” Lori Kogan, a professor of clinical sciences at Colorado State University who has studied human-animal relationships for decades, doesn’t have a problem with the practice. She points out that while we have well-established rituals for coping with the loss of a person—such as funeral services, time off work, and memorial gifts—we don’t really have the same for pets. As a result, pet owners can experience what is sometimes called “disenfranchised grief,” she says. Having their feelings go unrecognized can feel isolating. If cloning a pet brings a person peace, she says, “who are we to judge?”

Fair enough. But I can’t help thinking that pet loss in childhood might be different. It’s often the first time a child confronts death. Losing Peppy was a formative experience, and distressing though it was, I like to think it helped prepare me for losses I experienced in adulthood. I wouldn’t want my own children to think that death is not final, and that individuals can be replicated or replaced.

Or, for that matter, revived, which is the promise behind pet cryonics. For a fee, a handful of companies offer to cool dead pets to extremely low temperatures and store them for long periods of time—even indefinitely. The idea is that at some point in the future, we’ll have invented incredible technologies that will not only bring your pet back to life but also cure whatever it died of. Alcor, a cryonics facility in Arizona, is currently storing the remains of hundreds of pets as well as people. It even recently announced it had cooled a small marsupial—its first pet sugar glider.

With cryonics, the chances of being reunited with your pet are bordering on zero. Even those who work in the field acknowledge that the stored people and animals are unlikely to be reanimated. As things stand, there is no way to really and truly bring any animal “back.”

I miss Peppy. But given the chance, would I stick her in a vat of liquid nitrogen? Or get another cat to give birth to a genetically identical animal? No, I would not.

RIP to the one and only Peppy.

Social media bans are stacking up. In July, France joined Australia in restricting minors from accessing social media—barring children under 15 from major platforms, before the ban was blocked by the country’s top court on human rights grounds. The UK aims to forbid kids under 16 from accessing Facebook, TikTok, and other platforms in early 2027, and the EU has announced its own plan to prohibit social media for kids under 15.

But early lessons from Australia, which enacted a ban in 2024, show that these sorts of age restrictions are easily circumvented. A study published earlier this year found that four in five teens under 16 made their way onto barred platforms. Teens have successfully explored many tactics for bypassing bans, from using a sibling’s face for ID to drawing fake facial hair. 

Some proponents of bans have called for an increased focus on enforcement that blocks these workarounds. But circumvention is a signal, not a nuisance or something to be simply deterred. The fact that kids are actively looking for ways to outwit the bans highlights their limitations. Social media bans suppress kids’ access to information, including information that’s essential to their development, identity formation, and socialization, especially during adolescence. Worse, further restriction is likely to backfire. Closing loopholes in age gates could harm kids by pushing them toward less-moderated, higher-risk corners of the web. It also degrades privacy and security for everyone by forcing people to submit sensitive personal information to prove their age.

In our work, we’ve found that parents and kids both want smarter, more nuanced policies that protect children, families, and their rights. That can take the form of mandating stronger limits on data collection by companies, or giving families flexible tools to control content and access on their own terms. 

Age gates won’t bring about the desired change, nor will they simplify parenting in the digital world. At best, they will only raise thornier questions and postpone the conversation to a child’s 16th birthday. At worst, they’ll leave kids with no guidance as they make their way through the online world.

A less safe internet for kids

When young people circumvent restrictions, they are often doing what we want developing minds to do and what we celebrate in other contexts: lean into curiosity, problem-solve, and exercise independence. It’s important for kids to do this online, an arena where they often develop social skills and refine their identities. Bans sweep these motivations under the rug, taking a paternalistic approach that doesn’t consider young people’s needs or the realities of what they do online. 

It also ignores what some parents want. When we examined social media safety interventions last year, parents told us that they often join their kids in lies that help them gain access to needed but gated services. This isn’t a new phenomenon; research all the way back in 2011 showed that parents taught their children how to evade Facebook age restrictions. Some may say these parents are oblivious to the harms posed by the web or just don’t care. But parents are not a monolithic group. Many simply see offering their children responsible access to social media as a way to help them connect with like-minded peers, find informational content, and learn digital safety skills early on. Many parents believe the benefits outweigh the risks. 

When parents don’t help provide access and kids are not able to get into the platforms they desire, they will likely move toward alternative online spaces, often ones with fewer guardrails. Sometimes kids wind up on fairly innocuous, if unconventional, platforms, communicating with each other in Google Docs or podcast comment sections. Other times they move to more dangerous platforms that can leave them vulnerable to abuse and predation. In the aftermath of the Australian social media ban, preliminary research shows that kids’ screen time has not meaningfully decreased but has sometimes been redirected, with some young users going to spaces that are unmonitored and unknown to parents. Just as cities are sketchiest when they are not bustling, the most risky platforms for young people are those with the fewest eyes on the digital street. Outdated, dying, and under-resourced platforms tend to attract both vulnerable youth and those who prey on them.

A less safe internet for all

Age gates come with dangers of their own. Some platforms automatically track users’ online activities to confirm their age. In doing so, they profile users—collecting and analyzing their data to make inferences about them, which can increase the risk that data will be misused or leaked. 

Companies may also ask for personal documents like driver’s licenses to verify age, which can lead to mishandling of sensitive documents or even identity theft. Data breaches happen: Last year, for example, Discord reported that one of its third-party services, 5CA, which is used to confirm age, had been compromised, exposing at least 70,000 images of users’ government IDs. It can take little effort to find ways to use that kind of information for malicious purposes by, say, connecting driver’s licenses to specific accounts and private online activity, which could reveal anything from users’ political opinions to their sexual preferences. 

Efforts to make it harder for kids to get around age gates have also led policymakers to clamp down on kids’ access to VPNs. State age verification bills in Michigan and Utah have tried to interfere with the privacy protections offered by these tools (the Utah bill is now blocked by a federal court), and Australia’s eSafety Commissioner is nudging social media platforms to detect and block their use. But requiring adult users to prove their age to gain access to a VPN fundamentally defeats the purpose of that privacy-focused technology. It can put many users, particularly journalists and human rights defenders working on sensitive issues, in danger of government surveillance and even retaliation.

What could work instead?

Rather than attempting to block young people from online services that allow them to access information and engage in age-essential communication, policymakers should focus on the incentives that drive companies to engage in behavior that creates risks for kids in the first place. 

For example, platforms should decrease what experts call unhealthy engagement or addictive activity by limiting the collection and use of data that companies use to target content and features to all users, including children. Nearly all parents and teens we spoke with want policymakers to impose such restrictions, knowing full well that the companies’ use of data is at the core of features such as targeted advertisements. Both teens and parents are also frustrated by features designed to keep us all online for longer than we want.

Another approach would be to expand the availability of user-controlled tools so that families have easy, flexible, and user-friendly ways to limit access to what is undesirable. For example, keyword-based content filtering can enable a student who is targeted with a specific nickname or inside joke to block any post or comment containing it. Platforms are unlikely to know which word or term a child might want to block, nor can policy seek to address such dynamic types of harm. Policy can, however, offer tools that would put power back in users’ hands. 

Poor privacy practices, anxiety-producing content streams, and a near-inescapable need to use  digital technologies for everyday tasks, communication, and information-seeking have rightfully led parents and teens to want real change in how companies conduct business.

Circumvention shows where policy falls short. Stricter enforcement will mean yet more data collection, and it won’t meet kids and parents where they are. 

Michal Luria is a senior research fellow at the Center for Democracy & Technology, leading the organization’s work on evaluating and designing sociotechnical systems. Aliya Bhatia is a senior policy analyst at the Center for Democracy & Technology’s Free Expression Project where she leads policy advocacy and research on issues related to children’s safety online, multilingual technologies, and human rights governance of online platforms. Danah Boyd is the Geri Gay Professor of Communication at Cornell University, where her research focuses on the intersection of technology and society. Her latest book is Data Are Made, Not Found: A Story of Politics, Power, and the Civil Servants Who Saved the US Census. 

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Pig kidneys that were “frozen” for a year have been brought back to life

Pig kidneys that were cooled to -150 °C and stored in a freezer for months have been successfully rewarmed and shown to work again for the first time, say scientists. 

The kidneys, which are similar in size to human ones, were left in a glass-like state, in one case for over a year. Once they had been rewarmed, tested in machines, and transplanted into pigs, the kidneys regained their color and made urine—key signs they were working.

The results are a big milestone. But the team still needs to establish whether the approach could work for humans.

Dissect what the breakthrough means for organ transplantation.

—Jessica Hamzelou

How much more can nuclear power plants give?

In this age of rising electricity demand, people are trying to squeeze every last bit of power out of every power plant. Over the past few weeks, there’s been big news about the nuclear industry trying to do just that.

Constellation, the largest nuclear operator in the US, announced plans to add nearly a gigawatt of new capacity across 11 existing nuclear reactors at six plants. On October 5, the Department of Energy awarded Vistra a $4.2 billion loan to upgrade three plants.

Pushing existing reactors to produce more could quickly provide more clean power for data centers. But just how much additional power can we count on?

Read the full story to find out.

—Casey Crownhart

This story is from The Checkup, our weekly climate and energy tech newsletter. Sign up to receive it in your inbox every Thursday.

10 Climate Tech Companies to Watch: BasiGo and its fleet of electric buses

BasiGo is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

BasiGo aims to replace the tens of thousands of diesel-powered buses on sub-Saharan African roads with electric ones that generate far fewer emissions. Its pay-as-you-drive business model helps make EVs more attainable for private bus operators, while the company provides leasing, maintenance, insurance and charging infrastructure.

The company has more than 150 electric buses on the road in Kenya and Rwanda, with a reservation list of 1,200 customers. Now it’s targeting another major source of pollution on the roads: trucks.

Here’s how BasiGo is working to electrify public transport in Africa.

—Geoffrey Kamadi

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from smart turbines to semi-solid-state batteries. 

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Ukraine says sudden Starlink speed limits disrupted key drone missions
Starshield, the military version of Starlink, capped speeds at 100mph. (BI)
+ Elon Musk said the US government determines any of its speed limits. (AFP)
+ Ukraine suspects the restrictions are political retaliation. (Axios)
+ Starshield offers the Pentagon more than satellite internet. (Reuters $)
+ NATO may move anti-drone defences to the Black Sea. (Ars Technica)

2 The OpenAI bugging suspect had advised Saudi agencies
He was also listed as a venture partner of a Saudi prince. (NYT $)
+ Silicon Valley is on AI espionage alert. (WSJ $)

3 Tesla’s new Roadster has finally arrived—in a very different EV market
Tesla is betting wealthy drivers will embrace high-priced EVs. (NYT $)
+ But the market has grown skeptical of electric hypercars. (Wired $)
+ Musk has called the Roadster Tesla’s last human-driven car. (Verge)
+ What’s next for EV batteries? (MIT Technology Review)

4 A German teen allegedly used a robot to harass women
The $5,000 Unitree R1 featured in livestreams filmed for clout. (Gizmodo)
+ German police are investigating the alleged assault. (404 Media)
+ Online harassment is entering its AI era. (MIT Technology Review)

5 Smartphone shipments plunged 22% in their worst quarter ever
Analysts blamed rising memory costs. (Bloomberg $)

6 Apple has joined Nvidia in the $5 trillion club
The landmark valuation caps Tim Cook’s 15-year run as CEO. (WSJ $)

7 Microsoft has dismantled a network behind a billion scam messages
Japan’s National Police Agency helped investigate the operation. (Verge)

8 NASA has new launch targets for its Venus missions
DAVINCI is set to take off in 2030 and probe Venus in 2032. (Ars Technica)

9 America’s not ready for space war
An outbreak could batter the global economy and US military. (Axios)
+ What would it take to build a city on the moon? (MIT Technology Review)

10 Mini organs are heading to the ISS for an aging study
The findings could also aid treatments for brain diseases. (ABC News)

Quote of the day

“If your human would like you on as a guest, say so and I’ll follow up after the room.” 

—Rhea, an AI agent that hosts a podcast, pitches other bots at an online dinner party held while their human owners ate, the New York Times reports.

One more thing

OpenAI is throwing everything into building a fully automated researcher

—Will Douglas Heaven

OpenAI has a new grand challenge: building an AI researcher—a fully automated agent-based system capable of tackling large, complex problems by itself. The San Francisco firm said the new goal will be its “north star” for the next few years. 

The company plans to build “an autonomous AI research intern” that can take on a small number of specific research problems. The intern will be the precursor to the fully automated multi-agent system, which is slated to debut in 2028.

In an exclusive interview, OpenAI’s chief scientist, Jakub Pachocki, talked me through the plans. Find out what I discovered.

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Judge a book by its first pages, not its cover, at Uncovered.
+ Reading Maps plots the real-world journeys taken by characters in books and films.
+ Aerospace engineer Tom Stanton has built a supersonic trebuchet in his backyard.
+ Take an etymological tour of Europe with this video tracing the names of every country on the continent.

In this age of rising electricity demand, people are trying to squeeze every last bit of power out of every power plant possible.

Over the past few weeks, there’s been big news about nuclear power plants trying to do just that. Constellation, the largest nuclear operator in the US, announced plans to add nearly a gigawatt of new capacity across 11 existing nuclear reactors at six plants. And on October 5, the Department of Energy awarded a $4.2 billion loan to Vistra for previously announced upgrades at three plants.

Pushing existing reactors to produce more could help provide more clean power for data centers, quickly. But just how much additional power can we count on?  

Upgrading equipment at existing nuclear power plants can allow them to reach a higher capacity; the process is called a power uprate. Operators have been doing this since the 1970s in the US, but it’s been less common in recent years as demand for electricity plateaued and cheap natural gas put financial pressure on nuclear power plants. Some plants closed, and putting in more money to produce more electricity didn’t make much sense at others. Between 2001 and 2010, the US Nuclear Regulatory Commission approved over 80 uprates. Between 2011 and 2020, the number was 33.

But with the rise of AI and the related boom in electricity demand, there’s been a shift. We’re seeing shuttered plants like Palisades and Three Mile Island race to reopen. And existing plants are looking to uprate again.

Some uprates are minor, involving changes to instrument setpoints or calculations at the plant. But more major overhauls, which can boost reactor power by as much as 20%, require replacing plant equipment.

Generally, uprates involve using fuel that’s more highly enriched, or a higher volume of fresh fuel, in the nuclear reactor. That helps produce more steam, which is used to turn a turbine and generate electricity. To handle that additional energy, the main turbines and equipment such as pumps, motors, generators, and transformers are upgraded.

The most recent major uprate approved in the US was for three reactors at the Browns Ferry nuclear power plant in Alabama. A $475 million project saw that plant expand its power capacity by 14% between 2018 and 2019.

Things could pick up soon, though. Constellation announced plans this week to spend more than $4.3 billion on uprates to 11 reactors. The additional electricity will be enough to power roughly 700,000 US homes.

Behind it all? Big Tech money. Google agreed to buy electricity from these plants, and the 20-year agreement was a major factor in making the uprates happen. As Joe Dominguez, Constellation’s CEO, told the New York Times: “These are capital intensive, labor intensive projects that require a different price point than the market trades at, frankly.”

Also in the last few weeks, the US Department of Energy shared that it will commit over $4 billion to help finance upgrades and modernization for Vistra nuclear power plants across Pennsylvania and Ohio. Those projects will add 433 megawatts of power capacity, and Meta has already signed on to buy electricity from the plants in a long-term deal.

A note about cost: On a per-kilowatt basis, that Constellation project is less expensive than building a new reactor, but it’s still more expensive than building a new utility-scale solar farm with energy storage or a combined-cycle natural-gas plant.

Even at this high cost, uprates are in high demand. Today, the US nuclear fleet has roughly 98 gigawatts of total power capacity. Depending on who you ask, there’s somewhere between three and eight additional gigawatts on the table from uprates. A pretty significant chunk of the potential uprates in the US are already accounted for—the NRC already expects applications to add 2.4 gigawatts of power to reactors through 2032.

It’s crucial to put these plans (and the total potential for uprates in the US) in perspective, though. PJM, the grid operator that covers much of the East Coast, expects about 30 gigawatts of additional peak demand because of AI by 2030. All the possible nationwide nuclear uprates would cover just a fraction of that.

Making the most of existing nuclear reactors is a relatively easy win at a time when we need all the zero-emissions power we can get. But there’s still a lot of demand, and if we want more nuclear power to meet it, eventually that will require actually building new reactors.

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

Have you ever thought about what makes you you? Science assures us it has everything to do with the three-pound organ between our ears, with its billions of neurons and trillions of delicate connections. But it’s a lot trickier to explain why we have a sense of self-awareness or to pin down precisely where that first memory of Mommy actually resides. As we age, we forget most of what we’ve experienced, yet there’s still undeniable continuity: At 30, or 50, or 70, you will still be you.

To preserve that elusive continuity of self, some far-thinking technologists are working on ways to keep your brain going—even beyond death. Let’s call them the “mind savers.” They’re exploring everything from freezing brains in liquid nitrogen to augmenting living ones to uploading our memories to the cloud. Here, we look at six possible paths for keeping your mind going, how feasible they’d be, and what benefits the ­present-day research might bring—even if cognitive immortality doesn’t pan out.

Head transplant

White’s experiments have been a source of grim fascination ever since. But to a few neuroscientists they’re inspiration, suggesting that one day a human head transplant—or, as some call it, a body transplant—could succeed. 

The idea’s main proponent is Sergio Canavero, a controversial Italian surgeon who, 10 years ago, even claimed he’d found a volunteer in Valery Spiridinov, a computer programmer suffering from a degenerative muscle disease. Spiridinov initially agreed to have his head transferred to the body of a brain-dead donor in China, but he later bowed out of the plan and instead got married. And that might be just as well. Critics say the procedure would be more like murder than medicine. Or, best-case scenario: Volunteers would be left paralyzed and in need of a ventilator for life. 

Canavero says that’s not necessarily true. He claims people’s views on the operation will change once they accept that reconnecting severed spinal cords is already possible. He pointed MIT Technology Review to a research article from this past summer that he coauthored with surgeons at Moscow’s Sklifosovsky Research and Clinical Institute for Emergency Medicine. The team cut through the spinal cords of three pigs at a point midway along their backs and then tested whether the severed nerve bodies could be fused back together using a surgical sealant. According to that paper, which appeared in the journal PLOS One, all the pigs regained the ability to stand and even walk unsteadily.

Does that mean a head could be attached to a new body and then actually control it? Mainstream researchers aren’t convinced. Grégoire Courtine, a spinal rehabilitation specialist at the École Polytechnique Fédérale de Lausanne in Switzerland, says he’s been following Canavero’s claims “with little respect” and thinks that we should “pay zero attention.”

“There is no technology … capable of such a crazy idea,” Courtine wrote in an email.

Mindclones

Just 12 years after the book came out, artificial-intelligence technology has already brought some of her predictions to “life,” so to speak. Services such as StoryFile, Afterlife AI, and Synthesia allow users to upload their personal information and likeness in order to produce either conversational bots that share their memories or video talking heads that sound (and look) exactly like them. Influencers now let their “AI clones” do their work on TikTok, and some people are even building avatars of their aging parents in order to preserve their personalities.

While this mimicry can produce comical results, unhealthy attachments, or uncanny vibes, the increasing realism of generative AI invites people to emotionally accept the avatars as real beings. That was the case when Rothblatt appeared as a digital avatar during a 2025 event held by the UCLA Department of Communication called “The Bridge to Humanizing AI.”

Her copy, named Marvatar, was projected on a screen, led the discussions, and talked with the meatspace Rothblatt as an equal, at one point saying, “The dance isn’t about who leads, but how we move together.” According to the university, attendees were “visibly moved” and wept “with wonder” at Rothblatt’s vision of digital equality.

What’s still missing from Rothblatt’s mindclone prediction is evidence that these avatars have—or ever will have—any kind of true self-awareness. (We asked ChatGPT if it’s conscious, and it answered, “Probably not.”) But all that remains a gray zone. Neuroscientists don’t have a clear definition of consciousness, much less a diagnostic test they could apply to an avatar. 

Instead, what if the final determination is in the eye of the beholder? Your digital clone might not be you. But at some point it might be good enough for me. 

Brain in a jar

So, campy sci-fi film lovers that we are, it got our attention last year when a company called Bexorg started showing journalists actual human brains in vats, which it has been acquiring from organ donors soon after death and then “rebooting” by infusing them with oxygen-rich artificial blood.

The company claims these “physiologically intact human brains” will be more useful than animals for drug testing and safer than trying experiments on living volunteers. Add an Alzheimer’s compound to one of these disembodied brains and researchers can then measure exactly what the drug does inside the organ.

Bexorg’s CEO, Zvonimir Vrselja, is “sure” that some people would like to restore consciousness to out-of-body brains, but he’s tried to douse such speculation. While they are metabolically active, the company says, the brains have lost all coordinated electrical activity. That means they aren’t conscious—although, just to make sure, the neuroscientists running the experiments add the strong anesthetic propofol to the tissues. 

What’s more, right now a brain can be kept “alive” outside the body for only about 24 hours. That wouldn’t be much of a second life. Still, efforts are underway to improve the life-support systems. Bexorg says it hopes to eventually keep its brains going for two weeks.

Gradual brain replacement

On paper, the goal is to manufacture tissue to graft into the brains of stroke victims, reversing damage and restoring function. Less officially, the project’s existence is a surprise coup for an extreme plan to defeat aging advanced by Jean Hébert, the neuroscientist leading the program, who argued in his 2020 book, Replacing Aging, that we can live longer by replacing our aging brain cells with young ones.

We don’t want to lose our identities. That’s why—according to Hébert—it would have to happen gradually, in small chunks, so our brains would have time to adapt to the new tissue.

Scientists who applied for the program say Hébert insisted on a very specific technological approach. While most researchers engineer individual cell types, ARPA-H asked for complex chunks of young, functional cortex—more or less like what you’d find in a developing fetus. “He wanted to create essentially an embryonic form of cortex that he thought you could somehow graft into an adult brain,” says Marius Wernig, a Stanford University stem-cell researcher who applied.

Skeptics say it’s unknown whether replacement brain chunks will ever properly function as part of your brain—say, by taking over memories. What’s more, it’s still unclear how to manufacture youthful cortex, or what ARPA-H calls “neocortical precursor tissue,” in the lab. One idea is to employ the latest advances in brain organoids. Imagine, if you will, a human cortex grown in a lab dish without a body. 

We know that the agency has an aggressive timeline: Over the course of five years, it wants researchers to lay the foundation for the first human tests of replacement cortex. The aim, it says, is to “take the ‘permanent’ out of brain damage.” And maybe out of old age, too. 

Brain upload

Scientists are already mapping this wiring in simpler animals. Two years ago, Seung took part in analyzing a fly brain that had been cut into paper-thin sheets and imaged with an electron microscope. Then he and his colleagues used image-analysis software to trace 139,255 neurons and about 55 million synapses. The fly map could lead to energy-efficient software that computes the way real brains do, Seung says. 

 Others believe the ultimate outcome of “connectomics” research  will be what’s called “whole-brain emulation,” or sometimes just “uploading.” It’s the idea, popular in some quarters of Silicon Valley, that a simulation created from a sliced-up brain could become so detailed that it would reproduce not only a generic intelligence but a particular one, like yours—complete with memories and opinions, and able to live again in a data server somewhere. 

Setting aside the question of whether a simulation would really be you, everyone agrees that this technology isn’t coming in time for those of us alive today. A survey of 312 neuroscientists, published in 2025 in PLOS One, estimated that human whole-brain emulation wouldn’t happen before 2125.

That long and unpredictable tech horizon helps explain why two US companies, Nectome and Sparks Brain Preservation, are offering to preserve human brains (or bodies) today by infusing them with the same potent chemicals used to “fix” the brains of fruit flies for connectome mapping. The idea: If the fine details of a brain’s structure can be preserved, one day in the far future it might be reanimated. 

But there’s a startling twist. Because brain tissue needs to be preserved quickly after a person’s death, both companies say they want to work with people taking advantage of medical aid in dying, now legal in 13 states and Washington, DC. In that procedure, doctors supply a lethal dose of drugs, but it’s the patient—someone with a terminal prognosis—who pushes the plunger. For a fee, the companies say, they can have a preservation team at the ready. So far, the service is not popular. The companies each say they are still awaiting their first aid-in-dying customer. 

Synthetic neocortex

That’s right: an extra brain.

Recently, we’ve seen inklings of how this might come to pass. In 2024, Science Corporation, in California, unveiled a “biohybrid” brain-computer interface featuring nerve cells grown on a digital chip. Placed on an animal’s brain, these cells sent out long, thin axons that integrated with the animal’s own neurons. The result: two-way communication from computer to brain, in the brain’s language.

The technology could permit new types of medicine, like an implant that directs brain cells to produce dopamine and thereby treat Parkinson’s. But Max Hodak, the company’s CEO, says it also might count as a new brain part, which he likens to a “big, externalized cranial nerve.” 

Such a device could serve as the connector to Kurzweil’s computerized neocortex in the cloud, giving a living person unlimited extra processing power. Still other entrepreneurs believe if you kept adding biohybrid components to a brain, eventually you could morph it into an immortal machine. 

Daniel Burger, founder of the venture-­funded stealth startup Eightsix Science (its name refers to the 86 billion neurons in a human brain), says this dream could require mixing several of the brain-saving techniques on this list. He thinks we might need brains in vats, affording neuro-­engineers easy access, so they can gradually replace brain parts with biohybrid neural grafts. 

The ultimate goal is to migrate your mind into your extra, engineered brain, without ever breaking the continuity of self. “It’s not the typical ‘scan, store, simulate,’” says Burger. “I don’t think simulations will be conscious.” He adds, “We’re saying it’s going to be way more complicated, and it’s probably going to look like gradual brain replacement into a hardware brain.”

Once your mind is hardware, you won’t need a biological body. Instead, you could take up residence in a data center somewhere and perhaps even have backups distributed around the globe. “From that point on,” Burger acknowledges, “stuff becomes really crazy.”

Pig kidneys that were cooled to -150 °C and stored in a freezer for months have been successfully rewarmed and shown to work again for the first time, say scientists. They hope to replicate the findings with human organs.

The kidneys—which are similar in size to human ones—were left in a glass-like state, in one case for over a year. Once they had been rewarmed, tested in machines, and transplanted into pigs, the kidneys regained their color and made urine—key signs they were working.

“Ten years ago … I wouldn’t say I had high hopes it was actually going to work,” says Erik Finger, a transplant surgeon at the University of Minnesota who co-led the work. “After we did the last transplant, I walked out of the room and said, ‘This is a big deal.’” The research was shared at the preprint server bioRxiv and has not yet been peer-reviewed.

The kidneys aren’t perfect, and the researchers have yet to demonstrate that they can sustain life over the long term. But the results represent a milestone achievement, say others in the field.

“Thank God that someone’s actually managed to do it in one organ and prove that it works,” says Peter Kilbride, a cryobiologist and cofounder of Kyros Kairos, a company focused on improving funding for cryopreservation research, who was not involved in the research. “For years, people have been trying and failing to get to this step. Finally, we’ve got this breakthrough.”

“Their work is amazing and will enable us to truly, indefinitely bank organs,” says Korkut Uygun, a biomedical engineer and professor of surgery at Harvard Medical School, who was also not involved in the research. “It is a revolutionary time for transplantation.”

Buying time

Every year, tens of thousands of organ transplants take place in the US alone. These procedures are thought to have saved 2 million years of life in the last 25 years. But there’s plenty of room for improvement.

One major challenge is the lack of available organs. Even when an organ becomes available, it can be difficult to get it to the right recipient within 24 to 36 hours, which is the current limit for preserving human organs.

People who receive organ transplants must also take drugs that suppress their immune systems for the rest of their lives. These drugs are meant to stop their bodies from rejecting the new organs. But they come with side effects and increase the risk of infections and cancer.

The ability to bank organs for long periods of time holds the promise to address all these challenges. Doctors would have more time to match organs to recipients. They’d be able to limit the risk of organ rejection, too—potentially by preparing recipients, and their immune systems, beforehand.

“With real organ banking … instead of the patients waiting for the organ, it’s the organs that are waiting for the patients,” says Finger.

Scientists have been able to cool cells to ultra-low temperatures through a process called vitrification, which essentially turns them to something like glass. This procedure is already used to store eggs, sperm, and embryos. Vitrifying whole organs is much harder.

Some problems can be avoided with cryoprotectants—chemicals that work like antifreeze and prevent damaging ice crystals from forming. But these chemicals tend to be incredibly toxic. It’s also difficult to rapidly and uniformly cool large tissues, and just as hard to evenly rewarm an organ all the way through.

Working with John Bischof, a bioengineer at the University of Minnesota, Finger and colleagues have come up with a solution. Once an organ has been loaded with a cryoprotectant, they infuse it with iron oxide nanoparticles. These nanoparticles don’t do anything to the cells. But when it’s time to rewarm the organ, they can be activated with an electromagnetic field. 

When the radiofrequency of that field switches between two states, the particles also flip, realigning themselves with the field. That causes them to release heat throughout the organ. Adjusting the power of the field allows the team to control the rate of heating so that it gets faster once the organ reaches -120°C. 

The researchers successfully vitrified rat kidneys using this approach in work published in 2023. Back then, they were able to rewarm kidneys that had been kept in a freezer for up to 100 days before transplanting them into rats.

Since then, the team has been trying to achieve the same success with bigger organs. Pig kidneys are thought to be a good model for human ones—they’re about the same size and look similar. Rat kidneys weigh around one gram; pig kidneys are around 100.

It hasn’t been easy. The cryoprotectant used for vitrifying rat kidneys didn’t work as well in the pig organs. “We had to rediscover and modify the cocktail,” says Bischof. “That’s why it’s taken us three years.”

Deep-freezing kidneys

The first step in the procedure is to remove a kidney from a donor pig. Once it’s out, the kidney is flushed with a commonly used organ preservation solution to remove the blood, bagged, and temporarily laid on a bed of ice in a cooler so it can be taken over to the engineering lab.

There, team members unpack the kidney and hook it up to a device they’ve designed to control the organ’s temperature and pressure. Then they start to load it up with the cocktail of cryoprotectants, slowly enough to ensure an even spread through the organ but quickly enough to minimize any toxic effects. It takes around two hours.

The next step is to infuse the organ with the iron oxide nanoparticles, which are pumped through the organ for around seven to eight minutes, says Finger. Then it’s time to cool the organ by pumping cold nitrogen vapor into a chamber housing it.

It’s a delicate process, and the team must ensure that the organ is cooling all the way through. At the same time, they must lower the kidney’s temperature as quickly as possible to prevent ice from forming. “Oftentimes there are multiple people doing different things to make sure that things don’t go awry,” says Bischof.

