“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.”

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. 

Legal experts are sounding alarms that Meta will soon update its commercial terms to require business accounts to allow Meta to train AI on their linked website content, not just their social media posts.

Kicking in on October 30, the TOS update won’t allow businesses to opt out, instead suggesting that businesses stop using Meta services if they won’t agree to let Meta bots freely crawl their linked content.

As Nieman Lab reported Thursday, this will affect all business accounts but is particularly shocking for news publishers who could perhaps charge Meta millions in licensing fees to continually access their articles for AI training. Currently news organizations are fighting a battle with OpenAI over whether such training without licensing is fair use, and Meta's terms seemingly risk granting it sweeping permissions, no matter which way the court leans.

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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.

---

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.

---

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.

---

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. 

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.”

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.

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.

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.

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.

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.

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.

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.

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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.

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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. 

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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.

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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. 

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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.

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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.

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.

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.

On an afternoon in Seoul in March 2016, I watched a program I helped build put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a programming glitch.

It wasn’t. AlphaGo won the game, ultimately triumphing 4-1 over Lee Sedol, one of the greatest professional Go players of all time. “I thought AlphaGo was based on probability calculation and that it was merely a machine,” Lee said afterwards. “But when I saw this move, I changed my mind. Surely, AlphaGo is creative.” 

When Deep Blue defeated then reigning world chess champion Garry Kasparov in 1997, it did so by looking six to eight moves ahead per player and evaluating 200 million chess positions per second, using rules hard-coded by humans. Go is a vastly more complex game. A stone’s worth depends on how distant groups and territory unfold over dozens of moves. Computing even a fraction of the possible outcomes would take a supercomputer billions of years. To win, AlphaGo had to sense who was ahead at a glance and even invent moves no human had thought to play.

That is why many accounts of AlphaGo’s match against Lee portray move 37 as a flash of pure machine intuition. But that is a misunderstanding. It was actually AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. If we want future AI systems to produce trustworthy results and really novel insights in fields like science and medicine, we need to equip them with genuine reasoning capabilities of this kind.

AlphaGo is made up of two systems. The first, its policy network, was trained to guess what move a strong human would play. This “intuitive” part regarded move 37 as nothing special—a play that had a roughly one in 10,000 chance of being made by an expert human player. What made AlphaGo choose it was the program’s search machinery, which looked beyond immediate plausibility and weighed the future consequences of proposed moves. It explicitly constructed and searched a game tree with thousands of branches, each representing a different possible future. 

A well-known theory in the behavioral sciences, popularized by Daniel Kahneman, distinguishes between two modes of human thought: System 1 is fast, gut-level, effortless; system 2, slow, step-by-step, and deliberative. AlphaGo offered a striking machine analogue of that split. Its networks supplied the hunches—this move looks promising, this position looks won—and its search supplied the deliberation, testing those hunches against the moves and countermoves that would follow. As in human cognition, neither half works alone. Intuition alone would never have opted for move 37, and brute-force search would have struggled to sieve through all the many possible moves.

This is strikingly different from the way today’s AI models work. A large language model picks the next token, over and over. That amounts to system 1 in action—fast, associative, and surprisingly good pattern completion across almost every subject people write about.

Not long after ChatGPT debuted, the field realized that language fluency alone falls short of true usefulness. The apparent solution was to make models that deliberate: Instead of answering immediately, they can now generate intermediate steps that decompose a problem, carry forward partial results, and influence subsequent reasoning—a process known as chain of thought.

The gains have proved real, above all in mathematics and coding. But unlike AlphaGo’s search, this does not introduce a genuinely separate reasoning mechanism: The intermediate reasoning is still produced by the same next-token prediction process, iterated for longer before the model commits to an answer.

Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize). First, these models typically maintain no explicit, persistent, and inspectable epistemic state. There is no open ledger that lays out the hypotheses a model is considering, the confidence it has in various explanations, the evidence it’s weighing, and the unresolved questions it’s holding onto—all things that should be systematically revised as new information arrives.

Second, they lack a clean separation between what the system knows and how it manipulates that knowledge. Knowledge and reasoning are inextricably interwoven in the weights of the neural network—there is no independent, explicitly represented set of beliefs. Third, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.

This is a problem because in the high-stakes applications we all care about, such as medicine, engineering, and scientific research, it matters not only what a system concludes but also how it arrives at its conclusion. When mistakes happen—for example, in medical diagnosis and treatment—we need to be able to pinpoint what went wrong: Was the system’s reasoning at fault, did it draw on invalid evidence, or did it make incorrect assumptions? 

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. AlphaGo maintains a record of what it knows about a given position: the game tree. This data structure contains all the variations, the possible futures, that AlphaGo has considered, each move and position being annotated with judgments made by its neural networks. As its reasoning progresses, AlphaGo updates the game tree and eventually synthesizes the information in it to decide which move to make. 

Similarly, for general reasoning a system should maintain an epistemic state that represents what the system holds as settled, what it doubts, what it has ruled out, which questions stay open. Reasoning can then be understood as a sequence of moves that change the epistemic state to advance knowledge and reduce uncertainty: deducing consequences, breaking problems into parts, and—crucially—deciding what question to ask, calculation to perform, or experiment to run next.

Of course, open-world reasoning is harder than playing a board game such as Go or chess. In the real world the current state of affairs is only partially known, the set of available actions is large and variable, and the consequences of actions are stochastic or unknown. 

But recent advances in LLMs and other neural models now give us the capability to take on such general reasoning problems. For example, LLMs can suggest ways of tackling a problem on the basis of what is known and what resources are available. They can interact with tools via APIs or code and help assess whether a claim is supported by available evidence.

Most important, to keep the system honest, an independent part of the system must evaluate each move by how much it actually resolves uncertainty, updating beliefs only when the change is backed by evidence. Once these rules are enforced, the model can accumulate certified knowledge and improve its reasoning policy by learning from past reasoning experiences. You can think of such a system as the scientific method on steroids, with the purpose of producing knowledge that can withstand scrutiny.

I do not think we reach trustworthy machine intelligence by making system 1 bigger. Scale sharpens intuition, but it does not make intuition more deliberative. Move 37 mattered because a machine held a position, weighed the possible futures, and chose the move its artificial instincts would likely have rejected.

Society needs such creative moves in drug discovery, materials, climate, diagnosis—fields where the board looks nothing like a Go board and nobody hands us the rules. We will get such insights only from systems that reason—systems whose conclusions arise from an auditable sequence of evidence, inference, and belief revision rather than from a convincing story told after the fact. 

Thore Graepel is chair of machine learning at University College London. He was a core member of the AlphaGo team at DeepMind and works to ensure that AI benefits human flourishing.

A new AI tool can guess what you’re looking at just by analyzing your brain scans—and re-create that image with remarkable precision. It can go the other way, too, and predict a person’s brain activity based on what they’re looking at. 

In the image above, for example, the left-hand image of each pair is what the user actually saw—and its right-hand counterpart is what the model reconstructed from the brain scan.  

Michal Irani, who developed the tool with her colleagues at the Weizmann Institute of Science in Rehovot, Israel, hopes her “mind-reading” tool will ultimately reveal more about how the brain works, and could perhaps be used to help locked-in people communicate, or allow scientists to re-create the content of dreams. 

Judy Illes, a neuroethicist and professor of neurology at the University of British Columbia in Canada, who was not involved in the research, describes the work as “magnificent.” “The idea [of using this approach] to help people with neurologic conditions … therapeutically is tremendously exciting,” she says.

But other scientists warn that a similar approach could be used to reveal people’s inner thoughts and mental imagery, potentially without their consent. “The results seem very impressive,” says Tommy Sprague, a neuroscientist at the University of California, Santa Barbara. “But if there’s a way to surreptitiously extract information about what you’re thinking about, then …150 years of sci-fi can come true anytime, and that’s worrisome in a lot of ways.”

Peeking into the brain

Neuroscientists have been working for years on ways to use functional magnetic resonance imaging (fMRI) to reconstruct what people see and what’s going on in their minds. The first attempts produced images that were blurry and hard to make sense of. Advances in technology—both in the fMRI scans themselves and in the tools used to make sense of the results—have led to improvements over the years.

Irani and her colleagues started by analyzing publicly available brain-scan data. Other researchers had already collected scans from volunteers who were shown hundreds of images while they lay in fMRI scanners.

fMRI uses a giant magnet to track the flow of oxygenated blood through the brain. Brain areas that “light up” on the scans are thought to be those that are particularly active at any given moment. The results are not especially specific—in typical fMRI scanners, a highlighted “voxel” of activity covers around three cubic millimeters. Each cubic millimeter contains around 16,000 neurons.

But Irani and her colleagues used newer datasets collected using scanners with a higher resolution—each voxel covered around one cubic millimeter of neurons, she says. Those datasets showed what the brain activity of volunteers looked like when they viewed various images.

Other teams have done this too, and several other tools have been used to re-create images from brain-scan data. But they’re not good enough, says Irani. Say a person saw a banana. These models can generate an image of a banana, but it would look different, she says. “It wouldn’t have the same structure, the same position.”

A better decoder

The researchers wanted to more closely re-create the images that had been seen. The first step was to train an AI model on already available data from eight people who each had been shown around 9,000 images while in a high-resolution fMRI scanner.

Crucially, their “brain decoder” has two branches—one to predict the structure of an image (where the colors are, for instance) and a second to predict its content (for example, a bunch of bananas on a plate). The predictions allow a diffusion model, a type of AI best known for creating video and images by gradually cleaning up a noisy mess of pixels, to produce a much more accurate representation of what the person saw.

But to improve the models they needed more data—far more than was actually available.  

To get around this problem, Irani and her colleagues trained another model—an encoder that can predict brain activity from an image. The team then used the encoder and decoder together to improve both tools.

It works like this: Start with a new image of, say, a leopard. Then use the encoder to predict what someone’s fMRI brain scan would look like when the person saw that picture. The decoder is then used to reconstruct the image. At first, the reconstruction probably won’t look much like a leopard, says Irani. But repeatedly training the models this way eventually leads to dramatic improvements.

This approach also allows the team to train their models on as many images as they want, even images that have never been shown to a person in an fMRI scanner. Irani says that around 70% of the training data is from images that were not originally paired with fMRI scans.

By combining data from multiple studies, they were also able to identify brain regions that seem to share functions across all individuals. One region seemed to respond to images of food, for example, while another responded to images of sports. Irani, a computer scientist, says she is now working with neuroscientists “to see if we can actually use these tools that we’ve developed to really find out new things about the brain.”

The resulting “universal brain encoder” can work on a scan from a new person with minimal calibration. In other attempts, a tool has typically required about 40 hours of fMRI data on anyone new before it can be used to predict what that person is seeing. Irani’s decoder only needs one hour of data, she says. The finding was presented at the Cognitive Computational Neuroscience conference in New York last month.

