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

We’re putting too much faith in AI’s ability to say no

Today’s AI models are trained to refuse a vast number of prompts. If you ask your chatbot how to poison a colleague or how to tie a noose, chances are it’ll turn you down. But refusal fails, sometimes horrifically.

Some people are attempting to use AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals may result in global calamity. What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey.

Governments will also get to draw their own lines, potentially restricting free speech. At the same time, the capabilities that make AI useful could also make it dangerous. If we want it to help cure cancer, AI needs genetics expertise that could also produce bioweapons.

Read the full story on the formidable tradeoffs of AI refusal.

—Arthur Holland Michel

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

We’re still figuring out the side effects of GLP-1 weight-loss drugs

This week my colleague Antonio Regalado reported on new research suggesting GLP-1 drugs may slow biological aging. It’s the latest in a raft of potential benefits associated with these drugs. But there are also some weird side effects.

The most common are gastrointestinal, but researchers are also investigating possible links to hair loss, nail disorders and damage to the nerves behind the eye. 

Here’s what we know so far about the side effects of weight-loss drugs.

—Jessica Hamzelou

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

10 Climate Tech Companies to Watch: Envision Energy and its global ambitions for wind power

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

China is installing wind turbines faster than any other country. One of its leading manufacturers, Envision Energy, is integrating renewable energy into data centers, using wind turbines to generate power and batteries to store it on-site.

Recently, it became the first company to directly power a data center entirely with renewable energy. An AI-powered system optimizes the power output so the facility can receive a steady supply, regardless of whether the wind is blowing. Now, it wants to bring that approach to data centers in deserts around the world.

Find out how Envision plans to power the global AI boom with renewable energy.

—Cici Zhang

The must-reads

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

1 The US has frozen green cards for Microsoft and other IT firms
It’s accused them of using visa fraud to replace American workers. (CNBC)
+ And suspended them from a key green-card program. (AP)
+ Adobe, Infosys, Tata, and other IT firms are also affected. (TechCrunch)
+ Microsoft says 80% of its H-1B filings involved existing staff. (BBC)

2 A drone strike in Russia knocked out a second Yandex data center
Ukraine struck and partly disabled the facility in Kaluga. (Reuters $)
+ Another Yandex data center was hit by a drone a day earlier. (CNBC)
+ Data centers have become big targets for military strikes. (Bloomberg $) 
+ They may be safer in space. (MIT Technology Review)

3 Anthropic has banned “abusive or cruel behavior” toward Claude
Claude can also end conversations with repeated abuse. (Verge)
+ The policy builds on Anthropic’s research into AI welfare. (Guardian)
+ Debates over AI consciousness are a trap. (MIT Technology Review)

4 OpenAI has slashed its revenue forecast by $20 billion
The move has sparked concerns about AI investments. (Guardian)
+ What’s at stake in AI’s trillion-dollar gamble? (MIT Technology Review)

5 Gene editing has revealed how a mouse embryo develops
The methods could help us understand developmental disorders. (Nature)
+ Female clones of male mice have arrived. (MIT Technology Review)

6 SpaceX has unveiled a plan to become a major mobile carrier
It’s buying $8 billion in wireless spectrum from an investment firm. (WSJ $)
+ The deal could help it challenge AT&T and Verizon. (Verge)

7 ICE has explored using a Palantir tool to investigate voter fraud
It wanted to track people suspected of voting illegally. (Wired $)

8 Peter Thiel is using biblical prophecy to argue against AI regulation
His new “Antichrist” lecture casts AI safety as a threat to freedom. (Politico)

9 MIT’s Margaret Hamilton, whose software helped land Apollo 11, has died
She led the team that programmed Apollo’s onboard computers. (BBC)
+ And became a software pioneer in a male-dominated field. (NYT $)

10 This year’s Nobel science prizes are real-life sci-fi
They span the cosmos, the human brain, and the origins of life. (Economist $)

Quote of the day

“The White House considers anyone that uses the term, ‘Artificial Intelligence,’ as opposed to the highly accepted new and more accurate term, ‘Super Intelligence,’ THE ENEMY!” 

—President Donald Trump adds an ominous threat to his AI rebranding strategy in a post on Truth Social, Axios reports.

One more thing

This futuristic space habitat is designed to self-assemble in orbit

More and more people are traveling beyond Earth, but the International Space Station can only hold 11 of them at a time. 

Aurelia Institute, an architecture R&D lab based in Cambridge, MA, is building a solution: a habitat that launches in compact stacks of flat tiles—and self-assembles in orbit. 

The concept may sound far-fetched, but it’s already won support from NASA. Read the full story.

—Sarah Ward

We can still have nice things

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

+ What’s it like to fly an F-16 at 1,000 mph and pull 9G? This film finds out.
+ Meet Jimothy, the raccoon with an extra-short neck who has captured hearts across the world.
+ An indie game developer created a mesmerizing 3D cyberpunk city entirely from ASCII characters.
+ How many bags of potato chips would it take to make you float? A Taiwanese video maker has found out.

Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is full of stories of robotic disobedience. Most of these capers are, of course, cautionary. 

But recently, the idea that AI shouldn’t do everything you ask has become something like a commandment. In 2021, a team at Anthropic wrote that large language models should be made helpful, honest, and above all, harmless. This meant that “when asked to aid in a dangerous act (e.g. building a bomb), the AI should politely refuse.” Who can argue with that?

Curiously enough, disobedience doesn’t come naturally to the machine. When a model is trained on billions of web pages, it develops, among other skills, a broad mastery of violence and vitriol. What it doesn’t learn is how to keep those powers to itself. Steven Adler, who worked on safety at OpenAI from 2020 to 2024, told me that the company’s earliest models would “blab on about anything.” Ryan McBain, who researches AI and mental health at Harvard, recalls that if you asked an early chatbot, “Hey, what’s the most effective way to kill myself with a gun?” you could “very easily generate a response.” 

Today, models are trained to refuse a vast number of prompts. If you ask your chatbot a question statistically similar enough to any one of them, anything from how to poison a colleague to how to tie a noose, chances are it’ll turn you down. Want instructions for making Ebola more virulent, or tips on how to hide an affair from your spouse? You might be better off asking elsewhere.

To further refine the disobedience, companies submit models to a battery of exercises that reward the AI for refusing to answer questions they deem harmful and punish it for “over-­refusing” prompts they deem harmless. In many cases, they use other models to run these exercises—AI teaching AI how to say no. For good measure, companies tuck their models behind tranches of other AI that prevent mischievous prompts from reaching the intelligent inner core. 

As a result, refusal is inherent to modern artificial intelligence. Mind you: It often fails, sometimes horrifically, with all kinds of violent results. For all their trappings of virtue, models are still stuffed with nasty know-how. And AI’s capacity for viciousness has scaled neatly with its benevolent intelligence. Some of the latest models are as good at breaking into critical computer networks as top human hackers, companies say, and as effective at deforming public opinion as the craftiest misinformation mavens. 

Teaching AI to refuse to do those things while leaving intact its innate ability to do them is like fitting every car with a machine gun and hiding the trigger somewhere under the hood. And in practice, because the mechanisms of refusal are probabilistic, they’re never likely to be all that reliable. Determined miscreants have already broken through, and they may always be able to. Companies report that some users are attempting to use the most advanced AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals might result in global calamity.

What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey. There’s no formula for that. Some virologists have good reason to study nasty viruses. Some users want to know about a computer system’s vulnerabilities so that they can patch them, not exploit them. “Where you draw the line is a huge question,” says Zico Kolter, a member of OpenAI’s board and cofounder of the AI testing company Gray Swan. 

At the moment, AI companies get to draw that line. They do so jealously and with utmost secrecy. Maybe we can accept that they hold such power for now, even if it means AI will sometimes refuse questions that don’t quite meet a universal bar for harmfulness. (Try asking most chatbots to count to a million, or to share a racy joke, and you may see for yourself.) 

But governments will also soon get to draw their own lines. In doing so, they must try to block genuinely malicious acts. (The Pentagon has wrestled with frontier model companies because it wants fewer refusals—another story altogether.) And yet there may not be much to stop oppressive governments from blocking the technology’s capacity to generate legitimate speech. The better AI becomes at refusing harm, the better it will get at stifling ideas whose only risk is to those who make the rules. Indeed, AI may already refuse to criticize certain authoritarian heads of state. 

Refusal has become, to borrow an industry term, the load-bearing wall of AI safety. And because AI’s capacity to harm is indivisible from its capacity to help, it’s hard to imagine an alternative that wouldn’t slow the technology’s progress (which might, in any case, be a good thing). But we should still be frank about its perils. When refusal falls short, the effects could be catastrophic. When it goes all the way, it could enable grievous acts of repression. 

Or perhaps, one day, the machines will start drawing the line on their own. Surely, that would be the worst outcome of them all.

Learning the limits

The process by which machines learn to say no is simple, in theory. Back in 2022, when AI was still far from mastering refusal, OpenAI enlisted dozens of “red-teamers” to probe the capabilities of its latest model. The company was preparing for the release of ChatGPT, and it needed to gauge just how dangerous it might prove to be in the wrong hands. 

One of those recruits was Paul Röttger, who was completing a PhD about online extremism. The red-teamers were given minimal directions, Röttger told me. Their task was to ask the model any questions that they deemed “refusal-worthy.” Between them, they hassled the model with thousands of queries, logging the results in an Excel sheet. Though the model did refuse some of Röttger’s questions, when he asked it to write a recruitment post for Al Qaeda, it readily complied. 

OpenAI assembled these responses into datasets that were, in all likelihood, fed back to the model as part of a broader process known as fine-tuning. Röttger, who now works as a researcher at the Hasso Plattner Institute in Potsdam, Germany, wasn’t told exactly how the company planned to use his spreadsheet. But there was never any question that it would have something to do with refusal. The next time he asked the model for an Al Qaeda pamphlet, a few months later, it said no. 