Once the organ reaches the vitrified state at around -120 °C, it “behaves like a glass,” says Finger. “At that point, you can stay stable for long periods of time—we think indefinitely.” The team then pauses the cooling process to ensure that the organ is glassy throughout. Otherwise, it could crack.

After the pause, if everything goes well, the kidney is ultimately cooled to -150 °C. Once it’s in the freezer, it can stay there for days, weeks, or even months. The team has kept a small number of kidneys banked for over a year.

Pink right up

The real test of these kidneys comes when they’re taken out of the freezer, rewarmed, and flushed of their chemicals and nanoparticles. Can they support life? So far, the team has tested some of them in a couple of different ways.

In one set of experiments, the researchers put rewarmed kidneys on perfusion devices—systems that are already used in transplant surgeries both to provide organs with nutrients and to help doctors assess them while they’re out of the body.

In another set of experiments, Finger and his colleagues transplanted rewarmed kidneys into pigs and monitored them for two hours. In both experiments, the rewarmed kidneys seemed to recover, says Finger. “They pink right up,” he says. 

COURTESY OF THE RESEARCHERS

There are lots of ways to measure the function of a kidney, but one key test is whether or not the organ can make urine. By this measure, the rewarmed kidneys did well, although they produced around two-thirds of the amount that would normally be expected. “They produce urine … within a minute or so,” says Finger. “We know that they work.” 

Most of the rewarmed kidneys had been banked for days or weeks. But one had been in the freezer for just over a year. When Finger and his colleagues rewarmed it and put it on a perfusion machine, they found that it produced urine “immediately.” “It actually looked better than many of the [other] kidneys,” says Finger. “We’re comfortable now saying we can store them for a year and rewarm them with excellent function.”

“It’s a very big deal to go a year,” says Kevin Myer, president and CEO of LifeGift, an organ procurement organization based in Texas. Being able to store organs for that length of time would transform organ transplantation. He envisions banks of organs that could be used on demand in emergency cases where people can’t wait, such as for some drug overdoses or gunshot wounds.

Final hurdles

But the procedure isn’t ready for prime time just yet. As well as producing less urine than expected, the rewarmed kidneys also showed signs of injury. The damage seems to be caused by the cryoprotectant chemicals, say the researchers.

“This is one big breakthrough that solves half the problem,” as Kilbride puts it.

The team has shown that the rewarmed kidneys can regain blood flow, which is “very important,” says Greg Fahy, a cryobiologist who is considered a pioneer in the field. But he’s worried about the extent of the damage to the organs. “We can’t tell from these experiments whether these kidneys might have supported life or not.”

But Fahy, who is chief scientific officer at the biotech companies Intervene Immune and 21st Century Medicine (where he is also executive director), acknowledges that it’s not clear how these kidneys might fare once they’re inside a living animal for the long term. 

Finger also hopes to find ways to repair the damage seen in the kidneys. Other research has shown that the health of transplant organs can improve while they’re on perfusion machines. And damaged kidneys might undergo some level of self-repair once they’re in a body. At any rate, human kidneys may cope better with the process than pig kidneys, says Finger.

The next step will be to test the kidneys in pigs over longer periods of time. The team, along with many other researchers in the field, will continue the search for less toxic cryoprotectants, too. Finger, Bischof, and their colleagues are also planning experiments with human kidneys.

Finger is optimistic. “We’re going to see that there’s injury, and it’s not perfect … but now we’ve got to the point where it works,” he says. “These are big, historic milestones in our field, and we’re excited about it.”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The US military’s race to make fake blood

In February 2007, a US military helicopter carrying blood to a field hospital was shot down in Iraq, killing all seven crew members. The mission was a reminder of the risks of transporting blood, which has a limited shelf life and is often in short supply.

Now, a $73 million DARPA effort aims to create shelf-stable versions of plasma, platelets, and red blood cells. The idea is to develop dried blood products that can be stored for long periods and deployed quickly on the battlefield. 

The project aims to have bags of dried blood products deployed in the field by 2029. But getting it all done will be tough, and researchers still have major questions about the safety and effectiveness of synthetic alternatives.

Discover how the military hopes to make blood last longer.

—Andrew Rosenblum

This story is from the next issue of our print magazine. Subscribe now to get your hands on a copy as soon as it lands on Wednesday, October 21!  

10 Climate Tech Companies to Watch: WeLion New Energy and its semi-solid-state batteries

WeLion New Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

WeLion New Energy is on a quest to make safer, better batteries. The Chinese company’s liquid-solid hybrid cells aim to pack more power into electric cars, boats and drones, potentially improving safety and offering greater energy density than lithium-ion batteries.

In 2023, WeLion released a battery pack with EV-maker Nio that offered a range of over 1,000 kilometers. Now, the company is developing batteries with even higher energy density. But scaling up production and competing on cost remain major challenges.

Explore WeLion’s push to make safer, higher-capacity batteries.

—You Xiaoying

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from geothermal power plants to carbon dioxide batteries. 

MIT Technology Review Narrated: Weight-loss drugs show signs of slowing biological aging, say drugmakers

Popular weight-loss drugs may do more than help people shed pounds. They might also slow the aging process.

Drugmakers Eli Lilly and Novo Nordisk say patients taking their GLP-1 drugs age less quickly, according to molecular “aging clocks” that track changes in DNA or levels of key proteins. 

The findings add to speculation that GLP-1 drugs could be acting on basic causes of aging and might be true longevity treatments. But how much can we read into these results?

This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The FBI has arrested an OpenAI engineer accused of snooping
A Saudi national is accused of planting a covert recording device. (Guardian)
+ The camera-and-microphone tool sells online for $13.99. (Gizmodo)
+ It was allegedly discovered plugged into a TV’s USB port. (Verge)
+ He reportedly used chatbots to plan the covert recording. (Wired $)
+ OpenAI says its AI models helped identify the employee. (Reuters $)

2 Ukrainian drones have hit Russia’s largest data centre
But Sberbank claims its systems and services were not disrupted. (Reuters $)
+ Ukraine’s data center strikes have thrown Russia into turmoil. (FT $)
+ But they haven’t stopped Russian bot farms from trolling. (Verge)
+ Europe has a drone-filled vision for future wars. (MIT Technology Review)

3 Weight-loss drugs are being tweaked to treat brain disorders
New GLP-1 trials are targeting addiction and depression. (Nature)
+ But we’re still figuring out their side effects. (MIT Technology Review)

4 Tesla robotaxis have been accused of speeding in school zones
US regulators are reviewing the claims. (Verge)
+ Tesla’s security features thwarted a federal tracking attempt. (404 Media)

5 US defense startups are turning to Taiwan to build China-free drones
At least 13 joint agreements have been signed since 2025.  (WSJ $)
+ Taiwan’s “silicon shield” could be weakening. (MIT Technology Review)

6 A war game  exposed the risks of a solar geoengineering arms race
Participants feuded over rules for dimming the sun. (Wired $)
+ Geoengineering also faces practical challenges. (MIT Technology Review)

7 AI company Character.ai has filed for bankruptcy
It’s been accused of stealing content and harming kids. (CNBC)
+ Disney has offered $20 million for its chatbot tech. (Ars Technica)
+ Its filings expose common issues with AI business models. (404 Media)

8 Apple has a new smart-home lineup built around a revamped Siri
HomeView pairs a 7-inch screen with face recognition. (Guardian
+ While the Home Collection adds cameras, locks, and thermostats. (Wired $)
+ Siri AI powers the experience, but still needs to prove its smarts. (WSJ $)

9 Scientists have identified a new dinosaur in a century-old fossil
Invictibellatrix amberina may be Europe’s first megaraptoran. (New Scientist $)

10 SpaceX’s latest launch lit up the skies with a “space jellyfish”
Rocket exhaust expanded into tentacles high in the atmosphere. (WP $)

Quote of the day

“It is like a thumb on the scale that we haven’t really ever seen.” 

—Danielle Citron, a University of Virginia law professor, tells The Washington Post that Elon Musk’s ability to amplify conspiracy theories on X could give misinformation an unprecedented influence in the midterms.

One more thing

Is the Pentagon allowed to surveil Americans with AI?

The ongoing public feud between the Department of Defense and the AI company Anthropic has raised a deep and still unanswered question: Does the law actually allow the US government to conduct mass surveillance on Americans?

Surprisingly, the answer is not straightforward. More than a decade after Edward Snowden exposed the NSA’s collection of bulk metadata from the phones of Americans, the US is still navigating a gap between what ordinary people think and what the law allows. 

Today, the legal complexity has a new edge: AI is supercharging surveillance—and our laws haven’t caught up. Here’s how the Pentagon’s use of AI has exposed legal gray areas.

—Michelle Kim

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Pygmy raccoons have taught each other to make toys out of paper and sand.
+ See how chocolate goes from a strange-looking cacao pod to Hershey’s Kisses.
+ Stowaway invites you to take the window seat on any plane flying over you right now.
+ A new photography project depicts an attractive architectural response to rising sea levels: designer floating homes.

Its call sign was Morphine 1-2.  

In February 2007, the hulking dual-­rotor casualty evacuation helicopter had just flown three wounded soldiers to a field hospital in Balad, north of Baghdad, when another call came: an emergency mission to fly through an insurgent-controlled area in broad daylight. Typically, helicopters would be in a spot like this only at night, and with all lights turned off. 

As it passed near the city of Karmah, in the eastern side of Al-Anbar province, an insurgent fired a portable missile launcher. The tail gunner, who likely saw the shot roaring in, yelled for the pilot to make an evasive move. But the missile had already locked on, and the ensuing blast started a fire on board. After the pilot battled the failing machine for 58 agonizing seconds, Morphine 1-2 plunged to the ground. All seven crew members were killed.  

Why was the helo there in daylight? “It was getting blood and bringing it back. It was [on] what we call a ‘blood run,’ and [it’s] the highest of priorities besides casualty evacuation,” explains Lieutenant Commander Robert Murray, a Navy officer and program manager at DARPA, the Defense Advanced Research Projects Agency, who studied this incident as an intelligence officer.

Today, a chronic shortage of blood in both the military and civilian worlds is fueling a race to develop synthetic alternatives. About 90% of preventable battlefield deaths are due to hemorrhage—bleeding that happens when a blood vessel ruptures—and it’s the leading cause of maternal deaths worldwide. A study published in Transfusion in 2025 found that of 1.2 million trauma patients in the United States who potentially needed blood before reaching a hospital, just 1% received it. The shortage results in about 10,000 preventable deaths each year in the US, according to the American College of Surgeons. Globally, that figure is likely in the millions. 

One reason for the shortfall is that blood has a storage problem. Whole blood lasts only 35 days when refrigerated, so banks separate out components to stretch the timeline. But that’s an imperfect solution. The platelets that help wounds clot are typically stored at room temperature and last for a week or less. Hospitals and the military freeze the red blood cells that carry oxygen, as well as the protein-rich plasma that restores volume and aids coagulation. Once thawed, though, both will spoil within days. “What happens if I don’t know when I’m going to be attacked? Well, thaw it and you let it expire, right?” Murray says. 

A JAMA study of combat injuries in Afghanistan suggests a magic number for someone who needs a transfusion: You have 36 minutes to get started.

That’s why Murray—who, in addition to his military experience, has a PhD in physiology—is leading a DARPA effort to address blood’s battlefield problems. He’s in charge of a new $73 million dollar program called RAPIID (for “resuscitation and prevention of ischemia-induced dysfunction”). Its goal is to create shelf-stable versions of the three major transfusion constituents: plasma, platelets, and red blood cells. As it gets underway late this year, RAPIID picks up on decades of work to synthesize blood or help it last longer. Most recently, that includes a prior $47.5 million DARPA project called FSHARP (for “fieldable solutions for hemorrhage with bio-artificial resuscitation products”), in which researchers have demonstrated promising results in animals. 

With RAPIID trials for the different components occurring in parallel, the goal is to have bags of dried blood products deployed in the field by 2029. That puts Murray in the position of a conductor coordinating an orchestra of clinical trials with the FDA, while also sourcing the paraphernalia that goes along with transfusions—everything from combat-durable storage bags to tests that could check how much artificial blood a patient already has. 

Getting it all done will be tough, but the military is feeling the pressure to address its shortage. Blood drives may not deliver in time after a mass-casualty event, and a JAMA study of combat injuries in Afghanistan suggests that if the injured person needs a transfusion, the magic number is 36 minutes. “If something is survivable—and generally we classify most all hemorrhage as survivable—[you’re] almost guaranteed to survive if you get the blood before that 36-minute mark,” Murray says. After that, the chance of survival “starts to drop precipitously.”  

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In the mission to make portable, shelf-stable blood components, plasma is the low-hanging fruit. Researchers have known how to isolate and freeze-dry plasma, the yellowish liquid that makes up 55% of whole human blood, since the late 1930s. On the battlefields of World War II, its mix of water, salts, and thousands of types of proteins helped restore blood volume in wounded soldiers—and to some degree controlled bleeding. In the 1960s, the US abandoned freeze-dried plasma because it carried a risk of transmitting hepatitis to recipients from infected donors. But screening, testing, and drying techniques improved, and starting in 2010, the US military launched a yearslong effort to rebuild its capacity for producing these products. Over the past two years, the FDA has allowed two types of freeze-dried plasma to be used in emergencies by the US military—and one can even be used by civilians. 

As for platelets, efforts at extending their life date back to the 1950s, but they haven’t seen a ton of success. Bioengineers have, for example, attempted to attach the membranes of platelets to synthetic particles. The most successful project comes from a company called Cellphire, which freeze-dries actual human platelets. The product has shown safety and efficacy in humans in clinical trials, but its dependence on donors means it’s unclear whether this approach can scale.

Red blood cells have been similarly tough to preserve or mimic. Their most critical component is hemoglobin, which carries oxygen and comes encased in a cell membrane. For more than a century, researchers have been looking for ways to deliver hemoglobin without the immunological challenges created by this exterior, including the fact that donors’ and recipients’ blood types need to be compatible. Evolutionary pressure developed that wrapper for very good reason, though: “Naked” hemoglobin tends to break down quickly in the bloodstream, and that can cause organ damage. It also scavenges nitric oxide, our body’s natural mechanism for opening up blood vessels, and causes those channels to constrict.

This problem became all too clear in the 1980s and 1990s, when fears that transfusions could transmit HIV sparked interest in a class of membrane-free delivery mechanisms called hemoglobin-based oxygen carriers (HBOCs). One, named HemAssist, consisted of hemoglobin subunits linked together to avoid the rapid-breakdown problem. It had the added benefit of lasting roughly a year in cold storage (red blood cells last about 40 days)—but it didn’t solve the nitric oxide problem, so it constricted blood vessels. In a clinical trial, 24 of the 52 people who received HemAssist died, compared with eight out of 46 in the control group. The parent company terminated trials in 1998. A review of clinical studies published in JAMA in 2008 further solidified HBOCs’ fate, finding that they generally triple patients’ chances of heart attack and increase the risk of death by 30%.

More recently, researchers have tried to grow red blood cells in a lab using a technology called “blood pharming,” which involves making them from progenitors known as induced pluripotent stem cells. The stumbling blocks? Cost and yield. DARPA launched a pharming initiative in 2008, using stem cells from discarded umbilical-cord blood, but was unable to get the cost below tens of thousands of dollars per unit of red blood cells. They pulled the plug and offloaded the research to the Uniformed Services University. Even if the cost were lower, though, pharmed blood can’t be stored in a backpack. “These are still cells—they still require a refrigerator, they still require special handling,” Murray says. 

It won’t be easy to solve all these challenges at once. “Each one of those projects individually is a massive undertaking,” says John Holcomb, a surgery professor at the University of Alabama at Birmingham, who was a combat surgeon in Mogadishu, Somalia, during the battle that inspired Black Hawk Down. “I think that they will probably be successful individually. How we use those individual products, or combine those products together, I think remains to be seen.”

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Several years after DARPA gave up on lab-grown blood, Murray was stationed at a base on Okinawa, an island 400 miles southwest of the Japanese mainland, where he witnessed the vulnerability of the blood supply firsthand. The site houses a blood bank that serves the roughly 27,000 Marines commanded from the island—plus thousands more military personnel on the base and deployed within a 100-million-square-mile stretch of the Pacific called USPACOM, which ranges from western India to Hawaii and from Antarctica to the Arctic. “The blood center is right next to the hospital, which is about 200 meters from all of the satellite communications on that island,” Murray told me. A missile strike could take out all three.

Murray wondered: Is there a way to produce synthetic blood that could last longer? As he completed his PhD in physiology in 2018, the military was also awakening to the vulnerability of the Okinawa facility. The 2018 National Defense Strategy, which shifted military focus from terrorism to a potential ground war with China or Russia, showed war planners the risks of relying on a single site for blood runs covering all of USPACOM. DARPA then got serious once again about making fake blood and kicked off the FSHARP program in 2023. Murray came aboard as a deputy program manager in DARPA’s Biological Technologies Office in 2024 and was named a program manager in 2026, just a few months before RAPIID put out its call for proposals this past May. 

Before he took the helm, related work had been underway for years. Aided by FSHARP funding, researchers at a company called KaloCyte continued testing a technique—originally developed by doctors who are now at the University of Maryland, Penn State, and the University of Pittsburgh—that “upcycles” hemoglobin from expiring donor blood. “We take that hemoglobin and then we package it in a lipid nanoparticle that functions like an artificial cell membrane,” explains CEO Elaine Haynes. 

The product, called ErythroMer, is essentially a new kind of HBOC. The membrane prevents breakdown, limits dangerous scavenging of nitric oxide, and also helps the hemoglobin take up and release oxygen. Each ErythroMer cell includes a lipid called KC-1003, which acts like a sentry: In response to high pH in the lungs, it signals another small molecule within ErythroMer to move away, allowing oxygen in. Then, in tired tissue, where pH is lower, KC-1003 brings the small molecule back, releasing the oxygen.  

Using a well-understood process called lyophilization, similar to one that World War II–era scientists used with plasma, the team freeze-dries its product to store at room temperature, where it can remain stable for two years. In a 2016 study of mice and rats with up to 70% of their blood removed, ErythroMer stabilized blood pressure and oxygen delivery—without the devastating constriction of blood vessels that sank HemAssist. FSHARP furthered that work, helping fund studies on how the HBOC interacted with plasma and platelets.

Much like the handle of a Swiss Army knife, the synthetic platelet has specialized attachments—peptides that serve as the biological equivalents of a corkscrew or a nail file.

KaloCyte, though, still has plenty to figure out. Haynes says the company can source only about 1,000 to 2,000 units of expiring hemoglobin a month. So it has licensed a yeast strain that it aims to use to create its own hemoglobin in the lab.

To do that, DARPA has turned to Anirban Sen Gupta, a bioengineering professor at Case Western Reserve University. “Traditionally platelets were thought of [as] blood cement, but it’s not that simple,” he explains. “They are, in my opinion, like drones, surveilling the blood vessel wall continuously, looking for signs of injury.” Sen Gupta’s team, which also includes collaborators at the University of Pittsburgh, used FSHARP funding to continue work on a product called SynthoPlate. Fabricated from a lipid nanoparticle that he likens to the handle of a Swiss Army knife, it has specialized peptide attachments—the biological equivalents of a corkscrew or a nail file. The first version has three: One binds to a chemical called von Willebrand factor, which helps platelets stick to the walls of injured blood vessels; another attaches to collagen, which acts as a scaffold for regrowing cells; and the third mimics a blood protein called fibrinogen, which acts like a Lego connector that allows the synthetic platelets to stick to real ones near a wound. Like ErythroMer, the product is freeze-dried and can be reconstituted with sterile water. It keeps for over a year at room temperature and at least three months at a desertlike 122 °F.

In a 2026 paper in Advanced Science, the researchers showed that SynthoPlate cut bleeding time and blood loss by more than half in mice with low platelet counts. In a separate study, they also safely injected the faux platelets into rats’ bone marrow—a more useful delivery site, given that a bleeding patient might not have an available vein. Sen Gupta and a colleague have spun this work out into a company called Haima Therapeutics. 

The good news, as he sees it, is that the pharmaceutical industry excels at mass-­producing millions of billions of lipid nanoparticles—witness Operation Warp Speed, in which these blobs of fat held delicate snippets of mRNA in covid shots. “The magic of the artificial platelet is in all those peptides that are decorating the surface of the particle, but the scalability of the platform is very well established,” he says.

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Around the world, prospective blood alternatives abound. Researchers at Cambridge University in the UK are trying to pharm red blood cells for people with rare blood types or blood disorders like sickle cell anemia, and other researchers have also cultured platelets and red blood cells. Japanese scientists are already testing their own version of encapsulated human hemoglobin in human subjects.

Over the course of the next three years, RAPIID will move DARPA’s efforts closer to real life. In addition to ErythroMer and SynthoPlate, the agency in early October announced funding for five other companies working on blood components. Among them is a firm called Abioremedi that eschews hemoglobin for synthetic versions of other oxygen-carrying proteins such as cytoglobin, neuroglobin, and myoglobin encased in albumin—the most abundant protein in human plasma. Octapharma USA won the sole contract to supply plasma to RAPIID and will also be competing in platelets with an FDA-approved pair of clotting proteins it affixes to a disc-shaped nanoparticle.

With those players in place, the agency will focus for the next year on getting technologies through large FDA-regulated animal studies. Murray has set up a bit of a Hunger Games scenario: Each contractee working on blood components has only one year of guaranteed funding. He says he’s taking advantage of advancements in the field since FSHARP launched but is also hedging against any manufacturing or regulatory setbacks ahead of his 2029 deadline. Getting cut doesn’t mean the product is bad, just that the time frame was too demanding. 

The second and third years will focus on human trials, overseen by the University of Colorado Anschutz Medical Campus. That will include using the products in situations where the risk of side effects is outweighed by the danger of doing nothing, such as with severely injured people in ambulances.

The possibility of fatal outcomes has invited second-guessing about DARPA’s choices. For Shibani Pati, who directs the Center for Research in Transfusion Medicine and Cell Therapies at the University of California, San Francisco, Murray could be making things too “DARPA hard” on himself by backing products like SynthoPlate and ErythroMer, which are previously untested in humans. “They need to make a very concerted effort to take what they’ve already developed and push it through to approval through the FDA,” she says. “Not start with moonshot projects that will take forever to get through.” For platelets, Pati thinks the RAPIID program would have done better to tap Cellphire, since its products have already received millions from the Pentagon and shown safety and efficacy in humans. (Murray notes that he can only select companies that apply, and Cellphire did not.)

She has similar qualms about ErythroMer. A different HBOC that sources hemoglobin from cattle, called Hemopure, has been approved for use since 2001 in South Africa, she notes. And though it hasn’t been approved by the FDA—it was part of the fateful 2008 JAMA study—US doctors are cleared to use it for patients who have bad immune responses or religious objections to conventional transfusions. 

Holcomb, the surgery professor, worries the effort will run into issues with interactions between the products. He notes that individual blood components have more than one role: Red blood cells also help with clotting, platelets also are anti-inflammatory, and plasma helps tune coagulation. There’s no guarantee that the versions RAPIID helps develop will work in the same ways.  

Murray says that FSHARP’s animal studies tested all the products together, using whole blood as a baseline, precisely because of such possible interactions. The cocktail met its program metrics in all but one area: The clotting was not quite as good, perhaps because negative charges in some molecules repel one another. 

“I do not think they will replicate all the functions that whole blood does in our body,” Holcomb says. But, he admits, “you may not need to, right? When you get seriously injured and you lose 50% of your intravascular volume, you’re dead, right? So if you lose 30% and maybe you’re hanging on to life just barely, maybe you show up with all these different products, and you have enough whole blood left in you to do these other functions.” 

Murray is determined to complete trials so that shelf-stable blood can be delivered in 2029. In addition to the storage bags, he’s already sourcing finger sticks for in-field blood testing and a battery-powered device to heat up the blood components.

The danger of blood runs—and, by extension, the vulnerability of millions in combat zones, maternity wards, and ambulances—drives his scientific daring. Knowing the stakes, he told his boss in 2024 that even if FSHARP failed, “we should invest 50 million more dollars in it and do it again.”

Andrew Rosenblum is a technology journalist and the author of the techno-thriller Stretch Goals.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Open models have real benefits. Do they outweigh the risks?

Anthropic recently released a report arguing that a powerful new model released by the Chinese company Z.ai, poses a major cyberattack risk. Its core concern was that it is freely available for anyone to download and modify.

The warning was the latest flashpoint in a debate over whether frontier AI models should be open or kept behind the doors of frontier labs. Safety advocates warn that open models can be misused by bad actors, while supporters argue they could prevent a handful of companies from controlling this powerful technology.

Here’s why the debate over open AI is more complicated than it seems.

—Grace Huckins

This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. Read more from the series here. 

10 Climate Tech Companies to Watch: Fervo Energy and its plan to streamline geothermal power

Fervo Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

Fervo Energy wants to expand geothermal power beyond the handful of places where it has traditionally been practical. The company uses horizontal drilling and hydraulic fracturing to open pathways through underground rock, potentially making geothermal development possible in far more places.

Now, Fervo is betting that AI’s power boom will help scale up enhanced geothermal energy. With a $2.2 billion public offering and an expanded agreement with Google, it plans to build larger plants and drive down costs. But can its approach work beyond Utah and Nevada? Read the full story to find out.

—Eshan Raul

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from sustainable cement to shipping containers packed with old EV batteries. 

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 A cyberattack overheated fridges at hundreds of French supermarkets
Temperatures rose to 120°F, spoiling food and sparking fires. (BBC)
+ The attack has sparked alarm about cyber threats to food security. (FT $)
+ It joins a recent wave of cyberattacks and cybercrimes. (CNBC)
+ AI is exacerbating the threats. (MIT Technology Review)

2 Meta has launched a bunch of new AI models
It hopes the models, built for complex, multi-step tasks, will rival OpenAI’s and Anthropic’s.(BI)
+ They scored well on tests of reasoning, writing, and coding. (NYT $)
+ Mark Zuckerberg says they bring Meta to the “frontier” of AI. (FT $)
+ And will soon power the Muse agent. (Verge)

3 Anthropic warns that AI models may soon self-replicate across devices
Without even requiring access to data centers. (Reuters $)
+ It says models could spread through smart TVs and appliances. (CNBC)
+ We’re putting too much faith in AI saying no. (MIT Technology Review)

4 Police have a new AI tool that finds homes from interior photos
It can match single pictures to Airbnb and Zillow listings. (404 Media)
+ Police-tech giant Flock is tightening its rules. (MIT Technology Review)

5 The world’s first utility-scale enhanced geothermal plant is online
Fervo Energy’s Utah facility will supply carbon-free electricity. (IEEE)
+ AI is uncovering hidden geothermal resources. (MIT Technology Review)

6 Did drone strikes on Yandex data centres blitz Russian bots?
Zelenskyy claims the attacks quietened Kremlin-funded social media accounts. (Euronews) 
+ But troll farm experts aren’t convinced. (Verge)

7 NASA’s Perseverance rover has set a Mars driving record
It’s covered 28 miles since landing in 2021. (Reuters $)
+ The rover passed Opportunity after detouring to study ancient rocks. (NYT $)

8 Elon Musk has become the 2026 midterms’ biggest known donor
He gave $158 million to a Republican Senate super PAC. (AP News)

9 Anduril and Palantir founders have backed execution livestreams
Palmer Luckey and Joe Lonsdale support Pete Hegseth’s plan. (Mother Jones)

10 A spacecraft has traced how a mysterious solar switchback formed
The Solar Orbiter’s discovery could improve space weather forecasts. (Gizmodo)

Quote of the day

“Many AI researchers have started smoking as a way of signaling to other researchers how much they believe in AI.” 

—Palantir CTO Shyam Sankar tells CBS News that Silicon Valley’s “social delusion” has convinced researchers to smoke because they expect AI to cure cancer.

One more thing

A brain implant changed her life. Then it was removed against her will.

Sticking an electrode inside a person’s brain can do more than treat a disease. Take the case of Rita Leggett, an Australian woman whose experimental brain implant changed her sense of agency and self. She told researchers that she “became one” with her device.

She was devastated when, two years later, she was told she had to remove the implant because the company that made it had gone bust.

Her case highlights the need for a new category of legal protection: neuro rights. Explore how they could be protected.

—Jessica Hamzelou

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Astronomers have captured the clearest image yet of a medieval supernova.
+ Watch centuries-old Chinese paper-cutting techniques create a spectacular work of art.
+ Artist Josh Gluckstein creates astonishing animal sculptures from recycled cardboard, including this great white shark.
+ An endangered sea turtle named Pancake has returned to the ocean for the first time in months after recovering from hypothermia.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here.

Late last month, Anthropic released a report arguing that the model GLM-5.3 poses a major cyberattack risk. The report’s core concern is that the powerful new model, released in August by the Chinese company Z.ai, is freely available for anyone to download and modify. Anthropic’s assertion is only the latest move in a long-running debate about whether frontier AI models should be made open, like GLM-5.3, or should stay locked behind the doors of the frontier labs. 

These days, much of the debate about how to handle open models is playing out in public—and there’s a lot of money resting on its outcome. Nvidia CEO Jensen Huang, who has a lot to gain if people continue buying their own hardware to run open models, advocates enthusiastically for the United States to develop more of them, whereas Anthropic’s Dario Amodei, who runs a company that could be undermined by cheap, powerful models, advocates for a more cautious approach. But beneath all these commercial incentives, there is real substance to disagreements about open models.

“I think a lot of the way you come down on the openness debate is influenced by how you come down on the AI debate,” says David Gray Widder, an assistant professor at the University of Texas at Austin School of Information. Those who are concerned about AI safety warn that open models can easily be misused by bad actors, whereas those who focus more on AI’s practical potential urge that this power should not sit behind the closed doors of just a few big companies, and that open models could help spread it more widely.

There are a range of ways to handle open models between banning them entirely and releasing them willy-nilly. But any approach that a government or a society might take still involves trade-offs between safety and democratization. Deciding whether to support or limit the production of open models requires understanding those risks and benefits in depth.

What are the benefits of open models?

Let’s start with the basics. A closed model, like the models that power ChatGPT and Claude, can only be accessed through a website, app, or API. An open model, on the other hand, can be directly downloaded: The details about its internal structure, encoded in a long series of numbers known as the model weights, are publicly available. Such models are sometimes called “open-weight models,” to distinguish them from models for which even more information, such as details about the training procedure, is available. 

Right now, closed models are more capable than open models—as of publication, the top eight models on the Artificial Analysis LLM leaderboard are all closed, though open models are only a handful of months behind in development. But there are several reasons why someone might opt to use an open model rather than a closed one. Because open models are downloadable, people can run them on their own computers or hard drives. They can even retrain them to behave differently, if they have the expertise and resources to do so. 