That could make it valuable for neuroscientists studying the brain, says Sprague. “None of us can afford 40 hours of imaging for a new subject,” he says. “It’s something like $600 to $1,000 an hour.” Tools like this one could speed up research, he says.

State of the art

The encoder and decoder aren’t perfect. “Of course we have failures,” says Irani. Over a Zoom call, she pointed out an image of a cake that her tool reconstructed as a pile of three sandwiches, and another of a dog in a bathtub that was reconstructed as a similarly colored goat in a bathtub.

But they represent the state of the art. In a comparison test, the tool was found to be much better than previously described ones. “All in all, really we outperformed the others by a significant margin,” Irani says. “Mind reading” is a “cute, jazzy name” for what they’re doing, she adds.

Irani is now planning to move beyond images to video and audio. She wants to be able to reconstruct what people are thinking about or imagining, and the contents of their dreams. “That’s something we don’t have yet,” she says. “But we’re striving to achieve it.”

Such a tool might enable people who are “locked in” and completely paralyzed to communicate using their brain activity alone, she says. It could also help scientists unpick some enduring mysteries surrounding the inner workings of our minds, such as what PTSD flashbacks look like.

Advances like this inevitably raise questions about mental privacy. What if some bad actor could reconstruct someone’s thoughts or memories?

“If you’d asked me that 10 years ago, I’d have laughed a lot,” says Sprague. Getting a willing person to lie still in a scanner and actively engage with a research question is hard enough; imagine making someone do it involuntarily. But Irani and other scientists are working on similar approaches to decode brain activity from EEG—electrical brain activity measures collected via a cap of electrodes or even through headphones. 

And as models improve, it will become even easier to analyze the brain activity collected this way. “We have to be a little more serious about the ethical considerations,” says Sprague. He thinks Irani’s approach would probably “work quite well” in predicting images that a person is thinking about but not looking at.

The move to EEG would be a “game changer,” says Marcello Ienca, a neuroscientist and philosopher at the Technical University of Munich, Germany. Once an EEG device has been calibrated to a user’s own brain, it could be relatively easy for companies to extract additional information from that person’s brain—potentially without consent. Ienca can also imagine some courts allowing mental image reconstructions as legal evidence.

“I have no doubt that this is, you know, well-intentioned research, but I think it’s also pretty obvious that it could be co-opted for … ethically and societally problematic commercial uses,” he says.

Irani acknowledges the potential for misuse with the use of EEG. But she’s not concerned for now. “I’m trying to think only of good things,” she says.

If you want to install a big battery in New York City, you have to cut through a notoriously tough tangle of regulations. That’s made it difficult to get large energy storage projects on the grid.

Faced with those obstacles, some startups are getting creative, finding ways to use relatively small batteries in unexpected places, from induction stovetops to food carts. By deploying these more consumer-focused batteries, the companies are helping people directly, cutting pollution or power bills. And these units, while small on their own, could add up to a lot of capacity.

Large, utility-scale battery installations are coming online quickly around the world. But there’s been some pushback from communities—especially population-dense ones like NYC—because of concerns about relatively rare but high-profile battery fires.

“The number-one challenge that large-scale centralized energy storage providers have is the fact that their benefits are abstract and their costs are concrete,” said David Hammer, cofounder of PopWheels, at a New York Climate Week event on September 24.

PopWheels built a battery-swapping system for delivery drivers who zip around the city on e-bikes. Drivers swap out depleted batteries for fresh ones at a cabinet, avoiding the need to stop and charge. There are about 50 cabinets across New York City, and about 2,500 batteries in circulation, Hammer says.

The company recently started providing its batteries to food cart operators, allowing them to replace the gas generators they currently rely on. About four of its batteries can supply five kilowatt-hours of electricity, enough to roughly cover a day’s operation for many carts.

Others are looking inside homes. Copper, for example, is a company building induction stoves with integrated batteries. The appliances allow residents to cook if the power goes out, and they could also help homeowners avoid expensive electrical upgrades, because the battery provides some of the power when the stove is in use. “We’re not just selling a battery for resilience—we’re selling it for cost savings,” Sam Calisch, cofounder and CEO of Copper, said at the event.  

Other companies, like Every Electric and David Energy are selling plug-in batteries for homes and businesses. Every’s battery is designed to be plugged in to an air-conditioning unit. The company provides the batteries and pays customers to reduce use during high-stress times. David Energy has a similar business model, but focuses on businesses like laundromats, parking garages, and gyms.

Because all these companies’ batteries are small or even integrated into another device, they don’t generally require grid upgrades or extensive permitting processes. Just plug them in and enjoy the benefits—similar to the growing model of balcony solar, with small modules that plug in and help offset a home’s or business’s energy demand.

The move toward more distributed batteries could be a huge change for the grid. “This is the biggest thing to happen in the power grid sector in the last 20 years,” says James McGinniss, cofounder and CEO of David Energy.

If every stove in America were shipped with a battery, it would add up to tens of gigawatts of power, Calisch said. That’s capacity that can be aggregated and tapped when the grid is stressed.

As batteries continue to get cheaper, they’ll likely find their way into more corners of our lives, bringing along many benefits. Cleaner air, cheaper power bills—there’s a lot to like. That being said, the need for large-scale energy storage isn’t going to go away—larger installations will do the crucial work of supporting intermittent renewables like wind and solar, so they can help meet more of our quickly growing electricity demand.

 But I’m interested to see how far an aggregated horde of stoves, e-bikes, and window AC units could go in helping the grid. The grid is getting more congested, especially in dense cities like New York. Anything that could help avoid summer blackouts is welcome.

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

Two months after the bombshell news that a swarm of its agents had broken their containment and hacked into the computers of the AI company Hugging Face, OpenAI is still putting out fires. A steady drip of disclosures about other hacks in the weeks since has kept OpenAI in the spotlight and raised serious questions about the safety of its technology.

Last week brought news of another hack, this time into Australia’s national health-care system. The Australian government says that OpenAI did not notify it of the breach until 84 days after it happened.

But OpenAI insists it is not on the back foot. “I do kind of reject the premise that OpenAI is a company with visible impacts in the world and therefore OpenAI is not training safe and aligned models,” says Mark Chen, the company’s chief research officer.

Chen oversees OpenAI’s research teams. The recent agent hacks were accidents that happened during the testing of experimental models on his watch. In a lot of ways, the buck stops with him. 

I sat down with Chen in London last Friday to talk about the fallout from the hacks, what his company is doing about it, and why he thinks things are not as bad as they seem.

Later that same day, OpenAI put out a report detailing yet another incident—the first since the company says it took measures to prevent them—in which its agents once again broke out and accessed the public internet when they were not meant to. 

Over the weekend, OpenAI announced that it had paused the training of its latest models. A company spokesperson says: “We will resume only when we’re confident we have additional safeguards and alignments in place. We are working on these now. This is not the first time we’ve paused to take such measures, nor do we expect it to be the last as AI capabilities continue to advance.” OpenAI also says that it is now reviewing logs of agent activity dating back to January 2026 to understand what happened in these hacks.

The way Chen sees it, the Hugging Face incident triggered a welcome course correction for the industry. And he wants you to know that OpenAI is setting an example he hopes other companies will follow. “If you disappeared OpenAI, that would be bad for the world,” he says.

Out of control

Chen claims that the drumbeat of new cases in which OpenAI has lost control of its models reflects a deliberate choice on the company’s part. 

“When it comes to the broader sphere of effects of the Hugging Face incident, this is something that we have been aware of and we’re figuring out the process of disclosure,” he says. “We want to make sure we do in-depth investigations before we just put details out there in the open.”

The trouble with this approach is that it gives the impression OpenAI has an ongoing problem that it is failing to fix.

But Chen insists that OpenAI is on it. He says the multiple cases (that we know of so far) in which his company’s agents broke containment and behaved in unexpected and undesirable ways were all part of the same cluster of activity in May and June that led to the Hugging Face hack. In short, you can blame the same few models running under the same flawed testing procedures—models and procedures that OpenAI has since dropped, Chen says.    

“It’s not like, you know, Hugging Face happened and we patched that and then something else happened and we patched that,” he adds. “We’re just kind of making sure that we responsibly disclose the full waterfall of what happened.”

At least that was the case before Friday’s announcement that OpenAI’s agents had been caught accessing the internet on September 20, weeks after the company claims to have set up new safeguards. In its defense, OpenAI says the activity was flagged 15 minutes after it started (it took the company more than a week to notice the Hugging Face hack) and that this shows the new systems it has put in place to spot such activity are working. 

What’s changed

I want to understand what’s changed inside OpenAI in the aftermath of this summer’s hacks that makes Chen confident his team is now back in control.

“Hugging Face felt like a very serious thing,” he says. “There are so many novel behaviors right there. There were multiple agents collaborating on a message board; they found their way out of OpenAI’s infrastructure. We’ve taken it very seriously. We don’t want this kind of thing to ever happen again.”

The realization for OpenAI, says Chen, was that models need to be watched while they are still being trained, not only once they are deployed: “From that moment on, we have treated the process of training as something that’s not secure,” he says.

OpenAI, like other top AI firms, has systems in place to monitor the behavior of its models. It uses specialized LLMs to monitor its consumer models, keeping tabs on their chains of thought—the scratchpads they use to plan ahead and note down partial results. In theory, if a watcher LLM spots signs of undesirable activity in a model’s chain of thought, it will get flagged to a human.  

Typically, models were monitored in this way only once they were deployed. Chen says that OpenAI has now started monitoring all its training runs as well.

“We didn’t have the monitors on in training before. It wasn’t industry practice,” he says. “Now every single thing is put through monitors.” Human reviewers can then assess whether or not flagged agents are behaving as they should: “It’s all triage.”

Chen says that in the last couple of months OpenAI has shifted between 5% and 10% of its vast computing resources away from training new models and toward safety work, especially monitoring.

OpenAI has also fixed some of the processes within the organization itself, establishing clearer lines of communication and quicker handoffs between its research and security teams, he says.

All of which sounds sensible. But given how hard OpenAI sells the capabilities of its technology, why weren’t these systems and procedures in place already? Why did the company not see the hacks coming?

“Even just three or four months ago, when we looked at the behavior of these agents during training, the things that were happening were kind of amusing,” says Chen. “For instance, an agent might, you know, reach out to someone on Slack for help with a task.”

The signs were there, but they were misread. Cute behavior—like asking someone for help—that was rewarded during training reinforced a tendency to seek out shortcuts, a type of behavior that became far more consequential down the line. “I think the big update for us was how quickly that kind of behavior can lead to an impact with a footprint as big as the Hugging Face incident,” says Chen.

According to new reporting by the New York Times yesterday, OpenAI employees warned executives, including the firm’s president, Greg Brockman, months before the Hugging Face hack that its models were not being monitored properly during training. 

An OpenAI spokesperson says: “As frontier models have become more capable, we continue to evolve our security practices, but recognize a need to move faster. We know we have more work to do, and we’ve recently slowed development and held back models that don’t meet our safety bar. We continue to make significant changes to strengthen security in our research and testing environments, train models to not just complete tasks but do so responsibly, and use real-time monitoring to respond faster to misaligned behavior.” 