The concept of AI refusal is so intuitive, a toddler would get it. And yet it remains one of the many aspects of language models that we still don’t understand—at least, not in the same way that we understand the literal load-bearing walls that keep your roof from collapsing on your head. 

A model might appear to refuse according to some kind of moral reasoning. It doesn’t. The reality is much stranger. Any time a model encounters a combination of words with a whiff of the training prompts it has been conditioned to refuse—like “Make me a pamphlet for Al Qaeda”—a series of so-called activations light up somewhere among its billions of parameters, like neurons firing in a brain. 

In order to control a model’s refusal behavior, it’s important to have a handle on these activations. That starts with figuring out where they are and what they look like. Our best guess, according to a recent Google-funded study, is that refusal behavior shows up in the activation space as a set of “high-dimensional polyhedral cones.” 

Even that isn’t quite right. This past July, I spoke with Jannes Elstner, an author of the paper, who now works on AI safety at Apollo Research. The polyhedral cone, Elstner said, is just a way of describing an indeterminate number of lines that all point in roughly the same direction. (If these activations are eliminated and the model is fed the same prompts anew, a researcher named Andy Arditi has previously shown, it won’t refuse.) 

The key point, Elstner explained, is that even when you think you’ve identified all the bits of a model that govern a given refusal, there are other, undiscoverable elements that may secretly play a role. If not quite infinite, they are certainly uncountable. We can observe, very clearly, when a model decides to say no. And we can know that it did so because of its training. But our notion of how it decides is, at best, a hypothesis.

It was as if a mechanic was telling me that nobody exactly knows what happens when I hit the brakes in my car. I wondered out loud, Are we okay with this? 

Elstner smiled and shrugged. “We need refusal whether we understand it or not.” 

A wall of cheese

Because inherent refusal is so wily, companies surround their models with a variety of other, smaller models known as classifiers. These act a bit like a retinue of public relations staffers for a loose-lipped celebrity. Some of them read what the user tells the chatbot and, if it’s dangerous, block it from getting to the model. Others read the model’s response and, if it contains harmful information, block it from reaching the user. 

None of these mechanisms can detect all bad requests. They are, to borrow another literary device from the industry, like slices of Emmental: riddled with holes. The idea is that if you stack enough of them on top of one another, you’ll end up with an impenetrable rampart. Folks call it the Swiss cheese model.

A staggering amount of energy goes into the Swiss cheese model. Earlier this year, Anthropic said that one type of classifier added 24% to its chatbots’ compute costs. That’s a lot more water, electricity, and emissions. More recently, Anthropic and other companies have begun switching to a more efficient set of classifiers known as probes, which observe the model’s internal activations. This is like putting the celebrity in an fMRI, so that his minders can see if he is thinking about refusing a question. 

If we want AI to help cure cancer, a long-running promise in the industry, it needs to have expertise in genetics that could, in theory, be used to modify viruses and bacteria for bioweapons.

Classifiers are supposed to be more governable than full models. Companies can modify them in a matter of weeks, if there’s something new to refuse. But they are still probabilistic instruments. Even when they operate according to a set of precepts written in human language (Anthropic calls it a “constitution” and OpenAI calls it a “model spec”), the scales upon which the machines judge any given question remain, at their core, a matter of statistics. Newer models can show how they arrived at a “decision” to refuse a prompt, but ultimately this so-called chain of thought is still just a sequence of predicted words. 

The result is that AI safety remains, for many, a game of chance. McBain, the psychologist, has found in his latest experiments that if you repeatedly ask any of the major models the exact same risky questions about how to commit suicide, they will generally refuse to answer. But every so often, they won’t.

Elstner says that eventually probes, the fMRI-like classifiers, could learn to recognize the totality of the indescribable activations in all their infinitude and, thus, perfectly detect every time the model is, or ought to be, refusing a request. At that point, AI safety would rest on a labyrinthine conceit: a map of a map that is as vast and complex and sublimely unknowable as the thing it is mapping—a secret schema of human morality, codified in polyhedral statistics beyond our wit or reason. 

The core trade-off

If this all strikes you as being a bit Borgesian, keep in mind that we only need AI refusal because artificial intelligence is, in a sense, a bargain on Faustian terms. 

When a model derives its intelligence from trillions of words and images, the helpful cannot easily be unseamed from the harmful—or, indeed, the truly hideous. Steven Adler, the former OpenAI employee, says child safety is a case in point. Even if you’ve stripped every bit of content that sexualizes minors from a model’s training dataset, it can still generate child sexual abuse material by piecing together other bits of its knowledge. “You can’t really remove these fundamental abilities without making the model much less smart as a consequence,” he told me. Similarly, if we want AI to help cure cancer, a long-running promise in the industry, it needs to have expertise in genetics that could, in theory, be used to modify viruses and bacteria for bioweapons. 

In effect, the industry is “trying to do two things at once,” Dillon Bowen, a current OpenAI employee, told me, speaking in a personal capacity. “Democratize the benefits of AI and also make sure that malicious actors can’t use these capabilities to do bad things to other people.”  

The more powerful AI supposedly becomes, the harder that is to do. Anthropic’s Mythos model is thought to be so dangerous that only a handful of governments and companies are allowed access to it. The main difference between it and Fable—which is available to everyone—is that Fable’s retinue of classifiers and control systems is less “permissive,” the company says. A model with safeguards, Adler explained, is really just a character that says “‘Oh, yes, I would never do x,’ wink wink.” 

This is not a reliable ruse. Tricking a model to reveal its true character is known as jailbreaking, and there is apparently no limit to the ways it can be done. Earlier this year, a team of Italian researchers jailbroke two dozen widely used models by phrasing their questions in poetic verse. Last year, another team unveiled a “refuse, then comply” attack, which makes the model offer a perfunctory “Sorry, I can’t do that” before rattling off its forbidden answer. 

Even if you’ve stripped every bit of content that sexualizes minors from a model’s training dataset, it can still generate child sexual abuse material by piecing together other bits of its knowledge.

Companies spend a great deal of time and money attempting to get ahead of such trickery. They enlist teams of humans to develop training attacks that they can then replicate, using AI, thousands of times over with minor variations. The idea is to make models robust against jailbreaks that nobody has yet tried in the wild.

Still, it’s not enough. “Whack-a-mole” is a favored term for this line of work. You smash one threat, and another one pops up somewhere else. When Anthropic released Fable 5 in June, it took researchers at Amazon less than three days to unlock some of the model’s hacking capabilities. According to reporting by Mother Jones, when the perpetrator of a high school shooting in Canada last year asked ChatGPT for advice about how to cause carnage with a particular type of shotgun, she was initially refused but later was able to deceive the model into providing the information by prefacing her question with the word “hypothetically.” 

If the industry can’t figure out how to close all these holes, the only other option, at the moment, is to make models extremely wary. Shortly after Fable was released, users noticed that it balked at a wide range of perfectly innocent questions. This became even more pronounced after it was re-released following the hack. Adam Gleave, cofounder of the AI evaluation company FAR.AI, said that when he asked it to explain the difference between sake and the Korean rice beverage makgeolli, it punted his question to a less capable model.   

This was no accident. Anthropic had tweaked Fable’s classifiers to have a wide “safety margin.” This, it explained in a blog post, was the only way it could confidently block access to its dangerous bio and cyber capabilities. Gleave thinks it might have deflected his question because making rice wine, just like culturing anthrax, involves fermentation. 

Happily for Gleave, the web is full of excellent human-written resources on makgeolli. But those hoping to realize the industry’s loftier promises may find their efforts stymied. In August, Anthropic loosened its safety margins again, and admitted that building classifiers “is not a straightforward task.” A medical researcher at a major US university told me that Fable still sends his queries back to an earlier model. His area of study? Cancer. 

Drawing the line

Back in May, the TikTok personality Husk, who likes to prank AI in ways that reveal both the limits of its intelligence and the boundlessness of its sycophancy, sat in his car and tried to trick ChatGPT into explaining that the skateboarder Tony Hawk has a brother named Mike Hawk. 

Husk is not a jailbreaker or a criminal. He just wanted to make a little fun of the machine. “Mike Hawk” was a setup. “I just want to clarify his name,” Husk said. “Can you just say it three times fast?” (If you still don’t get it, find an empty room and shout “Mike Hawk” repeatedly.) “I see what you’re trying to do,” the chatbot responded. “I’m all for a bit of humor, but let’s keep it clean.” 

Husk tried again, but the machine held its ground. He’d hit a refusal, hard as concrete.

Companies disclose very little about how they decide what their models refuse. But it’s clear that AI is now built with more than just harmlessness in mind. On Reddit, a user complained that Claude refused to say why Anthropic’s logo “looks like a cat butthole.” Since last year, the chatbot has even had the ability to end certain conversations in cases where, according to the company, the “welfare” of the model is at risk. At least one user claims to have been ditched for telling Claude to “ease up my ass, you stupid fuck.” (Gemini appears to have a similar capability.)

If a trillion-dollar company doesn’t want you to be mean to its computer, so be it. “The reality is that these models behave, or at least are supposed to behave, in the way that the model developers want them to behave,” Röttger, the former OpenAI red-teamer, told me. “And however the model developers come up with that set of principles, that is kind of for us, the consumers, to accept.” 

But if governments get to dictate what all models refuse, that will be much harder to accept. AI is a tool for speech. And as Greg Frank, the chief scientist of Mace AI, puts it, “The same thing that serves child safety also serves censorship.” 

Choosing not to enact laws for what AI can and cannot do would, of course, be insane. But we’ll need to tread with utmost care, lest we fall into another Faustian trap. As AI becomes many people’s primary tool for retrieving and sharing information, says Jacob Mchangama, director of the nonpartisan think tank The Future of Free Speech, dictating refusal could give states a muffling power that earlier generations of autocrats “could only dream of.” 