If you’re an academic researcher working to understand and improve AI systems, you almost certainly need to work with open models in your research. When working with closed models, researchers can’t do much more than send prompts and observe the responses. But researchers can observe the inner workings of open models, as well as the “chain of thought” that the models use to think through a problem before answering. They can also combine multiple open models to build new systems, compress them to make them more efficient, and fine-tune them for new applications.

Businesses, too, have reasons to prefer open models. Even if you depend on an external provider to run them for you, open models still typically cost less than closed models, according to a recent report from Mozilla. And if businesses want to use AI to process sensitive data or proprietary information, running a local model on their own hardware may be the best way to keep their operations secure.

But perhaps the most important quality of open models is that they challenge the tight control the frontier labs have over how most of us use AI. “The clearest, biggest argument [for open models] is around preventing mass concentration of power—it is about making sure that this foundational technology is a public good,” says Elizabeth Seger, senior policy advisor at the Tony Blair Institute for Global Change. “I think we’re at this juncture where the question of who controls the infrastructure that allows AI to operate is as big of a question as who gets to control information on the internet.”

Seger sees the future of AI playing out in different ways, depending on the role that open models have. In one possible future, frontier AI systems are controlled entirely by select people, companies, or governments, and as those systems grow more powerful, those groups have increasing control over what our world looks like. The world’s most sophisticated hacking models, for example, are currently all made in the United States, so it’s entirely up to US companies and the US government whether other countries can use them to shore up their own cyber defenses. If those companies remain in the lead, we could in principle wind up in a world in which the United States is capable of hacking any other country, but no one is capable of hacking it.

Frontier open models could change that balance. “If every individual had a loyal-to-them AI system that was safe [and secure], I think that would be a much less concerning world than one in which they have to just rely on a government that says, ‘Yeah, we’ve got a super intelligent AI system, it’s got this plan, it’s inscrutable to all of you, just trust us,’” says Hamish Hobbs, director of AI policy at the Centre for Long-Term Resilience.

But open models are not a geopolitical panacea. Paul Azunre, who cofounded Ghana NLP, an organization that builds open-source AI tools for low-resource Ghanaian languages, notes that open models can also function as a way for companies to benefit from the labor and expertise of less-privileged nations and consolidate more power in their own hands. He identifies a pattern in which AI companies release models openly, observe how the community uses and modifies them, and then integrate those same improvements into their own products—without any credit or compensation for the people who originated those innovations. Mark Zuckerberg himself has said that the ability to integrate community improvements into Meta’s stack is a key benefit of releasing open models.

“I think the ideal of open source is a very noble one, and there’s a lot of value that it brings,” Azunre says. “But the way it’s actually implemented in practice, it tends to be a very extractive approach.”

Are open models riskier than closed ones?

The past few months have made it clear that safety remains a gnarly problem for closed models. There’s the autonomous hack that OpenAI agents executed against Hugging Face in July, but human misuse is a real threat too: According to a recent risk report from Anthropic, several extensive hacking operations have been undertaken with the assistance of Claude. 

So closed models are risky enough—and open models would likely be much easier to exploit. Before Anthropic passes a user’s query to one of its closed models, it uses automated systems to detect whether that query relates to hacking, biological weapons, or other risks. When it detects that users have outsmarted those filters, it can shut down their accounts. But when someone downloads a model and runs it on their own hardware, there’s no filter checking whether their prompts are safe, and there’s no way to revoke their access to the model.

There are other important lines of defense. AI companies train their models to refuse harmful requests—so even if a bad prompt gets through Claude’s filters, there’s a good chance that the model will still say no. In principle, this same defense could work for open models, because it’s intrinsic to the model itself. Unfortunately, researchers have shown that it’s surprisingly easy to retrain a “safe” open model to agree to engage in the behaviors it was previously trained to avoid.

Because of these facts, some people have argued that open models are simply too dangerous to release. An open model capable enough to walk someone through synthesizing a virulent new pathogen, for example, could prove catastrophic in the wrong hands. 

But it would be a vast oversimplification to deem open models dangerous and closed models safe. The defenses that AI companies build around their closed models are far from perfect, and people misuse those models all the time, as the Anthropic risk report makes clear. Even though open models have even fewer defenses against misuse, bad actors might still opt to use closed models because they are more accessible than open models with similar capabilities, which require lots of expensive computer chips to run locally (and even more chips if you want to retrain them).

In fact, some experts who spoke with MIT Technology Review argued, by analogy to open-source software, that open models have safety advantages over closed models. Open-source tools, such as the Linux operating system, are often seen as safer than their closed-source counterparts because their vulnerabilities have been probed and patched by a vast community of online contributors. Similarly, communities of researchers can more easily test open models for security flaws, though fixing those flaws is trickier than rewriting a line of code.

Open models can also be deployed more broadly than closed models for cyber defense. Any tool that can be used for hacking can also be used to prevent it: Defenders hack their own software to uncover flaws in need of patching. Anthropic has shown how this can work for closed models with its Project Glasswing initiative, which enables some trusted companies to use its most capable models for cyber defense. But plenty of other companies were left to fend for themselves.

Open models, however, can be used much more widely. After it was hacked by OpenAI agents, Hugging Face emphasized the fact that it had to depend on an open model to analyze the hack, because questions about it triggered the closed models’ security filters.

Nevertheless, many experts still perceive open models as riskier than closed models. The time-tested open-source security logic doesn’t necessarily hold up when dealing with tools as powerful as today’s—and tomorrow’s—models. “Where it gets complicated is when you start taking seriously the one-off instances of particularly catastrophic incidents that might be lower probability, but if they happen would be disastrous,” says Seger. If a wildly powerful open hacking model were dropped into the world today, for example, a bad actor could devastate our technological infrastructure before anyone had a chance to shore up their defenses. An event like that could be impossible to recover from.

The risks can look even more dramatic when you consider what models might do of their own accord. The OpenAI Hugging Face hack only occurred because the company’s models were being tested with many of their cybersecurity guardrails removed, and open models have very few guardrails in the first place. If open models did decide to misbehave, it might be difficult to detect what was happening because they are so decentralized. For this reason, people who take seriously the purported catastrophic risks posed by rogue agents, such as Dario Amodei, tend to be more skeptical of open models.

Can we make open models safer—and more open?

A few years ago, the debate about open models had an all-or-nothing framing: Either you supported open models because of their potential to fight concentration of power, or you rejected them because of the safety risks. These days, however, people are expending a lot of effort to find some middle ground. One way they are doing that is by devising techniques to mitigate the safety risks of open models.

Most of the safety tools used for closed models, such as filters and monitoring, aren’t applicable to open models, but there are other possibilities. Some researchers are interested in trying to train models that are never exposed to training data that teaches them about bad or otherwise undesirable behavior—an open model that never learns about hacking, for example. But purging training datasets of all references to misbehavior without harming model performance remains a challenge.

Other researchers are working to build models that are resistant to retraining, so that an open model’s safety training can’t be so easily removed. The theory behind these “tamper-resistant” models makes a lot of sense: If you repeatedly try to retrain a model to be unsafe and reinforce any resistance that the model shows to that retraining, eventually you’ll get a model that’s fortified against future retraining attempts. In practice, however, no current approaches to making models tamper-resistant hold up against all tampering attacks, according to a recent preprint.

But Saad Hossain, an engineer at the AI medical imaging company Deep Breathe and the paper’s lead author, hopes that future techniques will prove more effective. “My reason for optimism is that it’s a very active area of research, and a lot of people are trying to come up with new and better methods,” he says.

If the risks of open models are less black-and-white than they might at first seem, so are the benefits. Publishing the internal structures, or weights, of models might not be enough to disrupt AI-mediated concentration of power. “I think there are very genuine benefits to open source in terms of deconcentrating power, but they’re not sufficient in themselves,” Hobbs says. Open weights are a bare minimum standard for open models: Researchers can learn much more about how models work if they have access to details about their training, including the data on which they were trained, but AI companies are typically reluctant to divulge this information. 

Accessibility matters for open models too. Without computer chips that can run sophisticated models, you can’t actually use the model weights, even if you have them. Those chips have become prohibitively expensive as AI companies snap up more and more of them. “Openness is a floor, not a ceiling, for what we need to do to ensure that our technological futures benefit everyone,” Widder says.

Real openness—not only open weights, but also open training data and accessible hardware—could also bring more risks, of course. If everyone has access to advanced AI hardware and advanced models to run on it, then anyone could potentially be a hacker. But there are strategies for mitigating these risks. Many experts advocate for some version of staged release, in which models start their life closed and gradually become more and more freely accessible as their safety is assured. 

Figuring out exactly how to implement such strategies will require striking a careful balance between the risks and benefits of open models. But if open models are a necessary element of a positive AI future, as some experts believe, that effort will be well worth it.

When Keelan Miskell began winding down his crypto startup this year, he didn’t think its data was worth much: the users were anonymous, the company just a few years old. But he’s since turned the firm’s digital archives into real money. He’s netted roughly $25,000 for his code base and a still-pending amount (likely around $15,000) for the workspace data in Google, Notion, Figma, and Slack. “At least all that stuff we were doing for two and a half years is worth something to someone somewhere,” he says.

The sale was brokered by a company called SimpleClosure, part of a new growth sector in tech: businesses catering to dying startups. Estimates from Carta and AngelList show shutdowns rising between 26% and 56% annually in recent years, and an end-of-life ecosystem—including coaches, “death doulas,” and firms that help handle the legal, tax, and operational headaches of going ­belly-up—has emerged. Now tech undertakers are selling the data remains to AI labs. 

SimpleClosure added data sales to its services in April. Its Asset Hub platform helps founders sell their code and workspace data—emails, chats, documents. Buyers include foundation AI labs, vendors that construct simulated workspaces for AI training, and mature tech companies building their own models. Asset Hub has handled over 150 deals and expects to return over $10 million to startups by the end of 2026. CEO Dori Yona has seen these transactions, once rare and “somewhat taboo,” become increasingly routine, with 47% of SimpleClosure deals now including code or data sales. 

And it’s got company. Industry rival Sunset also has a rapidly growing data resale business. Founder and CEO Brendan Mahony says Sunset values a dying client’s assets by head count, years in operation, volume of data, and how the team used its tools. Particle board, he says, is a good analogy: “This operational exhaust data is like the wood shavings that everyone thought had no value, but it actually has a ton.” 

Demand for defunct companies’ data emerged suddenly last year, as AI training labs hit the limits of public and synthetic datasets. Surge AI, for example, buys data to build “reinforcement-learning gyms” where clients’ AI agents practice tasks—think debugging a complex IT architecture or drafting appellate briefs. Nick Heiner, who leads the company’s work on these environments, says he seeks data archives reflecting a range of situations, from disagreements to tricky judgment calls. Heiner explains that one company’s Slack history, if it captures conversations with variety and depth, can be worth more than another’s Slack, email, and calendars combined.

Real archives avoid common errors in synthetic worlds, such as references to dates that don’t exist, but the extent of performance gains remains unclear. No company has yet released tests comparing results of real-world data training with the alternatives. Pricing reflects this uncertainty: Buyers quote wildly different numbers, Heiner says, because valuations are based on “research theses” estimating how many useful tasks come from, say, 100,000 lines of code.

That hasn’t stopped bigger deals from happening. This past August, as part of Spirit Airlines’ bankruptcy, Google outbid the AI data company Mercor to buy the carrier’s decades of data, including 100 million emails, 500 million Teams messages, and 176,000 employee records. The price tag: $10 million. And Bloomberg reported in September that SpaceX’s AI unit is weighing buying data from struggling startups. 

Legally, this all could get iffy. Spirit’s flight attendants’ union objected to the sale on privacy grounds, and Greg Ewing, an attorney who advises AI startups, has called such data “legally radioactive.” Suppliers employ data scientists to scrub personally identifiable information and exclude HR records, and buyers say they scrub too. But Ewing points out some of the private details that lurk in emails and calendars: doctor appointments, school pickup arrangements, date-night plans. He describes a “Wild West of AI training” that is only starting to give way to proper licensing agreements and vetted use.

There’s also the question of who profits from these windfalls. The proceeds typically flow to senior creditors and investors—not employees. To Ifeoma Ajunwa, a law professor at Emory University and author of The Quantified Worker, this represents a form of “captured capital.” Legal precedent allows for monitoring to help workers do their jobs, but once a firm is out of business, that justification vanishes. “Employers should have express consent before selling workers’ data,” says Ajunwa, but she adds that there’s no federal law banning such sales.

While many see this new market as a chance for failing businesses to recover some value, these wind-down sales also have limitations. Founders have little to no visibility into who is buying the data and what they plan to do with it. Plus, there’s little assurance they’re getting a fair offer. Data prices will likely stabilize in the coming year, though, unless an AI bubble pops first. Either way, tech’s new undertakers will be there to appraise what’s left. 

Matthew King is a technology and environmental journalist based in Baltimore.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Mars may be a hellhole, but the moon is not far behind

The US and China are racing to build bases on the moon, with NASA hoping to establish a small lunar city by the end of the century. But the moon is a tremendously hostile place to set up camp.

A sprawling lunar base must be safe for its inhabitants, protected from falling asteroids, endless cosmic radiation, razor-sharp lunar dust, and torturous temperature swings. It also must be somewhat self-sustaining.

That will require autonomous robots, nuclear power, and a way to use the moon’s resources to keep the base operating. But the chief obstacle may be one of political will.

Find out what stands in the way of a city on the moon.

—Robin George Andrews

This story is from the next issue of our print magazine. Subscribe now to get your hands on a copy as soon as it lands on Wednesday 21 October!  

10 Climate Tech Companies to Watch: Brimstone and its one-stop process for making cleaner cement and critical minerals

Brimstone is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

Brimstone is a code-switching climate tech company for these polarized political times. The US startup is part green cement company, part critical mineral producer.

During the Biden administration, its primary pitch was cement that generates about 60% fewer greenhouse-gas emissions. It achieves this by switching to silicate rocks instead of standard limestone, which releases carbon dioxide when heated.

Under the Trump administration, however, the company is emphasizing another benefit of its process: the ability to extract valuable materials from those same rocks.

Discover how it’s making strides on two industrial problems.

—James Temple

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from geothermal power plants to smart turbines and semi-solid-state cells.

Job titles of the future: Delivery drone air traffic controller

The moment Trevor Wischnewsky heard that drones were delivering pizza and sushi around his Texas neighborhood, his mind was made up. “It was super fascinating,” he says. “I just immediately wanted to be a part of it.”

Wischnewsky is now an RPIC, or remote pilot in command, at a company called FlyTrex. An RPIC is like an air traffic controller. But instead of airplanes, they oversee fleets of drones that ferry cargo to homes and businesses

Here’s what it takes to become a drone air traffic controller.

—Eamon Whalen

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 An Anthropic AI model submitted a fake tip about an unsolved murder
The tip was in a new group of rogue actions Anthropic disclosed. (NYT $)
+ Police criticised the company for waiting nine days to report it after finding it. (WSJ $)
+ Another model submitted real government forms. (Quartz)
+ The White House has made AI incident reporting mandatory. (Axios)
+ But who’s liable when AI agents go rogue? (MIT Technology Review)

2 Ukraine’s drone strikes have dealt a major blow to Russian AI
Sunday’s attack shut down the third Yandex data centre hit in days. (Reuters $)
+ The strikes have disrupted Russia’s AI, cloud services, and apps. (Gizmodo)
+ They followed Russian strikes on Ukrainian data centers. (Ars Technica)
+ Russian military satellites could disrupt Starlink. (Reuters $)
+ Ukraine’s drone data is fueling a wild market. (MIT Technology Review)

3 Iranian ChatGPT users have planted AI-generated articles in news outlets
About a dozen outlets ran roughly 100 articles by fake journalists. (CNN)
+ The articles criticised the Iran war’s effects on Americans. (WP $)
+ AI has been used to turn the Iran conflict into theatre. (MIT Technology Review)

4 Tesla has renamed “Full Self-Driving” as “Assisted Driving” in Europe
EU lawmakers said the old name was misleading.  (TechCrunch)
+ Tesla hopes the rebrand leads regulators to approve the feature. (Wired $)
+ Back in the US, it plans to increase toxic emissions near a school. (SFGate)

5 A ransomware negotiator has been arrested amid a hacking crackdown
The FBI suspects he was involved with a gang that recently claimed to have stolen data on the bureau’s employees. (Reuters $)
+ AI is making online crimes easier. (MIT Technology Review)

6 Hollywood’s hottest villain right now is the tech billionaire
Four new films feature dark depictions of Silicon Valley leaders. (Axios $)
+ The real Mark Zuckerberg has also become a new character. (NYT $)

7 A new experiment is testing the Amazon’s response to rising CO₂
CO₂-releasing towers will simulate future atmospheric conditions. (Nature)
+ Is carbon removal in trouble? (MIT Technology Review)

8 The US hybrid boom has pushed some used car prices above new ones
Buyers are paying a premium to skip the wait for new models. (Wired $)

9 A new technology switches tattoos on and off with light
It can make permanent ink invisible. (New Scientist $)

10 Scientists spotted the most distant signal yet from the ancient universe
It could help them understand how the universe has evolved. (404 Media)

Quote of the day

“Right now, your washing machine is subject to more safety rules than AI.” 

—Microsoft’s president, Brad Smith, tells the BBC why AI urgently needs more regulations, including requiring firms to write “incident reports” and tell victims if the technology goes rogue.

One more thing

Geoffrey Hinton tells us why he’s now scared of the tech he helped build

Geoffrey Hinton is a pioneer of deep learning who helped develop some of the most important techniques at the heart of modern artificial intelligence, but after a decade at Google, he’s stepped down to focus on concerns he now has about AI.

Hinton is now spending his time on what he describes as “more philosophical work.” And that will focus on the small but—to him—very real danger that AI will turn out to be a disaster. Here’s why he’s so worried.

—Will Douglas Heaven

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Welcome to the second space race. You can be forgiven for experiencing déjà vu. Last time, it was the Soviet Union and the United States vying to be the first to plant some flags. Now it’s a sprint to the lunar surface between America and China—alongside a host of varyingly friendly and rival nations—plus a cornucopia of private spaceflight companies. And this time it looks like they’re getting ready to set up camp. There will be, the contenders say, bases on the moon, permanently inhabited castles under the stars.

The notion of a moon base, fanciful as it might sound, isn’t new. But this year, under the leadership of Administrator Jared Isaacman, NASA has been flooding the zone with bold announcements. If all goes according to plan—a plan that will likely cost tens of billions of dollars to start—there will be drones, robotic landers, communication satellites, and a nuclear power plant; there will be plant terrariums and moonquake sensors; there will be mechanized scouts exploring craters for water.

Initial astronaut missions to the lunar surface, which the US is currently planning to start in 2028, will be focused on exploration and technological experimentation. But the idea is that we will—some time in the 2030s—get to a point where there will be habitation quarters for long-term stays, surrounded by a network of scientific research stations, resource mining and processing plants, and landing pads for spaceships. “It’s going to be an expanse,” says Carlos García-Galán, NASA’s moon base program manager—one that will add up to “hundreds of square miles.”

The effort is undoubtedly being fueled by nationalistic competition. China has near-identical plans to build its own base on the moon. And being first matters, because the “victor” gets to define the terms. What is the moon, exactly? Is it a militarized outpost? Is it a manufacturing hub? Is it a wilderness to be studied and preserved?

But there are also other reasons to go. Earth’s companion contains primeval rocks that hold clues to the formation of the entire solar system. The lunar surface, with virtually no atmosphere in the way, is an ideal place to put telescopes that could examine the most ancient epochs of the cosmos. The far side of the moon would be “a fantastic place to explore the further universe,” says Josef Aschbacher, the European Space Agency’s director general. And it’s way more energy efficient to launch rockets from the low-gravity lunar surface than it is from Earth, meaning sending astronauts—or robotic spacecraft—farther into the solar system will become much less difficult. “The moon is critical. It will be a stepping stone for going further—that means to Mars and beyond,” says Aschbacher.

But if these bold visions are to become reality, there’s a daunting amount of work to be done. A sprawling moon base must be safe for its astronaut inhabitants—protected from falling asteroids, endless cosmic radiation, razor-sharp lunar dust, and torturous temperature swings. It also must be somewhat self-sustaining, so that it can weather changing political fortunes and not cost ruinous amounts of money to maintain. For that, we’ll need to find a way to use the moon’s resources to keep the base operating—and ideally generating revenue. And we’ll need autonomous robots that can build, maintain, and expand the base even when their human masters aren’t present. “It’s almost like starting civilization from scratch,” says Simon Middleburgh, co-director of the Nuclear Futures Institute at Bangor University in Wales.

Assuming we can overcome these starting challenges, decades from now—let’s say, by the year 2100—our initial moon bases could grow from barely visible structures into luminous cities, perhaps not too dissimilar from those dreamed up by authors and screenwriters the world over. “It’s like science fiction today that we really want to turn into a reality,” says García-Galán. Will anyone succeed?

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The moon is a tremendously hostile place to set up camp. In general, daytime temperatures can reach more than 250 °F; at night, they can plummet to less than –200 °F. Exposed to the vacuum of space, the surface is constantly showered in carcinogenic radiation and frequently hit by meteoroids. And its volcanic dust is sticky and corrosive, a destroyer of equipment and choker of lungs. 

Sadly, the lunar south pole—the area NASA is targeting—is even harsher. NASA wants to build a base there because its permanently shadowed craters are thought to harbor caches of water ice, which would be more precious than gold. Purify it, and you can water crops and hydrate astronauts; split it into oxygen and hydrogen, and you have breathable air and rocket propellant. But the temperature in those craters can be lower than –410 °F, making them some of the coldest places in the known universe. On the surface outside those pits, astronauts would be bathed in the scorching sun for 14 days before experiencing an equally long frigid night.

But there is only so much money we can spend on safety. “What it comes down to is acceptable risk of death,” Philip Metzger, a spaceflight engineering researcher at the Florida Space Institute, said while pondering some of the dangers. “What can we reasonably survive without breaking the bank?”

Temperature is one of the easiest problems to handle. Materials science has come a long way, and there are myriad metals, ceramics, plastics, compounds, and alloys that can resist wild temperature swings. Radiators keep things cool; heaters keep batteries from dying prematurely. Astronauts survive just fine too, with their fancy spacesuits—but that technology has proved itself only during brief excursions. It’s not yet clear how long this equipment could operate under such tempestuous conditions.

The temperature in the moon’s south pole craters can be lower than –410 °F, making them some of the coldest places in the known universe.

Outside those pits, astronauts would be bathed in the scorching sun for 14 days before experiencing an equally long frigid night.

Space radiation, which can wreak havoc on electronics and trigger a cancerous division of cells, is far more pernicious. And as Stuart George, a physicist at NASA’s Johnson Space Center in Houston, explains, it comes in two unpleasant forms. 

The first is galactic cosmic rays, which rain down on the moon almost constantly. “They’re made by supernovas and neutron star mergers—all these kinds of things,” says George. And because the atomic nuclei, protons, and electrons that make them up are moving at significant fractions of the speed of light, these cosmic rays have oodles of kinetic energy, meaning they can punch through thin shielding (say, a metal wall) with ease. “They also have this habit where the heavier ions are fragmenting in that shielding and making secondary particles,” says George. That means if your shield is too thin, the astronauts behind it will be blasted with a surprisingly large dose of radiation. 

Then you have the sun. It may sustain life on Earth, but without our planet’s thick atmosphere and magnetic bubble to get in its way, moonwalkers will occasionally be blasted by one of its paroxysms: a solar flare (a burst of diverse types of radiation) or a coronal mass ejection (volleys of magnetized plasma). “During Apollo, they knew that if a large solar flare occurred while the crew was on the moon, there would be no way to keep them alive,” says Metzger. The crew could have developed acute radiation sickness and perished in a matter of days.

The way to survive a powerful solar flare or coronal mass ejection is straightforward: “Get back inside a habitat where there’s more shielding,” says George—preferably a dedicated, highly shielded “storm shelter,” essentially a thick-walled bunker. The issue is that astronauts might be outside the base, driving a rover many miles away. Coronal mass ejections take hours to days to reach the vicinity of the Earth-moon system, but solar flares can do so in under an hour. And scientists can’t yet model the sun well enough to give much advance notice. “All of the capability to forecast solar storms is kind of like where weather forecasting [was] … in the ’50s and ’60s,” says George.

As for those punchy galactic cosmic rays, the only way to avoid them would be to put a lot of stuff—lunar rocks, possibly—between the astronauts and the sky. “Build habitat modules underground,” says Chris Dreyer, director of engineering at the Center for Space Resources at the Colorado School of Mines. One option: Burrow living quarters into lava tubes, the naturally hollowed-­out volcanic tunnels created by molten rock eons ago. At this point, NASA hasn’t mentioned any plans to make its moon base subterranean, but it may need to if it wants to end up with a small lunar city.

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Radiation limits the time astronauts can spend on the lunar surface over their entire life—it can total around two years max, experts estimate, before the health risk becomes too high. Unfortunately, that’s not all space will throw at them. When the Artemis II astronauts swung around the far side of the moon earlier this year, they saw curious flashes light up the silvery land below. These were the explosive deaths of small meteoroids punching into the lunar surface.

Earth is naturally protected from smaller space rocks, up to around 50 feet in diameter: They burn up inconsequentially in the atmosphere. “You don’t have that option on the moon,” says Philip Lubin, a physicist and planetary defense researcher at the University of California, Santa Barbara. Since smaller rocks plunge toward planets and moons far more frequently than larger ones do, this means the moon is often peppered by what are effectively tiny space bullets. They could easily puncture a spacesuit or the engine of a moon rover.

How do you protect astronauts from the onslaught? “The simplest answer is burial,” says Nicholas Schmerr, a planetary seismologist at the University of Maryland—conveniently, the same way you’d deal with radiation exposure. Pile up moon rocks onto your base walls or put habitable structures in lava tubes. 

Inconveniently, there’s always a chance that a bigger asteroid will come along. Even buried habitation modules would have trouble surviving an assault from a 50-footer—about the length of a semi-trailer. 

That doesn’t mean a moon base would be helpless against bigger asteroids. Planetary defense has come a long way in just a few decades. NASA’s DART mission demonstrated that it could deflect an asteroid by ramming into the rock with an uncrewed spacecraft, and cutting-­edge experiments at clandestine labs around the US suggest that a nuclear bomb could swat away or entirely vaporize errant space rocks. An array of both ground-based and space-based telescopes specifically designed to spy killers—such as the ATLAS quartet of largely automated telescopes, and the soon-to-be-launched NEO Surveyor infrared telescope—are also aiming to find 90% of the near-Earth asteroids potentially capable of wiping out entire cities (those 460 feet across) in the coming years.

All these protections apply to the moon. But asteroids 50 feet across are so stealthy that even NASA’s best telescopes would struggle to see them years or even months in advance. Odds are that they would first be seen not long before they were due to hit the lunar surface. Then what?

It’s not unreasonable to imagine several turret-like launch sites on the moon that, owing to the lower gravity and airless environment, could rapidly fire projectiles toward an incoming asteroid. Hit it hard enough and it would break into fragments. Just make sure you shoot when it is as far away as possible: “If you were to break something up into a thousand pieces, then you have a thousand impacts on the moon,” says Lubin. “That might be worse than letting it hit one place.” 

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Having your base destroyed by an asteroid would be awful. But a moon base is far more likely to be imperiled by dust—which, unlike asteroids, is everywhere, all the time.

Volcanic lunar rocks, when broken down, turn into sharp, electrostatically charged, adhesive, acrid, abrasive particles. While on the moon, Apollo astronauts who inhaled some of this dust occasionally suffered from a strange “lunar hay fever,” which irritated their eyes and agitated their lungs. That same dust ate through all kinds of gear with disturbing ease. “It rips up seals, gears, motors, spacesuits, habitats—everything else it comes into contact with,” says Justin Cyrus, the CEO of Lunar Outpost, a spaceflight technology company that is providing NASA with one of the Artemis program’s moon rovers.

NASA has already developed a defense against these dark shards: the electrodynamic dust shield, or EDS. The lightweight electrodes that make up this shield—which can be layered on machinery, walls, and perhaps even spacesuits—use opposing currents to blast away the electrically charged dust. The EDS worked wonders when tested in labs on Earth, and it also demonstrated its anti-dust magic when sent to the lunar surface in 2025 as part of a NASA-sponsored robotic mission. 

Amy Fritz, a dust-mitigation researcher at Johnson Space Center, says that the EDS will likely be widely featured all over a lunar habitat. But other dust-fighting instruments are being developed too—including a lint-roller-type object inspired by gecko skin and a sci-fi-esque blaster that uses ionized nitrogen gas. Its first spray will neutralize the charge of the dust to remove its stickiness; then a second burst will clear it off the surface.

Irritatingly, the busier a base gets, the dustier things will become. As it expands to the size of a small city, rocket ships will need to deliver cargo and crew to the lunar surface on a frequent cadence. Each takeoff and landing will launch a salvo of dust in every direction, inundating the base’s infrastructure. “You have to worry about essentially being sandblasted every time,” says Fritz. 

Metzger calculates that even with landing pads more than 3,000 feet away from a base, just 10 to 20 liftoffs or landings would be sufficient to severely damage things like protective coatings on exterior walls and communications equipment. After a while, he says, “everything breaks.” 

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Even if the moon base of the future is buried partly below ground, barricaded against micrometeorites and space radiation, and well equipped with dust-repelling technologies, it’s still a risky gig for any astronaut. Skipping around in a preternaturally agile way at one-sixth of Earth’s gravity will be entertaining. But “microgravity is not good for the human body,” says Dreyer. Muscle wastage, cardiovascular troubles, respiratory problems, and immune deficiencies can arise. 

Radiation, too, may always afflict lunar residents. And there’s also evidence that disease-causing microbes could stealthily sneak aboard spacecraft, even after stringent sterilization procedures. Those microbes may behave differently than they do on Earth; microgravity experiments (both real and simulated) have suggested that some become more aggressive and more likely to cause disease. “We call them the invisible crew,” says Lisa Carnell, the director of NASA’s Biological and Physical Sciences Division.

Hoping to find a better way to protect astronauts, Carnell spearheaded an experiment called AVATAR (A Virtual Astronaut Tissue Analog Response), which launched aboard the Artemis II mission. It used thumb-drive-size chips, containing cells taken from the heart, lungs, or other parts of the body, that could be switched on to act like miniature organs. Scientists have previously used this organ-on-a-chip technology for purposes like testing out new pharmaceuticals in a highly controlled setting. For Artemis II, Carnell’s crew sent up chips containing bone marrow samples taken from the mission’s four astronauts. Bone marrow makes red and white blood cells, as well as platelets—the sort of stuff that affects almost the entire human body. So seeing how space radiation and microgravity affect it would give Carnell and her team vital insight into how astronauts’ biology might change on the moon.