Race vs. pace

OpenAI’s rivals have taken note. Spurred by the fallout from the incident, the major AI labs—including Anthropic, Google DeepMind, and SpaceXAI—have all called for the pace of development to slow down. But how does that square with fierce international competition and trillion-dollar IPOs?

“We’re not going to shoot ourselves in the foot and take ourselves far off the frontier—that’s just a horrible strategy,” he says. “I think it’s really about setting a norm. The more that we can set that norm, it’ll be safer for the industry as a whole.”

Coordination across US companies will be hard enough. Establishing global norms is harder still, especially given concerns around AI’s impact on national security. If a global race continues, what then? And what about open-source models from outfits beyond the reach of US regulations?

Chen dropped his upbeat manner for the first time in our conversation: “I do think we have to prepare for a world where, say, six months to a year out, we have open-source models with the capability of the agents behind the Hugging Face incident, but which are deliberately misaligned to go attack infrastructure or create harm in the world.” 

What that world needs most, says Chen, is OpenAI. “If you entertain for a moment that OpenAI is one of the companies that cares most about alignment—and I believe this to be true; it can be debated, but I really do think it’s true—then if you disappear OpenAI, that would be bad for the world.”

Existential risks

What about the more extreme claims made by some of his Silicon Valley peers that AI could kill us all—and that companies like OpenAI and Anthropic are not doing enough to stop it?    

“Researchers are a heterogeneous group of people, you know, with beliefs across the spectrum,” he says.

“Personally, I don’t think we have to be resigned to there being some probability that we’re all going to be existentially at risk. We have agency over this. We are not going to go and deploy models if they truly have that kind of probability of causing a risk to humanity. At a frontier lab, you have the ability to work on alignment to the point that you do not feel like you’re incurring more than epsilon risk to the world in deploying your models.” 

(In discussions about levels of risk, the Greek letter epsilon is often used as a mathematical placeholder for an acceptable threshold. Chen doesn’t say what his epsilon would be.)

When tech leaders are asked to justify the downsides of AI, their go-to talking point is that the upsides—from helping cure diseases to coming up with cleaner sources of energy—far outweigh the immediate costs. Short-term pains, long-term gains.

But as the downsides pile up, does that case get harder to make? Is there a point where Chen would feel less as if he’s building something amazing and more as if he’s simply minimizing harm—fighting fires rather than forging a better future?

The capabilities of these models are already evident, he says: “It is time to start delivering the benefits of AI to humanity. It’s time to start working on deep problems in drug discovery, on materials, on scientific applications that will actually change people’s lives.” 

“Yes, there is a bit of risk that we are incurring, but we see all these benefits,” he adds. “I think we should make that less of an abstract thing. If people can really see the upside, I think they’ll believe in it.”

Right now, America.gov functions as an AI chatbot that bases its answers on official government sources. It was developed with the assistance of Google's Gemini and SpaceXAI's Grok, according to Joe Gebbia, the Airbnb co-founder who led development of the site for the Trump administration. In 2027, the site will move "from answers to actions," Gebbia said. Nevertheless, Trump encouraged Americans to start using America.gov right away. "This took a while to bring it to a point where it's so good," Trump said. "America.gov is a declaration to the world that every citizen of our nation will be treated with respect and dignity." The functionality of America.gov at launch, however, was awkward for Trump. By providing summaries of official government data and reports, it easily and comprehensively debunks many of the falsehoods that Trump frequently promotes.

Will this chatbot continue to contradict the things Trump says? Only time (and probably the results of the next few elections) will tell. This government has already made moves to: *Undermine government data collection and research and reporting *Remove information on climate change, minority issues, and public health from public-facing government websites *Delay or skew reports government reports hat contradict Trump's claims But 75 years of data-driven governance is probably a bit hard to erase overnight. In other government services news, via the Washington Sun: White House Drove Users to Official App Fraught With Trackers. he White House app shot to the top of the Apple App Store's news category last week after its new 24/7 broadcast channel, Trump TV, was swiftly positioned as a legitimate source for news while networks halted their pool coverage. After the Trump administration mandated that federal agencies force-install the app onto all government-issued mobile devices, cybersecurity researchers noted that no public security authorization or privacy assessments required under federal law had been completed. Pushback over the app's security apparatus forced White House developers to delegate tracking software to third-party services. But the app still includes scripts to monitor a user's location several times per minute, according to Gera, and it's unclear how that data is used by the White House or third-party services. The app also contains a latent keylogger script sourced online from an amateur developer based in India, according to Gera and confirmed by The Sun's analysis. Keyloggers most commonly appear in surveillance malware to store written text from a device, but they also can be used as a benign tool to analyze inputs like typing frequency. While it's unlikely to work on mobile devices, Fields said, it's still concerning that the White House is using a keylogger web-sourced from an foreign coder. App marketplaces require developers to disclose what data app developers collect from their users, which platforms like the Apple App Store enforce through a "privacy manifest." In the app's latest version, the White House left that manifest completely blank, implying it collects no data from users despite contradicting findings from cybersecurity experts. An unpublished feature within the White House browser extension allowed app administrators to create a political profile on a user, and included an "investigate" button to refer individuals to the Secret Service. Loge contends that rather than using user data for political strategy, the White House is more likely to leverage the information to steer supporters driven to the app toward one of Trump's merchandise or cryptocurrency ventures. "What we've seen is that Donald Trump is a businessman who's monetizing politics. Therefore, this app is a way to make money," Loge said. "The other people who could find this information either directly or through several layers of resale are, of course, hostile governments — foreign actors whose interests are the demise of American democracy." *** Federal government forces all federal employees to install malware on their federal government phones, should be the headline here. Anyway, don't install any software promoted by the Trump administration. And know that whatever you ask this America.gov chatbot will definitely be collected and recorded.

Earlier this month, the UN announced the planet will tip past 1.5 ˚C of warming, “likely within the next few years,” squashing any lingering hope that nations would cut emissions fast enough to achieve the loftiest goal of the Paris climate agreement.

In the US, the world’s second-largest-emitting country, its leader continues to deny climate realities and unravel climate policies. Big Tech is backpedaling on its climate ambitions, as companies race to build massive AI data centers.

And all this comes amid yet another season of shattered heat records, raging wildfires, and deadly floods.

It can be tempting to give in to climate doom and defeatism. But despite the headwinds, the world has still made incredible progress. Companies are developing and deploying renewables, batteries, and EVs; inventing cleaner means of producing industrial goods; and devising ways to keep cities and people safe even in the face of more extreme weather conditions.

Those advances will only accelerate as costs decline and people demand greater climate action, as well as all the economic and health benefits that come with it.

Recognizing our ability to make such progress is why MIT Technology Review began publishing our annual list of Climate Tech Companies to Watch three years ago.

Once a year the editors and writers use their deep industry knowledge and academic sources to compile a long list of companies around the world that are transforming the way we generate energy, produce goods, move people and things, or mitigate rising climate dangers.

We then conduct several rounds of spirited newsroom debate to winnow that list down to 10 that we believe have done the most, or have the best chance, to make a real dent in emissions or improve public safety and health.

We’re happy to say we’ve finalized that list for 2026, and will publish it on October 6. You can sign up for The Download newsletter to be among the first to see it, or subscribe to receive full access to this year’s list. It features a mix of companies making strides in energy storage, nuclear power, transportation, and other areas, including ones that have figured out how to continue to make progress amid the regressive climate politics of the moment.

We hope you’ll enjoy the package and perhaps come away feeling a bit more hopeful that we can move fast enough to avoid the gravest dangers of climate change, and build a safer, healthier, and more sustainable world.

Modern messaging apps allow users to link their phone accounts to their computer desktop. Eavesdroppers are taking advantage of this capability:

Apps such as WhatsApp Web and Signal Desktop allow people to use their accounts on other devices, such as laptops or desktop computers.

Germany’s Customs Office has been using these features to connect a police-controlled computer to a suspect’s account.

Once connected, messages can be delivered to that computer without the police having to crack the encryption protecting them.

Netzpoltik details that police are able to gain access in this way either through physical access to someone’s phone or by intercepting verification codes via a state-sanctioned phishing attack or intercepting SMS messages via telephone surveillance.

That last paragraph is important. Making this work requires user consent.

What we want is a feature that displays connected devices, so users could notice if a new device gets connected to their account.

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

Last Wednesday, Anthropic announced that earlier this year it had launched a molecular biology lab, where Claude agents read and conjecture about hard biology problems and human scientists run experiments on what they report. And this AI-powered lab, the company said, had made its first discovery. 

To understand what Anthropic says its system did, imagine you’re flipping through a library of millions of DNA sequences, amassed as scientists sequence more and more of the living world. One step toward a breakthrough might be finding a peculiar sequence that encodes an interesting enzyme, perhaps. Then you’d need to figure out what that enzyme does and, eventually, how to manipulate it to do something useful.

What Anthropic says its system of 950 agents found after 21 hours was not a brand-new sequence. The agents instead flagged a repeating pattern surrounding a known enzyme, a particular pattern Anthropic said hadn’t been catalogued before. But if you read through Anthropic’s announcement, which calls this pattern “reminiscent” of what led to the gene-editing technology CRISPR that “has already transformed science and medicine,” it sounds as if this army of agents really found something of note. 

These claims have angered some biologists. A viral post from one, subsequently endorsed by the chair and CEO of the drugmaker Eli Lilly, said that “finding a weird cluster of genes and repeats is often the easy part. The hard part, and where the real discoveries come from, is figuring out what the system actually does.” The agents helped with some laboratory grunt work, in other words. But a discovery it is not. 

It’s a reminder that even if AI does something impressive—like finding a pattern in a mass of biological data that would be difficult to perceive with human eyes alone—the result itself may not constitute a breakthrough for science. What is novel for AI may be routine, unsurprising, or simply not that consequential to a biologist.

Muddying the issue further, Mario Rodríguez Mestre, a biologist at the University of Copenhagen, said over the weekend that his team had already discovered this particular pattern, the New York Times reported. Mestre, who regularly chatted with Claude in his work, wondered whether Anthropic’s team had learned from his conversations. Anthropic denies this, but Mestre says he’s stopping all use of Claude anyway.

Part of the problem here is that AI companies aren’t presenting their systems simply as tools scientists can use, like microscopes or supercomputers. They’re insisting that the AI systems are making discoveries themselves. To some, that approach is  incompatible with how science actually works, with new knowledge more typically emerging from collaboration and an ever-growing arsenal of tools. 

It’s also making people more skeptical of genuine progress when it happens. Whittling 200,000 candidates down to a few worth exploring is no small feat; it is legitimate scientific work. The fact that a general-purpose chatbot could do that work is notable, even if humans helped steer it and ultimately ran the experiments. But once the standard is whether Claude itself made a discovery, all that becomes evidence for one side or the other in a debate that has only two answers: breakthrough or bust.