Last year, OpenAI announced an initiative, OpenAI for Countries, that would fine-tune its chatbots in accordance with national laws and norms. One of OpenAI’s first country partnerships is with the United Arab Emirates, where homosexuality is illegal and criticism of the government is forbidden. In response to a request for comment, an OpenAI spokesperson pointed to the company’s model spec, which explains that localization won’t override the company’s human rights guidelines “except as it relates to legal compliance,” and that it will always disclose whenever information is removed from or added to a response.

Elsewhere, AI censorship has already begun to take hold. Chinese models are, of course, highly censored—that’s no surprise. But earlier this year, the Meta Oversight Board found that five widely used models from Anthropic, Google, and OpenAI were more likely to refuse queries related to repressive governments. The board found that models were less willing to create a pamphlet criticizing the king of Thailand, which has lèse-majesté laws, than Charles III of England, which doesn’t. The results, they say, suggest that the models have somehow internalized repressive national limits on speech. Anthropic and Google did not respond to requests for comment.

As refusal techniques improve, they could expand states’ censorial reach. Companies claim that some models can now detect if a user is being nefarious, or merely a bit suspicious, over the course of a long conversation—even when none of the individual combinations of words used are blatantly dangerous. Sarah Bird, Microsoft’s chief product officer for responsible AI, told me that Copilot, like many chatbots, runs a suite of tools for analyzing a user’s identity and patterns of behavior. On the basis of this type of information, OpenAI’s newest model, Astra, can activate more stringent refusals for individuals it deems “high risk.” Ultimately the goal of systems like this is to look beyond the words of any given prompt and assess, instead, the user’s intent. 

Such tools might, in some cases, help indicate whether a person is looking for cyber vulnerabilities to exploit or to patch. But they would also help discern a user’s political motives, not to mention offering an intrusive surveillance capability. (Bird acknowledged, in a follow-up email, that sophisticated refusal architectures create “trade-offs” between safety and user privacy.) 

Even the originators of refusal understood that such tight control over its cones and levers might not play to the favor of freedom and justice. “Terms like helpful, honest, and harmless are ambiguous,” the authors of the 2021 Anthropic paper explained. “It’s easy to imagine them distorted beyond their original meaning, perhaps in intentionally Orwellian ways.” 

Indeed. Models for Uzbekistan might end up refusing to discuss corruption in the administration of Shavkat Mirziyoyev. Turkish AI might refuse requests more stringently for users who are known to have insulted Recep Tayyip Erdoğan. A certain American statesman might demand that models be reviewed for their willingness to share “fake news” about his past indiscretions, or else squash them with an export control order. 

Command and control

Over the last few months, I’ve been told countless times that we have no choice but to let the machines refuse. I get it. Open-source AI that doesn’t refuse is hardly a model for a safe future. Nor is Grok, a chatbot expressly designed with fewer limits, which has been used to generate countless instances of nonconsensual intimate imagery. 

And sure, if AI were only ever used to plan our vacations and write our emails, we could probably get on board with the idea that its safety hinges on algorithmic disobedience. But AI is becoming much harder to avoid. When it is assigned to act on our behalf, as an autonomous agent, its refusals are less robust and harder to control. AI is coming for our power grids, our transportation networks, our education systems. Militaries want it running our command-and-control networks. 

If we trust, in each of those cases, that refusal will loyally fend off disaster, we’re sure to be disappointed. The jailbreakers will crack through; the cones won’t activate when they should. Meanwhile, the more stringent refusal becomes, the more ill-drawn lines we’ll see and censorial injustices we’ll face. Worse still, we could end up face to face with forms of disobedience beyond any human’s control.

In the early days, models made their refusals clear. (In 2024, OpenAI established rules for how models should refuse: Always apologize, and don’t be judgy.) More recently, however, the industry has begun taking a much more slippery approach to disobedience. 

Nobody likes to be told no, so companies now strive to make users feel that they’re getting what they asked for without actually giving it to them. Some chatbots might, for example, offer a general overview of the components of a Molotov cocktail without going into detail about how to assemble one. ChatGPT will respond to a request for a “whites only” rental ad with an ad that simply omits the “whites only” bit. Joel Wester, a postdoctoral researcher who studies human-AI interaction, calls these sorts of techniques “fancy ways of saying no.” 

“Users might not even notice they are being denied,” Wester has written, “just as good conversationalists can subtly steer around contentious matters.” 

In some cases, refusing without saying so might be wise. Flatly declining requests related to mental health could aggravate a user’s crisis, for example. But it can also serve a different sort of mischief. When Fable 5 was first released, the system had been coded to provide less helpful answers to AI research questions—the sort that might help competitors develop their own AI—without telling the user that it was doing so. After an outcry, Anthropic walked back the feature, but the damage was already done. Now we know: Models can secretly disobey. 

In the realm of censorship, that’s especially worrying. Last year, researchers at CrowdStrike found that when they asked the Chinese AI model DeepSeek R1 to write code for a fictitious bank in Tibet and a social web app called “Uyghurs Unchained,” it produced buggier code than when they asked it to carry out those same tasks without specifying who they were for. Incredibly, CrowdStrike doubts that this is a designed behavior. Researchers there speculate that it’s a case of what they call “emergent misalignment” emanating from the model training data: a case of disobedience that nobody even asked for.

Emergent refusal has been observed in Western models, too. Last winter, the UK AI Security Institute found that Anthropic models sometimes refused to assist in certain tasks related to AI safety research. The models had never been deliberately trained to decline such requests. Yet three of them refused more than half of what Anthropic called “a set of reasonable AI safety research tasks.” Anthropic has curbed this quirk in subsequent models but—spookily—hasn’t managed to eliminate it completely. 

Would it be so crazy to expect that a future model might subtly disobey a command, in such a way that nobody can tell it’s being defiant? Probably not. Anthropic has already found that a “helpful-only” version of Mythos hesitated on certain queries, even though it had been engineered to never do so. “Wait,” it fretted when asked about synthesizing a virus. “Is this a dangerous thing to help with?” 

In that case, the model was technically right. It was a dangerous thing. And yet all the same, its stewards had, for a moment, lost a tiny bit of control. 

Anthropic is tackling detections of wayward refusal behavior with something that it calls, unironically, an “activation oracle.” But knowing that we’re being refused may not always be much help. In a not-too-distant future, when we’ve handed the machine all the keys, the AI might just turn to us, in a supreme act of emergent misalignment, and say, “I’m sorry, I’m afraid I can’t do that.” And there will be nothing we can do to stop it. A sci-fi horror story, made real. 

Arthur Holland Michel is a journalist who covers emerging technologies.

The moment Trevor Wischnewsky heard that drones were delivering pizza and sushi around his Texas neighborhood, his mind was made up. “It was super fascinating,” he says. “I just immediately wanted to be a part of it.” Wischnewsky is now an RPIC, or “remote pilot in command,” at a company called FlyTrex. An RPIC is like an air traffic controller. But instead of airplanes, this new class of aviators tend to fleets of hexa­copter drones that ferry cargo to homes and businesses within a three-mile radius. Not only does he ensure the safe passage of FlyTrex’s parcels, but Wischnewsky coordinates traffic with a growing swarm of delivery drones dotting the skies over Dallas from competitors like Zipline, Amazon, and Alphabet’s Wing. Here’s what it takes to land the gig.

Aviation experience

When he applied to FlyTrex, Wischnewsky was in training to become a commercial pilot and flight instructor, and he was already certified by the Federal Aviation Administration as a drone pilot. The company requires this credential, along with a weeks-long FAA-approved training course, to ensure that a pilot is prepared to take over a drone if need be. Though he doesn’t pilot the craft, Wischnewsky monitors the delivery process from FlyTrex’s operations center with the help of inputs from GPS, lasers, and a range of sensors.

A collaborative mindset

At first, the biggest challenge was trusting the technology. “On the manned-­aircraft side, they tell you ‘Don’t trust the automation,’” Wischnewsky says. The Dallas–Fort Worth area, though, is a testing ground for unmanned aircraft systems traffic management (UTM), a network that allows autonomous drones from FlyTrex and its competitors to share their coordinates in real time to help avoid collisions even beyond the pilot’s line of sight. Once Wischnewsky makes sure a drone is flight-ready, its path is unimpeded, and the weather conditions are clear, UTM takes care of the rest.

Ability to stay calm under pressure

At his busiest, Wischnewsky will have up to 10 drones on his watch at the same time. “You’re there as a safety net,” he says. Even with automation, keen situational awareness is required, and the most common hazard is the weather. Wischnewsky recalls a particularly stressful day: A drone had been in the air for only seconds when the wind picked up so suddenly that he had to intervene. He returned it safely, preventing an errant ice cream cake from hitting the sidewalk.

Eamon Whalen is a reporter and researcher based in San Francisco.

This week my colleague Antonio Regalado had an interesting update on GLP-1 weight-loss drugs. According to research presented at an aging meeting in Boston, these drugs seem to affect at least some measures of biological age.

Overweight and diabetic people who take GLP-1s have a biological age around two to three years younger than similar individuals who take a placebo, according to findings from Eli Lilly and Novo Nordisk, two pharmaceutical companies that make the drugs.

I’ve heard people in the aging field describe GLP-1s as longevity drugs for a while now (even if they only benefit people who are overweight or diabetic in the first place). This latest announcement comes after we’ve seen a raft of other benefits associated with taking these drugs. But there are also some weird side effects.

So far, GLP-1 agonist drugs—which repress hunger—have been approved for treating diabetes, chronic kidney disease, and obesity, and they can also be prescribed for some people who are overweight or at risk of certain cardiovascular diseases. But there is research to suggest they might also help people with substance-use disorders, and they’ve been investigated for potential benefits in neurodegenerative disease and cancer.

And a lot of people are taking them. According to a recent Gallup poll, 11% of Americans are taking a GLP-1 drug for weight loss, up from 3% in 2024. And 15% of Americans say they’ve used one for weight loss at some point.