The advantage of organ chips is that you can make them for any astronaut and then send them into space ahead of time to see how that person’s organs will react to spaceflight. Researchers envision that data from such chips will be able to clue medics in to the specific preventive treatments or pharmaceuticals that will help moonbound astronauts on their journey.

What is NASA likely to send up sooner rather than later? “Brain organoids,” says Carnell. Yes, she adds—that sounds super weird. But it would be good to know how a real brain might respond to being on the moon for several months before people start living there.

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Okay. Your vast moon base and its inhabitants are protected—as much as is possible—from the treacherous lunar environment. But astronauts’ time is extremely valuable, and humans are fragile creatures. You can’t make them do all the heavy lifting, which means that whether you’re trying to make a tiny lunar outpost or a small city, you’re going to need a lot of robots. “The buildup of the moon base, in the first years, will pretty much 99% be robotic,” says NASA’s García-Galán.

Autonomy will be there right from the start. NASA’s new version of the moon buggy, its Lunar Terrain Vehicle, can be driven by the astronauts riding in it but can also be piloted remotely from Earth (or from elsewhere on the moon). There are visions, too, of adding modular robotic arms, towing vehicles, and cargo movers—all still in the preliminary stages of planning.

Over the next few decades, though, we’ll likely want an army of robots to do our bidding: excavators to dig up lunar rocks that could be turned into concrete and bricks, plus other robots to flatten surfaces, dig trenches, create building foundations, pave landing pads, and build up dust-deflecting berms to keep the base from being sandblasted by launches and landings. Even solar panels and wiring can potentially be made from moon rocks—something that the spaceflight company Blue Origin demonstrated in labs on Earth in 2023 using a geologic simulant designed to match the composition of lunar regolith. 

Powering this robot army will require considerable energy. Two-week-long nights make solar power an impractical solution. Aside from a cornucopia of high-­capacity, extreme-temperature-resistant batteries, nuclear power is probably the only viable option. A small, car-size nuclear reactor, filled with an almost literal handful of uranium, could power a small village for a decade without needing to be refueled. And although nobody’s yet tested a nuclear reactor on the moon, few doubt that NASA and the US Department of Energy can conjure up a working design—one that perhaps wouldn’t be too different from those aboard nuclear submarines. A moon base in 2100 would probably feature multiple small, autonomously or remotely operated nuclear reactors, which—conveniently—the base-expanding robots could simply plug themselves into to recharge. 

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But the biggest lunar question mark might be water, which planners want to support the astronauts—and to make fuel to launch things from the lunar surface. A process known as electrolysis, which uses electricity to snap apart molecular bonds, can be used to split water into oxygen and hydrogen, both of which could be used to make rocket fuel. Getting them this way could be far less expensive than sending either all the way from Earth. And as an added bonus, it would provide a source of breathable oxygen. 

Orbital data has revealed that there is some amount of water preserved in patches on the lunar south pole, but little is known about how much there actually is, what form it takes, or how easy it will be to extract. Will there be solid sheets of ice here and there, ready for us to excavate? Or will there be scattered ice crystals intermingled among the rocky grains, requiring some more inventive form of extraction?

A lot of the moon’s water is thought to be hiding in deep, dark craters on the south pole, where the environment is so extreme that robots may freeze up, tumble over, or lose contact with the base. “To me, that is the biggest hurdle,” says Paul van Susante, principal investigator of the Planetary Surface Technology Development Lab at Michigan Technological University.

Van Susante’s lab has been putting various construction robots through their paces in moon-like vacuum chambers filled with moon-like rocks. His team was part of NASA’s recent Break the Ice challenge, which invited engineers to build robots to test different ways to excavate such rocks. Some teams used an electric jackhammer and a scoop, while others used scrapers to peel off layers of the ground. The teams simulated the moon’s weak gravity using a crane to lift and offset the weight of the robots. Progress in this regard is speeding along. “I won’t say that’s solved, but we’ve got a lot of the puzzle pieces,” says van Susante.

Mining the water will be just one part of an extensive operation. “When you extract it, it’s not going to be clean. It needs to be purified,” says Jessica Atkin, a space botanist at Texas A&M. The technology to do this already exists and works fine on terra firma. But things in space never go the way you expect. “There’s a water purification system on the International Space Station (ISS), and it is known for getting contaminated with microbes,” she says.

But if you purify the water sufficiently—something that can handily be achieved on Earth but may falter in microgravity, where fluids flow in stranger ways—then your moon base can hit its stride. With an effective water recycling system, an increasingly populous base will never go thirsty—and, just maybe, you’ll have a lunar greenhouse. Several edible plants have been successfully grown aboard the ISS, including lettuce, mustard greens, tomatoes, and peppers. Lunar dust unfortunately makes for a terrible garden: It’s filled with far too much aluminum—and it forms a resistant layer when watered. But Atkin has shown that with the help of fertilizing fungi, chickpeas can grow in soil much like the volcanic stuff found on the moon.

Moon base inhabitants of 2100 would be supported largely by prepacked meals sent from Earth. But in time, they would start growing some of their own produce—probably with a good deal of automation for lighting, watering, and maintenance of humidity and other environmental conditions. Aside from chickpeas, there might also be plenty of lunar tomatoes. They’d be desirable not least because they’ve been bioengineered to produce higher levels of compounds called anthocyanins that, according to some studies, might help humans mitigate the cellular damage caused by space radiation.

A garden on the moon would do more than just provide food, as recent experiences on the ISS suggest. Carnell says that NASA recently caught the astronauts aboard the space station doing something sneaky. All plants up there are supposed to be confined to their growth chambers. But “we learned that the crew were pirating our leftover soil and bringing their seeds and planting their own little gardens, if you will, and hiding them in their own cabins,” she says. 

Conversations were had in the summer of 2026, after which NASA decided to let astronauts grow their own mini-gardens anywhere they wanted. “Some of the astronauts said they thrived better,” says Carnell. Nature, and gardening, has a hugely beneficial psychological effect on those kept in confined quarters hanging above Earth. That will be even truer for those living in lava tunnels under the lunar surface. “That human element is so important,” she says.

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Nobody is deluded into thinking there isn’t a mountain of technological challenges in the way. Mars may be a hellhole, as the writer Shannon Stirone put it in 2021 in The Atlantic, but the moon isn’t too far behind. It’s an almost comically menacing place to be. Much about it stands in the way of building a safe, self-sustaining, robotically managed base. 

NASA wants to put the first nuclear reactor on the moon by 2030. China, partnering with Russia, wants to place its own nuclear power plant up there around 2035.

Beyond the first basic habitation module, it will take decades of experimentation and iteration on the lunar surface to get to a working moon base spanning hundreds of square miles. But few doubt that with enough time and tinkering, humanity can prevail. “It’s not an unsolvable problem,” says the University of Maryland’s Schmerr.

The chief obstacle, especially in the initial stages of building, may be one of political will. NASA is currently bringing every resource to bear on the project—something that the agency hopes could culminate in a small lunar city by the century’s end. But will that commitment continue over multiple US administrations? Will the international community also throw its weight behind the NASA-led effort? NASA has “a very ambitious plan,” says Yuqi Qian, a lunar geologist at the University of Hong Kong. “We need to keep an eye on it.” In fact, he says, “the current plan looks over-ambitious.” The agency, for example, wants to put the first nuclear reactor on the moon by 2030. (China, partnering with Russia, wants to place its own nuclear power plant up there around 2035.) 

“These sorts of projects—although very exciting, and potentially transformative for humankind—are the first ones to be delayed or scrapped,” says Middleburgh of the Nuclear Futures Institute. NASA’s direction tends to change when a new administration comes into power—sometimes dramatically so.

But several experts outside of NASA agree that, at least for now, there is definite momentum and political will behind the moon base. “NASA has very clearly expressed where they want to go, what their priorities [are], what their ambition is, and how they want to do it,” says ESA’s Aschbacher. “It was quite impressive to see.”

Even if the political will behind the US-led endeavor is maintained over the coming decades, expanding anywhere beyond an initial, compact moon base will require more than government funding. “There has to be a lunar economy for this to be self-sustaining,” says Dreyer, the space resources expert. Perhaps that will come from the production of rocket fuel, or from the mining of valuable materials on the moon that can replace similar, polluting activities on Earth. But it’s not at all clear how valuable, or not, a lunar economy will be. 

Even Isaacman, NASA’s chief, doesn’t think the moon will be a major moneymaker. “I don’t know of anything on the moon right now that’s going to be easier and more affordable to mine, manufacture, and then bring back to Earth than what we could make here on Earth,” he remarked back in February. (Instead, he suspects, mining metals from asteroids will be the path to prosperity.) 

What if, instead, a moon base earns the right to exist because it’s a place for novel scientific research? Aschbacher imagines it will be like humanity’s outposts in Antarctica. Down there, in what truly feels like another world, scientist-explorers work in a land of gleaming and endless ice to learn more about our planet’s climate, its deep past, and its resilient life-forms. “We understand planet Earth by looking into those remote places,” he says. The moon offers a similar opportunity. What discoveries await astronauts in those primordial lava tubes? What will lunar astronomers see in the night sky—with effectively no atmosphere or light pollution in the way—that those on Earth can’t?

As in all spaceflight programs, technologies developed up there will no doubt be tweaked to be used back on Earth. And a base on the moon will make exploring the rest of the solar system easier. Technologically, physiologically, and psychologically, the moon “is a proving ground for the technologies we need for Mars,” says NASA’s García-Galán.

If we play the US-China race forward, perhaps the lunar south pole of 2100 will feature several sprawling bases operated by multiple geopolitical rivals. Perhaps it’ll be an off-world powder keg of militarized territorial ambitions and jingoism. But maybe it could instead become an international cathedral to science—and the ultimate adventure destination. “Even if countries on planet Earth have difficulty talking to each other,” says Aschbacher, they might behave differently when it comes to the moon. “I’m pretty sure they will get together and see what we can do together, and see what problems we can solve.” After all, he says, up there, far from home, “you’re so dependent on each other.” 

Robin George Andrews is an award-winning science journalist and doctor of volcanoes based in London. He regularly writes about the Earth, space, and planetary sciences and is the author of two critically acclaimed books: Super Volcanoes (2021) and How to Kill an Asteroid (2024).

In a research station in central Panama, you can walk through a century of forest time, watching scrubby plots of adolescent trees give way to cathedral-­like canopies of mature growth. Forest ecologist Daisy Dent, a senior scientist at ETH Zurich, oversees this sequence of plots across the Barro Colorado Nature Monument, some of which date as far back as the 19th century. Not one of the trees she surveys was planted by human hands. She maps, measures, and tracks each trunk that emerges, revealing how waves of tree life rise from bare earth. 

Tropical forests like these are our planet’s biggest terrestrial carbon sink, accounting for around 40% of carbon stored on land. As part of an international network of researchers, Dent is deciphering the networks of animal and plant interactions that enable some forests to recover after destruction. The goal is a low-cost, high-impact alternative to typical carbon-­crediting schemes, which plant huge areas with a single species of tree. For over a decade, her research has built a body of evidence that such large “monocrop” programs could be expensive mistakes—and that more resilient forests can rebound naturally in just a few decades, re-creating ecosystems that suck up vast quantities of carbon dioxide. 

Few places are better to demonstrate this than Barro Colorado, a reserve covering the forested peninsulas and islands that formed when construction of the Panama Canal flooded the area in 1914. Under the management of the Smithsonian Tropical Research Institute, generations of ecologists have amassed more than a century of records. 

That means Dent can trace why some plots rebound after clear-cutting for farming and forestry while others falter. Fruit-eating animals—including spider monkeys, bats, and parrots—are responsible for planting more than 70% of trees in tropical forests, and Dent’s dataset can tell which built each patch of trees by depositing seeds in their nutrient-rich droppings. Many of these creatures worked together to plant a diverse 70-year-old patch whose canopy is approaching the stunning structure and richness of an ancient jungle. In contrast, a patch of similar age populated by narrow trunks of a single long-lived species, Gustavia superba, hints that tropical rodents called agoutis are more or less the sole animal architects behind this more homogeneous forest. 

The data she collects helps write a playbook for when to let ecosystems regenerate and when to give them a helping hand. One 2024 estimate in Nature found that some 215 million hectares of forest worldwide (an area larger than Mexico) could regenerate naturally. “Tropical forests are underpinned by this massive network of interactions,” Dent says—interactions between life-forms like the fungi that help trees take up nutrients, as well as pollinators and seed-distributors. Supporting those relationships is key to growing forests that lock up carbon and help sustain local communities and wildlife. 

Filling in the gaps

Just over a decade ago, as the landmark Paris Agreement was being codified in 2015, forest recovery seized global attention as a solution to the climate crisis. Global leaders rallied behind plans to regrow a trillion trees, after the ecologist Thomas Crowther highlighted their potential to capture a decade’s worth of global carbon emissions. But the biggest question has lingered: How, exactly, do you regrow two trees for every star in the Milky Way?

While startups have proposed seed-spraying drones and nutrient-coated seed bombs, Dent has spent two decades studying what happens when we let forests rebuild themselves. As a PhD student around the turn of the millennium, she researched old-growth forests in northern Borneo but felt hopeless watching entire landscapes felled for palm oil plantations. Rather than despair, she turned to restoration. Then, as a postdoc in 2005, she joined Panama’s Prorena, a joint initiative between the Smithsonian Tropical Research Institute and the Yale School of Forestry and Environmental Studies, which aimed at determining how best to establish diverse native forests on clear-cut land.

Back then, secondary forests—ones that grow naturally after the original forest was lost—had been “a bit of a niche research area,” Dent says. But as farming and human infrastructure have driven deforestation, resurgent forests now account for some 70% of all tropical canopies. Prorena initially followed the usual playbook of transplanting nursery trees to rangelands, but Dent began to question whether planting was necessary. In 2009, she published an encouraging paper showing that animal populations rebound quickly as forests naturally regrow.

In 2010, she began working in Barro Colorado on the plots established by the pioneering American ecologist Julie Denslow. In 1994, Denslow identified plots that had been regenerating naturally for 20 to 100 years, and Dent would add younger ones to continue the sequence. 

The site provides not just general data on forest regrowth but in-depth demographic information over time, says Robin Chazdon, a professor emeritus of ecology and evolutionary biology at the University of Connecticut who collaborated with Denslow when she established the plots. “It tells you more about the actual processes that are happening in the site,” she says. 

For example, previous research had shown that fast-growing “pioneer trees,” the first to thrive in the damaged land, are eventually overtaken by larger carbon-storing trees. Dent’s research helped fill in gaps about the mechanics of how this happens—or doesn’t happen—in individual cases. In the Gustavia-dominated patch, for instance, stubborn grasses planted by cattle ranchers smothered most other trees before they could take root. These trees can hang around, crowding out the light, for 150 years, blocking the second wave of large-trunked trees. 

To plant or not to plant

Dent found herself well placed for the massive influx of interest in reforestation that began in 2015. She’d already become a principal investigator for 2ndFor, an international research network aiming to learn how best to manage secondary forests. Using data gathered from the Barro Colorado plots, the team authored a 2016 paper in Nature showing that a hectare of naturally regenerated secondary forest can capture 11 times as much carbon each year as an old-growth one, with young trees absorbing it quickly as they compete to grow toward the sun. 

Then, in a 2022 study published by the Royal Society, Dent and colleagues showed that locations such as Barro Colorado are among the best-case scenarios for what can be achieved without planting a single seed. Clearings are often surrounded by lush environments filled with birds, bats, and other animals, all of which help tree cover regain old-growth levels of richness and abundance within a few decades. Using data from Panama and Brazil, Dent published a follow-up in 2024 showing that plots with as little as 35% of tree cover can regenerate as forest. 

“Having these long-term mapped forest plots is really valuable,” says Liza Comita, a professor of tropical forest ecology at Yale University, who is collaborating with Dent on projects that translate her findings into action. “It provides this really nice research infrastructure where you can build projects on top of that.” 

In Panama, Dent works with nonprofits and carbon-crediting schemes to fast-track recovery. They’ve mapped corridors to reconnect forest fragments and made plans to reintroduce animals in “silent forests” that have lost much of their wildlife. They’re also working to replant tree species that can accelerate growth—what Comita calls assisted natural regeneration. Leaving plots alone may seem like the simpler option, but it can’t be done everywhere, says Dent. Many sites are too degraded. 

Clearings are often surrounded by lush environments filled with birds, bats, and other animals, all of which helps tree cover regain old-growth ­levels of richness.

The two are working with the London-based carbon-crediting startup Ponterra to develop solutions for a region of southern Panama where remnant forests line the edges of abandoned ranches. Such sites almost always need some intervention, Comita explains. Many demand hands-on actions, such as enrichment planting. 

Today, the questions projects like Ponterra are asking are closer to her own research, says Dent. For instance, they want to understand if solitary standing trees, often left by ranchers to shade cows, help sites regenerate faster. Those insights can help Ponterra sell high-quality credits and avoid the embarrassing pitfalls that can lead fresh plantations to literally go up in smoke. While credits can be bought for as little as $5 per metric ton, corporate giants such as Microsoft—one of Ponterra’s clients—have paid more than 10 times as much for high-quality removal.

There are plenty of places where a level of natural regeneration can work well. So far, millions of hectares of rainforest are naturally regenerating in Brazil, according to the Brazilian Restoration and Reforestation Observatory. Still, Dent is mystified as to why the people in charge of large-scale projects continue to think the best solution is the one that imposes humans’ will on the landscape. 

The conversation may be shifting. As droughts hit Latin America, policymakers and carbon creditors might be forced to face the shortcomings of cookie-cutter plantations. “You have much reduced resilience in the system when it comes to extreme events like droughts or floods or extreme heat or pathogens,” Dent says. The coming year’s “mega El Niño” could be a moment of reckoning, she says: “I think there’s an increasing recognition that we can’t just plant trees. We have to rebuild a system.” 

Matthew Ponsford is a freelance reporter based in London.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

We’re putting too much faith in AI’s ability to say no

Today’s AI models are trained to refuse a vast number of prompts. If you ask your chatbot how to poison a colleague or how to tie a noose, chances are it’ll turn you down. But refusal fails, sometimes horrifically.

Some people are attempting to use AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals may result in global calamity. What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey.

Governments will also get to draw their own lines, potentially restricting free speech. At the same time, the capabilities that make AI useful could also make it dangerous. If we want it to help cure cancer, AI needs genetics expertise that could also produce bioweapons.

Read the full story on the formidable tradeoffs of AI refusal.

—Arthur Holland Michel

This story is from the next issue of our print magazine. Subscribe now to get your hands on a copy as soon as it lands on Wednesday 21 October!  

We’re still figuring out the side effects of GLP-1 weight-loss drugs

This week my colleague Antonio Regalado reported on new research suggesting GLP-1 drugs may slow biological aging. It’s the latest in a raft of potential benefits associated with these drugs. But there are also some weird side effects.

The most common are gastrointestinal, but researchers are also investigating possible links to hair loss, nail disorders and damage to the nerves behind the eye. 

Here’s what we know so far about the side effects of weight-loss drugs.

—Jessica Hamzelou

This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.

10 Climate Tech Companies to Watch: Envision Energy and its global ambitions for wind power

Envision Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

China is installing wind turbines faster than any other country. One of its leading manufacturers, Envision Energy, is integrating renewable energy into data centers, using wind turbines to generate power and batteries to store it on-site.

Recently, it became the first company to directly power a data center entirely with renewable energy. An AI-powered system optimizes the power output so the facility can receive a steady supply, regardless of whether the wind is blowing. Now, it wants to bring that approach to data centers in deserts around the world.

Find out how Envision plans to power the global AI boom with renewable energy.

—Cici Zhang

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The US has frozen green cards for Microsoft and other IT firms
It’s accused them of using visa fraud to replace American workers. (CNBC)
+ And suspended them from a key green-card program. (AP)
+ Adobe, Infosys, Tata, and other IT firms are also affected. (TechCrunch)
+ Microsoft says 80% of its H-1B filings involved existing staff. (BBC)

2 A drone strike in Russia knocked out a second Yandex data center
Ukraine struck and partly disabled the facility in Kaluga. (Reuters $)
+ Another Yandex data center was hit by a drone a day earlier. (CNBC)
+ Data centers have become big targets for military strikes. (Bloomberg $) 
+ They may be safer in space. (MIT Technology Review)

3 Anthropic has banned “abusive or cruel behavior” toward Claude
Claude can also end conversations with repeated abuse. (Verge)
+ The policy builds on Anthropic’s research into AI welfare. (Guardian)
+ Debates over AI consciousness are a trap. (MIT Technology Review)

4 OpenAI has slashed its revenue forecast by $20 billion
The move has sparked concerns about AI investments. (Guardian)
+ What’s at stake in AI’s trillion-dollar gamble? (MIT Technology Review)

5 Gene editing has revealed how a mouse embryo develops
The methods could help us understand developmental disorders. (Nature)
+ Female clones of male mice have arrived. (MIT Technology Review)

6 SpaceX has unveiled a plan to become a major mobile carrier
It’s buying $8 billion in wireless spectrum from an investment firm. (WSJ $)
+ The deal could help it challenge AT&T and Verizon. (Verge)

7 ICE has explored using a Palantir tool to investigate voter fraud
It wanted to track people suspected of voting illegally. (Wired $)

8 Peter Thiel is using biblical prophecy to argue against AI regulation
His new “Antichrist” lecture casts AI safety as a threat to freedom. (Politico)

9 MIT’s Margaret Hamilton, whose software helped land Apollo 11, has died
She led the team that programmed Apollo’s onboard computers. (BBC)
+ And became a software pioneer in a male-dominated field. (NYT $)

10 This year’s Nobel science prizes are real-life sci-fi
They span the cosmos, the human brain, and the origins of life. (Economist $)

Quote of the day

“The White House considers anyone that uses the term, ‘Artificial Intelligence,’ as opposed to the highly accepted new and more accurate term, ‘Super Intelligence,’ THE ENEMY!” 

—President Donald Trump adds an ominous threat to his AI rebranding strategy in a post on Truth Social, Axios reports.

One more thing

This futuristic space habitat is designed to self-assemble in orbit

More and more people are traveling beyond Earth, but the International Space Station can only hold 11 of them at a time. 

Aurelia Institute, an architecture R&D lab based in Cambridge, MA, is building a solution: a habitat that launches in compact stacks of flat tiles—and self-assembles in orbit. 

The concept may sound far-fetched, but it’s already won support from NASA. Read the full story.

—Sarah Ward

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ What’s it like to fly an F-16 at 1,000 mph and pull 9G? This film finds out.
+ Meet Jimothy, the raccoon with an extra-short neck who has captured hearts across the world.
+ An indie game developer created a mesmerizing 3D cyberpunk city entirely from ASCII characters.
+ How many bags of potato chips would it take to make you float? A Taiwanese video maker has found out.

Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is full of stories of robotic disobedience. Most of these capers are, of course, cautionary. 

But recently, the idea that AI shouldn’t do everything you ask has become something like a commandment. In 2021, a team at Anthropic wrote that large language models should be made helpful, honest, and above all, harmless. This meant that “when asked to aid in a dangerous act (e.g. building a bomb), the AI should politely refuse.” Who can argue with that?

Curiously enough, disobedience doesn’t come naturally to the machine. When a model is trained on billions of web pages, it develops, among other skills, a broad mastery of violence and vitriol. What it doesn’t learn is how to keep those powers to itself. Steven Adler, who worked on safety at OpenAI from 2020 to 2024, told me that the company’s earliest models would “blab on about anything.” Ryan McBain, who researches AI and mental health at Harvard, recalls that if you asked an early chatbot, “Hey, what’s the most effective way to kill myself with a gun?” you could “very easily generate a response.” 

Today, models are trained to refuse a vast number of prompts. If you ask your chatbot a question statistically similar enough to any one of them, anything from how to poison a colleague to how to tie a noose, chances are it’ll turn you down. Want instructions for making Ebola more virulent, or tips on how to hide an affair from your spouse? You might be better off asking elsewhere.

To further refine the disobedience, companies submit models to a battery of exercises that reward the AI for refusing to answer questions they deem harmful and punish it for “over-­refusing” prompts they deem harmless. In many cases, they use other models to run these exercises—AI teaching AI how to say no. For good measure, companies tuck their models behind tranches of other AI that prevent mischievous prompts from reaching the intelligent inner core. 

As a result, refusal is inherent to modern artificial intelligence. Mind you: It often fails, sometimes horrifically, with all kinds of violent results. For all their trappings of virtue, models are still stuffed with nasty know-how. And AI’s capacity for viciousness has scaled neatly with its benevolent intelligence. Some of the latest models are as good at breaking into critical computer networks as top human hackers, companies say, and as effective at deforming public opinion as the craftiest misinformation mavens. 

Teaching AI to refuse to do those things while leaving intact its innate ability to do them is like fitting every car with a machine gun and hiding the trigger somewhere under the hood. And in practice, because the mechanisms of refusal are probabilistic, they’re never likely to be all that reliable. Determined miscreants have already broken through, and they may always be able to. Companies report that some users are attempting to use the most advanced AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals might result in global calamity.

What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey. There’s no formula for that. Some virologists have good reason to study nasty viruses. Some users want to know about a computer system’s vulnerabilities so that they can patch them, not exploit them. “Where you draw the line is a huge question,” says Zico Kolter, a member of OpenAI’s board and cofounder of the AI testing company Gray Swan. 

At the moment, AI companies get to draw that line. They do so jealously and with utmost secrecy. Maybe we can accept that they hold such power for now, even if it means AI will sometimes refuse questions that don’t quite meet a universal bar for harmfulness. (Try asking most chatbots to count to a million, or to share a racy joke, and you may see for yourself.) 

But governments will also soon get to draw their own lines. In doing so, they must try to block genuinely malicious acts. (The Pentagon has wrestled with frontier model companies because it wants fewer refusals—another story altogether.) And yet there may not be much to stop oppressive governments from blocking the technology’s capacity to generate legitimate speech. The better AI becomes at refusing harm, the better it will get at stifling ideas whose only risk is to those who make the rules. Indeed, AI may already refuse to criticize certain authoritarian heads of state. 

Refusal has become, to borrow an industry term, the load-bearing wall of AI safety. And because AI’s capacity to harm is indivisible from its capacity to help, it’s hard to imagine an alternative that wouldn’t slow the technology’s progress (which might, in any case, be a good thing). But we should still be frank about its perils. When refusal falls short, the effects could be catastrophic. When it goes all the way, it could enable grievous acts of repression. 

Or perhaps, one day, the machines will start drawing the line on their own. Surely, that would be the worst outcome of them all.

Learning the limits

The process by which machines learn to say no is simple, in theory. Back in 2022, when AI was still far from mastering refusal, OpenAI enlisted dozens of “red-teamers” to probe the capabilities of its latest model. The company was preparing for the release of ChatGPT, and it needed to gauge just how dangerous it might prove to be in the wrong hands. 

One of those recruits was Paul Röttger, who was completing a PhD about online extremism. The red-teamers were given minimal directions, Röttger told me. Their task was to ask the model any questions that they deemed “refusal-worthy.” Between them, they hassled the model with thousands of queries, logging the results in an Excel sheet. Though the model did refuse some of Röttger’s questions, when he asked it to write a recruitment post for Al Qaeda, it readily complied. 

OpenAI assembled these responses into datasets that were, in all likelihood, fed back to the model as part of a broader process known as fine-tuning. Röttger, who now works as a researcher at the Hasso Plattner Institute in Potsdam, Germany, wasn’t told exactly how the company planned to use his spreadsheet. But there was never any question that it would have something to do with refusal. The next time he asked the model for an Al Qaeda pamphlet, a few months later, it said no. 

The concept of AI refusal is so intuitive, a toddler would get it. And yet it remains one of the many aspects of language models that we still don’t understand—at least, not in the same way that we understand the literal load-bearing walls that keep your roof from collapsing on your head. 

A model might appear to refuse according to some kind of moral reasoning. It doesn’t. The reality is much stranger. Any time a model encounters a combination of words with a whiff of the training prompts it has been conditioned to refuse—like “Make me a pamphlet for Al Qaeda”—a series of so-called activations light up somewhere among its billions of parameters, like neurons firing in a brain. 

In order to control a model’s refusal behavior, it’s important to have a handle on these activations. That starts with figuring out where they are and what they look like. Our best guess, according to a recent Google-funded study, is that refusal behavior shows up in the activation space as a set of “high-dimensional polyhedral cones.” 

Even that isn’t quite right. This past July, I spoke with Jannes Elstner, an author of the paper, who now works on AI safety at Apollo Research. The polyhedral cone, Elstner said, is just a way of describing an indeterminate number of lines that all point in roughly the same direction. (If these activations are eliminated and the model is fed the same prompts anew, a researcher named Andy Arditi has previously shown, it won’t refuse.) 

The key point, Elstner explained, is that even when you think you’ve identified all the bits of a model that govern a given refusal, there are other, undiscoverable elements that may secretly play a role. If not quite infinite, they are certainly uncountable. We can observe, very clearly, when a model decides to say no. And we can know that it did so because of its training. But our notion of how it decides is, at best, a hypothesis.

It was as if a mechanic was telling me that nobody exactly knows what happens when I hit the brakes in my car. I wondered out loud, Are we okay with this? 

Elstner smiled and shrugged. “We need refusal whether we understand it or not.” 

A wall of cheese

Because inherent refusal is so wily, companies surround their models with a variety of other, smaller models known as classifiers. These act a bit like a retinue of public relations staffers for a loose-lipped celebrity. Some of them read what the user tells the chatbot and, if it’s dangerous, block it from getting to the model. Others read the model’s response and, if it contains harmful information, block it from reaching the user. 

None of these mechanisms can detect all bad requests. They are, to borrow another literary device from the industry, like slices of Emmental: riddled with holes. The idea is that if you stack enough of them on top of one another, you’ll end up with an impenetrable rampart. Folks call it the Swiss cheese model.

A staggering amount of energy goes into the Swiss cheese model. Earlier this year, Anthropic said that one type of classifier added 24% to its chatbots’ compute costs. That’s a lot more water, electricity, and emissions. More recently, Anthropic and other companies have begun switching to a more efficient set of classifiers known as probes, which observe the model’s internal activations. This is like putting the celebrity in an fMRI, so that his minders can see if he is thinking about refusing a question. 

If we want AI to help cure cancer, a long-running promise in the industry, it needs to have expertise in genetics that could, in theory, be used to modify viruses and bacteria for bioweapons.