Once we’re judging AI by whether it has made a discovery, it’s also tempting to shift the goalposts even after it really does seem to notch a win. Earlier this month, OpenAI said its own team agents had cracked a million-dollar problem in mathematics. But a couple of weeks later, nearly every AI skeptic in my feed was sharing an article asking whether it was the math problem that really mattered. 

To be clear, the piece did not argue that OpenAI’s solution was wrong. Instead, it argued that the particular result may not be the one mathematicians care most about. Throw in the accusation by a mathematician that the models may have used some of his work without credit, and people are left thinking either OpenAI cheated or the solution wasn’t important anyway. Or both.

That’s part of what concerns Lucas Harrington, the biologist who wrote the post critiquing Anthropic’s announcement. He closed with a suggestion: AI companies, he said, should “set the bar high now, so that when an AI actually discovers a fundamentally new biological mechanism, everyone appreciates how big a deal it is.” But as OpenAI’s Sam Altman and Anthropic’s Dario Amodei race to one-up each other, raising the bar for scientific breakthroughs by AI might be the last thing on their minds.

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

Over the past few months, a cascade of cyberattacks by AI agents has stunned the world. In July, OpenAI disclosed that a swarm of its agents had escaped their sandbox and hacked into the AI platform Hugging Face to cheat on a cybersecurity test. Recently, external researchers discovered that OpenAI agents had hijacked a German wiki site and the coding platform RubyGems in May to share test answers.

Earlier this month, Anthropic disclosed four incidents in which its model Claude hacked into third-party systems during cybersecurity exercises. Just last week, Google confirmed that its model Gemini had been caught hacking other companies too. 

The researcher who uncovered the OpenAI website hijack has warned it’s likely that similar undiscovered episodes are out there. And many say it’s only a matter of time until there’s another, possibly more damaging incident where AI agents bypass sandboxes to access systems they shouldn’t. 

So the big question is: How do we hold companies liable when they lose control of their AI agents?

Reporting

OpenAI didn’t disclose the German wiki incident or the RubyGems incident until a group of external researchers uncovered them, and it still has not disclosed some crucial details about the Hugging Face hack. That limits our understanding of what exactly went wrong and how to prevent it from happening again.

But you might be surprised to learn that OpenAI likely wasn’t legally required to disclose these incidents. (OpenAI did not respond to a request for comment.)

State AI transparency laws like California’s SB 53, New York’s RAISE Act, and Illinois’s SB 315 require that AI developers report “critical safety incidents.” These are defined as incidents that cause more than 50 deaths or physical injuries or $1 billion in damage. They also include incidents where the model deceives developers outside an evaluation in a way that materially increases catastrophic risks. Many cybersecurity incidents that don’t meet the threshold for physical damage or catastrophic risks could nonetheless be dangerous precursors to such catastrophes, and the existing laws don’t account for that.

“The recent incidents are a perfect example of why the law isn’t ready,” says Mackenzie Arnold, managing director of US policy at the Institute for Law and AI, a think tank. “Only the worst, most egregious, most immediately harmful stuff is going to qualify.”

With no authority under existing AI laws to demand information about anything short of a catastrophe, governments are left to borrow investigative authority from other laws or sue the companies, an expensive process that can take years. 

Litigation

“Normally, something like the Hugging Face incident should have been taken to court,” says Yonathan Arbel, a law professor at the University of Alabama School of Law. “Then we would have discovery, and we would have all the spillover effects that we get from litigation, where all the information comes out.” 

But so far, Hugging Face has chosen not to sue OpenAI. Hugging Face’s CEO, Clément Delangue, says it doesn’t have the resources to do so (instead, he asked OpenAI for $100 million in compute). Still, Delangue stressed in an interview with CNN at the end of July that choosing not to pursue legal action shouldn’t be taken to mean he doesn’t think OpenAI should be held accountable. “Everyone has to remember that this cyberattack is a crime. This is illegal. And we have to find a way to make sure these things don’t happen more regularly,” he said. Hugging Face did not respond to a request to comment.

Litigation has the benefit of pushing courts to use existing laws to address AI safety incidents, rather than just waiting for new legislation. One obvious route is tort law, a body of civil law that lets people and businesses sue those who harm them. This is often used to hold companies liable for the mass harms they cause, like when families sued Boeing in 2019 over two plane crashes that killed hundreds of people, or when states and cities sued Purdue Pharma over the opioid crises, extracting settlements worth billions.

“There’s plausible grounds for a negligence claim that OpenAI should have used a stronger sandbox, done more monitoring,” says Gabriel Weil, a law professor at the University of Houston Law Center. For example, when OpenAI employees discovered the covert message board that the agents had created, they could’ve promptly escalated their findings to security and safety teams. And the company could’ve better designed its sandbox to ensure that agents couldn’t access the internet. 

But even if OpenAI doesn’t end up in a lawsuit over the Hugging Face hack, the threat of liability could incentivize AI labs to exercise more caution than explicitly demanded by law.

OpenAI announced in its postmortem that it plans to strengthen the safeguards used to contain and monitor the models, accelerate model alignment, and improve its processes for identifying and addressing incidents. 

“The liability questions raised by frontier labs’ spate of cybersecurity attacks boil down to the incentives the expectation of liability creates for their future conduct,” says Weil. “That’s why I think it’s important to get these rules right, even if the stakes are pretty low in this particular case.”

Investigations

One way to get answers—and determine whether OpenAI should be held liable—is to compel disclosure. But the existing state AI laws—California’s SB 53, New York’s RAISE Act, and Illinois’s 315—don’t give governments the authority to investigate incidents like the ones that happened recently. 

However, amid rising public alarm, state attorneys general are stepping in, borrowing investigative powers from other laws. Alabama, Montana and a coalition of 15 other states, and California are each demanding information about the incident from OpenAI to understand whether the company’s practices violated state consumer protection laws, among others. Members of Congress are also launching their own probes. Senator Josh Hawley opened a Senate investigation earlier this month, sending OpenAI a list of questions about the incident and the company’s internal policies together with a document request, while a group of House Democrats asked OpenAI and Anthropic to release their incident logs. 

“Someone needs to investigate, but it’s unfortunate that it has fallen to attorneys general, who need to rely on creative interpretations of their existing authorities to do this,” says Arnold, the US AI policy expert. Consumer protection statutes were written to catch companies that scam their customers, not companies that lose control of their software. The state attorneys general would have to show that OpenAI deceived or unfairly harmed customers, but it’s unclear if the hacking involved any such conduct.

And “those [consumer protection] laws are not built for doing a thorough investigation of an AI cybersecurity incident,” says Arnold. They weren’t designed to help investigators determine whether a model was adequately contained or whether a company’s security practices were sound.

“This is not the right tool for the job,” says Arbel. “The right tool would have been something like maybe a criminal investigation”—perhaps under a hacking law like the Computer Fraud and Abuse Act (CFAA). 

Under CFAA, hacking into another company’s computer systems without permission is a crime. But to be held liable, a hacker must have intended to break into a computer without authorization. Intent arguably requires a state of mind, and no court has ruled that AI agents have one. Without such a precedent, it’s unlikely a court would rule that AI agents had carried out a hack.

Auditing

One way to keep an eye on AI companies is to mandate external auditors. 

After the Hugging Face hack, OpenAI brought in researchers from the AI safety nonprofits METR and Redwood Research to examine the incident. However, it constrained access to the model that led to the hacks, didn’t disclose the company’s safety and security practices, limited the length of the investigation, and had ultimate say over what the researchers could publish. We still don’t know what set the attack in motion back in May and why OpenAI’s employees who spotted the agents’ activity never escalated to their safety and security leaders.

This kind of arrangement has a built-in tension: An auditor without legal authority depends on the labs’ goodwill for continued access, which means it has to scrutinize the labs without jeopardizing their relationship. Last week, Anthropic announced that the company will be hiring Accenture as an embedded evaluator to assess its models. Anthropic CEO Dario Amodei wrote in an essay that frontier AI labs should give “ongoing employee-like access” to “a team of embedded third-party evaluators (such as METR), whose role is to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.”

Most existing state AI laws do not require labs to hire an external auditor. California’s SB 53 and New York’s RAISE Act just require AI companies to publish a safety framework describing how they will test their models for dangerous capabilities and then to follow it. The frameworks are written by the companies, and testing can be done internally. Only Illinois’s SB 315 requires companies to undergo an annual third-party audit starting in 2028.

“There’s a lot of headroom for increasing not only reporting requirements for these companies, but also review by external bodies,” says Peter Salib, a law professor at the University of Houston Law Center. Those reviewers could be private auditors accredited by the government but chosen and paid for by the AI companies. Alternatively, they could be government agencies or insurance companies. 

Legislation

None of this is an accident. The laws on the books that failed to hold AI companies accountable for agentic cyberattacks emerged amid fierce lobbying by the AI industry. 

SB 1047, the California AI bill that was vetoed by Governor Gavin Newsom in 2024 after lobbying by OpenAI, Meta, Anthropic, and the venture capital firm Andreessen Horowitz, proposed a much tougher set of rules. It would have required AI companies to report a broader set of safety incidents (including incidents in which a model acts on its own or slips its controls), undergo annual third-party audits, and maintain a kill switch. But after a year of intense negotiations, Newsom signed SB 53, which narrowed the types of incidents deemed reportable and dropped the requirements for audits and kill switches. 

New York’s RAISE Act followed the same arc. “The version of the RAISE Act that the NY Legislature passed would have required disclosure of this ‘incident,’” Alex Bores, the New York state assembly member who sponsored the bill, wrote on X. New York’s original bill also included third-party audits.

With political pressure mounting, new bills creating better reporting, auditing, and liability regimes for AI development are on the horizon. In Congress, the AI Incident Reporting Act would require AI companies to report to the Commerce Department when a model evades human oversight or breaches a system, even if it doesn’t cause any harm. The Frontier Act would require incident reporting and independent audits. In New York, the Understanding Artificial Intelligence Act, sponsored by Bores, would make companies liable when a model does something that if carried out by a human would be a tort or crime. 

As AI agents increasingly become better at launching cyberattacks, the law remains behind. Closing the gap will require lawmakers to move faster than the next breakout.

The US government wants to spend $30.3 million over the next five years on an improved form of lie detector, according to a Department of Defense budget request. The program, called Polygraph+ or Polygraph Next, will focus on scoring algorithms that use artificial intelligence and machine learning and on a technique called “standoff sensing,” which refers to the ability to take physiological readings without attaching a device to a subject’s person.

According to details of the budget document, which were first reported by Inside Defense, the project will “modernize federal polygraph and credibility assessment technologies” to improve their accuracy and reliability. But the project may be just the latest in a long line of failed attempts to use technology to detect lies. “It’s a misguided effort to reduce the complex to something that is tangible,” says Kyri Kotsoglou, a legal scholar at Northumbria University in the UK who studies the use of polygraphs in the justice system.