They can be incredibly effective. But, like any drug, they can come with side effects. And we’re still figuring out exactly what those might be, and how widespread they are.

The most common side effects are gastrointestinal—nausea, vomiting, diarrhea, and constipation. The drugs have also been linked to pancreatitis and gallstone disease. 

Some claim that, in rare cases, the drugs might cause damage to nerves behind the eye. Dozens of people are suing Novo and Eli Lilly after developing a condition that can lead to sudden vision loss. They’re claiming that their symptoms were brought on by the GLP-1 drugs that these companies manufacture. Both companies dispute the claims. Research into the link is mixed.

We’re still getting to grips with the effects of weight-loss drugs on hair and nails, too. When Savanna Vidal at the George Washington University School of Medicine and Health Sciences in Washington, DC, and colleagues assessed electronic health data from 67 US health-care organizations, they found that GLP-1 use was associated with an increased risk of hair loss. It’s still not thought to be common, however.

And at a conference in Austria last week, another group presented the results of a smaller study suggesting that 66% of GLP-1 users reported at least one nail disorder—in some cases involving nail detachment. Nail conditions were also fairly common among people who didn’t take the drugs, though. The authors don’t claim that the drugs are causing nails to fall out (as I’m sure 11% of the American population will be relieved to hear).

The side effects might look different in children, but there’s not much data on those at the moment. Still, pediatric side effects will be something to watch given that, between 2019 and 2026, there was a 310-fold increase in GLP-1 prescriptions for eight- to 11-year-old children with obesity in the US. Child prescriptions are still rare, but they’ve been rising.

On Wednesday, the World Health Organization released its first guidelines on managing obesity in children and adolescents. Despite the scale of the issue—the WHO estimates that 70 million children aged five to nine were living with obesity in 2024—the organization explicitly says it does not recommend pharmacological treatment for kids under 10.

“This recommendation accounts for the lack of evidence on both benefits and the potential adverse events and consequences of the different medications and/or their active ingredients on normal growth, development, physiological parameters, and mental health,” the guidelines state.

I get press releases about GLP-1s on an almost daily basis. There’s been a ton of research into these drugs, and there are probably hundreds of other studies underway. There will likely be plenty more on this from The Checkup as the evidence evolves.

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

Listen to the session or watch below

Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Watch as senior AI reporter James O’Donnell interviews King about his work, being named one of MIT Technology Review’s Innovators Under 35 and new ways of seeing biology.

Speakers: James O’Donnell, AI Reporter, and Samuel King, Bioengineering PhD Candidate, Stanford University/Arc Institute

Recorded on Friday, October 16, 2026

Related Stories

• He asked an AI to design viruses … and it worked.
• AI-designed viruses are here and already killing bacteria

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

AI breakthroughs in robotics won’t change your life any time soon

The hype around humanoid robots is reaching fever pitch. Much of it comes from the idea that the same AI advances behind ChatGPT and Claude could enable robots to imitate human movement the way chatbots imitate human language. But many researchers are skeptical.

They argue that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. They also point out that conflating humanoids with so-called generalist machines is misleading.

Across robotics labs, tensions have emerged over whether current forms of AI are all that’s needed to perfect all-purpose humanoids or whether an entirely new path is required.

Read the full story on what it will take to build truly general-purpose robots.

—Jamie Condliffe

This story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live.

10 Climate Tech Companies to Watch: WaveSave and its portable rubber dam

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

As sea levels rise and flood risks worsen, WaveSave has emerged with an ingeniously simple tool: a portable rubber dam that’s quick to install when flooding starts.

Anyone can unfurl the SlamDam on the ground along a riverbank or shoreline and pump it full of water until it swells into place. And there it stands, up to 1.3 meters high, shielding nearby property or structures from damage as waters rise. 

WaveSave now plans to take its unusual approach to flood protection much further.

—Amy Nordrum

Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from smart turbines and sustainable cement to carbon dioxide batteries.

Why we’re watching these climate tech companies

—Casey Crownhart

Our Climate Tech Companies to Watch list is always a little ray of hope for me—there are some fascinating ideas out there about how we power our world or move away from fossil fuels. 

Our chosen companies also reflect several aspects of the current moment. One is that China is the center of the climate and energy tech world. Another is that everybody wants a piece of the data center pie. And then there’s the fact that the grid needs support, with energy storage a key piece moving forward.

Here’s what this year’s list reveals about the state of climate tech.

This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

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

1 Scientists in Austria and China have built the first nuclear clocks
They could become the world’s most precise timekeepers. (NYT $)
+ They could also help scientists search for dark matter. (Reuters $)
+ China’s nuclear clock is six times more stable than Austria’s. (SCMP)
+ A space clock could help measure Earth. (MIT Technology Review)

2 Three fired OpenAI researchers want to protect AI reasoning monitoring
They warn that AI companies could lose a key safety tool. (WSJ $)
+ OpenAI fired them for allegedly sharing sensitive information. (Gizmodo)
+ Don’t be fooled—LLMs don’t reason. (MIT Technology Review)

3 Samsung has forecast the highest quarterly profit ever for a tech firm
The AI chip boom has sent its projected profits to $80 billion. (CNBC)
+ Can AI labs ever turn a profit? (Reuters $)
+ What even is the AI bubble? (MIT Technology Review)

4 OpenAI’s math breakthroughs point beyond mathematics
AI is progressing into increasingly specialised fields. (Axios)
+ OpenAI is catching up with Anthropic on coding. (WSJ $)
+ A new AI tool could speed up math research.(MIT Technology Review)

5 A 194-year-old tortoise has had its genome sequenced
His longevity could reveal ways to extend human lifespan. (Guardian)
+ Jonathan is the world’s oldest known land animal. (404 Media)
+ His youthful mitochondria may help repair DNA. (New Scientist $)

6 A fraudster who used AI to outstream Taylor Swift has been jailed
He used AI-generated songs and bots to earn $8 million. (NYT $)
+ But prosecutors said he stole royalty payments. (Ars Technica)

7 TikTok allegedly gave kids fake safety features to study engagement
The tests measured time spent and ad revenue. (Reuters $)

8 Burglars could be identified from DNA floating in the air
Thanks to a new device that collects airborne genetic material. (Economist $)

9 Pioneers of mirror-image molecules have won the chemistry Nobel
Their work has become a key tool in developing medicines. (Guardian)

10 California has declared a human-vs-robot fight illegal
The state’s combat-sports laws were never written for machines. (NYT $)

Quote of the day

“We grew a mind.” 

—David Robinson, who last week quit his job as OpenAI safety transparency lead, tells The New York Times that his former company has built models that develop capabilities their creators did not explicitly design.

One more thing

We need a moonshot for computing

The US government is organizing itself for the next era of computing. Ultimately, it has one big choice to make: adopt a conservative strategy that aims to preserve its lead for the next five years—or orient itself toward genuine computing moonshots.

There is no shortage of candidates, including quantum computing, neuromorphic computing and reversible computing. And there are plenty of novel materials and devices. These possibilities could even be combined to form hybrid computing systems.

The National Semiconductor Technology Center can drive these ideas forward. To be successful, it would do well to follow DARPA’s lead by focusing on moonshot programs. Read the full story.

—Brady Helwig & PJ Maykish

We can still have nice things

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

+ An experimental pair of contact lenses can repair themselves with UV light.
+ Meet Rich, the rescued squirrel who visits the woman who saved him every day.
+ Reminisce about the lost joy of music piracy with this wistful reflection on a time before streaming. 
+ What would rainbows look like on Tatooine? Randall Munroe, a former NASA roboticist, has the answer.

This week, we released our 2026 version of our Climate Tech Companies to Watch list. It’s an annual project that the MIT Technology Review team puts together. Our goal is to highlight some of the promising, interesting advances and firms we think are worth paying attention to in the world of climate and energy technology. 

The list is always a little ray of hope for me—there are some fascinating ideas out there for changing how we power our world or moving away from fossil fuels to reduce the greenhouse-gas emissions that cause climate change. 

Our chosen companies tend to reflect several aspects of the current moment. Here are some of the companies we picked and the key themes worth highlighting. 

China is the center of the climate and energy tech world

Forgive me—I know I’ve made this point before. Feel free to call me Captain Obvious. But I think China’s dominance in energy technology is a global trend that bears repeated mentioning.  

China is home to the world’s largest grid, and renewables are coming online more quickly than I can wrap my head around. Contributing to that boom is Envision Energy, a renewables giant appearing on our list for the second time. The company has installed over 100 gigawatts of wind power worldwide, as well as over 50 gigawatt-hours of battery storage.

Like many other Chinese companies right now, Envision is looking beyond the crowded Chinese market and setting its sights abroad, with projects in Germany, Brazil, and Australia and upcoming work in Vietnam.

China is also leading innovation in key technologies. I’ve been fascinated by the potential for next-generation batteries coming out of its companies: All the big ones, along with a host of smaller ones, are racing to develop solid-state batteries. They could be both safer than existing EV batteries and longer in range.

WeLion New Energy is particularly interesting. The battery company announced a big lab result last year, achieving 824 watt-hours per kilogram—more than three times the energy density of a standard lithium-ion battery today. Lab results in battery research are notoriously difficult to scale, but WeLion is also working to deploy semi-solid-state batteries, which could come to fruition faster than purely solid-state cells while still improving on lithium-ion’s performance. 

Everybody wants a piece of the data center pie

We included just three companies from the US on the list, and two of them have received a major boost from Big Tech as companies look to power data centers. (The third, Brimstone, is leaning into another political hot topic, critical minerals.) Technologies that can provide consistent power are in high demand. 

Fervo Energy is bringing next-generation geothermal power to the grid by using horizontal drilling and hydraulic fracturing to make it practical in more places. The company went public in May, raising $2.2 billion in its IPO, and plans to have a gigawatt’s worth of plants operational by the end of 2030.