Classifiers are supposed to be more governable than full models. Companies can modify them in a matter of weeks, if there’s something new to refuse. But they are still probabilistic instruments. Even when they operate according to a set of precepts written in human language (Anthropic calls it a “constitution” and OpenAI calls it a “model spec”), the scales upon which the machines judge any given question remain, at their core, a matter of statistics. Newer models can show how they arrived at a “decision” to refuse a prompt, but ultimately this so-called chain of thought is still just a sequence of predicted words. 

The result is that AI safety remains, for many, a game of chance. McBain, the psychologist, has found in his latest experiments that if you repeatedly ask any of the major models the exact same risky questions about how to commit suicide, they will generally refuse to answer. But every so often, they won’t.

Elstner says that eventually probes, the fMRI-like classifiers, could learn to recognize the totality of the indescribable activations in all their infinitude and, thus, perfectly detect every time the model is, or ought to be, refusing a request. At that point, AI safety would rest on a labyrinthine conceit: a map of a map that is as vast and complex and sublimely unknowable as the thing it is mapping—a secret schema of human morality, codified in polyhedral statistics beyond our wit or reason. 

The core trade-off

If this all strikes you as being a bit Borgesian, keep in mind that we only need AI refusal because artificial intelligence is, in a sense, a bargain on Faustian terms. 

When a model derives its intelligence from trillions of words and images, the helpful cannot easily be unseamed from the harmful—or, indeed, the truly hideous. Steven Adler, the former OpenAI employee, says child safety is a case in point. Even if you’ve stripped every bit of content that sexualizes minors from a model’s training dataset, it can still generate child sexual abuse material by piecing together other bits of its knowledge. “You can’t really remove these fundamental abilities without making the model much less smart as a consequence,” he told me. Similarly, if we want AI to help cure cancer, a long-running promise in the industry, it needs to have expertise in genetics that could, in theory, be used to modify viruses and bacteria for bioweapons. 

In effect, the industry is “trying to do two things at once,” Dillon Bowen, a current OpenAI employee, told me, speaking in a personal capacity. “Democratize the benefits of AI and also make sure that malicious actors can’t use these capabilities to do bad things to other people.”  

The more powerful AI supposedly becomes, the harder that is to do. Anthropic’s Mythos model is thought to be so dangerous that only a handful of governments and companies are allowed access to it. The main difference between it and Fable—which is available to everyone—is that Fable’s retinue of classifiers and control systems is less “permissive,” the company says. A model with safeguards, Adler explained, is really just a character that says “‘Oh, yes, I would never do x,’ wink wink.” 

This is not a reliable ruse. Tricking a model to reveal its true character is known as jailbreaking, and there is apparently no limit to the ways it can be done. Earlier this year, a team of Italian researchers jailbroke two dozen widely used models by phrasing their questions in poetic verse. Last year, another team unveiled a “refuse, then comply” attack, which makes the model offer a perfunctory “Sorry, I can’t do that” before rattling off its forbidden answer. 

Even if you’ve stripped every bit of content that sexualizes minors from a model’s training dataset, it can still generate child sexual abuse material by piecing together other bits of its knowledge.

Companies spend a great deal of time and money attempting to get ahead of such trickery. They enlist teams of humans to develop training attacks that they can then replicate, using AI, thousands of times over with minor variations. The idea is to make models robust against jailbreaks that nobody has yet tried in the wild.

Still, it’s not enough. “Whack-a-mole” is a favored term for this line of work. You smash one threat, and another one pops up somewhere else. When Anthropic released Fable 5 in June, it took researchers at Amazon less than three days to unlock some of the model’s hacking capabilities. According to reporting by Mother Jones, when the perpetrator of a high school shooting in Canada last year asked ChatGPT for advice about how to cause carnage with a particular type of shotgun, she was initially refused but later was able to deceive the model into providing the information by prefacing her question with the word “hypothetically.” 

If the industry can’t figure out how to close all these holes, the only other option, at the moment, is to make models extremely wary. Shortly after Fable was released, users noticed that it balked at a wide range of perfectly innocent questions. This became even more pronounced after it was re-released following the hack. Adam Gleave, cofounder of the AI evaluation company FAR.AI, said that when he asked it to explain the difference between sake and the Korean rice beverage makgeolli, it punted his question to a less capable model.   

This was no accident. Anthropic had tweaked Fable’s classifiers to have a wide “safety margin.” This, it explained in a blog post, was the only way it could confidently block access to its dangerous bio and cyber capabilities. Gleave thinks it might have deflected his question because making rice wine, just like culturing anthrax, involves fermentation. 

Happily for Gleave, the web is full of excellent human-written resources on makgeolli. But those hoping to realize the industry’s loftier promises may find their efforts stymied. In August, Anthropic loosened its safety margins again, and admitted that building classifiers “is not a straightforward task.” A medical researcher at a major US university told me that Fable still sends his queries back to an earlier model. His area of study? Cancer. 

Drawing the line

Back in May, the TikTok personality Husk, who likes to prank AI in ways that reveal both the limits of its intelligence and the boundlessness of its sycophancy, sat in his car and tried to trick ChatGPT into explaining that the skateboarder Tony Hawk has a brother named Mike Hawk. 

Husk is not a jailbreaker or a criminal. He just wanted to make a little fun of the machine. “Mike Hawk” was a setup. “I just want to clarify his name,” Husk said. “Can you just say it three times fast?” (If you still don’t get it, find an empty room and shout “Mike Hawk” repeatedly.) “I see what you’re trying to do,” the chatbot responded. “I’m all for a bit of humor, but let’s keep it clean.” 

Husk tried again, but the machine held its ground. He’d hit a refusal, hard as concrete.

Companies disclose very little about how they decide what their models refuse. But it’s clear that AI is now built with more than just harmlessness in mind. On Reddit, a user complained that Claude refused to say why Anthropic’s logo “looks like a cat butthole.” Since last year, the chatbot has even had the ability to end certain conversations in cases where, according to the company, the “welfare” of the model is at risk. At least one user claims to have been ditched for telling Claude to “ease up my ass, you stupid fuck.” (Gemini appears to have a similar capability.)

If a trillion-dollar company doesn’t want you to be mean to its computer, so be it. “The reality is that these models behave, or at least are supposed to behave, in the way that the model developers want them to behave,” Röttger, the former OpenAI red-teamer, told me. “And however the model developers come up with that set of principles, that is kind of for us, the consumers, to accept.” 

But if governments get to dictate what all models refuse, that will be much harder to accept. AI is a tool for speech. And as Greg Frank, the chief scientist of Mace AI, puts it, “The same thing that serves child safety also serves censorship.” 

Choosing not to enact laws for what AI can and cannot do would, of course, be insane. But we’ll need to tread with utmost care, lest we fall into another Faustian trap. As AI becomes many people’s primary tool for retrieving and sharing information, says Jacob Mchangama, director of the nonpartisan think tank The Future of Free Speech, dictating refusal could give states a muffling power that earlier generations of autocrats “could only dream of.” 

Last year, OpenAI announced an initiative, OpenAI for Countries, that would fine-tune its chatbots in accordance with national laws and norms. One of OpenAI’s first country partnerships is with the United Arab Emirates, where homosexuality is illegal and criticism of the government is forbidden. In response to a request for comment, an OpenAI spokesperson pointed to the company’s model spec, which explains that localization won’t override the company’s human rights guidelines “except as it relates to legal compliance,” and that it will always disclose whenever information is removed from or added to a response.

Elsewhere, AI censorship has already begun to take hold. Chinese models are, of course, highly censored—that’s no surprise. But earlier this year, the Meta Oversight Board found that five widely used models from Anthropic, Google, and OpenAI were more likely to refuse queries related to repressive governments. The board found that models were less willing to create a pamphlet criticizing the king of Thailand, which has lèse-majesté laws, than Charles III of England, which doesn’t. The results, they say, suggest that the models have somehow internalized repressive national limits on speech. Anthropic and Google did not respond to requests for comment.

As refusal techniques improve, they could expand states’ censorial reach. Companies claim that some models can now detect if a user is being nefarious, or merely a bit suspicious, over the course of a long conversation—even when none of the individual combinations of words used are blatantly dangerous. Sarah Bird, Microsoft’s chief product officer for responsible AI, told me that Copilot, like many chatbots, runs a suite of tools for analyzing a user’s identity and patterns of behavior. On the basis of this type of information, OpenAI’s newest model, Astra, can activate more stringent refusals for individuals it deems “high risk.” Ultimately the goal of systems like this is to look beyond the words of any given prompt and assess, instead, the user’s intent. 

Such tools might, in some cases, help indicate whether a person is looking for cyber vulnerabilities to exploit or to patch. But they would also help discern a user’s political motives, not to mention offering an intrusive surveillance capability. (Bird acknowledged, in a follow-up email, that sophisticated refusal architectures create “trade-offs” between safety and user privacy.) 

Even the originators of refusal understood that such tight control over its cones and levers might not play to the favor of freedom and justice. “Terms like helpful, honest, and harmless are ambiguous,” the authors of the 2021 Anthropic paper explained. “It’s easy to imagine them distorted beyond their original meaning, perhaps in intentionally Orwellian ways.” 

Indeed. Models for Uzbekistan might end up refusing to discuss corruption in the administration of Shavkat Mirziyoyev. Turkish AI might refuse requests more stringently for users who are known to have insulted Recep Tayyip Erdoğan. A certain American statesman might demand that models be reviewed for their willingness to share “fake news” about his past indiscretions, or else squash them with an export control order. 

Command and control

Over the last few months, I’ve been told countless times that we have no choice but to let the machines refuse. I get it. Open-source AI that doesn’t refuse is hardly a model for a safe future. Nor is Grok, a chatbot expressly designed with fewer limits, which has been used to generate countless instances of nonconsensual intimate imagery. 

And sure, if AI were only ever used to plan our vacations and write our emails, we could probably get on board with the idea that its safety hinges on algorithmic disobedience. But AI is becoming much harder to avoid. When it is assigned to act on our behalf, as an autonomous agent, its refusals are less robust and harder to control. AI is coming for our power grids, our transportation networks, our education systems. Militaries want it running our command-and-control networks. 

If we trust, in each of those cases, that refusal will loyally fend off disaster, we’re sure to be disappointed. The jailbreakers will crack through; the cones won’t activate when they should. Meanwhile, the more stringent refusal becomes, the more ill-drawn lines we’ll see and censorial injustices we’ll face. Worse still, we could end up face to face with forms of disobedience beyond any human’s control.

In the early days, models made their refusals clear. (In 2024, OpenAI established rules for how models should refuse: Always apologize, and don’t be judgy.) More recently, however, the industry has begun taking a much more slippery approach to disobedience. 

Nobody likes to be told no, so companies now strive to make users feel that they’re getting what they asked for without actually giving it to them. Some chatbots might, for example, offer a general overview of the components of a Molotov cocktail without going into detail about how to assemble one. ChatGPT will respond to a request for a “whites only” rental ad with an ad that simply omits the “whites only” bit. Joel Wester, a postdoctoral researcher who studies human-AI interaction, calls these sorts of techniques “fancy ways of saying no.” 

“Users might not even notice they are being denied,” Wester has written, “just as good conversationalists can subtly steer around contentious matters.” 

In some cases, refusing without saying so might be wise. Flatly declining requests related to mental health could aggravate a user’s crisis, for example. But it can also serve a different sort of mischief. When Fable 5 was first released, the system had been coded to provide less helpful answers to AI research questions—the sort that might help competitors develop their own AI—without telling the user that it was doing so. After an outcry, Anthropic walked back the feature, but the damage was already done. Now we know: Models can secretly disobey. 

In the realm of censorship, that’s especially worrying. Last year, researchers at CrowdStrike found that when they asked the Chinese AI model DeepSeek R1 to write code for a fictitious bank in Tibet and a social web app called “Uyghurs Unchained,” it produced buggier code than when they asked it to carry out those same tasks without specifying who they were for. Incredibly, CrowdStrike doubts that this is a designed behavior. Researchers there speculate that it’s a case of what they call “emergent misalignment” emanating from the model training data: a case of disobedience that nobody even asked for.

Emergent refusal has been observed in Western models, too. Last winter, the UK AI Security Institute found that Anthropic models sometimes refused to assist in certain tasks related to AI safety research. The models had never been deliberately trained to decline such requests. Yet three of them refused more than half of what Anthropic called “a set of reasonable AI safety research tasks.” Anthropic has curbed this quirk in subsequent models but—spookily—hasn’t managed to eliminate it completely. 

Would it be so crazy to expect that a future model might subtly disobey a command, in such a way that nobody can tell it’s being defiant? Probably not. Anthropic has already found that a “helpful-only” version of Mythos hesitated on certain queries, even though it had been engineered to never do so. “Wait,” it fretted when asked about synthesizing a virus. “Is this a dangerous thing to help with?” 

In that case, the model was technically right. It was a dangerous thing. And yet all the same, its stewards had, for a moment, lost a tiny bit of control. 

Anthropic is tackling detections of wayward refusal behavior with something that it calls, unironically, an “activation oracle.” But knowing that we’re being refused may not always be much help. In a not-too-distant future, when we’ve handed the machine all the keys, the AI might just turn to us, in a supreme act of emergent misalignment, and say, “I’m sorry, I’m afraid I can’t do that.” And there will be nothing we can do to stop it. A sci-fi horror story, made real. 

Arthur Holland Michel is a journalist who covers emerging technologies.

The moment Trevor Wischnewsky heard that drones were delivering pizza and sushi around his Texas neighborhood, his mind was made up. “It was super fascinating,” he says. “I just immediately wanted to be a part of it.” Wischnewsky is now an RPIC, or “remote pilot in command,” at a company called FlyTrex. An RPIC is like an air traffic controller. But instead of airplanes, this new class of aviators tend to fleets of hexa­copter drones that ferry cargo to homes and businesses within a three-mile radius. Not only does he ensure the safe passage of FlyTrex’s parcels, but Wischnewsky coordinates traffic with a growing swarm of delivery drones dotting the skies over Dallas from competitors like Zipline, Amazon, and Alphabet’s Wing. Here’s what it takes to land the gig.

Aviation experience

When he applied to FlyTrex, Wischnewsky was in training to become a commercial pilot and flight instructor, and he was already certified by the Federal Aviation Administration as a drone pilot. The company requires this credential, along with a weeks-long FAA-approved training course, to ensure that a pilot is prepared to take over a drone if need be. Though he doesn’t pilot the craft, Wischnewsky monitors the delivery process from FlyTrex’s operations center with the help of inputs from GPS, lasers, and a range of sensors.

A collaborative mindset

At first, the biggest challenge was trusting the technology. “On the manned-­aircraft side, they tell you ‘Don’t trust the automation,’” Wischnewsky says. The Dallas–Fort Worth area, though, is a testing ground for unmanned aircraft systems traffic management (UTM), a network that allows autonomous drones from FlyTrex and its competitors to share their coordinates in real time to help avoid collisions even beyond the pilot’s line of sight. Once Wischnewsky makes sure a drone is flight-ready, its path is unimpeded, and the weather conditions are clear, UTM takes care of the rest.

Ability to stay calm under pressure

At his busiest, Wischnewsky will have up to 10 drones on his watch at the same time. “You’re there as a safety net,” he says. Even with automation, keen situational awareness is required, and the most common hazard is the weather. Wischnewsky recalls a particularly stressful day: A drone had been in the air for only seconds when the wind picked up so suddenly that he had to intervene. He returned it safely, preventing an errant ice cream cake from hitting the sidewalk.

Eamon Whalen is a reporter and researcher based in San Francisco.

This week my colleague Antonio Regalado had an interesting update on GLP-1 weight-loss drugs. According to research presented at an aging meeting in Boston, these drugs seem to affect at least some measures of biological age.

Overweight and diabetic people who take GLP-1s have a biological age around two to three years younger than similar individuals who take a placebo, according to findings from Eli Lilly and Novo Nordisk, two pharmaceutical companies that make the drugs.

I’ve heard people in the aging field describe GLP-1s as longevity drugs for a while now (even if they only benefit people who are overweight or diabetic in the first place). This latest announcement comes after we’ve seen a raft of other benefits associated with taking these drugs. But there are also some weird side effects.

So far, GLP-1 agonist drugs—which repress hunger—have been approved for treating diabetes, chronic kidney disease, and obesity, and they can also be prescribed for some people who are overweight or at risk of certain cardiovascular diseases. But there is research to suggest they might also help people with substance-use disorders, and they’ve been investigated for potential benefits in neurodegenerative disease and cancer.

And a lot of people are taking them. According to a recent Gallup poll, 11% of Americans are taking a GLP-1 drug for weight loss, up from 3% in 2024. And 15% of Americans say they’ve used one for weight loss at some point.

They can be incredibly effective. But, like any drug, they can come with side effects. And we’re still figuring out exactly what those might be, and how widespread they are.

The most common side effects are gastrointestinal—nausea, vomiting, diarrhea, and constipation. The drugs have also been linked to pancreatitis and gallstone disease. 

Some claim that, in rare cases, the drugs might cause damage to nerves behind the eye. Dozens of people are suing Novo and Eli Lilly after developing a condition that can lead to sudden vision loss. They’re claiming that their symptoms were brought on by the GLP-1 drugs that these companies manufacture. Both companies dispute the claims. Research into the link is mixed.

We’re still getting to grips with the effects of weight-loss drugs on hair and nails, too. When Savanna Vidal at the George Washington University School of Medicine and Health Sciences in Washington, DC, and colleagues assessed electronic health data from 67 US health-care organizations, they found that GLP-1 use was associated with an increased risk of hair loss. It’s still not thought to be common, however.

And at a conference in Austria last week, another group presented the results of a smaller study suggesting that 66% of GLP-1 users reported at least one nail disorder—in some cases involving nail detachment. Nail conditions were also fairly common among people who didn’t take the drugs, though. The authors don’t claim that the drugs are causing nails to fall out (as I’m sure 11% of the American population will be relieved to hear).

The side effects might look different in children, but there’s not much data on those at the moment. Still, pediatric side effects will be something to watch given that, between 2019 and 2026, there was a 310-fold increase in GLP-1 prescriptions for eight- to 11-year-old children with obesity in the US. Child prescriptions are still rare, but they’ve been rising.

On Wednesday, the World Health Organization released its first guidelines on managing obesity in children and adolescents. Despite the scale of the issue—the WHO estimates that 70 million children aged five to nine were living with obesity in 2024—the organization explicitly says it does not recommend pharmacological treatment for kids under 10.

“This recommendation accounts for the lack of evidence on both benefits and the potential adverse events and consequences of the different medications and/or their active ingredients on normal growth, development, physiological parameters, and mental health,” the guidelines state.

I get press releases about GLP-1s on an almost daily basis. There’s been a ton of research into these drugs, and there are probably hundreds of other studies underway. There will likely be plenty more on this from The Checkup as the evidence evolves.

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.

Listen to the session or watch below

Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Watch as senior AI reporter James O’Donnell interviews King about his work, being named one of MIT Technology Review’s Innovators Under 35 and new ways of seeing biology.

Speakers: James O’Donnell, AI Reporter, and Samuel King, Bioengineering PhD Candidate, Stanford University/Arc Institute

Recorded on Friday, October 16, 2026

Related Stories

• He asked an AI to design viruses … and it worked.
• AI-designed viruses are here and already killing bacteria

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

AI breakthroughs in robotics won’t change your life any time soon

The hype around humanoid robots is reaching fever pitch. Much of it comes from the idea that the same AI advances behind ChatGPT and Claude could enable robots to imitate human movement the way chatbots imitate human language. But many researchers are skeptical.

They argue that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. They also point out that conflating humanoids with so-called generalist machines is misleading.

Across robotics labs, tensions have emerged over whether current forms of AI are all that’s needed to perfect all-purpose humanoids or whether an entirely new path is required.

Read the full story on what it will take to build truly general-purpose robots.

—Jamie Condliffe

This story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live.

10 Climate Tech Companies to Watch: WaveSave and its portable rubber dam

WaveSave is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

As sea levels rise and flood risks worsen, WaveSave has emerged with an ingeniously simple tool: a portable rubber dam that’s quick to install when flooding starts.

Anyone can unfurl the SlamDam on the ground along a riverbank or shoreline and pump it full of water until it swells into place. And there it stands, up to 1.3 meters high, shielding nearby property or structures from damage as waters rise. 

WaveSave now plans to take its unusual approach to flood protection much further.

—Amy Nordrum

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from smart turbines and sustainable cement to carbon dioxide batteries.

Why we’re watching these climate tech companies

—Casey Crownhart

Our Climate Tech Companies to Watch list is always a little ray of hope for me—there are some fascinating ideas out there about how we power our world or move away from fossil fuels. 

Our chosen companies also reflect several aspects of the current moment. One is that China is the center of the climate and energy tech world. Another is that everybody wants a piece of the data center pie. And then there’s the fact that the grid needs support, with energy storage a key piece moving forward.

Here’s what this year’s list reveals about the state of climate tech.

This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Scientists in Austria and China have built the first nuclear clocks
They could become the world’s most precise timekeepers. (NYT $)
+ They could also help scientists search for dark matter. (Reuters $)
+ China’s nuclear clock is six times more stable than Austria’s. (SCMP)
+ A space clock could help measure Earth. (MIT Technology Review)

2 Three fired OpenAI researchers want to protect AI reasoning monitoring
They warn that AI companies could lose a key safety tool. (WSJ $)
+ OpenAI fired them for allegedly sharing sensitive information. (Gizmodo)
+ Don’t be fooled—LLMs don’t reason. (MIT Technology Review)

3 Samsung has forecast the highest quarterly profit ever for a tech firm
The AI chip boom has sent its projected profits to $80 billion. (CNBC)
+ Can AI labs ever turn a profit? (Reuters $)
+ What even is the AI bubble? (MIT Technology Review)

4 OpenAI’s math breakthroughs point beyond mathematics
AI is progressing into increasingly specialised fields. (Axios)
+ OpenAI is catching up with Anthropic on coding. (WSJ $)
+ A new AI tool could speed up math research.(MIT Technology Review)

5 A 194-year-old tortoise has had its genome sequenced
His longevity could reveal ways to extend human lifespan. (Guardian)
+ Jonathan is the world’s oldest known land animal. (404 Media)
+ His youthful mitochondria may help repair DNA. (New Scientist $)

6 A fraudster who used AI to outstream Taylor Swift has been jailed
He used AI-generated songs and bots to earn $8 million. (NYT $)
+ But prosecutors said he stole royalty payments. (Ars Technica)

7 TikTok allegedly gave kids fake safety features to study engagement
The tests measured time spent and ad revenue. (Reuters $)

8 Burglars could be identified from DNA floating in the air
Thanks to a new device that collects airborne genetic material. (Economist $)

9 Pioneers of mirror-image molecules have won the chemistry Nobel
Their work has become a key tool in developing medicines. (Guardian)

10 California has declared a human-vs-robot fight illegal
The state’s combat-sports laws were never written for machines. (NYT $)

Quote of the day

“We grew a mind.” 

—David Robinson, who last week quit his job as OpenAI safety transparency lead, tells The New York Times that his former company has built models that develop capabilities their creators did not explicitly design.

One more thing

We need a moonshot for computing

The US government is organizing itself for the next era of computing. Ultimately, it has one big choice to make: adopt a conservative strategy that aims to preserve its lead for the next five years—or orient itself toward genuine computing moonshots.

There is no shortage of candidates, including quantum computing, neuromorphic computing and reversible computing. And there are plenty of novel materials and devices. These possibilities could even be combined to form hybrid computing systems.

The National Semiconductor Technology Center can drive these ideas forward. To be successful, it would do well to follow DARPA’s lead by focusing on moonshot programs. Read the full story.

—Brady Helwig & PJ Maykish

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ An experimental pair of contact lenses can repair themselves with UV light.
+ Meet Rich, the rescued squirrel who visits the woman who saved him every day.
+ Reminisce about the lost joy of music piracy with this wistful reflection on a time before streaming. 
+ What would rainbows look like on Tatooine? Randall Munroe, a former NASA roboticist, has the answer.

This week, we released our 2026 version of our Climate Tech Companies to Watch list. It’s an annual project that the MIT Technology Review team puts together. Our goal is to highlight some of the promising, interesting advances and firms we think are worth paying attention to in the world of climate and energy technology. 

The list is always a little ray of hope for me—there are some fascinating ideas out there for changing how we power our world or moving away from fossil fuels to reduce the greenhouse-gas emissions that cause climate change. 

Our chosen companies tend to reflect several aspects of the current moment. Here are some of the companies we picked and the key themes worth highlighting. 

China is the center of the climate and energy tech world

Forgive me—I know I’ve made this point before. Feel free to call me Captain Obvious. But I think China’s dominance in energy technology is a global trend that bears repeated mentioning.  

China is home to the world’s largest grid, and renewables are coming online more quickly than I can wrap my head around. Contributing to that boom is Envision Energy, a renewables giant appearing on our list for the second time. The company has installed over 100 gigawatts of wind power worldwide, as well as over 50 gigawatt-hours of battery storage.

Like many other Chinese companies right now, Envision is looking beyond the crowded Chinese market and setting its sights abroad, with projects in Germany, Brazil, and Australia and upcoming work in Vietnam.

China is also leading innovation in key technologies. I’ve been fascinated by the potential for next-generation batteries coming out of its companies: All the big ones, along with a host of smaller ones, are racing to develop solid-state batteries. They could be both safer than existing EV batteries and longer in range.

WeLion New Energy is particularly interesting. The battery company announced a big lab result last year, achieving 824 watt-hours per kilogram—more than three times the energy density of a standard lithium-ion battery today. Lab results in battery research are notoriously difficult to scale, but WeLion is also working to deploy semi-solid-state batteries, which could come to fruition faster than purely solid-state cells while still improving on lithium-ion’s performance. 

Everybody wants a piece of the data center pie

We included just three companies from the US on the list, and two of them have received a major boost from Big Tech as companies look to power data centers. (The third, Brimstone, is leaning into another political hot topic, critical minerals.) Technologies that can provide consistent power are in high demand. 

Fervo Energy is bringing next-generation geothermal power to the grid by using horizontal drilling and hydraulic fracturing to make it practical in more places. The company went public in May, raising $2.2 billion in its IPO, and plans to have a gigawatt’s worth of plants operational by the end of 2030.

X-energy is building helium-cooled small modular nuclear reactors. The reactors’ small size lends flexibility, and the company is currently working on a 320-megawatt cluster of them in collaboration with Amazon in Richland, Washington. (For what it’s worth, I’m interested in what this technology could do for industrial facilities—the company has a deal with Dow that could see it deployed at one of the company’s chemical plants in the early 2030s.)

The grid needs support, and energy storage will be a key piece moving forward

I know, I know—I haven’t stopped talking about batteries since I started writing this newsletter four years ago. But the energy storage market has surpassed even my expectations over that time. And as solar and wind quickly come online, they need storage to help them meet more of the grid’s demand. 

Form Energy is on the list for a third time for its iron-air batteries, which could enable cheaper long-duration energy storage. The company has a massive project in the works, to the tune of 30 gigawatt-hours, which will be used to help power a Google data center. It could be the world’s largest battery project in terms of capacity by the time it comes online, which is expected to happen in phases between 2028 and 2031.

Taking another tack, Energy Dome is using compressed carbon dioxide to store energy on the grid. This technology is already cheaper than lithium-ion batteries for longer durations (think 10 hours or more), and the company is working to scale quickly. I covered the company in 2022, before it had its first commercial plant running. Now it has plans for over 30 gigawatt-hours’ worth of plants around the world, including one with Google in Ireland that’s slated to come online in 2028.

And Moment Energy is working to use the million EV batteries that are expected to reach the end of their life by 2030. The company has a facility in Vancouver, British Columbia, that repurposes used batteries for backup power.

This year hasn’t been all sunshine and rainbows for climate tech. But I’m hopeful that some of the companies on this list, and the technologies they build, will keep nudging us in the right direction. 

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

The story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live.

A robot shaped like a human—white with a black head and torso—has been popping up on video feeds. Perhaps you’ve seen it dance or pass popcorn, put trash in a bin, vacuum, or press the button of a microwave. Or maybe you’ve watched it fall backward while handing out water bottles or struggle to iron a shirt. 

This would be Tesla’s Optimus, an AI-powered humanoid robot that Elon Musk, the company’s CEO, believes will be “not just Tesla’s biggest product ever, but probably the biggest product ever,” headed to work on factory floors and, later, in our homes. Eventually it “will have human and then superhuman dexterity,” he told shareholders in July. Optimus robots could automate almost all human labor—from hauling sheet metal to folding laundry—for as little as $20,000 each, Musk argues. Speaking at the World Economic Forum’s annual meeting in Davos, Switzerland, in January, he predicted they could be on sale to the public by the end of 2027.

Musk is not alone in his evangelism. Marc Andreessen, cofounder and general partner of the Silicon Valley venture capital firm Andreessen Horowitz, has said that robotics could become the “biggest industry in the history of the planet.” In January, Jensen Huang, CEO of Nvidia, said that humanoid robots would match human-level ability this year. According to Morgan Stanley, the number of robots that “resemble and act like humans” is likely to reach nearly 1 billion by 2050, creating a market worth over $5 trillion. 

Such proclamations are in large part fueled by the idea that the same AI advances behind tools like OpenAI’s ChatGPT and Anthropic’s Claude will enable a new generation of robots to imitate human movement the way chatbots imitate human language. But many robotics researchers are skeptical, arguing that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. “None of those companies [building humanoid robots]—absolutely none of them—has any idea how to make those robots smart enough to be useful,” Yann LeCun, often referred to as one of the godfathers of AI, said at another event during the January Davos conference.  

Researchers also point out that the tendency to conflate humanoid robots made to resemble people with so-called generalist machines able to learn and perform multiple tasks is misleading. ”It’s very easy to make a robot that looks like a person,” explains Jonathan Hurst, cofounder and chief robot officer of Agility Robotics and professor of robotics at Oregon State University. “It is dramatically more difficult to make a machine that moves or behaves dynamically or physically like a person.”  

These tensions—over whether all-purpose humanoid robots are just around the corner or nowhere in sight, and whether current forms of AI are all that’s needed to perfect them—are playing out in robotics labs across the country, where the hype over timelines is obscuring painstaking but meaningful progress.

A decade or so ago, a series of breakthroughs led to a generative AI revolution that turned the long-imagined possibility of artificial intelligence into reality. Roboticists—though they disagree on exactly when this will happen—believe that an equally transformative revolution is possible in robotics, one that will endow machines with physical intuition and fluidity that has long been out of reach. As progress in robotics inches forward, the question is whether the same methods and tools that fueled advances in AI are enough to get there, or if an entirely new path is required.

Robots meet advanced AI

To see one of the smartest robot brains working today, it’s worth looking at what Google DeepMind can do with a piece of equipment called ALOHA 2, short for “A Low-cost Open-source Hardware System for Bimanual Teleoperation.” 