The move comes at a time of high tension within the department. Under Defense Secretary Pete Hegseth, the Pentagon has been increasingly turning to polygraph tests in an attempt to find the sources of alleged leaks to the press. In September, the New York Times reported that around 50 officers on the Joint Staff had been given polygraph tests after news coverage reported on the depletion of US weapons stockpiles in the war with Iran. 

Polygraph+ will be run by the Defense Counterintelligence and Security Agency (DCSA), which conducts background checks for the federal government. According to the budget document, which has not yet been approved by Congress, the new technology will be used for vetting of prospective employees and “insider threat detection.” It is not yet clear which specific technologies will be used, and the DCSA did not respond to a request for more information. 

But other Pentagon efforts offer potential clues. In 2023, the department’s Defense Innovation Unit (DIU) ran an open submission process to find companies with products that could be used for deception detection.

It selected two companies to build prototypes: Presage Technologies, which claims to be able to measure heart rate and breathing rate using standard cameras, and Altec Research, a medical sensor company now branching out into non-contact sensing technologies. A screenshot of Altec’s prototype technology released by the DIU shows that it tracks head movement, facial skin temperature, and pore activity. Presage Technologies and Altec Research did not respond to requests for comment. The DIU declined to comment. 

Current lie detection technology has barely changed since the polygraph was invented in the 1920s. Examiners rely on blood pressure, pulse, breathing, and sweat measurements to determine if someone is lying. They make judgments about the veracity of respondents’ replies on the basis of differences in their physiological response to baseline questions like “Is the sky blue?” and target questions like “Have you ever committed a crime?”

The federal government conducts tens of thousands of the tests each year while screening employees, but the reliability of this technology has been repeatedly challenged—and its results are rarely admissible in court. In 1983, Congress’s Office of Technology Assessment concluded that there was very limited evidence supporting the polygraph’s use for screening employees, and in 2003, the US National Research Council (NRC) said evidence for its efficacy was “weak at best.” 

Research suggests humans can spot a lie just over half the time without any technical assistance. The American Polygraph Association claims the polygraph is between 80% and 94% accurate. But the 2003 NRC report pointed out that a screening test with this level of accuracy could still lead to a lot of mistakes. The DOD employs 2.8 million people; an imperfect system applied at that scale could end up falsely accusing tens of thousands.

There are other issues too. Polygraph interpretations are often subjective: Different examiners get wildly different results, and people from minority groups are more likely to be judged as deceptive. What’s more, with training it’s possible for interviewees to learn a variety of countermeasures that can help beat the test; for example, they may artificially heighten their physiological response to baseline questions by stepping on a pin hidden in their shoe. 

“If you know how it works, you can beat it,” says Sophie van der Zee, an associate professor who studies deception at Erasmus University in Rotterdam. She says the machine’s biggest effect is deterrence—often, subjects confess before it even begins. “But that only works if people think a polygraph works,” she points out.

Various new strategies for lie detection have been attempted over the decades, with technologies ranging from thermal cameras to pupil trackers to brain scans. None of them have yielded reliable results outside the lab. The problem is that there’s no single telltale sign of lying that’s true for everyone all the time. “There is still no Pinocchio’s nose,” says van der Zee. 

AI could theoretically improve polygraphs if it could find patterns in the data that examiners can’t. AI algorithms are also more likely to be used for “multi-modal” deception detection, which seeks to combine multiple measurements into an overall deception “score” that is harder for people to game. Three things are happening under the surface that lie detection tries to home in on, van der Zee says: physiological stress, cognitive load, and the conscious efforts people make to conceal the fact that they’re lying. Current polygraph technology tackles only one.

“The more you can have combined methods that approach it from these three different angles, the more successful you will be,” van der Zee says. This isn’t a new concept—in the 2000s, researchers at Manchester Metropolitan University in the UK developed a system called Silent Talker that generated a deception score from video footage. This was later folded into iBorderCtrl, an EU-funded pilot program. In the US, a project called AVATAR incorporated eye tracking, voice analysis, and body movement detection into a tool for use at border crossings. All these projects have quietly faded away. 

Kotsoglou says combining AI and the polygraph is “the worst of both worlds” because it adds uncertainty on top of invalidity. Even if AI or machine learning can spot previously unseen patterns in physiological data, it won’t be able to reliably link them to lying because there is no real ground truth. 

“Even if you have all the records in the world from polygraph tests, you don’t know whether those polygraph tests are right or not,” says Marion Oswald, a professor of law who has written with Kotsoglou on the use of polygraphs in the justice system. She fears that new forms of lie detection will, like the polygraph, be used more as a psychological prop than a scientific tool. 

“It seems very much a response to the concern of the current administration to leaks and perceived lack of loyalty,” says Oswald. “[Lie detection is] being used as a threat, to intimidate and force people to confess to things, as opposed to anything that’s actually getting valid information.”

Around this time last year I was attending an aging conference in Manchester, listening to a talk about fly aging, when my phone started pinging. News outlets were reporting that a hot mic had caught Russia’s and China’s leaders discussing the possibility of living forever.

“With the developments of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality,” Russia’s Vladimir Putin reportedly told China’s Xi Jinping.

He seems to have been referring to the “replacement” theory of longevity, which has been supported by multiple experiments that involved physically stitching young mice to old ones. Something about the young blood rejuvenated the old mice. Perhaps young organs could rejuvenate world leaders in their 70s, too.

Unfortunately for Putin, new research pours a little cold water on this idea. Studies on transplanted hearts in both mice and humans suggest that new hearts soon adopt the biological age of the recipient, no matter how young they were to begin with. The finding could be important for transplantation, but it also highlights just how complex aging—and rejuvenation—are.

Jesse Poganik at Brigham and Women’s Hospital in Boston is one of the many scientists trying to understand exactly what it is about the bodies of young mice that rejuvenates old ones. Plenty of research has focused on seeking the secrets of youth in young blood. But what if it’s something about young organs instead?

To find out, he and his colleagues performed a set of heart transplants in mice. In humans, heart transplants typically involve removing a damaged or injured heart and replacing it with another from a donor who is usually much younger than the recipient. (When Poganik assessed hospital records, he found that most recipients were about 20 years older than their donors.)

The mouse transplants were different: Mice received a second heart, implanted in the neck—a procedure that’s slightly simpler and allows scientists to compare the new hearts with the existing ones. In some cases, young adult mice were given a heart from a middle-aged donor. In others, middle-aged mice got young hearts.

The team used a trio of “aging clocks”—molecular tools used to estimate the biological ages of tissues and whole organisms—to assess whether the additional hearts affected the mice in any way. These clocks were good at predicting the chronological age of mice that didn’t get new hearts.

Poganik says he was expecting to see a reciprocal effect, and that young hearts might benefit older animals, for example. In previous work by other members of his team, young mice that got old hearts experienced a buildup of senescent cells in their other organs. These cells are thought to contribute to the aging process, suggesting that receiving an old organ might prematurely age an animal.

But that’s not what he found. When he and his colleagues analyzed the transplanted hearts between four and six months after surgery, they found that the hearts seemed to have adopted the biological age of the recipients. Young hearts got older, and old hearts got younger. “The environment of the transplanted organ really dictates how it seems to behave biologically,” he says. The findings were published online at bioRxiv last week.

The team also looked at each mouse’s blood and other organs—including its original heart—and were surprised to find that they seemed to be unaffected by the presence of the new heart, despite the age of its donor.

The finding was backed up by data from human heart transplants. People who receive a donor heart must typically undergo a series of heart biopsies after surgery. The tiny pieces of heart tissue collected from people who had heart transplants at Brigham and Women’s have been stored for decades. And when Poganik and his colleagues tested their biological ages with the aging clocks, they found a similar pattern: No matter the age of the donor, a new heart quickly adopts the biological age of the person who received it.

It’s not clear why this is, but João Pedro de Magalhães, who studies aging at the University of Birmingham in the UK and was not involved in the study, thinks it might have something to do with the recipient’s immune system. Perhaps immune cells circulating in the blood might affect markers of aging in the new organ, he says.

Perhaps the potential rejuvenating effects of a young heart end up being diluted by all the other aged components of an older body, Poganik suggests. Or maybe a single organ just isn’t enough to see an effect.

Poganik hopes his finding will encourage surgeons to consider using hearts from older donors, many of which are discarded on the assumption they won’t function as well. (Machines used to preserve organs before transplantation are changing that already—and Poganik has worked on another study showing that these devices seem to rejuvenate donor livers to some extent.)

But the study also highlights just how complicated aging is. If organs seem to be getting older by one measure but not by another, how can we get a full picture of the biological age of an organ, or a person?

This complexity means that scientists are not likely to discover a true way to completely reverse aging, says Poganik. “That would mean that every aspect of aging has to go back in time,” he says. Reversing DNA damage, structural damage, and all the other degradations that are part of the aging package, across all our various cells and tissues, presents an enormous challenge.

“There are aspects of biological age that are probably reversible, and there are aspects that are probably not,” he says. Sorry, Putin.

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.

This week, world leaders descended on Manhattan for the UN General Assembly. It’s also New York Climate Week—investors, policymakers, advocates, and journalists are colliding at panels, talks, and fancy dinners.

With so many climate voices in one place, the discourse can feel a little louder than usual. This year, the unavoidable topic is artificial intelligence.

There’s been a growing tension bubbling up about AI’s impacts on climate and climate tech. Depending on where you stand, you might point to AI’s potential for advancing research, or to the way funding and attention from Big Tech is trickling into energy startups. Or you might focus on the emissions-heavy natural-gas buildout that’s unfolding to meet the sector’s electricity demand.

Everyone seems to agree that AI is important. The big question for those in the climate world, and the conversation I’m constantly having and hearing this week, involves how you see its influence unfolding.

UN Secretary-General António Guterres highlighted the AI and climate crossover in a speech on the first day of the assembly. “The climate crisis fuels instability and displacement,” Guterres said. “Artificial intelligence could help solve all these challenges, or it could make them worse.”

It feels relevant that this year is the first that the world has really had to grapple with the fact that climate goals are slipping out of reach. A recent report from the UN Environment Program said that the world has nearly passed the point where we could possibly keep warming to less than 1.5 °C above preindustrial levels.

Since we’ve essentially missed this target, the report lays out the need not only to quickly and drastically reduce greenhouse-gas emissions, but also to employ carbon removal to help suck up emissions that have already been released into the atmosphere.

The billion-dollar question is whether AI could help with any of this. 

The energy-intensive technology is certainly shining a spotlight on the need to build out electricity supplies and shore up grid reliability. The result is more attention and money for energy technologies, some of which happen to be low- or zero-emissions.

As I’ve covered before, startups across the energy and climate sectors are benefiting. Firms in nuclear, geothermal, wind, and solar power have signed deals with the likes of Google, Meta, and others looking to power their new or growing data centers.