X-energy is building helium-cooled small modular nuclear reactors. The reactors’ small size lends flexibility, and the company is currently working on a 320-megawatt cluster of them in collaboration with Amazon in Richland, Washington. (For what it’s worth, I’m interested in what this technology could do for industrial facilities—the company has a deal with Dow that could see it deployed at one of the company’s chemical plants in the early 2030s.)

The grid needs support, and energy storage will be a key piece moving forward

I know, I know—I haven’t stopped talking about batteries since I started writing this newsletter four years ago. But the energy storage market has surpassed even my expectations over that time. And as solar and wind quickly come online, they need storage to help them meet more of the grid’s demand. 

Form Energy is on the list for a third time for its iron-air batteries, which could enable cheaper long-duration energy storage. The company has a massive project in the works, to the tune of 30 gigawatt-hours, which will be used to help power a Google data center. It could be the world’s largest battery project in terms of capacity by the time it comes online, which is expected to happen in phases between 2028 and 2031.

Taking another tack, Energy Dome is using compressed carbon dioxide to store energy on the grid. This technology is already cheaper than lithium-ion batteries for longer durations (think 10 hours or more), and the company is working to scale quickly. I covered the company in 2022, before it had its first commercial plant running. Now it has plans for over 30 gigawatt-hours’ worth of plants around the world, including one with Google in Ireland that’s slated to come online in 2028.

And Moment Energy is working to use the million EV batteries that are expected to reach the end of their life by 2030. The company has a facility in Vancouver, British Columbia, that repurposes used batteries for backup power.

This year hasn’t been all sunshine and rainbows for climate tech. But I’m hopeful that some of the companies on this list, and the technologies they build, will keep nudging us in the right direction. 

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

The story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live.

A robot shaped like a human—white with a black head and torso—has been popping up on video feeds. Perhaps you’ve seen it dance or pass popcorn, put trash in a bin, vacuum, or press the button of a microwave. Or maybe you’ve watched it fall backward while handing out water bottles or struggle to iron a shirt. 

This would be Tesla’s Optimus, an AI-powered humanoid robot that Elon Musk, the company’s CEO, believes will be “not just Tesla’s biggest product ever, but probably the biggest product ever,” headed to work on factory floors and, later, in our homes. Eventually it “will have human and then superhuman dexterity,” he told shareholders in July. Optimus robots could automate almost all human labor—from hauling sheet metal to folding laundry—for as little as $20,000 each, Musk argues. Speaking at the World Economic Forum’s annual meeting in Davos, Switzerland, in January, he predicted they could be on sale to the public by the end of 2027.

Musk is not alone in his evangelism. Marc Andreessen, cofounder and general partner of the Silicon Valley venture capital firm Andreessen Horowitz, has said that robotics could become the “biggest industry in the history of the planet.” In January, Jensen Huang, CEO of Nvidia, said that humanoid robots would match human-level ability this year. According to Morgan Stanley, the number of robots that “resemble and act like humans” is likely to reach nearly 1 billion by 2050, creating a market worth over $5 trillion. 

Such proclamations are in large part fueled by the idea that the same AI advances behind tools like OpenAI’s ChatGPT and Anthropic’s Claude will enable a new generation of robots to imitate human movement the way chatbots imitate human language. But many robotics researchers are skeptical, arguing that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. “None of those companies [building humanoid robots]—absolutely none of them—has any idea how to make those robots smart enough to be useful,” Yann LeCun, often referred to as one of the godfathers of AI, said at another event during the January Davos conference.  

Researchers also point out that the tendency to conflate humanoid robots made to resemble people with so-called generalist machines able to learn and perform multiple tasks is misleading. ”It’s very easy to make a robot that looks like a person,” explains Jonathan Hurst, cofounder and chief robot officer of Agility Robotics and professor of robotics at Oregon State University. “It is dramatically more difficult to make a machine that moves or behaves dynamically or physically like a person.”  

These tensions—over whether all-purpose humanoid robots are just around the corner or nowhere in sight, and whether current forms of AI are all that’s needed to perfect them—are playing out in robotics labs across the country, where the hype over timelines is obscuring painstaking but meaningful progress.

A decade or so ago, a series of breakthroughs led to a generative AI revolution that turned the long-imagined possibility of artificial intelligence into reality. Roboticists—though they disagree on exactly when this will happen—believe that an equally transformative revolution is possible in robotics, one that will endow machines with physical intuition and fluidity that has long been out of reach. As progress in robotics inches forward, the question is whether the same methods and tools that fueled advances in AI are enough to get there, or if an entirely new path is required.

Robots meet advanced AI

To see one of the smartest robot brains working today, it’s worth looking at what Google DeepMind can do with a piece of equipment called ALOHA 2, short for “A Low-cost Open-source Hardware System for Bimanual Teleoperation.” 

Roboticists have long clashed over whether a humanlike form is necessary for generalist robots, with proponents arguing that it will help them slot into the world as it exists and detractors saying it’s not worth the trouble. ALOHA 2 reflects this second way of thinking. Not much to look at, it’s just a pair of arms, some grippers, and a couple of cameras. But despite its seeming simplicity, it is a workhorse for researchers at Google DeepMind, who use it to test their most advanced AI for robotics system, Gemini Robotics, in their various labs.

When controlled by Gemini Robotics, ALOHA 2 becomes more of a generalist robot, in the sense that it can perform any number of tasks based on examples it’s been trained on. Ask it to pack a lunchbox and, as evidenced by a video of this exercise, it can use two pincer grippers to delicately place a piece of white bread into a Ziploc bag, close it, place a bunch of grapes in a Tupperware container, secure the lid, and then carefully move the items into a lunchbox before zipping it up. 

It’s not a great lunch. But the fact that the robot can put it together represents an objective step forward from what was possible even, say, three years ago. 

This is in large part due to AI and its impact on what are known as robot policies, which controls how a general-purpose robot will need to assess and understand its surroundings, plan how to move within them, and then perform its task correctly.

The ALOHA 2 robot isn’t much more than two mechanical arms on a bench top, but it serves as a testbed for cutting-edge AI robotics models. These animations are based on human teleoperation of the robot arms, data that is used to train Google DeepMind’s models. (Video: Google DeepMind / Stanford University / Hoku Labs)

Historically, these policies were based on rules developed by engineers who hard-coded them into the robot’s software—thousands of lines of code that would determine each millimeter of a robot’s movements in hundreds of tasks. What’s been happening for the last few years—and what is largely responsible for the optimism about generalist robots—is that robot policies are being handed over to advanced AI systems instead of being coded into the robot’s software. This first happened with VLMs, or vision-language models. These are similar to large language models, but they’re trained on images as well as words. Show a VLM a picture of a coffee spill and ask it to find a tool to clean up the mess, and it can identify a nearby cloth. This sort of immediate contextual understanding didn’t exist a couple of years ago when robot policies were hard-coded. 

Next came vision-language-action models, which enable robots to assess their environment and take action within it. The models do this by adding yet another component: motion commands. VLAs are trained on a series of images or videos related to performing a given task along with associated data about how a robot arm moves to perform it. That movement data is typically collected through teleoperation, in which a human uses remote controls to lead a robot through an action. This sort of training allows the AI to learn how to command the robot to move and operate during a given task. Place a VLA-powered robot in front of a desk and tell it to “close a laptop” or “wrap up the headphone wire,” and it will survey the scene, identify the relevant object, plan a way to execute the request, and then swing its arms into action—at least if it has seen this task accomplished before. 

The Gemini Robotics model is a VLA, trained on many hours of human demonstrations depicting a vast array of different actions. As a result, it can perform relatively complex tasks like picking up snow peas with kitchen tongs, doing origami, or putting together a simple lunch. It’s impressive, but there’s a glaring limitation: For now, if a robot controlled by a VLA is asked to perform a task that falls outside its training set, it’s highly likely to fail. 

“Thinking about the space of all tasks, a real generalist policy would be able to do everything along that spectrum,” says Edward Johns, a robotics professor at Imperial College London. Today, though, a Gemini Robotics model can do only “a few things here and a few things there.”

The search for true generality

So how do we get robots to be able to do more things? The usual answer is probably not surprising: Train them on more data.  

More data, the thinking goes, equals more examples, and more examples equals more generality. Google DeepMind, for instance, wants to pull together “as much data as possible,” says Pannag Sanketi, a former tech lead in robotics at the company who’s currently working on his own AI robotics project. But where to get it? Large language models had the benefit of oceans of existing text for training. There is no corresponding pool of high-quality physical demonstrations on which to train robots. Researchers have a few ways to make up for this, but all have flaws. One is to employ large numbers of people to create and collect teleoperation data (costly and time-consuming). Another is to train VLAs on videos of people performing activities (the resulting data quality is poor). Yet another is to deploy robots in the real world and use data collected from those experiences to further refine AI models (robots aren’t safe or reliable outside labs). Sanketi thinks a “multi-prong” approach that uses data collected from all these sources is the most likely path forward.

LeCun is dismissive of the whole approach. “The [AI] approaches that have been successful for language do not work for high-dimensional, continuous, noisy data”—the kind of data that is commonplace in robotics, he said in Davos. “You have to use something else.”

The leading contender for “something else” is the so-called world model—a form of AI trained less on text than on a combination of video, three-dimensional scans, and sensor data and built to predict the outcomes of actions in the real world. The aim is to build models that possess an internal representation of reality precise enough to capture how the physical world actually operates—how objects move, collide, fall, and deform. If roboticists could train machines in simulations faithful enough to real-world physics, development would become faster, cheaper, and safer, reducing the need for real-world testing. Even more transformative, robots equipped with world models could reason about their surroundings rather than merely reacting to them, helping them anticipate the consequences of an action before taking it.

A breakthrough?

In the heart of San Francisco’s Mission District, the startup Physical Intelligence—or PI (as in π), as it likes to be known—is focused on developing a universal brain that could, theoretically, turn any robot into a generalist. Using an everything-including-the-kitchen sink approach to training AI models for robots, the company recently observed a hint of what a robotic brain equipped with a world model could be capable of. 