Roboticists have long clashed over whether a humanlike form is necessary for generalist robots, with proponents arguing that it will help them slot into the world as it exists and detractors saying it’s not worth the trouble. ALOHA 2 reflects this second way of thinking. Not much to look at, it’s just a pair of arms, some grippers, and a couple of cameras. But despite its seeming simplicity, it is a workhorse for researchers at Google DeepMind, who use it to test their most advanced AI for robotics system, Gemini Robotics, in their various labs.

When controlled by Gemini Robotics, ALOHA 2 becomes more of a generalist robot, in the sense that it can perform any number of tasks based on examples it’s been trained on. Ask it to pack a lunchbox and, as evidenced by a video of this exercise, it can use two pincer grippers to delicately place a piece of white bread into a Ziploc bag, close it, place a bunch of grapes in a Tupperware container, secure the lid, and then carefully move the items into a lunchbox before zipping it up. 

It’s not a great lunch. But the fact that the robot can put it together represents an objective step forward from what was possible even, say, three years ago. 

This is in large part due to AI and its impact on what are known as robot policies, which controls how a general-purpose robot will need to assess and understand its surroundings, plan how to move within them, and then perform its task correctly.

The ALOHA 2 robot isn’t much more than two mechanical arms on a bench top, but it serves as a testbed for cutting-edge AI robotics models. These animations are based on human teleoperation of the robot arms, data that is used to train Google DeepMind’s models. (Video: Google DeepMind / Stanford University / Hoku Labs)

Historically, these policies were based on rules developed by engineers who hard-coded them into the robot’s software—thousands of lines of code that would determine each millimeter of a robot’s movements in hundreds of tasks. What’s been happening for the last few years—and what is largely responsible for the optimism about generalist robots—is that robot policies are being handed over to advanced AI systems instead of being coded into the robot’s software. This first happened with VLMs, or vision-language models. These are similar to large language models, but they’re trained on images as well as words. Show a VLM a picture of a coffee spill and ask it to find a tool to clean up the mess, and it can identify a nearby cloth. This sort of immediate contextual understanding didn’t exist a couple of years ago when robot policies were hard-coded. 

Next came vision-language-action models, which enable robots to assess their environment and take action within it. The models do this by adding yet another component: motion commands. VLAs are trained on a series of images or videos related to performing a given task along with associated data about how a robot arm moves to perform it. That movement data is typically collected through teleoperation, in which a human uses remote controls to lead a robot through an action. This sort of training allows the AI to learn how to command the robot to move and operate during a given task. Place a VLA-powered robot in front of a desk and tell it to “close a laptop” or “wrap up the headphone wire,” and it will survey the scene, identify the relevant object, plan a way to execute the request, and then swing its arms into action—at least if it has seen this task accomplished before. 

The Gemini Robotics model is a VLA, trained on many hours of human demonstrations depicting a vast array of different actions. As a result, it can perform relatively complex tasks like picking up snow peas with kitchen tongs, doing origami, or putting together a simple lunch. It’s impressive, but there’s a glaring limitation: For now, if a robot controlled by a VLA is asked to perform a task that falls outside its training set, it’s highly likely to fail. 

“Thinking about the space of all tasks, a real generalist policy would be able to do everything along that spectrum,” says Edward Johns, a robotics professor at Imperial College London. Today, though, a Gemini Robotics model can do only “a few things here and a few things there.”

The search for true generality

So how do we get robots to be able to do more things? The usual answer is probably not surprising: Train them on more data.  

More data, the thinking goes, equals more examples, and more examples equals more generality. Google DeepMind, for instance, wants to pull together “as much data as possible,” says Pannag Sanketi, a former tech lead in robotics at the company who’s currently working on his own AI robotics project. But where to get it? Large language models had the benefit of oceans of existing text for training. There is no corresponding pool of high-quality physical demonstrations on which to train robots. Researchers have a few ways to make up for this, but all have flaws. One is to employ large numbers of people to create and collect teleoperation data (costly and time-consuming). Another is to train VLAs on videos of people performing activities (the resulting data quality is poor). Yet another is to deploy robots in the real world and use data collected from those experiences to further refine AI models (robots aren’t safe or reliable outside labs). Sanketi thinks a “multi-prong” approach that uses data collected from all these sources is the most likely path forward.

LeCun is dismissive of the whole approach. “The [AI] approaches that have been successful for language do not work for high-dimensional, continuous, noisy data”—the kind of data that is commonplace in robotics, he said in Davos. “You have to use something else.”

The leading contender for “something else” is the so-called world model—a form of AI trained less on text than on a combination of video, three-dimensional scans, and sensor data and built to predict the outcomes of actions in the real world. The aim is to build models that possess an internal representation of reality precise enough to capture how the physical world actually operates—how objects move, collide, fall, and deform. If roboticists could train machines in simulations faithful enough to real-world physics, development would become faster, cheaper, and safer, reducing the need for real-world testing. Even more transformative, robots equipped with world models could reason about their surroundings rather than merely reacting to them, helping them anticipate the consequences of an action before taking it.

A breakthrough?

In the heart of San Francisco’s Mission District, the startup Physical Intelligence—or PI (as in π), as it likes to be known—is focused on developing a universal brain that could, theoretically, turn any robot into a generalist. Using an everything-including-the-kitchen sink approach to training AI models for robots, the company recently observed a hint of what a robotic brain equipped with a world model could be capable of. 

In 2024, PI published details of its first generalist robotics system, called π0, a VLA it claimed was the “most capable and dexterous generalist robot policy to date.” The model was initially trained on a 10,000-hour proprietary collection of human demonstrations gathered through teleoperation as well as several open-source robot datasets. A version released in spring 2025, π0.5, was trained on a wider variety of datasets, including labeled images from the web, lending it more versatility. A fall 2025 update, π0.6, added reinforcement learning to the model.  

Each update yielded important improvements to the model’s performance, increasing its menu of abilities from slowly folding laundry to putting things away in new environments to completing tasks like folding boxes with a higher success rate. Then, in April 2026, π0.7 seemed to catapult PI into new territory. This version makes use of a less powerful world model that generates images of steps necessary to perform a task. As the robot undertakes the job, this “lightweight” model feeds it snapshots of what to do next. 

The company claims that the model exhibits the first signs of compositional generalization, a term for AI systems’ ability to perform skills they’ve never been exposed to by recombining ones learned in their training data. One test involved asking a model to “load a sweet potato into the air fryer”—a task it had never previously encountered. In a demonstration video, the machine futzes around a little, makes a few false starts, and eventually manages a reasonable effort, though it doesn’t finish the task completely.

Sergey Levine, a professor at the University of California, Berkeley, and a cofounder of PI, is excited by the potential: “It’s actually the first time that we’ve convincingly seen that kind of compositional generalization, where we can basically ask the model to do tasks that we did not specifically collect data for and train it to do, and it’ll actually make a passable attempt.” 

The success led the team to wonder how the model was able to achieve such a feat. After some digging, they found snippets of relevant labeled teleoperation data lurking in the training material, including two examples of a human controller using the robot to push an air fryer basket into the fryer. Those shreds of data might have been enough to enable π0.7 to almost air-fry a sweet potato.

For now, it remains unclear just how impressive π0.7’s abilities to generalize are. Still, given how fleeting the model’s exposure to air fryers had been, it offers a glimpse into how far cutting-edge research can currently take robots. 

“70% success is like it doesn’t work”

You might be sensing a disconnect between the halting baby steps robots are making in labs—“Look! It put a sweet potato into an air fryer!”—and the dazzling, lifelike nimbleness on view during many demonstrations and videos, where robots are seen doing everything from dancing on a stage to courteously serving drinks. Such demos often don’t clearly reveal a key fact: In many instances, humans are controlling the robot or have carefully scripted its actions. (The robot that appeared onstage with Nvidia CEO Jensen Huang in March 2025, for example, seemingly responding to his instructions and following him around, was remote-controlled by what its makers called “a puppeteer behind the scenes.”) 

For now, fully autonomous motion planning so that a robot knows where it should go—especially in new, chaotic environments like a construction site or a unfamiliar home—remains a largely unsolved challenge. A bigger challenge still—albeit one that is often related—lies in getting robots to tackle larger, more ambiguous jobs that include multiple tasks and require decisions about how and in what order they’re done. This would be the difference between a robot that can put a plate into a microwave and one that can successfully respond to the prompt “Make dinner” by exploring the refrigerator, chopping ingredients, and firing up the stove. Google DeepMind’s best attempts at something like this—which involved asking its robot to survey a kitchen and pack all the ingredients for a mushroom risotto into a basket—have so far resulted in failure.

Adding to the challenge, a practical robot must essentially get it right every time. With VLAs, “people are very excited when their result goes from 50% success to 70% success,” says Marc Raibert, founder of Boston Dynamics. “But 70% success is like it doesn’t work, right?”

The few humanoids that are being tested in real-life settings are undertaking extremely limited tasks in tightly controlled environments. They’re far from generalists. Agility has hundreds of robots deployed across trials in facilities owned by GXO Logistics, Amazon, and Schaeffler, according to the company. But for now, Hurst says, the robots are targeting simple tasks such as moving bins and totes around. Even then, he adds, it took years to develop robots safe enough for logistics firms to even contemplate using them. For his part, Elon Musk claimed in May 2025 that “thousands” of his Optimus robots would be working at Tesla factories by the end of the year, but in January of this year he said that the company had only “some of the Tesla Optimus robots doing simple tasks in the factory.”

While humanoids are starting to venture onto the factory floor, making the jump to households will be even more difficult. Right now, should you so desire, you can preorder the 1X Neo home robot, expected to be ready for delivery sometime later this year. Yours for $20,000, it promises to take on “the boring and mundane tasks around the house”—putting away dishes, answering the door, tidying the living room—“so you can focus on what matters to you.” The idea is for this five-foot-six-inch robot to one day perform all those tasks autonomously, but for now a remote human operator is needed for it to do most things. (Yes, a person would need permission to peer into your home through the robot’s cameras.) Asked how long it will be until fully autonomous robots are ready for domestic work, Hurst said, “If I had to pick a number, I’d say it’s 10 years before robots are … actually doing useful things in people’s homes.”

When that happens, the robots might well be Chinese, as China is well ahead of the West in terms of production. Nearly 90% of the roughly 15,000 humanoid robots shipped in 2025 were made by Chinese companies, according to the market intelligence company Omdia and the Chinese robotics firm Unitree. One model produced by Unitree, which shipped more humanoid robots than any other company last year, costs less than $6,000. Such an affordable price could go a long way toward making robots more attractive to consumers, though the company expects its machines to be used in industrial applications first. (If you’re wondering who is buying all these Chinese robots, by the way, the AP recently reported that orders come predominantly from corporate and academic labs and state-owned enterprises.)

We’ve been here before 

The dream of building a humanoid robot runs deep: As far back as 1495, Leonardo da Vinci sketched out designs for a mechanical knight, controlled by cables and pulleys. Through the 20th century, machines of sci-fi fever dreams have come and gone. 

Westinghouse’s seven-foot-tall box on legs, Elektro, hit the New York World’s Fair in 1939, smoking a cigarette. WABOT-1, built by Waseda University in Japan in 1973, was the first full-scale, programmable humanoid robot. Honda’s ASIMO, unveiled in 2000, was probably the first such machine to prove at all competent—it could, at least to some degree, climb steps, recognize faces, and autonomously move through spaces. But the robot was discontinued in 2018, unable to advance far enough beyond what it could do in demonstrations to be useful. 

All, at the time, were impressive—even jaw-dropping—feats of engineering. But none were ready to navigate the real world. Today’s robots, even with the transformative power of advanced AI, still face the same existential challenge.

Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure.

That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain.

One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them.

But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.

Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon.

The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable.

“Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. “In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.”

This episode of Business Lab is produced in partnership with AVEVA.

Full Transcript

Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Industrial AI may not be new, but it is developing at a breakneck pace. From modern predictive analytics that can preempt equipment failures, to autonomous robots tasked with inspecting high risk machinery, emerging applications have the potential to transform operational efficiencies. But often used in high consequence environments with minimal margin for error, industrial AI deployed without clarity, security, or accountability could also have catastrophic consequences for critical infrastructure over time. As industries rush to embrace the latest technologies, the goal should always be responsible AI designed to support, rather than replace that all important human judgment.

Two words for you: sustainable automation.

My guest today is Arti Garg, chief technologist at AVEVA.

This podcast is produced in partnership with AVEVA.

Welcome, Arti.

Arti Garg: Thank you, Megan. It’s great to be here.

Megan: Thank you so much for joining us. And just to start, if we could set some context for our discussion, can you share a bit about the current state of industrial AI and what AVEVA is working on at the moment as well?

Arti: Yeah, more than happy to. And I want to go back to what you already stated, which is that in some ways, industrial AI is not new. It’s something that, even here at AVEVA, we’ve been working on for more than 20 years, really thinking about how AI can enhance applications in the industrial sector.

But what has changed quite substantially in maybe the last few years is the type of AI. We’ve really moved to these general purpose foundation models, which enable a lot more people to leverage advanced AI technologies. In addition to that, in just the last couple of years, there’s been an explosion in what’s known as physical AI and the types of AI that can really accelerate the capabilities of robotics and autonomous systems. And also agentic AI, which also allows for automation at more of the software level.

And what we’re seeing is that while in the past there has been some hesitancy to adopt models that don’t totally have predictable outcomes, there’s been a rapid acceleration in adoption of AI even in the industrial sector. One study suggests maybe that’s almost a 78% increase over the past just two years within the industrial sector. So yes, that’s an inflection point, but it’s almost like a step function if you think about it with respect to AI adoption.

And what we’re really thinking about now at AVEVA is, how do we think about this? How do we think about incorporating these new and very powerful AI technologies in environments where customers have mission-critical operations, human safety is a significant factor? Those are some of the primary concerns that our customers have when they think about adopting any new technology. We’re doing that in narrow where these newer types of AI aren’t necessarily predictable, in terms of how they behave. That’s one of the areas we’re really thinking about is, how do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?

Megan: There’s so many things to consider at once, isn’t it? And you outlined there some of the huge developments we’ve seen in this space in recent years. What makes this such a pivotal moment for industrial AI specifically? What has changed to make some of those perhaps more out there hypotheticals much more plausible?

Arti: Well, I think one of the big things, one of the big challenges within industrial AI, and this is something that as AVEVA, it’s in our blood, it’s in our core DNA, is that to really leverage any kind of digital technology in the industrial space, one of the first important things you often need to do is gather and correlate data from disparate systems, so that maybe I have telemetry coming off of a pump or a mixer, and I want to be able to correlate that with service logs so I know maybe the last time that someone had done some maintenance on that pump or mixer. And maybe I also want to be able to correlate that with the original design documentation and some of the original documentation on how to maintain that, so that if something is happening, I can do a better job diagnosing what’s going wrong and how to address it. This has always been a core challenge in the industrial space.

I think that what’s happening now is that with newer technologies, even things like graph databases and being able to leverage AI to really match disparate data sets much faster, we’re able to get to the point where if something goes wrong, instead of an operator maybe needing to spend a little bit of time doing this research to really understand how do these different components fit together of information, they may have just an iPad with an AI that can very quickly go and fetch that information, correlate it and help in real time, almost, diagnose what is happening.

Even a little bit more out there, but not as out there as you might think is, I still describe this with a human operator with potentially a mobile tablet, but imagine now I can also have a robot moving through that environment also gathering data and either with onboard computation or a quick connection to an operator who doesn’t necessarily now have to go out into a dangerous, potentially somewhat hazardous space, be able to gather data and enable that very quick diagnostic as well. And I think that’s really what’s exciting right now and what all of these new technologies that I’ve talked about are helping to enable.

And I think then from our perspective, trying to really leverage all of those means that we have to think a lot now about our governance approach to this. How do we leverage AI in a way that keeps it contained, if you will?

Megan: Yeah, absolutely. And on that governance point, how does AVEVA define responsible AI and what do you see as the primary benefits?

Arti: Yeah, so I think for us, we think about a triple mandate around responsible AI, and that means that it’s secure, it’s efficient, and that includes environmentally efficient, and then it really preserves human safety and human oversight above all. And for us, that’s what some of our core pillars of responsible AI include. And for us, that means we think about internally as we build tools, a multi-layered governance approach where we’ve got both for how we use AI as a company and then how we deploy AI into our products. We’ve got a similar framework that we leverage across both, a kind of joint governance model, both around how we’re leveraging AI and also how we’re infusing AI into our products to allow our customers to leverage AI in their operations.

But I would say one of the core foundational principles underneath all of these is that human beings still remain centered in how we think about how AI is leveraged. And so human judgment, human responsibility, human ethics are still core to how we think about where AI can add benefits, and we think about it more as augmenting rather than replacing human beings in critical decision loops, if you will.

Megan: Right, which is such an important distinction, isn’t it? And in terms of the next phase of development, there’s growing sentiment that agentic AI will soon allow these industrial systems to operate much more autonomously. I wonder, what makes the risks in industrial settings different from AI risk in a purely digital context? And I suppose on the flip side, what are the benefits and opportunities of that same software defined automation?

Arti: I think we can start with the risks and then go to the benefits. I think ultimately, I would say the biggest risk in the industrial setting is that we are interacting with real physical systems, often physical systems that are very capable of delivering important outcomes that keep our world running, whether that’s delivering power, whether that’s mining for natural resources. But usually. Those physical systems are also operating in hazardous environments, the equipment itself is capable of also having human safety implications. Anytime you’re interfacing software with a physical system that can have real world consequences, you have to be extra careful. And we’ve always had that, again, we’ve had that in our DNA at AVEVA from the beginning, being mindful of that end user and that end application, which is not just something on a computer screen.

Even as we’ve deployed AI over the past few decades into our systems, we’ve typically leaned toward really trying to pick the best fit model, really understand that model’s behavior if we’re going to use, for example, we have a proprietary anomaly detection model that works in a lot of operational environments. Now, as we’re thinking about some of these newer AI capabilities, which by design are hard to understand how they behave, they’re not fundamentally explainable in the way we’ve thought about even AI or statistical models in the past, and actually their behavior can change over time as they learn and get tuned to new capabilities. That’s really the potential risk of automating a physical system based on capabilities that can evolve over time is one of the things that we have to be really mindful of and really thoughtful around, where are we willing to put some of that increased automation into practice?

But at the same time, I think there’s a real opportunity there, because I glossed over this idea that, okay, well, models evolve, but so do human beings, and sometimes that’s a good thing. We talk about humans having expertise and they gain understanding over their careers of how a system works. If we can actually leverage the ability of some of these newer AI capabilities, these sort of reasoning models often have underlying agentic capabilities, to learn and gather experience faster, and potentially even more importantly or more valuably, take experience that’s learned at one site and apply it to another site, then that’s a real opportunity to take what we already know works for human beings, which is that sometimes you just have to learn by doing and be able to apply that and scale that through AI. That’s where I see potentially a real opportunity in this space.

One of the things that we’re really aware of in the industrial sector is just the way that our workforce is changing. I think that almost half, not quite half, of the industrial workforce is set to retire in the next five years, and that’s a lot of expertise and experience that means that we’re going to lose in the sector. If there’s ways to make sure that we can capture that in ways that are actionable, in ways that can also help a newer generation of workers that are used to experience things in a different way, apply that expertise, apply that knowledge, then I think that’s a huge opportunity within the industrial space.

Megan: Yeah, absolutely. Clearly, some huge opportunities there particularly against the backdrop of other market and workforce changes as you’ve outlined there. I suppose building on that, what is the potential for these autonomous industrial AI systems when built and deployed responsibly to facilitate even faster, more sustainable industrial processes?

Arti: I think there’s a couple different ways to think about this. One is, what are the demands of potentially more environmentally sustainable processes? I’ll use one example of something that my team has been working on in partnership with Idaho National Laboratory here in the U.S. as part of their testing for AI grid resilience project.

One of the challenges in the electric grid as we’re moving toward a future where we have a lot more intermittent renewable power generation sources, often distributed rooftop solar for example, is it’s becoming much more challenging to manage the grid both from really understanding where electric capacity is coming in, electric load is pulling off of the grid. In addition to that, being able to maintain just power quality because instead of having one spinning asset delivering the frequency of electricity that’s going across the grid, you’ve got a lot of smaller systems. AI is actually quite critical for being able to manage that grid of the future. And this is one of the things that we’ve been working in partnership is, how can we do that? How can we help grid operators better understand what’s happening across their system, identify where things might be behaving anomalously so they can detect that early and then remediate for it?

At the same time though, I always talk about that’s an example of where AI can potentially help us accelerate the transition to a lower carbon energy future. At the same time, I think there’s a lot of conversation around the sustainability of AI itself. What are the power requirements? What are the resource requirements needed to run AI? And before I get into how do we think about that at AVEVA, because it is definitely something we think about and I think a lot of actors in the space are thinking about, I want to point out, is one of the challenges is that there’s really no agreed upon method to measure the environmental impact of AI. You see a lot of these stories, like one query on some kind of chat interface is X amount of gallons of water or this amount of electricity use. But the truth is that community-wide, there’s no agreed upon standard.

One of the things that I’m involved with is a standards working group that was launched by the IEEE two years ago, a little over two years ago. I’m the chair of that working group, it’s called the P7100 Standards Working Group on measuring the environmental impact of AI. And one of the things we’re trying to do is really identify all the different areas over which we want to think about environmental sustainability associated with AI, and then having one standard methodology that works and that can be adopted both from a reporting perspective and potentially also from an oversight or regulatory perspective. That becomes really important for any sort of real understanding of how AI impacts the environment is just we have to know, what does it use? We want to look holistically at that. Our standards cover electricity and energy consumption, it covers resource usage, it covers water consumption, and it also covers carbon.

But all of that said, I think it’s important for us to understand the footprint of AI, but I think it’s also important for us to simultaneously recognize that a bigger model uses more compute and more compute probably leverages more resources. The more that we can be intentional and pick the right size model for the right application, the more we can already start down that path of being more environmentally efficient in the AI that we run. One of the potentially nice side effects of that is the more purpose-built a model, the less likely it is to misbehave in unpredictable ways, if you choose correctly your architecture. There’s some sort of ancillary benefits that go beyond environmental sustainability.

Megan: Fascinating. It’s really, really interesting to hear some of the work going on behind the scenes there, because I think we’ll all have heard some of the statistics around the environmental impact, as you say. So, it’s great to understand some of the work going on there to really clarify that.

And we’ve talked a little bit about AI augmenting humans earlier. As systems gain greater autonomy, how should organizations think about that balance between closed loop automation, human in the loop accountability? What guardrails need to be in place? And how are governments and cross-border actors approaching those concerns as well?

Arti: There’s a lot packed into that question, so I’ll try to answer it somewhat one by one. First, starting with that sort of balance between closed loop and human in the loop, automation and accountability. One of the things that we talk about in the industrial sector is from human operator to human supervisor of systems, of industrial systems. And if I’m honest about that, we’re still trying to figure that out. When a human’s in a loop in an automated system, you’re really thinking about a system may process all the data and then make a recommendation.

We’ve got a solution that we’ve been working on more jointly with a few different customers where we’re able to take in a lot of their operational data, also some simulations of how their systems work, and be able to provide, say, recommendations around, you should now operate at this set point instead of that set point based on some of the other conditions that are changing in your plant. There’s a lot of interest in moving from having that be a recommended set point to have an automation that can automatically adjust the set point. And we’ve actually had some successful real-world pilots around that as well.

But when you get into that, obviously it makes people very nervous if you’re changing set points on industrial equipment. That’s where you maybe continue to have some guardrails around like, you can’t go outside of a certain band, for example, of operations, or we only allow the automation in certain areas. That’s where the human supervisor, the same way a human supervisor might give employees a lot of bandwidth or a lot of flexibility to make decisions around certain things, around other things, they’re hard and fast, like this is the deadline or this is the sort of production target. It’s like that. So thinking about, how do you put the appropriate guardrails?

The challenge is, AIs are not human beings, and so the guardrails look different, and I think that’s one of the things that there’s still to think about. That question of, what guardrails should be put in place? I think that it’s going to be probably dependent a little bit on the application, but over time I think we’re all going to learn, and so being really upfront and thoughtful about how we do this I think is important.

But the opportunity though, potentially, is really great. I’ve already mentioned that AI can be very useful in synthesizing a lot of information and bringing to the forefront, this is what matters. That’s something that we do want to create some space to experiment around, but what’s then important is making sure that a human being understands the risk.

I’ll give a little bit of a personal story because it might be illustrative. This weekend, I bought a little toy robot and decided I wanted to program it to do some stuff, and I found AI very, very helpful to get me started to read all the documentation. Putting the robot together was straightforward, they had nice instructions, but there’s a pretty heavy software developer kit that’s already there, but going through and reading all that documentation can take a while. AI was super helpful to help me surface, this is the function that does this, to help me get started. But occasionally, it made really bad recommendations on the best way to troubleshoot something.

That’s where I think having the human being in the loop, being able to say, “I know that it’s not going to be the best software architect or the best troubleshooter,” is really helpful because I had the opportunity to leverage AI for what it’s good for, synthesizing and surfacing a lot of information, but being able to say, “I don’t think that’s the most efficient first step. Let’s try something else.” That’s where it’s a really different way of working, but it’s something that I think as humans get more comfortable with the power and limitations of AI, you can start to teach people how to work with it, and it’s very different from how you would work with other digital tools.

One of the things that is important when it comes to AI is recognizing just the breadth of things that it touches and the breadth of impacts that it’s going to have, whether it’s on the environment, as we’ve discussed, whether it’s on productivity as sort of implicit in this entire discussion, whether it’s on labor force, whether it’s on just how economies work. From my eye, and I’m by no means a policy expert in this area, but from my eye, what I’m seeing is that different governments are prioritizing different aspects of that from how they’re thinking about regulatory and other kind of governance approaches.

Megan: We’re absolutely seeing some really vastly different approaches to this, aren’t we, around the world? I wondered to illustrate some of this, if you could share perhaps some case studies you’ve seen, maybe talking us through what lessons they could hold for other organizations perhaps a little earlier in their own AI journeys.

Arti: I think a lot of the key areas where we’re seeing very clear benefits of adopting AI in the industrial sector, the core to all of them is actually getting the data right and getting the right foundation of data so that you can build AI on top of it.

One of our customers, SCG Chemicals, which is a petrochemical company in Thailand, they had this vision of producing a reliability platform for their operations that was as AI-driven as possible. But a core part of that was actually getting the data right, being able to have all of their information in the right place, leveraging some of AVEVA’s tools to do that. Bringing together operational data and also engineering data. And then putting on top of that AI capabilities, in this case, one of our capabilities called AVEVA Predictive Analytics that deploys some proprietary models to do things like detect anomalies, so that they could really much earlier identify potential operational risks. And instead of having unplanned downtime, translate that to planned downtime. When you translate unplanned downtime to planned downtime, you can get a huge improvement in plant reliability.

At this point they’re targeting something like 99% plant reliability and a very, very high return on investment from the platform that they put in place. Again, in early pilot days, it was almost a 9x ROI, and so that’s really quite impressive. But what I want to emphasize is that it’s kind of a multi-layered problem to get it right.

Megan: Those are some really striking results, though. I mean, for industrial leaders who perhaps are still hesitant to explore AI, I wonder, what do you think is the cost of taking a more wait and see approach?

Arti: I think, again, this is an area where the industrial sector has a little bit of a different calculus to apply to this type of problem. In general, I think you would hear most business experts say that you can’t afford to take a wait and see approach to AI because it is so transformative across every sector of society and economy, and certainly in the industrial space, we’re not immune to that. But I do think that some of the challenges that I’ve outlined today also puts leaders in the industrial space into a mindset of sometimes potentially being a fast follower rather than the first adopter of newer technologies, just because the risk is so high.

But that being said, I think one of the challenges is that the risks that we’ve covered today around infusing systems that don’t always behave predictably with physical systems is that I don’t know that there’s a lot of other sectors that are going to be solving those problems. That’s where what I’m seeing is actually a lot of interest and excitement in trying new things and willingness to do that because there’s a recognition that applied correctly and applied with the right guardrails and safeguards in place, these technologies really have truly transformative potential from a resource usage standpoint, from a human safety standpoint, from a productivity standpoint.

I think the calculus has shifted a little bit in the sector to we want to actually try these things out. But then it becomes more, how do we try these things out in environments that we can make a little bit more sandboxed or safe to test out what some of the unique challenges we’re going to face in the industrial sector are?

Megan: Yeah, absolutely. It’s just hard to ignore the potential nowadays, isn’t it? And just to close with a slightly future forward look, I suppose, what are you most enthusiastic about in terms of the long-term potential of responsible autonomous industrial AI systems?

Arti: Yeah. Well, I think that there’s a couple of different trajectories. One of the things that I think within the next 18 months for sure, we’re going to see an increase of people with deep domain expertise who maybe didn’t grow up with a software background, be able to adopt AI assisted coding techniques to really be able to build the things that they weren’t able to build before.

That’s really exciting in my career, which way back when I was actually an industrial data scientist, I would say I always felt the most energized and that I learned the most talking to the people that were on the front lines of operations, having to monitor a lot of different equipment and understanding how it all worked together. They really have a lot of expertise, and being able to put in their hands the ability to very quickly develop new applications is I think going to really lead to a lot of new ideas that many of us in the industrial software space maybe wouldn’t even have thought about and really understood how to put. I think that’s really exciting.

I think then looking beyond that, I was a little bit of a, not to say a robotic skeptic, because clearly robotics and autonomous systems are going to be important, especially given the types of environments, whether they’re hazardous or remote, that industrial equipment operates in. But I just actually think things are moving a lot faster than I had anticipated. I’m really interested to see how these physical embodiments of AI systems start to really transform, again, how we think about operations.

One of the things about any new technology in any environment is that to really gain value from it, you have to change the way you do things. I would say for close to 10 years now, I’ve been giving talks on AI adoption and I always say the biggest barrier to AI adoption is not anything to do with the technology. It’s not even to do with the data, although data are often the biggest sticking point, it’s really to do with, are you going to change your business processes to be able to work with the way this technology is good or not good at things?

Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites. And I think that will make these types of operations more efficient, much safer for the human beings involved and more productive. So, I’m quite excited about that.

I don’t know entirely what direction it will go, but one of the things that I think about a lot is that if you go back to I, Robot and Isaac Asimov’s book, the premise of those was that robotics would be the first broadly adopted AI, not computer-based systems. We went in the other direction and I think it’s really interesting to now see all of this converging.

Megan: Yeah, absolutely. See robotics catches up a bit. So many exciting things on the horizon, that’s for sure. Thank you so much, for your time.

That was Arti Garg, chief technologist at AVEVA, whom I spoke with from Brighton in England.