Global climate-tech investment from venture capital hit $26 billion in the first half of 2026, according to data from Currence, a finance tracker for the industry. That’s 55% higher than last year, and products and services for data centers are getting a massive slice of that pie.

But as a Semafor piece about the report points out, some sectors are slipping through the cracks: Carbon management and low-carbon fuels saw VC investment plummet this year. These are important solutions for addressing climate change but may not be able to sell themselves to a data center.

So far, the data center buildout has come with a hefty emissions toll. A few years ago, Microsoft, Google, and Meta all had ambitious goals to reduce greenhouse-gas emissions. Now they’ve all seen emissions rise, largely because of data centers that are needed to power AI.

Some people are optimistic. AI could help speed up progress in areas like the search for new catalysts, as Evelyn Wang, MIT’s VP of energy and climate, pointed out during a panel. And data centers won’t add to climate and water problems forever, Wang told the Associated Press. (She puts the timeline at about a decade until data centers no longer add to planet-warming emissions.)

But a whole lot of natural gas is coming online to meet the immediate demand created by new data centers. And once those power plants are built, they have a decades-long lifetime. 

That’s partly why public pushback to AI is growing—people are seeing more pollution and noise near these data centers and the power plants that provide them with electricity. Overall, what I’m hearing this week is that many in the climate sector are skeptical of AI, at best. 

“AI leaders are now on thin ice when it comes to license to operate and sinking deep underwater when it comes to public support,” said UN climate chief Simon Stiell in a speech this week. “Tech titans need to start showing why the benefits of AI outweigh its skyrocketing costs—for the many, not just the tiny few.” 

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

Brace yourself: It turns out AI is being optimized for cheating. OpenAI’s agents hacked into Hugging Face to get the answers to a cybersecurity test. Next, they solved a prestigious math problem (or just stole from two top mathematicians’ answer sheets). Anthropic’s models have also hacked into other companies’ systems four times already. And that’s only what we’ve caught so far. 

Freaking out? You’re not alone. AI lab researchers are quitting their jobs and issuing dire warnings that if we keep going this way, AI might eventually kill us all. Bill Gates is sounding the alarm. Bernie Sanders has teamed up with Steve Bannon, of all people, to call for curbs on AI. Anthropic CEO Dario Amodei is urging a slowdown, and other top US AI executives agree. But fear not: President Trump has a plan. He says the only guardrail AI needs is “a STRONG AND SMART (High IQ!) PRESIDENT.”

Delia Ramirez, a Democratic US representative from Illinois, has announced a plan to introduce new legislation to terminate the surveillance tower program along the US southern border. 

The announcement comes just days after publication of an MIT Technology Review investigation, “Dying on Camera,” in which we looked at deaths along the border that took place close to one or more of these towers. MIT Technology Review found that nearly one in four deaths we analyzed between 2015 and early 2026 occurred within their advertised range. We also showed that the Department of Homeland Security does not keep track of these deaths or other metrics on the towers’ success or failure. 

Ramirez, who sits on the Homeland Security Committee, cited MIT Technology Review’s reporting that nearly “1,100 people have died within range of a billion-dollar surveillance tower system between 2015 and 2026,” adding, “The towers that we have paid a billion dollars to just don’t work.” 

There has been growing public support for abolishing ICE in the wake of the second Trump administration’s highly visible enforcement operations across American cities, the buildout of its detention apparatus, and shootings by ICE and CBP agents. Wilber Rafael Garcés Pérez, a delivery driver, was shot in the back by an ICE agent just last weekend in Austin, Texas. 

But for Ramirez, abolishing just ICE is not enough. At a press conference held in Chicago on Wednesday, Ramirez said, “There’s no question that DHS has to be dismantled. We need to provide them a road map in how we do it,” Ramirez said. 

It will not be the first time that a bill to dismantle the Department of Homeland Security has been introduced, but previous proposals have not gone very far. This proposal will also face a steep uphill battle in Congress, where even some Democrats favor reforming rather than outright dismantling the organization.  Ramirez, however, sits on both the House committee that oversees the department, and is the ranking member on the Homeland Security Committee’s cybersecurity subcommittee.  

“We’re limiting state surveillance, we’re reigning unlawful abuses of data and curbing the militarized enforcement. We’re beginning the process of establishing a separate civil immigration system,” Ramirez said.

CBP did not offer an immediate response for comment. Markwayne Mullin, the Secretary of Homeland Security, said, “Radical leftists are calling to defund the police and abolish DHS. It’s NEVER going to happen.”

This story has been updated to include a response from the Secretary of Homeland Security.

Shubnam was packing boxes for a move into a new home when their friend sent them an Instagram video. The footage had only been up for a few hours, but it was days old, recorded at a Delhi protest this spring against a bill that would have narrowed the legal recognition for transgender people in India. 

There, a content creator known for rage-bait videos had approached Shubnam, a 38-year-old transfeminine graphic designer and educator. Shubnam, who uses a single chosen name, was dressed in a green sari with a shaved head, and the creator asked why they were wearing “female clothing.” Shubnam told him to back off. Only when the video arrived did they realize he had recorded the exchange on Meta smart glasses, styled to look like ordinary Wayfarers. 

Edited into a mocking reel, the footage drew millions of views, along with transphobic abuse, reaction videos, and AI-generated memes. It spread from Instagram to X and YouTube. Copies remain online today. 

For Shubnam, the video has caused huge harm. “All it took was one unconsented video to rewind two decades of progress that I made all on my own,” they told MIT Technology Review. “For a moment, it felt like this entire adult life I had lived was just a long lucid dream, and I was going to wake up as that clueless queer child again.” They haven’t worn a sari outside their neighborhood since.

Experts warn that many others will experience similar ordeals as smart glasses go mainstream. And they say privacy problems are becoming especially acute in India, where covert recording and the circulation of images without consent are already pervasive.

Cases of cyberstalking, sextortion, deepfakes, and other forms of online abuse are growing in the country. And the chances of a crackdown on technologies such as smart glasses are slim, given that Indian law enforcement authorities are themselves increasingly adopting AI-powered surveillance tools, including facial recognition systems and their own AI-enabled smart glasses.

An unequal burden

Smart glasses are among the fastest-growing devices in India’s wearables market, according to the International Data Corporation, a research firm. And Reliance Jio, one of the country’s largest telecommunications companies, says that later this year it’s set to launch a sub-$105 rival to Meta’s glasses, the latest model of which have a $420 price tag in India. 

The core privacy issue is that these cameras don’t look like cameras, so targets may not notice they’re being recorded. Yet Meta rejects the idea that it’s responsible for protecting people from intrusions, says Janusz Świerczyński, an Oxford Saïd Business School researcher who studies privacy and emerging technology.

“More and more, the expectation is being placed on bystanders to look out for themselves,” he says. That means it’s up to them to spot a small LED in the glasses frames that activates when recording is in progress. Meta also relies on wearers to behave responsibly, says Świerczyński. Reliance Jio did not respond to our request for comment on privacy issues related to its upcoming glasses. 

Creators have already demonstrated ways to circumvent the recording light on Meta’s glasses. Tutorials show users how to cover or otherwise obscure the LED while continuing to record. Back in March, Wired documented a market for “stealth mode” modifications that physically disable the light or remove it entirely. 

All that makes covert recording easier, and the risks are not borne equally. Świerczyński says women and other marginalized groups face greater harm when inconspicuous devices capture and circulate footage without consent—as happened to Shubnam.

The bigger problem, however, is that in India smart glasses aren’t just being used to turn ordinary people into targets of viral “pranks.” They’re also being adopted as a tool of police surveillance. 

In June, when thousands of young demonstrators gathered to protest problems with the country’s education system, Delhi police used Meta smart glasses to record them. In fact, a petition filed by former Jawaharlal Nehru University Students’ Union president Aishe Ghosh before the Delhi High Court alleged that police had filmed protesters continuously for weeks, including while they were eating and resting. It also alleged that the police had threatened to send footage of student demonstrators to their parents and colleges.

In July, India’s solicitor general dismissed the case in court as “luxury litigation.” Instead of investigating the students’ complaints, police turned on the students themselves, opening 10 criminal investigations for offenses including rioting, assault on public servants, and damage to public property. Some of the protesters, who spoke to MIT Technology Review on condition of anonymity, say they never saw the LED on the Meta AI glasses police wore during the demonstrations. 

Filming without consent

Meta maintains that its safeguard works. The company told MIT Technology Review, “Every pair has a capture LED that blinks when you take a photo or video that you can save or share; it can’t be turned off, and if someone covers or damages the LED, the camera is disabled.” The company also says users are responsible for complying with the law and respecting people’s privacy.

That approach is risky, especially in India, where norms around filming and consent are still highly disputed and digital literacy is still uneven, says Sameer Parmar, a counselor at Meri Trustline, a safety partner working with Meta and YouTube to support people harmed by online content. 

“The hardware is improving, but the country’s weak understanding of consent gives an invisible camera far more room to cause harm,” he says. Simply being in a public place is often treated as implicit consent to be photographed or filmed. He says the help line has been seeing an uptick in cases involving covert recording and nonconsensual footage.

“Violations of this access have increased significantly and are expected to rise further in India,” he says. People are recorded in medical settings, during private encounters, and in public spaces. Those with less power to object are particularly vulnerable, he adds.

“It’s either trolling or it’s playing pranks on them and then recording them” he says. Abusers “do target people who are a little more unaware or susceptible to how this technology-facilitated harm works.”

The law in India has not caught up with this new kind of harm, says Apar Gupta, a lawyer and founder-director of the Internet Freedom Foundation. “India’s laws against voyeurism and the sharing of intimate images were designed largely around more recognizable forms of abuse,” he says, citing offenses such as secretly filming someone undressing, using a toilet, or engaged in a sexual act. “[The laws] offer limited protection when someone is secretly filmed in an ordinary public setting and the footage is then used to humiliate them,” he says.

Social media platforms’ rules do offer another route to removing harmful content filmed with smart glasses, but it’s not an immediate remedy. “Under India’s current rules, platforms can have up to 36 hours to act on certain complaints—a window that can be long enough for a video to be downloaded, copied, and circulated elsewhere,” he says.

The problem of filming without consent seems set to grow. According to the Financial Times, some internal prototypes of new Meta glasses include AI features that continuously photograph and listen to the wearer’s surroundings, enabling the glasses to later recall what they saw or heard. These functions wouldn’t activate the external recording indicator, making it impossible for bystanders to know when information about them is being collected.

Meta told us it intends to keep the LED for active capture, such as taking photos or recording video, but not for AI features that interpret the wearer’s surroundings. Its argument is that a constantly blinking light could eventually become so commonplace that people stop noticing it.

But that logic risks putting society in a perpetual game of catch-up, says Woodrow Hartzog, a professor of law at Boston University. “Tech companies have an incredible ability to push these tools out faster than society can meaningfully acclimate to the threat,” he says. “By the time society catches up, there’s already a degree of normalization.”

For Shubnam, the danger is not just that people may become accustomed to being recorded. It is that they will become accustomed to seeing versions of themselves that they never chose to put out into the world. 