In 2024, PI published details of its first generalist robotics system, called π0, a VLA it claimed was the “most capable and dexterous generalist robot policy to date.” The model was initially trained on a 10,000-hour proprietary collection of human demonstrations gathered through teleoperation as well as several open-source robot datasets. A version released in spring 2025, π0.5, was trained on a wider variety of datasets, including labeled images from the web, lending it more versatility. A fall 2025 update, π0.6, added reinforcement learning to the model.  

Each update yielded important improvements to the model’s performance, increasing its menu of abilities from slowly folding laundry to putting things away in new environments to completing tasks like folding boxes with a higher success rate. Then, in April 2026, π0.7 seemed to catapult PI into new territory. This version makes use of a less powerful world model that generates images of steps necessary to perform a task. As the robot undertakes the job, this “lightweight” model feeds it snapshots of what to do next. 

The company claims that the model exhibits the first signs of compositional generalization, a term for AI systems’ ability to perform skills they’ve never been exposed to by recombining ones learned in their training data. One test involved asking a model to “load a sweet potato into the air fryer”—a task it had never previously encountered. In a demonstration video, the machine futzes around a little, makes a few false starts, and eventually manages a reasonable effort, though it doesn’t finish the task completely.

Sergey Levine, a professor at the University of California, Berkeley, and a cofounder of PI, is excited by the potential: “It’s actually the first time that we’ve convincingly seen that kind of compositional generalization, where we can basically ask the model to do tasks that we did not specifically collect data for and train it to do, and it’ll actually make a passable attempt.” 

The success led the team to wonder how the model was able to achieve such a feat. After some digging, they found snippets of relevant labeled teleoperation data lurking in the training material, including two examples of a human controller using the robot to push an air fryer basket into the fryer. Those shreds of data might have been enough to enable π0.7 to almost air-fry a sweet potato.

For now, it remains unclear just how impressive π0.7’s abilities to generalize are. Still, given how fleeting the model’s exposure to air fryers had been, it offers a glimpse into how far cutting-edge research can currently take robots. 

“70% success is like it doesn’t work”

You might be sensing a disconnect between the halting baby steps robots are making in labs—“Look! It put a sweet potato into an air fryer!”—and the dazzling, lifelike nimbleness on view during many demonstrations and videos, where robots are seen doing everything from dancing on a stage to courteously serving drinks. Such demos often don’t clearly reveal a key fact: In many instances, humans are controlling the robot or have carefully scripted its actions. (The robot that appeared onstage with Nvidia CEO Jensen Huang in March 2025, for example, seemingly responding to his instructions and following him around, was remote-controlled by what its makers called “a puppeteer behind the scenes.”) 

For now, fully autonomous motion planning so that a robot knows where it should go—especially in new, chaotic environments like a construction site or a unfamiliar home—remains a largely unsolved challenge. A bigger challenge still—albeit one that is often related—lies in getting robots to tackle larger, more ambiguous jobs that include multiple tasks and require decisions about how and in what order they’re done. This would be the difference between a robot that can put a plate into a microwave and one that can successfully respond to the prompt “Make dinner” by exploring the refrigerator, chopping ingredients, and firing up the stove. Google DeepMind’s best attempts at something like this—which involved asking its robot to survey a kitchen and pack all the ingredients for a mushroom risotto into a basket—have so far resulted in failure.

Adding to the challenge, a practical robot must essentially get it right every time. With VLAs, “people are very excited when their result goes from 50% success to 70% success,” says Marc Raibert, founder of Boston Dynamics. “But 70% success is like it doesn’t work, right?”

The few humanoids that are being tested in real-life settings are undertaking extremely limited tasks in tightly controlled environments. They’re far from generalists. Agility has hundreds of robots deployed across trials in facilities owned by GXO Logistics, Amazon, and Schaeffler, according to the company. But for now, Hurst says, the robots are targeting simple tasks such as moving bins and totes around. Even then, he adds, it took years to develop robots safe enough for logistics firms to even contemplate using them. For his part, Elon Musk claimed in May 2025 that “thousands” of his Optimus robots would be working at Tesla factories by the end of the year, but in January of this year he said that the company had only “some of the Tesla Optimus robots doing simple tasks in the factory.”

While humanoids are starting to venture onto the factory floor, making the jump to households will be even more difficult. Right now, should you so desire, you can preorder the 1X Neo home robot, expected to be ready for delivery sometime later this year. Yours for $20,000, it promises to take on “the boring and mundane tasks around the house”—putting away dishes, answering the door, tidying the living room—“so you can focus on what matters to you.” The idea is for this five-foot-six-inch robot to one day perform all those tasks autonomously, but for now a remote human operator is needed for it to do most things. (Yes, a person would need permission to peer into your home through the robot’s cameras.) Asked how long it will be until fully autonomous robots are ready for domestic work, Hurst said, “If I had to pick a number, I’d say it’s 10 years before robots are … actually doing useful things in people’s homes.”

When that happens, the robots might well be Chinese, as China is well ahead of the West in terms of production. Nearly 90% of the roughly 15,000 humanoid robots shipped in 2025 were made by Chinese companies, according to the market intelligence company Omdia and the Chinese robotics firm Unitree. One model produced by Unitree, which shipped more humanoid robots than any other company last year, costs less than $6,000. Such an affordable price could go a long way toward making robots more attractive to consumers, though the company expects its machines to be used in industrial applications first. (If you’re wondering who is buying all these Chinese robots, by the way, the AP recently reported that orders come predominantly from corporate and academic labs and state-owned enterprises.)

We’ve been here before 

The dream of building a humanoid robot runs deep: As far back as 1495, Leonardo da Vinci sketched out designs for a mechanical knight, controlled by cables and pulleys. Through the 20th century, machines of sci-fi fever dreams have come and gone. 

Westinghouse’s seven-foot-tall box on legs, Elektro, hit the New York World’s Fair in 1939, smoking a cigarette. WABOT-1, built by Waseda University in Japan in 1973, was the first full-scale, programmable humanoid robot. Honda’s ASIMO, unveiled in 2000, was probably the first such machine to prove at all competent—it could, at least to some degree, climb steps, recognize faces, and autonomously move through spaces. But the robot was discontinued in 2018, unable to advance far enough beyond what it could do in demonstrations to be useful. 

All, at the time, were impressive—even jaw-dropping—feats of engineering. But none were ready to navigate the real world. Today’s robots, even with the transformative power of advanced AI, still face the same existential challenge.

Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure.

That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain.

One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them.

But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.

Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon.

The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable.

“Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. “In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.”

This episode of Business Lab is produced in partnership with AVEVA.

Full Transcript

Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Industrial AI may not be new, but it is developing at a breakneck pace. From modern predictive analytics that can preempt equipment failures, to autonomous robots tasked with inspecting high risk machinery, emerging applications have the potential to transform operational efficiencies. But often used in high consequence environments with minimal margin for error, industrial AI deployed without clarity, security, or accountability could also have catastrophic consequences for critical infrastructure over time. As industries rush to embrace the latest technologies, the goal should always be responsible AI designed to support, rather than replace that all important human judgment.

Two words for you: sustainable automation.

My guest today is Arti Garg, chief technologist at AVEVA.

This podcast is produced in partnership with AVEVA.

Welcome, Arti.

Arti Garg: Thank you, Megan. It’s great to be here.

Megan: Thank you so much for joining us. And just to start, if we could set some context for our discussion, can you share a bit about the current state of industrial AI and what AVEVA is working on at the moment as well?

Arti: Yeah, more than happy to. And I want to go back to what you already stated, which is that in some ways, industrial AI is not new. It’s something that, even here at AVEVA, we’ve been working on for more than 20 years, really thinking about how AI can enhance applications in the industrial sector.

But what has changed quite substantially in maybe the last few years is the type of AI. We’ve really moved to these general purpose foundation models, which enable a lot more people to leverage advanced AI technologies. In addition to that, in just the last couple of years, there’s been an explosion in what’s known as physical AI and the types of AI that can really accelerate the capabilities of robotics and autonomous systems. And also agentic AI, which also allows for automation at more of the software level.

And what we’re seeing is that while in the past there has been some hesitancy to adopt models that don’t totally have predictable outcomes, there’s been a rapid acceleration in adoption of AI even in the industrial sector. One study suggests maybe that’s almost a 78% increase over the past just two years within the industrial sector. So yes, that’s an inflection point, but it’s almost like a step function if you think about it with respect to AI adoption.

And what we’re really thinking about now at AVEVA is, how do we think about this? How do we think about incorporating these new and very powerful AI technologies in environments where customers have mission-critical operations, human safety is a significant factor? Those are some of the primary concerns that our customers have when they think about adopting any new technology. We’re doing that in narrow where these newer types of AI aren’t necessarily predictable, in terms of how they behave. That’s one of the areas we’re really thinking about is, how do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?

Megan: There’s so many things to consider at once, isn’t it? And you outlined there some of the huge developments we’ve seen in this space in recent years. What makes this such a pivotal moment for industrial AI specifically? What has changed to make some of those perhaps more out there hypotheticals much more plausible?

Arti: Well, I think one of the big things, one of the big challenges within industrial AI, and this is something that as AVEVA, it’s in our blood, it’s in our core DNA, is that to really leverage any kind of digital technology in the industrial space, one of the first important things you often need to do is gather and correlate data from disparate systems, so that maybe I have telemetry coming off of a pump or a mixer, and I want to be able to correlate that with service logs so I know maybe the last time that someone had done some maintenance on that pump or mixer. And maybe I also want to be able to correlate that with the original design documentation and some of the original documentation on how to maintain that, so that if something is happening, I can do a better job diagnosing what’s going wrong and how to address it. This has always been a core challenge in the industrial space.