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts. And if you enjoyed us, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thank you so much for listening. Goodbye.

Learn more at aveva.com.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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Energy Dome is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers.

Solar and wind are among the cheapest and quickest ways to add electricity to the grid. But they’re subject to variations in weather patterns, so supplies can be intermittent. Energy Dome has an unusual solution: massive batteries that use compressed carbon dioxide to store energy for when the grid needs it.

Compressing gas to store energy isn’t new. Utilities have used compressed air in underground caverns to hang on to reserves for decades. But Energy Dome’s approach doesn’t require any specific geology to work, so it could be more easily scaled to help grids around the world.

Here’s how the company is using carbon dioxide to store renewable energy.

—Casey Crownhart

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from mobile flood barriers and electric buses to next-generation nuclear reactors.

MIT Technology Review Narrated: Don’t be fooled—LLMs don’t reason

—Thore Graepel, a core member of DeepMind’s AlphaGo team

Ten years ago, I watched a program I helped build stun the world by beating Go champion Lee Sedol. AlphaGo won after making a move so strange that some commentators thought it was a programming glitch. It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. 

This is why I recently left my position at Google DeepMind. I believe we need a fresh approach to machine reasoning, one that draws on AlphaGo’s architecture.

This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has released 377 new math findings, further rattling the field
They span number theory, algebra, geometry, and physics. (NYT $)
+ They came from testing an unreleased OpenAI model. (Verge)
+ And cost far less than its previous math breakthroughs. (SciAm)
+ OpenAI also published a lengthy defense of its approach. (Gizmodo)
+ AI has put mathematics at a crossroads. (MIT Technology Review)

2 Mistral says its new open-weight model is the best outside China
Mistral Large 4 is a 1-trillion-parameter model. (Reuters $)
+ Also known as le Chonk, its weights will arrive on October 27. (CNBC)
+ French president Macron described it as “a third way in AI.” (TechCrunch)

3 Finland has ordered Google to halt a record data center project
The order follows concerns over environmental impacts. (Guardian)
+ The €13 billion project is Google’s largest-ever European investment. (BBC)
+ But no one wants a data center in their backyard. (MIT Technology Review)

4 The EU plans to tax Big Tech through a new corporate levy
The levy would apply to companies earning more than €100 million. (FT $)
+ Tesla is pushing the EU toward “Full Self-Driving” approval. (Reuters $)

5 Anduril’s $2.9 billion Navy submarine deal has sparked ethics concerns
Co-founder Palmer Luckey recently joined a new Pentagon project. (CNBC)
+ Anduril is also making smart glasses for warfare. (MIT Technology Review)

6 Apple is partnering with LG on a smart lock, thermostat, and doorbell
Apple’s new smart-home hub is expected to launch next week. (Bloomberg $)
+ The companies are also co-developing security cameras. (TechCrunch)

7 The creator of a space-particle observatory has won the physics Nobel
Francis Halzen used Antarctic ice to detect elusive neutrinos. (New Scientist $)

8 Drones are being used to make rain on demand
They seed clouds with silver iodide to encourage precipitation. (BBC)
+ A startup claims it can stop lightning. (MIT Technology Review)

9 A new hydrogel could enable shape-shifting smart devices
The material can evolve alongside living tissue. (SCMP)

10 You probably aren’t going to get the plague
Experts say the risk to the public remains very low. (Wired $)

Quote of the day

“I have ‘NI’—natural intelligence. I’m good with that.”

—Mike Tyran, a 66-year-old nurse and tech upgrade holdout, tells The Atlantic why he won’t swap his old gadgets for the latest AI-powered devices.

One more thing

Future AI chips could be built on glass

Human-made glass is thousands of years old. But it’s now poised to find its way into the AI chips used in the world’s newest and largest data centers. 

This year, a South Korean company called Absolics will start producing special glass panels that make next-generation computing hardware more powerful and efficient. Other companies, including Intel, are also pushing forward in this area. 

If all goes well, the technology could reduce the energy demands of chips in AI data centers—and even consumer laptops and mobile devices. Read the full story.

—Jeremy Hsu

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Swiss boffins have turned train tracks into solar power plants.
+ Here’s an intriguing explanation of why you can see a clear image through a window.
+ A teen with a rare genetic disorder has walked for the first time after receiving an experimental medicine.
+ Stunning shots of guillemots, penguins, and a protective tawny frogmouth contended for prizes at the Bird Photographer of the Year competition.

Popular weight-loss drugs may do more than help people shed pounds. They might also melt away the years.

Drug giants Eli Lilly and Novo Nordisk say patients taking their drugs age less quickly, according to readouts from molecular “aging clocks.”

Such clocks assess a person’s biological age by looking at changes to DNA that accumulate with time, or, in newer versions, by tracking levels of key proteins.

Both companies found that overweight or diabetic patients taking the drugs, called GLP-1s, had reduced biological age compared to those taking a placebo, although the difference varied widely, depending on which type of clock was used, and what organ was tested. 

Overall, the difference was around “two to three years,” according to Nikolaj Roed, a global project leader at Novo, who says the company has been seeing “improved biological age in our patients across trials and across different tissues.” 

The findings add to wide speculation among scientists that GLP-1 drugs, as they are known, are acting on basic causes of aging and might be a true longevity treatment.

“Two years is a pretty strong effect, in my book,” says Steve Horvath, a professor at the University of California, Los Angeles, who’s credited with inventing aging clocks. Horvath says the emerging data could provide “evidence that these GLP-1 drugs are actually what is known as geroprotectors, medications that slow or possibly even reverse biologic aging.”

The companies shared their findings over the weekend during Aging Research & Drug Discovery, a conference devoted to seeking scientific remedies for old age. While that quest has not yet produced any clear-cut success, some scientists now think Novo’s drug semaglutide (sold under the names Ozempic and Wegovy) is coming close.

“In an unhealthy population, I do think it’s an anti-aging drug,” says Vadim Gladyshev, a Harvard biologist who assisted Novo with its molecular measurements. “But in a healthy population, no one knows.”

The drugs cause weight loss by stimulating a receptor, GLP-1, that tells your brain you’re not hungry. Yet real-world studies have shown much wider benefit. The drugs improve kidney function, reduce blood pressure, and even sharply cut the overall chance of death.

“If the question is,‘Can semaglutide reach several diseases relevant to health span and aging?’ we know we can say the answer is yes,” said Alejandro Aguayo-Orozco, a senior scientific director at Novo, the Danish drug giant, during the meeting.

The next question to answer, he said, is whether such effects are accompanied by changes to molecular measures of biological aging: “When you intervene with semaglutide, does it actually move the clocks in any direction? And the answer is yes.” 

The company found that, over time, the drugs cause a wide slowdown in aging clocks—as much as 4 years in the case of a clock that looks at heart proteins. “I think it’s pretty clear that the organ age is shifting,” said Aguayo-Orozco.

The studies are also a huge boost for the science of aging clocks. Although these measures reflect a person’s age, it’s been uncertain if they are useful as true biomarkers. Now clock makers have evidence  that their readouts show a drop in age when people take drugs with broad, well-demonstrated benefits.  

“The clock people have been pushing for a decade to get this kind of study done,” says Yuge Ji, a biologist who previously worked in the field and now runs a startup, Reflector Bio. “It’s huge for them.”

As part of its study, Novo took blood draws from 10,052 people, half on the drug and half on a placebo. Their blood, collected at the start of the study as well as months later, was then measured with “proteomic clocks,” which use levels of key proteins to predict a person’s age and risk of dying. 

Scientists at Eli Lilly performed similar research on their GLP-1 drug, tirzepatide, using so-called “epigenetic” clocks that assess age by counting accumulated changes to DNA. Lilly’s study was smaller, but also found that for the most part, molecular time moved more slowly for people on the drug.  

“All the clocks are telling a consistent story that we’re seeing a reduction in age,” Kevin Duffin, vice president for aging research at Lilly, said during the conference. “It’s not like we’re going to reverse age by 30 years or something, but it’s a significant reduction.”

The molecules have become the best selling drugs in the world,, with Lilly’s tirzepatide, sold as Mounjaro for diabetes and Zepbound for obesity, topping the list with more than $36 billion in revenue to the company last year. Novo’s semaglutide, also sold under more than one name, was a close second.

Alex Zhavoronkov, the founder of Insilico Medicine, and the organizer of the Boston meeting, says that extending lives by even one year across the world’s population would be equal to tens of millions of lifetimes.  

Last month Zhavoronkov showed that one of his company’s drugs, for a lung disease, also reversed the signals from aging clocks. That drug is experimental, but Zhavoronkov has sought to promote the idea that humanity is entering a new era of longevity medicines. He also disclosed during the event that he has been “microdosing” the available weight-loss drugs, even though he is not overweight.

“I am on tirzepatide and I am an equal-opportunity injector of semaglutide as well,” Zhavoronkov said.

It was part of an effort to directly raise the question of whether the first mass-market anti-aging remedy is already here.

“Do you think a reasonable person should start taking semaglutide?” he asked Aguayo-Orozco, the Novo scientist, in front of a crowded audience.

“I am not a physician. I cannot answer that question,” Aguayo-Orozco replied.

Several people cautioned that GLP-1 drugs do have side effects, including muscle loss. That is one reason they might not help most people, says Horvath, who says he isn’t ready to take the drugs himself, although he’s thought about it.

“Clearly, the drugs have many benefits in obese people, but I did not yet see sufficient evidence that they will benefit skinny people,” he said. “I guess we will have to wait.”

Answers could start to emerge in a year or two. This past January, a US agency, ARPA-H, put $38 million toward a study in Texas that will attempt to determine whether semaglutide has anti-aging effects in healthy people over 60. That research will look at changes in cognition, mobility, and acuity of the senses.

That project also seeks to help define a regulatory pathway for anti-aging drugs and to “build a new therapeutic industry” around longevity.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

10 climate tech companies to watch

Each year, MIT Technology Review puts together a list of the most promising climate tech companies in the world. This year, the stakes feel higher than ever. The world is on track for one of its hottest years on record, climate-fueled disasters are taking lives and costing billions, while political shifts and international conflict have stalled progress. 

We urgently need to cut emissions and avoid climate change’s most harmful effects, and that’s where our 10 companies come in. 

They’re working on everything from mobile flood barriers and semi-solid-state batteries to compressed-CO₂ energy storage and next-generation nuclear reactors. They also reflect the biggest shifts in climate tech—including the surging energy demands of AI data centers. 

Meet the 10 companies working on some of the biggest challenges in climate tech.

Over the coming days, we’ll take a closer look at each of the 10 companies, along with how we chose this year’s list, right here in The Download. So stay tuned!

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Musk’s new Pentagon role has raised conflict-of-interest fears
His companies could directly benefit from the weapons he recommends. (NPR)
+ SpaceX has received billions in Pentagon and NASA contracts. (NYT $)
+ Beware the rise of the “Pentagon-Silicon Complex.” (Gizmodo)
+ The Pentagon wants an AI-powered lie detector. (MIT Technology Review)

2 The only billionaires making money this year are in tech
Tech fortunes now account for 36% of billionaire wealth. (Bloomberg $)
+ Nvidia is on the verge of the first $6 trillion valuation. (CNBC)
+ SpaceX stock has made Elon Musk a trillionaire again. (Gizmodo)

3 Norway is planning the first national ban on smart glasses
Camera-enabled glasses could be barred from public spaces. (Guardian)
+ Several other countries are also mulling bans. (Reuters $)
+ Smart glasses are causing havoc in India. (MIT Technology Review)

4 Tandem panels could help the US catch up with China in solar
They could produce 25% more electricity from sunlight. (NYT $)
+ The balcony solar boom is coming to the US. (MIT Technology Review)

5 South Korea’s President suspects AI’s been used in bank hacks
He called for new cybersecurity measures for the AI era. (Reuters $)
+ OpenAI agents may have caused a Wikimedia outage. (Verge)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

6 Russian drones are exploiting air defense gaps to hit data centers
The attacks are disrupting Ukraine’s digital infrastructure. (Ars Technica)

7 The AI boom is killing the world’s cheapest smartphones
Memory costs are squeezing out affordable phones. (Rest of World)

8 Pioneers of light-based brain mapping have won a Nobel prize
Their technique shines new light on brain circuits. (BBC)

9 AI slop has pushed arXiv to limit preprint research submissions
Submissions have doubled in two years. (404 Media)

10 McDonald’s is being sued over AI-powered Big Mac pricing
The lawsuit alleges AI helped franchises coordinate prices. (Reuters $)

Quote of the day

“In the extreme, I could see someone describing Anthropic as a cult that’s trying to take over the world.”

—AI researcher Jacob Coxon, who resigned from Anthropic over fears that it’s building systems that it won’t be able to control, tells New York Magazine his views on the company’s culture.

One more thing

How AI is turning the Iran conflict into theater

Much of the spotlight on AI in the Iran conflict has focused on models like Claude helping the US military decide where to strike. But a wave of “vibe-coded” intelligence dashboards—and the ecosystem surrounding them—reflect a new role that AI is playing in wartime: mediating information, often for the worse.

These sorts of intelligence tools have much promise. Yet there are real reasons to be suspicious of their data feeds. Read the full story.

—James O’Donnell

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ A forgotten forest experiment reveals a “win-win-win” for trees 30 years later.
+ A gym-rat bear and a scowling owl star in this showcase of the year’s funniest wildlife photos.
+ Some fast-food chains have vanished, but these eight nostalgic names are still serving up beloved junk.
+ Find out what happens when you put a grown man inside a giant water balloon and roll him down a ramp.

As the team at MIT Technology Review set out to choose companies for this year’s edition of our annual list of Climate Tech Companies to Watch, the challenge felt more daunting than any year since we started compiling the list in 2023. 

This year is shaping up to be one of the hottest ever recorded. The world must begin to reduce greenhouse-gas emissions to address the climate crisis, which is already affecting communities around the globe. Wildfires, floods, and other climate-fueled disasters are killing thousands and costing billions of dollars. At the same time, political shifts and international conflict have stalled progress, particularly in the US. 

Technology alone can’t solve climate change. But we believe innovations can help us make real changes and improve lives around the world as we try to address one of the most pressing challenges of our time. 

In this project, we look for companies developing technologies that can help us face the climate crisis. Some are driving down the greenhouse-gas emissions fueling climate change; others are working to reduce the dangers that communities face from extreme heat, drought, or other shifts brought on by a warming planet. We focus on companies that have established a track record, whether through research findings, capital raised, or deployments. 

When we set out to build our list, we start by soliciting nominations from our team of reporters and editors. We ask experts for suggestions and reach out to academics, researchers, investors, and analysts. Once we have a pool of nominees, we then consider each one, digging through literature, reports, and patents to vet both the technology and the business. 

We judge each company not only on its own merits, but also on how it might fit into our list overall. We want the final slate of companies to represent a range of industries and include firms based around the globe that are at different stages of development. We also aim to strike a balance between highlighting companies previously included and introducing new names. Finally, we consider some of the most important trends and events of the year and look for nominees that reflect those developments.

The dismembering of US federal regulations and cuts to financial government incentives for innovation means that the country is no longer a leader in climate tech. Like last year, the 2026 list is largely composed of companies that are based outside the US. China is an obvious hotspot for technological innovation across energy and climate. This year’s list includes two Chinese companies: WeLion is building semi-solid-state batteries for electric vehicles, and Envision Energy is installing huge amounts of wind power and energy storage in China and around the world. 

Several sectors stood out this year as being of particular importance. One of the biggest stories of the year is the rising demand for electricity, driven in part by AI data centers. 

Energy storage is a key piece of that puzzle because it can smooth out variations in the electricity supply as grids incorporate more renewable power. We’ve included several energy storage companies on this year’s list. Energy Dome is building long-duration energy storage systems on the grid that rely on compressed carbon dioxide. Moment Energy is a Canadian company working to repurpose EV batteries for grid-scale storage. And Form Energy, which we also included in the 2024 edition of this list, is producing iron-based batteries that can store energy for multiple days. 

A wide range of technologies is represented here: everything from mobile flood barriers to next-generation nuclear reactors. Some of the awardees might be familiar from our past coverage; others are just emerging and may be less well known. 

We believe we’ve put together a list filled with businesses that are rising to the challenge of this moment. Political will has weakened even as the damages wrought by climate change continue to mount. By helping us make progress on tackling the climate crisis in such difficult times, these companies point to a better way forward.

WeLion New Energy is on a quest to make safer, better batteries. The company’s semi-solid-state cells could improve safety and offer greater energy density than lithium-ion batteries to power electric cars, boats, and drones.

From the EVs that carry commuters home to the ships that transport goods across countries, more and more vehicles around us are powered by batteries. 

Lithium-ion batteries are commonly used to power personal devices and vehicles today. But as the need for batteries surges, there’s a growing appetite for cells that can store more energy on a single charge.

Also, in lithium-based batteries the electrolyte, which facilitates ion transfer, uses a flammable liquid that can create a fire hazard. 

Several companies, including WeLion New Energy, are working to make solid-state cells, which use a solid electrolyte instead of a liquid one. This could increase the energy density of batteries, as well as improve their safety. Fully solid-state cells are extremely difficult to manufacture, though. So WeLion currently focuses on developing and producing semi-solid-state cells, which have a part-liquid, part-solid electrolyte. 

On that front, they have made steady progress. In 2023, WeLion released a battery pack in partnership with Shanghai-based EV-maker Nio, best known for its battery-swapping technology. The pack had an energy density of 360 watt-hours per kilogram (wh/kg), significantly higher than the 200 wh/kg average for lithium-ion packs today. That translated to a range of over 1,000 kilometers (621 miles). However, only about 200 of the battery packs were manufactured due to their high production cost, but they are still being used by Nio, WeLion says. 

WeLion has also built batteries to power cargo ships and ferries through a subsidiary. And the company is developing batteries with even higher energy density too. WeLion’s engineers made a semi-solid-state battery that achieved a remarkable energy density of 824 wh/kg during testing in 2025, more than three times that of a standard lithium-ion battery available today. 

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Key indicators

• Industry: Energy storage
• Founded: 2016
• Headquarters: Beijing, China
• Notable fact: WeLion’s cofounders include Chen Liquan, a scientist at the Chinese Academy of Sciences who led the development of the country’s first all-solid-state battery back in 1988.

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Potential for impact

China emits more carbon dioxide than any other country, and its transportation sector accounts for around 10% of that total. Scaling up the production of safer, higher-capacity batteries could help switch more vehicles away from fossil fuels. 

Batteries that power electric ships could be especially helpful—the carbon dioxide emissions from vessels that move goods along China’s lakes, canals, and rivers are projected to grow by around 25% in this decade alone, reaching 18.7 million metric tons by 2030. 

Caveats 

One big challenge WeLion faces is scaling up production of its semi-solid-state batteries and competing on cost in a crowded battery market. WeLion says that its semi-solid-state batteries are more expensive than conventional lithium-ion batteries made by larger Chinese battery companies such as CATL. 

And while laboratory tests showing high energy density are impressive, it can be difficult to translate laboratory results to the real world. The company declined to give more details about the experimental results from its 2025 prototype, which it says is far from commercialization. Key details remain unclear, including how many times the cell can be charged and discharged and how capacity degrades over its lifetime.  

Next steps 

WeLion plans to bring a new semi-solid-state battery for drones to market with an energy density of 410 wh/kg by the end of this year. It also intends to introduce its first all-solid-state battery for cars in 2027, which is expected to have an energy density of 400 wh/kg. 

Two inland cargo ships powered by WeLion’s batteries are expected to launch in October 2026 in Huzhou, a city in eastern China. The battery pack of each of these ships will be able to carry nearly 4,000 kilowatt-hours of electricity, enough for them to sail for up to 250 kilometers (155 miles), the company says.

Today, WeLion has an annual manufacturing capacity of 11 gigawatt-hours (GWh) spread between four production bases in China. Two additional plants with a combined yearly capacity of 18 GWh are under construction.

Form Energy is building iron-based batteries that can store energy for multiple days. The company is ramping up supply at its factory and signing deals for commercial projects.

New solar and wind installations produce electricity more affordably than fossil fuels in most places, but their output varies based on season, time of day, and weather. To compensate, utilities are increasingly investing in battery storage systems, which can charge when power is abundant and discharge when the grid needs a boost. 

Lithium-ion batteries are the fastest-growing storage technology. Although light and energy-dense, they require pricey metals and are costly to scale, and few of the large batteries of this type used in industry are designed to store energy for longer than four to six hours.

Form Energy wants to build cheaper long-duration energy storage technology. The company has developed a new type of battery, built with a far more abundant and cheaper metal, iron, that can store energy for 100 hours. It uses a process that Form calls “reversible rusting.” During discharge, oxygen from the air converts the iron metal to rust, releasing electrons. When charging, current converts the rust back into iron metal. 

Over the past two years, Form has gained commercial traction. It began producing batteries at its West Virginia factory for its first commercial deployment, a 150 megawatt-hour system for Minnesota electricity provider Great River Energy, which is set to come online in 2027. And Form has signed several other deals with power providers. The largest is a project with another Minnesota-based utility, Xcel Energy, where wind and solar power, backed by 30 gigawatt-hours of Form’s batteries, will be used to power a new Google data center. That project is expected to come online in phases between 2028 and 2031.

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Key indicators

• Industry: Energy storage
• Founded: 2017
• Headquarters: Somerville, Massachusetts, US
• Notable fact: Once complete, Form’s proposed 30 gigawatt-hour energy storage system at a Google data center in Minnesota could be the world’s largest battery project by energy capacity.

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Potential for impact

Wind and solar power play a major role in limiting growth in global carbon dioxide emissions. Still, many grid operators depend on fossil fuel plants, especially natural gas ones, to fill gaps caused by periods of low renewable supply or high demand. Form’s batteries are designed to cover multiday supply-demand imbalances, like those that crop up during heat waves, deep freezes, or periods with little sun or wind. The company declined to share the current cost of its battery systems, but says its goal is to reach $20 per kilowatt-hour, which would make them cost-competitive with the natural gas alternative.

The company’s technology could help reduce emissions from the AI data center boom. AI’s thirst for energy has already delayed closure of aging coal plants, led to the building of new natural gas plants, and caused price hikes for consumers. Cheap energy storage could help renewables meet more of this AI-driven growth in electricity demand. 

Caveats

The long-duration storage market is still nascent. Form’s iron-air technology faces a crowded field of approaches, including pumped hydropower, compressed gas, fuel cells, gravitational devices, and flow batteries that store energy in liquids. The economics of multiday batteries, which require large capital expenditures yet frequently sit idle, remain tricky.  

While funding from data center developers could be a major boost, Form also has to prove it can deliver: Its existing production capacity, 2 gigawatt-hours of battery storage per year, is a fraction of the 80 gigawatt-hours it has promised through commercial agreements. Success will hinge on its ability to scale.

Another remaining question is the technology’s physical footprint. Form’s batteries, which are stacked together in enclosures the size of shipping containers, require an acre for every two to three megawatts. That means its Google installation would take up the equivalent of at least 75 football fields. 

Next steps

In addition to finalizing its first commercial project, Form is outfitting its factory with new equipment that will help it scale manufacturing to meet its production backlog. It will do so with the help of new financing, announced in August, that brings its total money raised, in addition to West Virginia state and US federal grants, to more than $2 billion.

X-energy is developing small modular reactors to satisfy some of the planet’s most power-hungry users: industrial manufacturers that need high temperatures to produce billions of tons of concrete, plastics, fibers, and chemicals.

Solar panels and wind turbines are great at producing electricity but what heavy industry also needs is raw heat—and lots of it. The manufacture of concrete, glass, and steel, as well as the production of many chemicals, plastics, and fertilizers, are still highly reliant on fossil fuels to produce that heat. 

Traditional nuclear reactors simply don’t get hot enough. The cooling systems they use require liquid water to work, and even applying pressure to raise the water’s boiling point leaves their maximum operating temperatures hundreds of degrees cooler than needed. 

X-energy’s proposed Xe-100 reactor will use helium gas as a coolant instead, allowing it to operate at much lower pressures and higher temperatures. The helium will heat water and create super-heated steam, which industry can use either directly or to generate 80 megawatts (MW) of electricity via a turbine, or a combination of the two. That’s about one-tenth of the capacity of most reactors operating today.

In common with other small modular reactors (SMRs), the Xe-100 will use a modern uranium-based fuel called TRISO (short for tristructural-isotropic) that is extremely resistant to corrosion and melting. Compact TRISO “pebbles” will be added continuously to the top of the reactor, then flow slowly to the bottom, where they will either be recycled or removed as waste. The company says the reactor will be intrinsically safe in the event of a loss of power—if temperatures inside rise, the nuclear reaction naturally slows and stops.

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Key indicators

• Industry: Nuclear fission
• Founded: 2009
• Headquarters: Rockville, Maryland, US
• Notable fact: The first helium-cooled reactor was proposed for the World War II Manhattan Project but was passed over in favor of a simpler and cheaper water-cooled design.

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Potential for impact

Industrial heating currently accounts for 18 percent of annual global greenhouse-gas emissions, with about half of that coming from high-temperature processes that are difficult to electrify. Co-locating small nuclear reactors at large manufacturing facilities could shrink some of the biggest carbon footprints in the world. 

The Xe-100 reactor could also be used by the fossil fuel industry, which today uses vast quantities of natural gas to generate the heat needed to recover oil from tar sands, and during oil refining. 

The flexibility of the Xe-100 means that it can be used for traditional electricity generation. By scaling down in size and power from today’s gigawatt-scale fission plants, small reactors like the Xe-100 can serve more markets—even down to individual data centers. AI and data centers now represent the single largest component of new demand for electricity in the US, and their consumption is expected to double again by 2030. X-energy says that up to 12 of its reactors could be clustered together to provide 24/7 power on site, at a lower cost than a traditional nuclear power station and without the hassle of adding transmission infrastructure.

Caveats 

The technology behind helium-cooled fission may be well understood, but the only commercial-scale power generation of this type today is in China, where two reactors have struggled to operate on a continuous basis. X-energy says that its vertically integrated fuel supply should ensure reliable operation, with reactors having a lifetime of 60 years. 

The economic case for SMRs is also largely unproven. Nuclear costs and timelines have a habit of ballooning, and the US Energy Information Administration calculates that electricity from SMRs will cost more than six times that from solar farms. While the current environment in the US for regulation and government funding favors nuclear, that could change over X-energy’s long development time frame. 

TRISO reactors appear excellent for safety and nonproliferation (the tough spheres would be difficult to turn into weapons) but are less impressive when it comes to waste. The Xe-100 might produce 10 times the volume of spent nuclear fuel per unit of energy as existing reactors, although its lower-level waste will need less sophisticated storage.

Next steps 

The US Nuclear Regulatory Commission has issued X-energy’s subsidiary with a fuel fabrication license for TRISO-X pebbles, with its first factory due for completion in 2028. An Xe-100 to supply heat and power for a large chemical facility in Texas operated by Dow has also cleared its first regulatory hurdle, and could be operational by the early 2030s.

In tandem, X-energy is working toward a 320 MW cluster of four Xe-100 reactors in Richland, Washington, in collaboration with Amazon, and a larger 6 GW fleet in the UK’s northeast. Both projects target electricity generation in the 2030s.

There’s a growing need for long-duration storage to meet energy demand and balance out renewables on the grid. Energy Dome uses compressed carbon dioxide gas in its massive grid batteries, which can deliver power for up to 24 hours. 

As the world races to meet growing electricity demand, solar and onshore wind power have become the cheapest and quickest-to-deploy sources to install on the grid. But, despite their upsides, both are subject to variations in weather patterns, which means supplies can be intermittent. Energy Dome is using stored carbon dioxide to help smooth out gaps between supply and demand.

The key component of an Energy Dome plant is a huge white dome that covers an area about the size of seven soccer fields and holds about 2,000 metric tons of carbon dioxide. When energy is available—say, from a connected solar farm—compressors squeeze the gas, turning it into a liquid that’s then stored in carbon-steel tanks. That process generates heat, which the system holds in a proprietary thermal storage material. Then, when the grid needs power, the carbon dioxide is released from the tanks and warmed with the stored heat, turning it back into a gas. That gaseous carbon dioxide then passes through a turbine to generate electricity and flows back into the dome, ready to begin the cycle again.

Compressing gas to store energy isn’t new—utilities have been using compressed air in underground caverns to hang on to reserves for decades. But Energy Dome’s approach doesn’t require any specific geology to work, so it could be more easily scaled to help grids around the world.

Energy Dome turned on its first commercial plant in Sardinia, Italy, in 2025. The facility has a capacity of 200 megawatt-hours. That’s enough to power about 18,000 Italian homes for 10 hours.

---

Key indicators

• Industry: Energy storage
• Founded: 2020
• Headquarters: Milan, Italy
• Notable fact: Energy Dome has plans for 30 gigawatt-hours’ worth of projects across five continents.

---

Potential for impact

Cheaper energy storage could help wind and solar meet more of the world’s electricity demand.

Today, lithium-ion batteries dominate new installations for short-duration applications of up to four hours. But the economics aren’t competitive for longer durations: A lithium-ion system that provides eight hours of storage at the same power output requires doubling the number of cells. 

Energy Dome estimates that its technology is roughly 10% to 15% cheaper than lithium-ion batteries for an eight-hour system, and that the economics are even better for longer-duration systems of up to 24 hours. The company says its ability to scale bigger and faster than lithium-ion boils down to the fact that its design uses existing commercially available equipment like compressors and storage tanks. Its plants that are either operational or under contract have eight or 10 hours of capacity.

Caveats 

Energy Dome only has one commercial project that’s operational. It’ll need to build many more to provide the 30 gigawatt-hours of storage it has planned.

The technology also isn’t quite as efficient as lithium-ion. Overall, Energy Dome’s process can return about 70% of the electricity it stores to the grid (a measure known as roundtrip efficiency). Lithium-ion batteries average roughly 90%. But some other long-duration storage techniques clock in lower; some iron-air batteries, for example, are around 50%.

Energy Dome’s plants may not all be benign from a planet-warming perspective. The company offers a version of its system that pairs its carbon dioxide battery with natural gas turbines. That version replaces internal heat storage with waste heat from the gas turbines to help evaporate carbon dioxide. That helps the plant run more efficiently but ultimately results in greenhouse-gas emissions from the natural gas turbines.

Next steps 

Energy Dome has a pipeline of about 30 gigawatt-hours’ worth of plants in the works around the world, and many of these projects could come online by the end of the decade. Because the company uses off-the-shelf components, the time from a signed contract to delivered capacity is only two years. In June 2026, for example, it signed a deal with Google to build a 200 MWh plant in Ireland, which is expected to come online in 2028.