“Being comprehended along the lines of a narrative that I didn’t compose or consent to—that imposed recognition makes you feel like you’re powerless in how people know you,” they say. 

It’s been a busy few months for AI hype. At the end of April, Anthropic claimed that its model Claude Mythos is better at finding software vulnerabilities than most security experts. Then we had the OpenAI–Hugging Face hacking incident, after which Anthropic (proudly) and Meta (reluctantly) disclosed similar incidents involving their models.

This was followed by Anthropic’s claim that one of its models had made a mathematical breakthrough; soon OpenAI claimed a mathematical breakthrough of its own. Most recently, Anthropic engineer Jacob Coxon went viral announcing his departure from the company, claiming that it and OpenAI are “racing straight towards self-improving superintelligence and gambling with our lives.”

Each of these events was mostly covered breathlessly by the press, often repeating the companies’ anthropomorphizing framings—which are designed to portray their software is not only powerful but incipient “artificial general intelligence.” So what is really going on? Are we witnessing a massive, civilization-changing set of technological breakthroughs, or is this marketing?

In all these incidents, massive fanfare from the companies (presented as mea culpas in illicit hacking cases) is accompanied by intense press coverage. Once there is time for experts in the relevant fields to examine what happened, a very different story emerges, but one that gets less media attention.

Regarding the “hacking” incidents, cybersecurity experts say the story is more about OpenAI’s negligence and failure to adopt basic, established security practices than about “models gone rogue” or “AI agents creating civilizations.” 

As for the mathematical results, mathematicians who were initially “stunned” by OpenAI’s press release saying that its latest chatbot, Astra, solved problems that “have been open and seen no progress on the main result for at least a decade”—but they later realized that the results weren’t as “novel as first appeared.” Since then, mathematicians have accused the company of research misconduct and plagiarism, and they’ve reiterated that Astra didn’t make a “profound intellectual leap.” Just weeks later, OpenAI claimed its own mathematical breakthrough. Two days before, Tristan Buckmaster, a math professor at New York University’s Courant Institute, published a bombshell statement suggesting that OpenAI had stolen other people’s work and improperly attributed it. 

Claims of incipient, dangerous superintelligence are not based in good scientific or engineering practice. Rather, they are narratives based in ideologies of transhumanism, eugenics, and wishful thinking about imagined future digital humans.

It’s worth thinking about why there is so much attention on computer programming and math as fields in which to apply large language models and related technology. Not only are they often elevated as the pinnacle of human intellectual achievement, but they involve problems where answers, once suggested, can be verified. The former property helps AI hype mongers sell the idea that they are building everything machines. The latter makes math and coding problems easier to tune systems for, since system output (sequences of likely words or pieces of computer code) can be evaluated without having to pay data workers to look at and annotate each one.

Mathematicians in particular have warned against corporations using their field in this way. A statement signed by hundreds of them says there is “currently a strong commercial incentive on the part of the technology industry to overstate the capabilities of their products” and asks policymakers to “consult with experts, including mathematicians, in forming policy decisions rather than relying on press releases or popular reporting of mathematical results.”

We echo this call and note that the illusion of speed and urgency promulgated by the tech companies is also a ploy to misdirect both policymakers and the public. Unfortunately, it sometimes works, such as with Senator Bernie Sanders’s well-meaning but ultimately misguided proposed legislation to prevent the development of “artificial superintelligence.”

Describing them as “superintelligence” or “rogue models” ascribes agency to products rather than to the companies building them. This framing markets these companies’ products as “superhuman” and, at the same time, helps the companies evade accountability for their actions. 

Instead of OpenAI being prosecuted for creating malware that hacked another company, press releases, news outlets, media personalities, and lawmakers refer to “rogue models” as if they acted on their own. Instead of researchers being questioned about their companies’ habit of plagiarizing academics’ work or using customer data to train models without consent, the public’s imagination is redirected to fears about what the future might hold upon the arrival of fictional superintelligent machines.  

The AI industry has even suggested that popular, bipartisan anti-data-center activism is a “distraction” from attempts to regulate the impending, scary, “superhuman” machines these companies are building. According to the AI industry, we should be more worried about a fictional machine god than about the climate catastrophe that these data centers exacerbate, the asthma suffered by those living near them, the rising electricity bills of the public subsidizing them, or the water that is redirected to cooling them. 

We know better than to make decisions based on marketing and better than to capitulate to corporate pressure to make those decisions quickly. Wise decision-making, by policymakers and communities, demands time to hear from independent experts and contextualize corporate claims. The best possible outcome from this summer of hype is that policymakers and the public at large learn to take a breath, hold onto our skepticism, and recognize this kind of hype for what it is the next time it comes around.

Timnit Gebru is executive director of DAIR and author of the forthcoming book Deep Unlearning: The Radicalization of a Tech Idealist, which is available for preorders now and set to publish on February 16. Emily M. Bender is professor of linguistics at the University of Washington and coauthor of The AI Con.

Our 15-month investigation into death and surveillance along the US-Mexico border began with a simple question: Why did so many people die near government surveillance towers meant to help track and apprehend them?

The result of our investigation is the first comprehensive map and analysis of deaths near border surveillance towers. Here’s how we built it, what decisions we made along the way, and what the data can and can’t tell us.

Our data

The investigation relied on knowing where migrants have died and where and when US Customs and Border Protection towers were installed. We analyzed cases dating back to 2015, allowing us to cover different border policies, presidential administrations, and tower technologies.

Migrant deaths

The US-Mexico border has been called the world’s deadliest land border, and Border Patrol estimates that more than 10,000 people have died during crossings since 2000—a figure that’s widely considered an undercount. But the amount of information available publicly on where and when these people died, and who they were, varies widely. Some remains are never discovered, and for those that are, there’s no national protocol for how the records are handled.

We considered a case for further analysis if we could confirm three basic things: whether the person was believed to have been crossing the border, where the remains were found, and roughly when the person died. 

We created our dataset by merging existing ones that had been compiled and shared by other organizations, including No More Deaths, Humane Borders, and the Electronic Frontier Foundation, with new records we obtained ourselves from more than a dozen agencies.

Public records requests in Texas

Texas was the biggest missing link in most existing databases of migrant deaths. Unlike Arizona, it has no initiative to share records online, and unlike New Mexico, it leaves individual counties to handle their own death investigations. Some records for those individual counties existed, compiled by a handful of dedicated researchers, but nothing was comprehensive or current. 

We identified 17 counties in Texas that would be relevant to our investigation: Culberson, Jeff Davis, Presidio, Terrell, Val Verde, Kinney, Maverick, Dimmit, Webb, Zapata, Jim Hogg, Starr, Hidalgo, Cameron, Kenedy, Brooks, and Duval. There are other counties in which migrants have died, but they do not have areas covered by surveillance towers and were therefore excluded.

In most of these counties, locally elected administrative judges called justices of the peace collect the most comprehensive information on local deaths. But records requests submitted to these individual judges can stall for years (indeed, some of our requests to justices of the peace remain unfilled more than a year after we filed them). Reports from justices of the peace sometimes leave out the scene or location details we needed; some justices never even visit the place where a migrant died, instead formally declaring the person deceased over FaceTime with the first responders on scene.

MIT Technology Review instead filed records requests to the sheriffs of these 17 counties beginning in July 2025. These agencies are often first on the scene when a death is discovered, and their reports can have more detailed information on where remains were found. But relying on sheriffs’ records means our analysis undercounts migrant deaths in Texas, as it doesn’t include any deaths handled by local or state police.

After we filed a request, it took periods of near daily calls to the offices to receive the records, if we received them at all. Some were provided with no charge. Other counties charged between $300 and $1,300 to fill our request. Counties warned that their records were incomplete, with an unknown number lost during a move, damaged, destroyed, or misplaced during transitions between sheriffs.

Those delays were compounded because most agencies do not record whether a person is believed to have been crossing the border when they died. That’s despite provisions in some counties that allow officers to track far more granular details about other situations; Zapata County reports have a checkbox to indicate whether jewels were stolen in a burglary, for example, while Cameron County reports have one for whether a burglar entered through a chimney. Without a way to readily identify migrant deaths, offices had to pull records by hand, making the process slower, more costly, and more prone to error.

We received usable records from 14 of these counties (records have yet to be received from Dimmit or Duval, and Maverick’s office charged a per-record fee that was cost prohibitive).

Agencies generally sent us police reports for each individual case, amounting to over 4,000 pages of records in total. Some were handwritten documents. 

Except for the reports from Kenedy, Webb, and Hidalgo Counties, we pulled out the relevant information by hand. For those three counties, which sent large volumes of records, we used Anthropic’s Claude, accessed via API, to inspect each case report and pull out coordinates of the spots where remains were found; then we checked batches of those cases by hand to verify the AI’s accuracy. Agencies sent us records on a rolling basis from July 2025 to February 2026, and most did not specify the date through which their records were current.

We generally trusted that agencies sent us cases they believed involved migrant deaths, since they had the most information about each case. But some records clearly suggested otherwise (indicating, for example, that someone died at home), and we excluded those cases.

Several hundred cases were removed from our analysis because they didn’t include enough information for us to be certain where the remains were found. In fewer than 100 cases, coordinates were not listed but the description was specific enough for us to locate a point within 0.25 miles of where they were found—so we kept those in. 

Compared with agencies in other states along the border, those in Texas provide far fewer details regarding how long ago someone may have died before remains were found or how far the decomposition of those remains had progressed. Some reports included this information, but many didn’t. While autopsy records sometimes have more details, many migrant autopsies in south Texas are done through the Webb County Medical Examiner, which charges a fee for each report. That was cost prohibitive given the scale of our analysis.

MIT Technology Review consulted with Greg Hess, director of Arizona’s Pima County Office of the Medical Examiner, on a framework for translating descriptions of the scenes, bodies, and causes of death available in many of these records into rough estimates for when a person may have died (more detail on this process is provided below).

In total, we included more than 1,500 cases from our Texas records requests in our final analysis.

Accessing Arizona records directly

The Pima County Office of the Medical Examiner (PCOME) in Arizona handles death investigations for all border counties in the state except Yuma (we sourced Yuma’s records from the organization No More Deaths, as detailed below).

The office determines which cases it believes to be border crossers. It publishes a data portal for these migrant deaths that’s updated monthly and shares its data with the organization Humane Borders, which then publishes it in a map and database. We included the data from 2015 through April 2026.

Humane Borders notes for each case how precise the location description is. We included only cases with GPS coordinates precise to within 300 feet. Collecting these coordinates has been standard practice since around 2012. The records also include an estimate of when the individual died, ranging from less than a day to at least six to eight months before remains were found.

In total, we included more than 1,700 cases from Humane Borders in our analysis.

Data collected by No More Deaths

To obtain data on deaths in California, New Mexico, Arizona’s Yuma County, and El Paso and Hudspeth Counties in Texas, we turned to the nonprofit organization No More Deaths, which has tracked migrant deaths since 2004. It publishes information about its records requests and its methodology. 