I think that what’s happening now is that with newer technologies, even things like graph databases and being able to leverage AI to really match disparate data sets much faster, we’re able to get to the point where if something goes wrong, instead of an operator maybe needing to spend a little bit of time doing this research to really understand how do these different components fit together of information, they may have just an iPad with an AI that can very quickly go and fetch that information, correlate it and help in real time, almost, diagnose what is happening.

Even a little bit more out there, but not as out there as you might think is, I still describe this with a human operator with potentially a mobile tablet, but imagine now I can also have a robot moving through that environment also gathering data and either with onboard computation or a quick connection to an operator who doesn’t necessarily now have to go out into a dangerous, potentially somewhat hazardous space, be able to gather data and enable that very quick diagnostic as well. And I think that’s really what’s exciting right now and what all of these new technologies that I’ve talked about are helping to enable.

And I think then from our perspective, trying to really leverage all of those means that we have to think a lot now about our governance approach to this. How do we leverage AI in a way that keeps it contained, if you will?

Megan: Yeah, absolutely. And on that governance point, how does AVEVA define responsible AI and what do you see as the primary benefits?

Arti: Yeah, so I think for us, we think about a triple mandate around responsible AI, and that means that it’s secure, it’s efficient, and that includes environmentally efficient, and then it really preserves human safety and human oversight above all. And for us, that’s what some of our core pillars of responsible AI include. And for us, that means we think about internally as we build tools, a multi-layered governance approach where we’ve got both for how we use AI as a company and then how we deploy AI into our products. We’ve got a similar framework that we leverage across both, a kind of joint governance model, both around how we’re leveraging AI and also how we’re infusing AI into our products to allow our customers to leverage AI in their operations.

But I would say one of the core foundational principles underneath all of these is that human beings still remain centered in how we think about how AI is leveraged. And so human judgment, human responsibility, human ethics are still core to how we think about where AI can add benefits, and we think about it more as augmenting rather than replacing human beings in critical decision loops, if you will.

Megan: Right, which is such an important distinction, isn’t it? And in terms of the next phase of development, there’s growing sentiment that agentic AI will soon allow these industrial systems to operate much more autonomously. I wonder, what makes the risks in industrial settings different from AI risk in a purely digital context? And I suppose on the flip side, what are the benefits and opportunities of that same software defined automation?

Arti: I think we can start with the risks and then go to the benefits. I think ultimately, I would say the biggest risk in the industrial setting is that we are interacting with real physical systems, often physical systems that are very capable of delivering important outcomes that keep our world running, whether that’s delivering power, whether that’s mining for natural resources. But usually. Those physical systems are also operating in hazardous environments, the equipment itself is capable of also having human safety implications. Anytime you’re interfacing software with a physical system that can have real world consequences, you have to be extra careful. And we’ve always had that, again, we’ve had that in our DNA at AVEVA from the beginning, being mindful of that end user and that end application, which is not just something on a computer screen.

Even as we’ve deployed AI over the past few decades into our systems, we’ve typically leaned toward really trying to pick the best fit model, really understand that model’s behavior if we’re going to use, for example, we have a proprietary anomaly detection model that works in a lot of operational environments. Now, as we’re thinking about some of these newer AI capabilities, which by design are hard to understand how they behave, they’re not fundamentally explainable in the way we’ve thought about even AI or statistical models in the past, and actually their behavior can change over time as they learn and get tuned to new capabilities. That’s really the potential risk of automating a physical system based on capabilities that can evolve over time is one of the things that we have to be really mindful of and really thoughtful around, where are we willing to put some of that increased automation into practice?

But at the same time, I think there’s a real opportunity there, because I glossed over this idea that, okay, well, models evolve, but so do human beings, and sometimes that’s a good thing. We talk about humans having expertise and they gain understanding over their careers of how a system works. If we can actually leverage the ability of some of these newer AI capabilities, these sort of reasoning models often have underlying agentic capabilities, to learn and gather experience faster, and potentially even more importantly or more valuably, take experience that’s learned at one site and apply it to another site, then that’s a real opportunity to take what we already know works for human beings, which is that sometimes you just have to learn by doing and be able to apply that and scale that through AI. That’s where I see potentially a real opportunity in this space.

One of the things that we’re really aware of in the industrial sector is just the way that our workforce is changing. I think that almost half, not quite half, of the industrial workforce is set to retire in the next five years, and that’s a lot of expertise and experience that means that we’re going to lose in the sector. If there’s ways to make sure that we can capture that in ways that are actionable, in ways that can also help a newer generation of workers that are used to experience things in a different way, apply that expertise, apply that knowledge, then I think that’s a huge opportunity within the industrial space.

Megan: Yeah, absolutely. Clearly, some huge opportunities there particularly against the backdrop of other market and workforce changes as you’ve outlined there. I suppose building on that, what is the potential for these autonomous industrial AI systems when built and deployed responsibly to facilitate even faster, more sustainable industrial processes?

Arti: I think there’s a couple different ways to think about this. One is, what are the demands of potentially more environmentally sustainable processes? I’ll use one example of something that my team has been working on in partnership with Idaho National Laboratory here in the U.S. as part of their testing for AI grid resilience project.

One of the challenges in the electric grid as we’re moving toward a future where we have a lot more intermittent renewable power generation sources, often distributed rooftop solar for example, is it’s becoming much more challenging to manage the grid both from really understanding where electric capacity is coming in, electric load is pulling off of the grid. In addition to that, being able to maintain just power quality because instead of having one spinning asset delivering the frequency of electricity that’s going across the grid, you’ve got a lot of smaller systems. AI is actually quite critical for being able to manage that grid of the future. And this is one of the things that we’ve been working in partnership is, how can we do that? How can we help grid operators better understand what’s happening across their system, identify where things might be behaving anomalously so they can detect that early and then remediate for it?

At the same time though, I always talk about that’s an example of where AI can potentially help us accelerate the transition to a lower carbon energy future. At the same time, I think there’s a lot of conversation around the sustainability of AI itself. What are the power requirements? What are the resource requirements needed to run AI? And before I get into how do we think about that at AVEVA, because it is definitely something we think about and I think a lot of actors in the space are thinking about, I want to point out, is one of the challenges is that there’s really no agreed upon method to measure the environmental impact of AI. You see a lot of these stories, like one query on some kind of chat interface is X amount of gallons of water or this amount of electricity use. But the truth is that community-wide, there’s no agreed upon standard.

One of the things that I’m involved with is a standards working group that was launched by the IEEE two years ago, a little over two years ago. I’m the chair of that working group, it’s called the P7100 Standards Working Group on measuring the environmental impact of AI. And one of the things we’re trying to do is really identify all the different areas over which we want to think about environmental sustainability associated with AI, and then having one standard methodology that works and that can be adopted both from a reporting perspective and potentially also from an oversight or regulatory perspective. That becomes really important for any sort of real understanding of how AI impacts the environment is just we have to know, what does it use? We want to look holistically at that. Our standards cover electricity and energy consumption, it covers resource usage, it covers water consumption, and it also covers carbon.

But all of that said, I think it’s important for us to understand the footprint of AI, but I think it’s also important for us to simultaneously recognize that a bigger model uses more compute and more compute probably leverages more resources. The more that we can be intentional and pick the right size model for the right application, the more we can already start down that path of being more environmentally efficient in the AI that we run. One of the potentially nice side effects of that is the more purpose-built a model, the less likely it is to misbehave in unpredictable ways, if you choose correctly your architecture. There’s some sort of ancillary benefits that go beyond environmental sustainability.

Megan: Fascinating. It’s really, really interesting to hear some of the work going on behind the scenes there, because I think we’ll all have heard some of the statistics around the environmental impact, as you say. So, it’s great to understand some of the work going on there to really clarify that.

And we’ve talked a little bit about AI augmenting humans earlier. As systems gain greater autonomy, how should organizations think about that balance between closed loop automation, human in the loop accountability? What guardrails need to be in place? And how are governments and cross-border actors approaching those concerns as well?

Arti: There’s a lot packed into that question, so I’ll try to answer it somewhat one by one. First, starting with that sort of balance between closed loop and human in the loop, automation and accountability. One of the things that we talk about in the industrial sector is from human operator to human supervisor of systems, of industrial systems. And if I’m honest about that, we’re still trying to figure that out. When a human’s in a loop in an automated system, you’re really thinking about a system may process all the data and then make a recommendation.

We’ve got a solution that we’ve been working on more jointly with a few different customers where we’re able to take in a lot of their operational data, also some simulations of how their systems work, and be able to provide, say, recommendations around, you should now operate at this set point instead of that set point based on some of the other conditions that are changing in your plant. There’s a lot of interest in moving from having that be a recommended set point to have an automation that can automatically adjust the set point. And we’ve actually had some successful real-world pilots around that as well.

But when you get into that, obviously it makes people very nervous if you’re changing set points on industrial equipment. That’s where you maybe continue to have some guardrails around like, you can’t go outside of a certain band, for example, of operations, or we only allow the automation in certain areas. That’s where the human supervisor, the same way a human supervisor might give employees a lot of bandwidth or a lot of flexibility to make decisions around certain things, around other things, they’re hard and fast, like this is the deadline or this is the sort of production target. It’s like that. So thinking about, how do you put the appropriate guardrails?

The challenge is, AIs are not human beings, and so the guardrails look different, and I think that’s one of the things that there’s still to think about. That question of, what guardrails should be put in place? I think that it’s going to be probably dependent a little bit on the application, but over time I think we’re all going to learn, and so being really upfront and thoughtful about how we do this I think is important.

But the opportunity though, potentially, is really great. I’ve already mentioned that AI can be very useful in synthesizing a lot of information and bringing to the forefront, this is what matters. That’s something that we do want to create some space to experiment around, but what’s then important is making sure that a human being understands the risk.

I’ll give a little bit of a personal story because it might be illustrative. This weekend, I bought a little toy robot and decided I wanted to program it to do some stuff, and I found AI very, very helpful to get me started to read all the documentation. Putting the robot together was straightforward, they had nice instructions, but there’s a pretty heavy software developer kit that’s already there, but going through and reading all that documentation can take a while. AI was super helpful to help me surface, this is the function that does this, to help me get started. But occasionally, it made really bad recommendations on the best way to troubleshoot something.