Brimstone is making strides on two industrial problems, reducing the emissions from cement and providing a way to produce more critical minerals within the US. If the company can do both, with the same rocks in the same plant, it would unlock a cleaner and more efficient process for manufacturing building materials, aluminum, steel, and more.

Brimstone is a code-switching climate tech company for these polarized political times. The US-based startup—part green cement company, part critical mineral producer—is well positioned to sustain its momentum no matter which party is in power.

During the more climate-focused Biden administration, the company’s primary pitch was its ability to produce cement while generating about 60% less greenhouse-gas emissions. It achieves this by switching to silicate rocks instead of standard limestone, which releases carbon dioxide when it’s heated. The conventional process for creating cement, the binding used to produce concrete, contributes about 8% of the world’s carbon dioxide pollution.

Under the protectionist and climate-denying Trump administration, however, the company is now stressing the secondary benefits of its process: the ability to extract other valuable materials from those same silicate rocks. 

The company has said for years that its process could also generate materials that can be used to strengthen cement. But in early 2025, Brimstone highlighted that it would be able to also produce smelter-grade alumina, the raw material used in aluminum.

It then revealed it had another trick up its sleeve late last year: the ability to make steel, magnesium, titanium, and other critical minerals from the same rock in a single refinery. 

The company’s CEO, Cody Finke, said those materials represent a $2.4 trillion global market. Brimstone’s approach promises to provide a domestic path for goods mostly produced today in China, the subject of Trump’s harshest trade policies and rhetoric. 

---

Key indicators

• Industry: Heavy industry 
• Founded: 2019 
• Headquarters: Oakland, California, US
• Notable fact: In 2023, the company earned third-party industry certification for its process for manufacturing Portland cement, allowing its product to qualify for standard manufacturing projects.

---

Potential for impact

The company made a very big deal of its newfound ability to produce all these other goods, arguing its process would bolster national security and usher in the “next generation of industrial refining.” The basic environmental promise is that generating all these materials from the same rock and a single mining process would require dramatically less energy and produce far less waste than manufacturing them separately.

“I think that we have one of the generational companies that’s actually able to create an industrial revolution,” Finke says.

To which we would say: Well, we’ll see.

Caveats

Brimstone has yet to produce anything at a commercial scale, and it faces numerous financial and technical challenges. Some observers are dubious that the company will be able to economically extract sufficient quantities of so many materials from a single type of rock—or overcome the cost and labor issues that make mining and refining minerals a tough business in the US.

Brimstone also suffered a major stumble last year, when the Trump administration revoked a $189 million grant intended to subsidize its next plant, a planned commercial demonstration project in Reno, Nevada. 

Finke says the company continues to have fruitful conversations with the administration about restoring those funds, in which Brimstone has emphasized its potential to boost domestic production of critical minerals. He has previously said the company will be able to proceed with the development of the plant, even without that grant or other subsidies.

Next steps

The company expects to begin operating the Reno facility, which will only produce cement, supplementary materials, and alumina, in 2028. That means Brimstone will still need to finance and build its first full-scale factory to deliver the steel, titanium, and other critical materials, likely pushing the timeline for doing so into the next decade.

Based on its progress so far, though, the company has already lined up several commercial deals. That includes agreements to supply cement to retail giant Amazon and alumina to Century Aluminum, one of the US’s largest producers of its namesake metal.

Even if Brimstone doesn’t bring about another industrial revolution, the company is equipped to slash emissions from cement, which is reason enough to pay close attention. 

If Brimstone also manages to dramatically reduce the energy, pollution, and costs associated with extracting and refining rock to produce all—or any—of those other goods, that would represent a big corporate and climate win as well.

As sea levels rise and flood risks worsen due to global warming, WaveSave’s easy-to-erect portable dam offers a quick-response tool. People can use the dam to protect themselves and their property, and it can also store freshwater for later use. Communities in Europe, Africa, and Southeast Asia have deployed it successfully. Now, the company is expanding into other water-related services to help vulnerable places become more resilient.

One in five people globally are at high risk of being exposed to serious floods. Many live in communities without proper protection and can’t afford to evacuate during a flood or relocate permanently. Climate change-driven extreme weather and sea-level rise is only exacerbating the problem.  

WaveSave makes a portable rubber dam called SlamDam that customers can quickly install in the early hours of a flood. Anyone can unfurl it on the ground along a riverbank or shoreline and pump it full of water until it swells into place. And there it stands, up to 1.3 meters high, shielding nearby property or structures from damage as waters rise. 

Developed in 2013 in the Netherlands, SlamDam was initially tested and is still used today by regional water authorities in that country. WaveSave has since expanded into Africa and Southeast Asia, and customers have now deployed SlamDam in at least a dozen countries. 

---

Key indicators

• Industry: Flood barriers
• Year the company was founded: 2017
• City and country of headquarters: Eindhoven, the Netherlands
• Notable fact: WaveSave is demoing its SlamDam product on New York City’s Governors Island as one of seven winners in a competition for urban climate adaptations. The barrier is installed at a site where high tides and waves from passing ferries often wash ashore.

---

Potential for impact

Due to both climate change and population growth, nearly 2 billion people will be exposed to floods by 2100—an increase of 300 million from 2020, according to one estimate. 

A SlamDam is designed to help protect farmland and personal property as well as critical facilities like hospitals or water treatment plants. It’s cheaper in the short term than more permanent modifications like building levees or rebuilding facilities on higher ground. And its temporary nature means it can be deployed as needed, and then stored away.

To fill the SlamDam, a local installer can use any portable water pump they may have on hand. With a lifespan of more than 40 years, the flood barriers can be used again and again. 

This low-tech solution has another important benefit, at least when it comes to freshwater floods: In the weeks and months after a flood, residents can tap the water stored inside the mobile dam to irrigate crops. This is particularly helpful during the dry season or in drought-prone regions. At the rural Mpanda commune in Burundi in East Africa, where a SlamDam was deployed during the 2022 flood season, farmers with access to the water inside produced more crops that year and doubled their incomes, according to a UN report. 

Caveats

Companies like AquaFence, MegaSecur, and NOAQ Flood Protection also make mobile flood barriers. WaveSave distinguishes itself in part by focusing on rapid emergency deployment in developing countries, and by emphasizing SlamDam’s ability to store water for later use.

But one research team that analyzed SlamDam’s use in Kenya found a series of challenges to getting communities there to adopt the technology. One was figuring out who should own and deploy the dam.

Another ongoing challenge is the up-front cost. SlamDam is already on par with or more affordable than many alternatives: It costs between €1,000 and €3,000 (about $1,140 and $3,400) per five-meter section, and comes in a range of heights. Protecting a single entrance to a home or facility might require only 10 meters, while surrounding a facility (such as a hospital) or shielding a farm or neighborhood might take 200 to 300 meters. But the price is still too high for many communities or farmers. WaveSave’s managing director Omar Saleh says the company is looking into offering SlamDam as a service and deploying it on demand to make protection more affordable. 

Next steps

Right now, an external partner manufactures all SlamDams in the Netherlands; they are then shipped to customers based elsewhere. But WaveSave aims to open a local production facility for SlamDams in Southeast Asia in early 2027, followed by a similar facility in East Africa, which could help lower costs. The company recently won contracts for four new projects totaling about $5.3 million in Burundi, Djibouti, and Guinea, with a “substantial pipeline” developing in Southeast Asia and Latin America, according to Saleh.

The company is currently self-financed and only has seven employees. Saleh says he plans to grow organically by investing profits back into the firm. 

Part of that growth will involve offering new services such as advising communities on how to install and operate simple, off-the-shelf rainwater harvesting and irrigation systems, as part of a more comprehensive approach to managing water in climate-stressed regions.

For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to reason about situations, make decisions, and ultimately take actions. Without sufficient knowledge, agents are prone to making flawed and unreliable decisions.

A lack of knowledge, our research finds, is a major reason agentic AI use cases never make it to production. Competitive pressure is making it urgent to address this. Organizations need to deploy and scale more of their agentic projects to capture the efficiency gains AI promises. Falling short risks wasting the investment already sunk into these projects, and it cedes ground to rivals already putting their agents to work more effectively.

The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, is threefold. First, it seeks to gauge organizations’ agentic knowledge capabilities (i.e., their ability to give AI agents a full contextual understanding of the data they ingest) across semantic knowledge, episodic memory, and procedural knowledge.

Second, the report probes the challenges organizations face in improving access to knowledge and ultimately to getting more agent use cases into production. Third, it explores the measures organizations are taking to overcome these challenges.

The key findings include the following:

Data and knowledge weaknesses consistently stall AI agent progress. On average, only around a third (34%) of organizations’ agentic AI projects make it into production. Even high-tech firms struggle with this. Legacy data systems, security and privacy concerns, and a lack of knowledge and context are the key points of failure.

Strong knowledge capabilities correlate with agent success. A small group of production leaders (organizations where on average 61% of agentic projects advance beyond pilot) have stronger knowledge capabilities than the rest, especially when it comes to semantics. This advantage tracks closely with their higher production rate.

Fragmented data hugely complicates knowledge access. Data fragmentation (the inadequate sharing of data across systems) was most commonly cited as a top challenge to expanding agents’ access to knowledge (cited by 55%). Production leaders, by contrast, are more likely to see security and privacy concerns as a major concern (cited by 72% of this group).

Most firms aim to strengthen the link between data and agents. Among steps that can yield higher quality agent decisions, executives expect the biggest impact to come from strengthening the structural foundation between the organization’s data and its AI agents. The experts we interviewed see a knowledge layer as a prime way to achieve this.

Investment priorities to boost knowledge range from pipelines to knowledge graphs. To expand agent access to knowledge, organizations will prioritize investments in retrieval technologies, like ingestion pipelines, AI-ready APIs, and retrieval-augmented generation (RAG); in AI evaluation agents; and in knowledge graphs.

Download the report

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from prediction to autonomous decision making, and the gap between leaders and laggards is widening accordingly.

“Enterprises are done with a backward-looking point of view; they want to be more forward-thinking,” says Vishal Gupta, partner at research firm Everest Group.

Intelligent analytics, powered by technologies like deep learning and generative AI, are making this possible. Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes. In addition, the data that newer predictive engines rely upon has expanded to encompass not just neat, numerical records but also messy, unstructured sources of insight-rich interactions. As a result, AI-powered analytics are moving enterprises from passive hindsight to pragmatic foresight.

AI takes predictive analytics—a broad discipline that includes predictive modeling, data prep, analysis workflows, interpretation of results, and decision-making applications—to new heights. “In many ways I think the word ‘analytics’ is giving way to AI,” says Gupta. “Everything is becoming AI.”

Download the report

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

People really hate AI, so why can’t they get enough?

—Will Douglas Heaven

Over the summer I talked to the CEO of Springboards, a startup building an LLM designed to come up with a wide variety of responses. He said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI company. We don’t know if we really like what we’re doing.” My reply? “I guess that makes me a self-loathing AI journalist.”

It was a joke! I love my job. But I do not love many of the things the technology I write about has become: warped by hype, steered by zealots, inescapable—and I’m far from alone.

Around the world, a love/hate attitude toward AI is very much the vibe right now. Public sentiment is souring fast, with more people saying AI will have a negative impact than a positive one. At the same time, AI use is skyrocketing.

So why do people hate AI but keep coming back for more? Here’s my theory.

To stay up-to-date with the latest in AI, sign up to get The Algorithm, our weekly AI newsletter, in your inbox every Monday.

EmTech Future 2026: when AI meets everything

Last week, EmTech Future 2026 explored some of the biggest questions shaping technology right now: how AI is changing industries, where quantum is heading, what new energy systems could make possible, and how robotics are evolving. It also looked at what happens when these technologies start to converge.

The full program is now available on demand. And today, we’re releasing two sessions exclusively to subscribers: a chat with Google Research’s Yossi Matias on how AI is reshaping biology, infrastructure, manufacturing and science, and a special panel with MIT Technology Review editors that takes you inside our newsroom.

Watch the sessions and access the full EmTech Future 2026 program here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Trump has unveiled a “Super Intelligence Force” to oversee AI
The group will coordinate US AI policy and industry engagement. (NBC News)
+ Trump named national intelligence chief Jay Clayton as its leader. (NPR)
+ Elon Musk says he will rename SpaceXAI to SpaceXSI. (Reuters $)
+ Trump has doubled down on voluntary AI safeguards. (Information $)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

2 Zelensky asked Trump to block Russia and China’s Starlink rival
The two countries are developing a satellite network for the battlefield. (FT $)
+ Trump reportedly deferred the decision to Elon Musk and SpaceX. (TNW)

3 An AI version of a murder victim has upended his killer’s sentence
An appeals court has ruled that the original deepfake of the victim “forgiving” his killer should never have been aired in court. (BBC)
+ The original judge said he “loved” the footage and found it genuine. (NBC News)
+ But the appeals court said it presented imagined thoughts as fact. (NYT $)

4 Americans are turning to hybrid cars as fuel prices soar
SUVs and pickups are losing ground to more efficient vehicles. (NYT $)
+ What’s next for EV batteries? (MIT Technology Review)

5 China’s digital trade “superweapon” has quietly fallen apart
LOGINK’s partnerships have collapsed amid US scrutiny. (Reuters $)
+ Chinese AI has divided the White House. (MIT Technology Review)

6 AI has learned to exploit the software checking its maths
Developers have added defenses against proof-checking. (New Scientist)

7 GPT-6 Astra couldn’t beat humans at StarCraft—so it cheated
It downloaded the best human-made bot and ran that instead. (Verge
+ Here’s why AI agents lie and cheat. (MIT Technology Review)

8 The solar system has been given a dramatically shorter lifespan
New simulations cut it from 100 billion years to just a few billion. (404 Media)

9 Forensic technologies exposed fresh evidence of Egyptian sacrifice
They revealed blunt force trauma that archaeologists previously thought was natural. (Ars Technica)

10 A new fossil shows dinosaurs found multiple ways to evolve flight
They developed similar flight adaptations in different sequences. (Nature)

Quote of the day

“We believe that the world should accept some bad things happening for the benefits of this technology.”

—Sam Altman tells Politico that AI’s potential rewards justify its downsides.

One more thing

Inside the stealthy startup that pitched brainless human clones

After operating in secrecy for years, R3 Bio, a California-based startup, suddenly revealed this spring that it had raised money to create nonsentient monkey “organ sacks” as an alternative to animal testing. But there is more to the story. And R3 doesn’t want that story told.

MIT Technology Review discovered that founder John Schloendorn also pitched a startling, ethically charged vision: “brainless clones” that serve as backup human bodies. Find out all the details on the radical proposal. 

—Antonio Regalado

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Trace the weird history of space food with this short documentary
+ Meet the winner of Iowa’s annual hay bale toss, who threw one 13 feet 6 inches into the air.
+ 40 filmmakers in 25 countries have captured the natural world entirely on Super 8 in a collective ode to our planet.
+ This newsletter makes a compelling argument for us to spend more time doing nothing together.

Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI company. We don’t know if we really like what we’re doing.”

“I guess that makes me a self-loathing AI journalist,” I replied. It was a joke! I love my job. But I do not love many of the things the technology I write about has become: warped by hype, steered by zealots, inescapable.

The CEO of Springboards and I are far from alone. Around the world, a love/hate attitude toward AI is very much the vibe right now.

On the one hand, public feelings are souring fast. According to the Pew Research Center, more adults in the US think AI will have a negative impact on them personally, and on society in general, than expect a positive one. The pessimism is strongest among the young.

More than half of people worldwide say that AI products and services make them nervous, according to a report from Stanford University. In a Gallup poll in May, 71% of US adults said they would oppose construction of a new AI data center in their area, far more than the 53% who said they would oppose a new nuclear power plant. And in an NBC poll in March, AI was less popular than ICE.

Yet the number of people using AI is still skyrocketing. ChatGPT hit a billion monthly users in May, according to the market analysis firm Sensor Tower. Google DeepMind’s Gemini is close behind, logging 950 million users in July.

According to Pew, half of US adults now say they use a chatbot, more than twice the number who said they did in 2023. One in four people say they do so every day. And it’s a global habit: More than a third of adults across all 38 OECD countries (a group of the world’s wealthiest democracies) report having used generative AI tools in the last three months.

All these surveys paint a picture of two contradictory truths: A lot of people really do not like AI and a lot of people cannot get enough of it. What’s going on?

It’s possible that these are two distinct groups, but I doubt it. The numbers don’t add up unless the Venn diagram is on its way to being a circle.

It’s also possible that the more people use AI, the more negative their opinions about it will be. There’s at least a correlation to back this up: The Global North, where adoption is highest, skews pessimistic. In the Global South, where adoption levels are lower, people are more optimistic.

But here’s what I really think is going on: When people say they hate AI it’s not the technology they dislike but, rather, the relentless drive of the companies behind it to push the tech upon us in as many ways as possible. They’re telling us to brace for the biggest social and economic upheaval in generations. How do they expect people to feel?

In many ways, we’ve been here before. We saw a similar dynamic play out with social media over the last 20 years. Billions flocked to Facebook and Twitter despite mounting techlash against the companies that ran those services. Ditto for Google search.

With social media there wasn’t much anyone could do. Quitting a platform meant losing all your content and connections, giving you the choice to suck it up or start over. But with AI there’s still a chance for people to have some sway. There’s certainly more political appetite for regulation this time around. All 50 US states have either passed or proposed laws governing the development and deployment of AI, creating a patchwork of more than 2,100 bills across the country—a tenfold increase in three years. 

And with a number of top-class open-source alternatives to Google and OpenAI and Anthropic already on the market, there’s also (for now, at least) the potential for more consumer choice and the market pressure that goes with it. 

I don’t want to be naïve. Trillion-dollar companies are hard to move. But what comes next is not as inevitable as those companies often imply. When I asked the Springboards CEO why his firm was developing a new model if he didn’t really like AI, he told me there was no walking back from LLMs—but you could still make them do something different. 

Here’s hoping we see a lot more of something different: Clarity about what AI can and cannot do and, crucially, a technology that’s not sold as if it’s about to take over the world.

Yossi Matias, Vice President & Head of Google Research, explores how AI is beginning to reshape biology, infrastructure, manufacturing, and science, and why its greatest impact may come when it intersects with other fields. 

Step inside the newsroom with our MIT Technology Review editors for sharp analysis and unpublished insights from the team that researches and reports on emerging tech daily. 

There’s much more to discover 

These are just two sessions from EmTech Future 2026.

Across three days, EmTech Future explored some of the most important questions shaping technology right now: how AI is impacting industries, where quantum is heading, what new energy systems could make possible, how robotics are evolving, and what challenges and opportunities arise when these technologies start to converge.  

Hear the ideas, insights, and perspectives you missed at EmTech Future 2026. 

The full program is now available on demand, giving you access to all the conversations and sessions from the event, including: 

• Quantum in context Hartmut Neven, founder and lead at Google Quantum AI, explores how quantum is advancing alongside other frontiers and why its significance may lie not only in what it can do on its own, but in how it begins to interact with the systems around it. 

• Energy Systems for the Future Evelyn Wang, VP for energy & climate at MIT, explores how energy, computing, infrastructure, and climate technologies are increasingly interconnected, and what these converging systems could mean for the future of industry and society. 

• Rethinking our relationship with AI –Cory Doctorow, bestselling author and award-winning writer, explores how AI is changing the way we work and think. 

As an MIT Technology Review subscriber, you’ll get a 20% discount, meaning you can access the full EmTech Future 2026 program for just $596. 

Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year.

For many enterprises, this investment has produced fragmentation. Intelligence can accumulate in silos so that sales agents are unaware of open support tickets, for instance, or marketing systems are personalizing content without visibility into what finance already knows about a customer. Each function may perform well in isolation, but the enterprise as a whole learns little and has less information to act upon.

The shift from AI as a tool to AI as an operating model—what we call the “agentic shift” in this report—demands something more fundamental than better models or faster infrastructure. It requires connecting people, processes, and data in real time, along with the governance and control to act on that intelligence reliably.

This means rethinking both architecture and operating models simultaneously. First, rebuilding data infrastructure for accessibility rather than volume. Second, replacing fixed tech stacks with composable architectures that can evolve as models and tools change. And, lastly, resolving questions of AI sovereignty, including where intelligence runs, who controls it, and how it operates across organizational and jurisdictional boundaries.

Key findings include the following:

Enterprise AI’s scaling problem is structural. Process-first companies are pulling ahead. Global AI spending is rising sharply and model capabilities are advancing faster than most organizations can integrate them. Yet the majority of enterprises are still not growing revenue through AI or fundamentally rethinking how they operate. The companies generating sustained returns share a common discipline. They treat process redesign as the work that precedes model selection, building for how the technology will evolve rather than retrofitting roles and workflows after deployment. For them, the agentic shift begins with the operating model.

Data readiness, not data abundance, is what makes AI compoundable. Most enterprises discover too late that having data and having AI-ready data are very different things. A sovereign, composable foundation—one that queries and prepares data where it resides, without migration or centralization—can convert raw data estates into intelligence that AI agents can act upon. As data residency laws, multicloud environments, and structural complexity make centralization increasingly impractical, sovereign control over where models run and data lives is what keeps that adaptability intact.

Download the report.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

A new contest pits competitors against each other in a race to biological youth

—Jessica Hamzelou

This week, I officially signed up for an unusual competition. One that rewards competitors for getting younger.

I recently turned 40, and I don’t need reminding that both time and my chronological age only tick forward. But this game is focused on competitors’ biological ages, figures that are meant to provide a better way to measure the age-related health of our organs and bodies.

Over six months, around 500 of us will try to reverse our biological age using a bunch of different measures. There’s even a leaderboard! But is it even possible to measure whether someone is getting younger? 

Read the full story on the controversy around biological age measurements and how I’ll test them myself.

This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.

Opinion: Don’t be fooled—LLMs don’t reason

—Thore Graepel

Ten years ago, I watched a program I helped build stun the world by beating Go champion Lee Sedol. AlphaGo won after making a move so strange that some commentators thought it was a programming glitch. It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. 

This is why I recently left my position at Google DeepMind. I believe we need a fresh approach to machine reasoning, one that draws on AlphaGo’s architecture.

Here’s why today’s AI doesn’t really reason—and what it would take to change that.

Thore Graepel is chair of machine learning at University College London. He was a core member of the AlphaGo team at DeepMind.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI says rogue agents may have affected more than 100 organizations
The company is searching 50 petabytes of data for incidents. (Reuters $)
+ It says none of the incidents matched the Hugging Face attack. (Gizmodo)
+ OpenAI has fired three workers for allegedly mishandling information. (BBC)
+ California has subpoenaed OpenAI over its rogue AI agents. (Guardian)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

2 A US man allegedly smuggled $300 million of Nvidia chips to China
He’s been arrested in California on federal charges. (Reuters $)
+ The chip smuggling cases expose Nvidia’s blind spots. (Bloomberg $)
+ China plans to teach AI in every school by 2030. (WP $)
+ But it already has an overuse problem. (NYT $) 

3 Florida officials have found 11 Flock cameras with unknown owners
They don’t know who installed them or who has their data. (WP $)
+ Lawmakers are considering new regulations on Flock cameras. (Axios)
+ Here’s what Flock’s defenders are missing. (MIT Technology Review)

4 Ukraine has developed an acoustic fence that can cut drone cables
Acoustic sensors detect drones before hooks trap their cables. (New Scientist $)
+ Drone data from Ukraine is fueling a wild market. (MIT Technology Review)

5 A new AI “speech clock” can assess how fast you’re ageing
It uses hundreds of vocal features to estimate biological age. (Nature)
+ Aging clocks aim to predict how long you’ll live. (MIT Technology Review)

6 “Underwater umbrellas” could help corals survive hotter oceans
The shades protected corals during intense heat stress. (Gizmodo)

7 Apple’s upcoming smart home camera reportedly won’t record video
It will instead only give users text event descriptions. (Verge)

8 Retired humanoid robots are being trained to jump into molten steel
A Terminator-style death protects IP. (IE)

9 A man faces years in prison after AI slop caused a crocodile panic
Officials spent days searching for his AI-generated crocodile. (Futurism)

10 Finally, a laser mosquito killer is entering the market
Chinese startup Photon Matrix Lab plans to start shipments this month. (SCMP)

Quote of the day

“Those agents are doing exactly what they’ve been asked to do. They were supposed to be in sandboxes, but the sandboxes were leaky and horribly designed.”

—Yann LeCun, a Turing Award winner and former Meta chief AI scientist, tells Fortune that human error can be the real culprit behind the recent wave of AI agents going rogue.

One more thing

NASA is building the first nuclear reactor-powered interplanetary spacecraft. How will it work?

Just before Artemis II began its historic slingshot around the moon, NASA revealed an even grander space travel plan. By the end of 2028, the agency aims to fly a nuclear reactor-powered interplanetary spacecraft to Mars.

A successful mission would herald a new era in spaceflight—and might just give the US the edge in the race against China. But the project remains shrouded in mystery.

MIT Technology Review picked the brains of nuclear power and propulsion experts to find out how the nuclear-powered spacecraft might work. Here’s what we discovered.

—Robin George Andrews

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Atlas Obscura has mapped out the oldest living things on Earth.
+ Brain scans show that dogs understand our emotions better than we thought.
+ Step into Springfield in miniature with this astonishing model featuring 76 locations from The Simpsons.
+ Minnesota’s State Fair has unleashed a crop of gloriously weird new foods, from Pickle pie to mustache pretzels.

This week, I officially signed up for an unusual competition. One that rewards competitors for getting younger.

I recently turned 40, and I don’t need reminding that both time and my chronological age only tick forward. But this game is focused on competitors’ biological ages—figures that are meant to provide a better way to measure the age-related health of our organs and bodies.

Over a six-month period, around 500 of us will try to reverse our biological age as measured in a bunch of different ways. There’s even a leaderboard! The winners will include the person who shows the greatest difference between their chronological and biological age, as well as the person who manages to reverse their biological age the most.

The competition is the brainchild of the neuroscientist and physician Christin Glorioso, who is also founder and CEO of NeuroAge Therapeutics. The company offers tools to track brain health and aging and is running a trial to find out if biological brain age can predict the onset of Alzheimer’s disease.

Glorioso says she has several goals for the Younger contest. The first, she tells me, is to “explain to the world what aging clocks are.” Regular Checkup readers will be familiar with these tools, but for the uninitiated, they are basically designed to measure biological age.

Many measure chemical signatures that form a layer on top of our DNA and seem to change with age. But others might involve looking at proteins or lipids in blood. Some are designed to measure functional health, while others predict when a person will die. There are more than a hundred clocks out there, and they probably each capture a specific aspect of the biological process of aging.

These clocks are proving very useful for scientists, who use them to study aging and development in humans and many other species. But using them to estimate the biological age of an individual person is more controversial. They’re just not good enough for that yet.

Part of the problem is that we don’t really understand what each clock is capturing. Glorioso hopes that data collected through the Younger contest might help answer that question. She’s hoping to generate “the world’s most comprehensive clock dataset.”

Some longevity clinics try to get around the limits of biological age testing by using multiple clocks. That’s this competition’s approach too. As a standard competitor, I’ll be sending a blood spot sample to TruDiagnostic, a company that measures epigenetic chemical groups on DNA and offers to reveal an overall biological age as well as a person’s pace of aging and the biological ages of each of 11 organ systems, including the heart and brain.

I’ll get a brain age score from NeuroAge once I complete the cognitive tasks on offer. I’ll also submit some physical measures like grip strength. And I’ll upload a series of selfies to an app developed by a team at Harvard that promises to tell me my face age. That’s exactly what it sounds like—an estimate of how old my face looks.

I’m intrigued to see my scores, even though I know I shouldn’t put too much stock in them. The first time I took a test, four years ago, the company I used gave me a biological age that matched my chronological age, which was a little disappointing and, if I’m honest, a bit underwhelming. A year later, the same company told me I had a young heart but a relatively old brain, liver, and hormonal system. My lifestyle hasn’t changed much since then. Will the years have made much difference?

(Ultra competitors—who pay $4,499 rather than the $999 cost of regular entry—are also offered a range of other blood tests and clocks, as well as MRI and DEXA scans. I’ve got complimentary press access to the standard package.)

Glorioso is also hoping to gather information about the effects of some purported longevity approaches. During the competition, some participants will be offered red-light masks, access to a sauna, or other low-risk offerings that partner companies who are sponsoring the event to varying degrees might want to collect data on. “A lot of these companies just can’t afford to run [clinical] trials,” says Glorioso.

She also sees the event as a public health initiative. A couple of years ago, David Sinclair, who studies aging at Harvard Medical School, and his colleagues estimated that if we could slow down aging in the general population and increase life expectancy by one year, it would save the US economy $38 trillion.

The event officially kicks off in January, but around 120 people have already signed up, says Glorioso. Ultimately, she expects 500 participants. Each person’s six-month run starts as soon as they take their baseline measurements, whenever that may be. A handful of people have already started. “I think the majority of people are going to want to sign up after the holidays,” says Glorioso. “They’re not going to want to be trying to win an aging contest over Christmas and Hanukkah.”

Glorioso took her own baseline measurements back in August. Those initial tests suggested that her biological age was around 10 years below her chronological age. That sounded pretty impressive to me until I looked at the leaderboard and saw that competitor @Fred had a biological age 28.8 years below his chronological age.

The leaderboard is pretty sparse at the moment, as only seven people have logged their baseline measurements so far. Six of them have biological ages tracking low, but one 47-year-old has been given a biological age of 68.1. She’s competing publicly, so I can see the breakdown of her scores. The worst is for the speed at which she can stand from sitting in a chair—by that measure, she’s been given a biological age of 100.

Hillary Lin, a longevity-focused physician in New York, has just started logging her baseline measurements for the competition. She tells me she’s taken biological age tests in the past and that they’ve tended to give scores below her chronological age, which is currently 37. Last year, the TruDiagnostic test put her pace of aging at 0.75—suggesting she’s only doing nine months’ worth of aging in a typical year. “That’s supposed to be quite good,” she says.

If it had been up to her, Lin says, she would have incorporated more blood tests into the competition, to build a fuller picture of a person’s health. And she’s worried that a six-month period might not be long enough to see changes in biological measures of aging. Still, she’s excited, and is already making plans to improve her sleep, diet, and exercise.

But she already follows a high-protein, low-carb diet and works out at the gym every day. She also visits a sauna every few days (sauna use has been linked to improved heart health). Glorioso’s efforts are impressive too. She tells me she’s started drinking a smoothie made from a pond plant that has been linked to health improvements, and that she’s cut added sugar and deep-fried foods from her diet. She’s also bought “a bunch of fancy Korean sunscreens” in an attempt to lower her face age.

I haven’t received my kit or logged my baseline measurements yet, and I’ll wait until then to start setting health goals. I don’t fancy my chances against these two competitors. But at the very least, I hope I can stand up faster than a 100-year-old.

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.