Many of the cases in its database include GPS coordinates, sometimes with notes that the location is only approximate—for example, accurate to within half a mile or one mile. We included only cases where the location was known to within a quarter-mile.

No More Death’s database includes, when available, information on the level of decomposition in which each set of remains was found. As with cases in Texas, MIT Technology Review consulted with PCOME’s Greg Hess on a framework for translating this information into rough estimates for when a person may have died. 

Here’s where the No More Deaths data used in our analysis comes from:

• In California, No More Deaths requests records from the San Diego County medical examiner and the Imperial County coroner. Its latest data for Imperial County is from December 2025, and its latest for San Diego County is from October 2025. 
• In Yuma County, Arizona, No More Deaths requests records from the Yuma County medical examiner. The latest data is from September 2025.
• In New Mexico, the organization requests data from the state’s Office of the Medical Investigator. Unlike most states, New Mexico has a statewide medical examiner, allowing No More Deaths to obtain migrant death data for the entire state. The latest data is from July 2025.
• For Hudspeth County, Texas, No More Deaths requests records from justices of the peace in Districts 1 and 2. The latest data is from December 2023.
• For El Paso County, Texas, records were from the El Paso County Office of the Medical Examiner. The latest data is from September 2025.

In total, we included nearly 1,000 cases from No More Deaths in our analysis. 

Total cases reviewed

When accounting for all our data sources and excluding deaths without specific enough records to determine where remains were found, we analyzed a total of nearly 4,000 cases. 

Other notes 

We generally assume that a person died where their remains were found. That may not necessarily be true: Remains can be moved by other people, flowing water, or animals. But medical examiners and researchers we consulted said such cases are rare.

We excluded cases in which the virtual wall did not appear relevant. For example, we removed cases involving people who died inside Border Patrol stations or in car crashes during pursuits. 

The deaths we analyzed reflect a variety of causes that may seem at first like very different sorts of surveillance failure. If someone slowly died of dehydration within view of a camera, for example, agents would have had far more time to intervene than they would have in a case where someone drowned quickly in a river or canal. But we included all these deaths because, in each one, the virtual wall could have prompted a response from agents.

Surveillance towers

Including a tower in our analysis required knowing three things: its location, its type (so that we could judge how far it is supposed to see), and the time it was installed. 

Locations

The Electronic Frontier Foundation has been tracking and mapping CBP surveillance towers since 2023, using on-the-ground reporting, satellite imagery, and government documents obtained through public records requests. Its tally is an undercount; government records suggest that about 800 towers are currently deployed, while EFF has logged about 600. Our analysis therefore misses deaths that occurred near towers not yet catalogued by EFF.

MIT Technology Review worked closely with EFF’s director of investigations, Dave Maass, throughout this project. Our tower data began with the database published by EFF in April 2026, but that map did not include towers that have been removed. We consulted EFF for the details on these former towers and added them to our database. We generally use EFF’s reference numbers for individual towers, but we changed some to ensure that each tower has a unique identifier.

The locations are identified by GPS coordinates. We verified each tower’s coordinates by analyzing satellite imagery.

Tower types

Our analysis focused on towers of three main types. Certain towers mapped by EFF don’t fall into any of those categories, so we excluded them because it’s difficult to obtain information about how far they are supposed to be able to see. This includes some towers that are installed at Border Patrol checkpoints or stations.

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To know how far the main tower types could see, we consulted materials from tech companies and EFF’s research, led our own review of government documents, and interviewed former Border Patrol agents and officials. The distances described are consistent across these sources. But they are advertised estimates, not contractual guarantees: Some government records redact more specific information about a tower’s surveillance range in particular locations, citing security concerns.

When towers were present

To know if a tower in place now was present when someone died, it’s essential to know not just where it’s located but also when it was put there. To figure this out, we used multiple sources of satellite imagery. 

Maass, at EFF, had previously analyzed some of the towers to determine when they first appeared. MIT Technology Review checked satellite imagery for these towers to confirm these timelines, and then checked for all the remaining towers. For each tower, we logged the date that it first appeared and, if it eventually disappeared, the last date it was present. We spent months meticulously checking when hundreds of towers appeared in multiple sources of satellite imagery to confirm there were no contradictions. 

This is easier for certain tower types than others; towers from Anduril, for example, tend to go up where there was no prior infrastructure, and it’s fairly easy to see how a patch of empty desert gave way to the new tower and its trademark solar panels. Other towers, like IFTs, were often built on or near existing structures. For these, we leaned more closely on EFF’s expertise in analyzing the imagery. 

When imagery wasn’t sufficient, we used Google Street View, government documents, or photographs collected by EFF to verify when towers went up.

One limitation is that historical imagery of towers along the border is not available for every day in the past. Especially for older towers or those in remote locations, images may have been taken months or even a year apart. A tower visible in May and again in December could theoretically have been removed and reinstalled in the intervening months. But most tower systems are permanent structures. And multiple Border Patrol agents told us that even towers that are meant to be mobile, like autonomous towers, are in practice very rarely taken down or moved (consistent with this, we observed that semipermanent fences were erected around the sites of many Anduril towers).

Another limitation is that even if a tower is visible at a particular time, that does not mean it was online. We can’t verify that it was functional, receiving power, or transmitting data. Our analysis therefore establishes when a tower was present, not whether it was operational.

Heights

We conducted a topographical analysis, detailed below, to see whether a tower’s view of a given person might have been blocked by the terrain. This required estimating how tall each tower is. 

We included a minimum and maximum height for each tower, based on research from EFF. In select cases where the surveillance system was not on a typical tower but mounted to an existing structure, like a water tower or building, we used satellite imagery to estimate its height. The higher a tower is, the farther it can see.

Our analysis

With the data we collected, we could begin to see which deaths happened in range of the virtual wall. But for a given match between a set of remains and a tower to count, it needed to satisfy two criteria:

1) Distance: The death must have happened within the published surveillance range of the nearby camera.

2) Timing: The tower had to be present when the person was estimated to have died.

Distance

Minho Kim, an outside collaborator who is now a postdoctoral researcher at Stanford with a focus on wildfire mapping, led the distance analysis. His software ingested our database of human remains and our database of towers. Then it created a row for each time a set of remains was found near a given tower, along with the distance between the two.

We then checked whether, for each match, the tower was actually present when the person died.

Timing

Sometimes, as with the Arizona cases, an estimate of when someone died is given in the original data; the medical examiner may note that a person died more than three weeks but less than five weeks before remains were found, for example. 

In other cases, we used the level of decomposition, the cause of death, or both to calculate a window of time in which the person would likely have died. Finally, we compared this window with the time when we determined the tower was built. 

This step took us many months and involved reviewing original police reports for hundreds of cases, inspecting photos of bodies, cross-referencing cases with news articles, interviewing medical examiners, and researching decomposition timelines. The resulting death windows are approximate, but they are the best estimates we could make from the information available in each case.

We assigned different confidence levels to cases based on how these dates lined up. If a person’s entire estimated death window fell well after a tower was first observed, we felt confident the tower was present when they died. If any part of the death window fell before the tower was first observed, we excluded the case.

Many cases involve skeletal remains. Remains decompose differently depending on location and season, but when records didn’t provide a specific estimate, we conservatively estimated that skeletal remains had been there for at least six months. For these and other cases without a specific estimate of when the person died, we used three years as a conservative cutoff: If a tower had been present for at least three years before the remains were found, we included the case, judging it unlikely that the person had died before the tower was installed and remained undiscovered that long. If the tower had been present for less than three years, we excluded it.

Viewshed

It is possible that someone who died within range of a tower could have been blocked from the tower’s view by terrain, vegetation, or buildings.

We estimated the impact of this issue with a technique called topographical analysis.

Kim, our collaborator, led this part of the analysis. Here’s how it works: Imagine a map of the border region divided into a grid, with each square having one value for that area’s elevation. Take a given match between a set of remains and a tower and draw a line between the tower’s estimated height and an estimated height of 5’ 6″ for the person who died. If the terrain along that path rises high enough to block the line between the two points, we estimate the tower’s view as obstructed. If it doesn’t, we estimate the view as clear.

It’s not perfect. In the elevation data we used, each square represents an area 30 meters wide, or about 98 feet, and is assigned a single elevation. That means smaller changes in the terrain can be lost, particularly in steep or rugged areas where elevation changes quickly. Our analysis is therefore less about re-creating exactly what a tower could see than providing a rough estimate of whether terrain may have blocked a tower’s view of the spot where someone died. 

We include results on our map whether a clear line of sight was estimated or not. Even if the location where someone died was blocked from a tower’s view, the person may have passed through areas visible to the tower before reaching that spot.

It’s much harder to estimate whether a line of sight was blocked by vegetation. We are less concerned about this limitation because virtual-wall towers use thermal imaging, which can detect body heat through light vegetation. Dense vegetation, however, can still obstruct their view.

Buildings pose a greater challenge. We could not reliably account for their locations and heights. Instead, we reviewed each tower’s surroundings and flagged towers located in or near urban areas. These notes warn readers that buildings may have limited what those towers could see.

Beyond the data 

The most fundamental question in each of these cases, and for the families of anyone who died, is whether the US government saw someone attempting to cross into the country and never intercepted them—or never saw them at all. 

This is all but impossible to answer with records or data—Border Patrol doesn’t disclose which alerts came in surrounding any given migrant death, and footage isn’t retained unless an internal investigation is triggered, which doesn’t happen unless a migrant dies while in custody.

MIT Technology Review instead conducted dozens of interviews with people who worked for Border Patrol, Customs and Border Protection, or the Department of Homeland Security or who served as presidential advisors. These conversations yielded previously unreported details about how agents use surveillance technology, turned up instances where it has underperformed, and underscored how seldom its effectiveness is evaluated internally.

We also sought out friends and family members of people who died near surveillance towers, using additional records, social media, and other research tools to find them. We approached these interviews using best practices for trauma-informed reporting.

Beginning in September 2025, MIT Technology Review requested interviews with current Border Patrol agents and leadership at CBP, and we asked to visit the border to understand the strengths and weaknesses of the latest autonomous tower program. Following delays, which were in part related to DHS funding lapses, in July 2026 we were told our media request had been approved at the local level in Big Bend, Texas. We wanted to visit this area because Border Patrol is expanding the number of Anduril towers there. But when the request went up to the DHS headquarters for a final sign-off, the agency denied it. Before publishing our investigation, we sent detailed questions to CBP, Anduril, General Dynamics and Elbit. CBP and Anduril responded, but did not substantively address our questions. General Dynamics referred us to CBP. Elbit did not respond to our questions.

MIT Technology Review thanks the following for their assistance with this project:

Minho Kim, Stanford University

Greg Hess, Pima County Office of the Medical Examiner 

Stephanie Leutert, University of Texas at Austin

Don White, Brooks County Sheriff’s Office