That’s where I think having the human being in the loop, being able to say, “I know that it’s not going to be the best software architect or the best troubleshooter,” is really helpful because I had the opportunity to leverage AI for what it’s good for, synthesizing and surfacing a lot of information, but being able to say, “I don’t think that’s the most efficient first step. Let’s try something else.” That’s where it’s a really different way of working, but it’s something that I think as humans get more comfortable with the power and limitations of AI, you can start to teach people how to work with it, and it’s very different from how you would work with other digital tools.

One of the things that is important when it comes to AI is recognizing just the breadth of things that it touches and the breadth of impacts that it’s going to have, whether it’s on the environment, as we’ve discussed, whether it’s on productivity as sort of implicit in this entire discussion, whether it’s on labor force, whether it’s on just how economies work. From my eye, and I’m by no means a policy expert in this area, but from my eye, what I’m seeing is that different governments are prioritizing different aspects of that from how they’re thinking about regulatory and other kind of governance approaches.

Megan: We’re absolutely seeing some really vastly different approaches to this, aren’t we, around the world? I wondered to illustrate some of this, if you could share perhaps some case studies you’ve seen, maybe talking us through what lessons they could hold for other organizations perhaps a little earlier in their own AI journeys.

Arti: I think a lot of the key areas where we’re seeing very clear benefits of adopting AI in the industrial sector, the core to all of them is actually getting the data right and getting the right foundation of data so that you can build AI on top of it.

One of our customers, SCG Chemicals, which is a petrochemical company in Thailand, they had this vision of producing a reliability platform for their operations that was as AI-driven as possible. But a core part of that was actually getting the data right, being able to have all of their information in the right place, leveraging some of AVEVA’s tools to do that. Bringing together operational data and also engineering data. And then putting on top of that AI capabilities, in this case, one of our capabilities called AVEVA Predictive Analytics that deploys some proprietary models to do things like detect anomalies, so that they could really much earlier identify potential operational risks. And instead of having unplanned downtime, translate that to planned downtime. When you translate unplanned downtime to planned downtime, you can get a huge improvement in plant reliability.

At this point they’re targeting something like 99% plant reliability and a very, very high return on investment from the platform that they put in place. Again, in early pilot days, it was almost a 9x ROI, and so that’s really quite impressive. But what I want to emphasize is that it’s kind of a multi-layered problem to get it right.

Megan: Those are some really striking results, though. I mean, for industrial leaders who perhaps are still hesitant to explore AI, I wonder, what do you think is the cost of taking a more wait and see approach?

Arti: I think, again, this is an area where the industrial sector has a little bit of a different calculus to apply to this type of problem. In general, I think you would hear most business experts say that you can’t afford to take a wait and see approach to AI because it is so transformative across every sector of society and economy, and certainly in the industrial space, we’re not immune to that. But I do think that some of the challenges that I’ve outlined today also puts leaders in the industrial space into a mindset of sometimes potentially being a fast follower rather than the first adopter of newer technologies, just because the risk is so high.

But that being said, I think one of the challenges is that the risks that we’ve covered today around infusing systems that don’t always behave predictably with physical systems is that I don’t know that there’s a lot of other sectors that are going to be solving those problems. That’s where what I’m seeing is actually a lot of interest and excitement in trying new things and willingness to do that because there’s a recognition that applied correctly and applied with the right guardrails and safeguards in place, these technologies really have truly transformative potential from a resource usage standpoint, from a human safety standpoint, from a productivity standpoint.

I think the calculus has shifted a little bit in the sector to we want to actually try these things out. But then it becomes more, how do we try these things out in environments that we can make a little bit more sandboxed or safe to test out what some of the unique challenges we’re going to face in the industrial sector are?

Megan: Yeah, absolutely. It’s just hard to ignore the potential nowadays, isn’t it? And just to close with a slightly future forward look, I suppose, what are you most enthusiastic about in terms of the long-term potential of responsible autonomous industrial AI systems?

Arti: Yeah. Well, I think that there’s a couple of different trajectories. One of the things that I think within the next 18 months for sure, we’re going to see an increase of people with deep domain expertise who maybe didn’t grow up with a software background, be able to adopt AI assisted coding techniques to really be able to build the things that they weren’t able to build before.

That’s really exciting in my career, which way back when I was actually an industrial data scientist, I would say I always felt the most energized and that I learned the most talking to the people that were on the front lines of operations, having to monitor a lot of different equipment and understanding how it all worked together. They really have a lot of expertise, and being able to put in their hands the ability to very quickly develop new applications is I think going to really lead to a lot of new ideas that many of us in the industrial software space maybe wouldn’t even have thought about and really understood how to put. I think that’s really exciting.

I think then looking beyond that, I was a little bit of a, not to say a robotic skeptic, because clearly robotics and autonomous systems are going to be important, especially given the types of environments, whether they’re hazardous or remote, that industrial equipment operates in. But I just actually think things are moving a lot faster than I had anticipated. I’m really interested to see how these physical embodiments of AI systems start to really transform, again, how we think about operations.

One of the things about any new technology in any environment is that to really gain value from it, you have to change the way you do things. I would say for close to 10 years now, I’ve been giving talks on AI adoption and I always say the biggest barrier to AI adoption is not anything to do with the technology. It’s not even to do with the data, although data are often the biggest sticking point, it’s really to do with, are you going to change your business processes to be able to work with the way this technology is good or not good at things?

Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites. And I think that will make these types of operations more efficient, much safer for the human beings involved and more productive. So, I’m quite excited about that.

I don’t know entirely what direction it will go, but one of the things that I think about a lot is that if you go back to I, Robot and Isaac Asimov’s book, the premise of those was that robotics would be the first broadly adopted AI, not computer-based systems. We went in the other direction and I think it’s really interesting to now see all of this converging.

Megan: Yeah, absolutely. See robotics catches up a bit. So many exciting things on the horizon, that’s for sure. Thank you so much, for your time.

That was Arti Garg, chief technologist at AVEVA, whom I spoke with from Brighton in England.

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts. And if you enjoyed us, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thank you so much for listening. Goodbye.

Learn more at aveva.com.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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

Weight-loss drugs show signs of slowing biological aging, say drugmakers

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

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

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

Read the full story on what the new findings reveal about GLP-1 drugs and aging.

—Antonio Regalado

10 Climate Tech Companies to Watch: Energy Dome and its carbon dioxide batteries

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

Solar and wind are among the cheapest and quickest ways to add electricity to the grid. But they’re subject to variations in weather patterns, so supplies can be intermittent. Energy Dome has an unusual solution: massive batteries that use compressed carbon dioxide to store energy for when the grid needs it.

Compressing gas to store energy isn’t new. Utilities have used compressed air in underground caverns to hang on to reserves for decades. But Energy Dome’s approach doesn’t require any specific geology to work, so it could be more easily scaled to help grids around the world.

Here’s how the company is using carbon dioxide to store renewable energy.

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Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from mobile flood barriers and electric buses to next-generation nuclear reactors.

MIT Technology Review Narrated: Don’t be fooled—LLMs don’t reason

—Thore Graepel, a core member of DeepMind’s AlphaGo team

Ten years ago, I watched a program I helped build stun the world by beating Go champion Lee Sedol. AlphaGo won after making a move so strange that some commentators thought it was a programming glitch. It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. 

This is why I recently left my position at Google DeepMind. I believe we need a fresh approach to machine reasoning, one that draws on AlphaGo’s architecture.

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

The must-reads

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

1 OpenAI has released 377 new math findings, further rattling the field
They span number theory, algebra, geometry, and physics. (NYT $)
+ They came from testing an unreleased OpenAI model. (Verge)
+ And cost far less than its previous math breakthroughs. (SciAm)
+ OpenAI also published a lengthy defense of its approach. (Gizmodo)
+ AI has put mathematics at a crossroads. (MIT Technology Review)

2 Mistral says its new open-weight model is the best outside China
Mistral Large 4 is a 1-trillion-parameter model. (Reuters $)
+ Also known as le Chonk, its weights will arrive on October 27. (CNBC)
+ French president Macron described it as “a third way in AI.” (TechCrunch)

3 Finland has ordered Google to halt a record data center project
The order follows concerns over environmental impacts. (Guardian)
+ The €13 billion project is Google’s largest-ever European investment. (BBC)
+ But no one wants a data center in their backyard. (MIT Technology Review)

4 The EU plans to tax Big Tech through a new corporate levy
The levy would apply to companies earning more than €100 million. (FT $)
+ Tesla is pushing the EU toward “Full Self-Driving” approval. (Reuters $)

5 Anduril’s $2.9 billion Navy submarine deal has sparked ethics concerns
Co-founder Palmer Luckey recently joined a new Pentagon project. (CNBC)
+ Anduril is also making smart glasses for warfare. (MIT Technology Review)

6 Apple is partnering with LG on a smart lock, thermostat, and doorbell
Apple’s new smart-home hub is expected to launch next week. (Bloomberg $)
+ The companies are also co-developing security cameras. (TechCrunch)

7 The creator of a space-particle observatory has won the physics Nobel
Francis Halzen used Antarctic ice to detect elusive neutrinos. (New Scientist $)

8 Drones are being used to make rain on demand
They seed clouds with silver iodide to encourage precipitation. (BBC)
+ A startup claims it can stop lightning. (MIT Technology Review)

9 A new hydrogel could enable shape-shifting smart devices
The material can evolve alongside living tissue. (SCMP)

10 You probably aren’t going to get the plague
Experts say the risk to the public remains very low. (Wired $)

Quote of the day

“I have ‘NI’—natural intelligence. I’m good with that.”

—Mike Tyran, a 66-year-old nurse and tech upgrade holdout, tells The Atlantic why he won’t swap his old gadgets for the latest AI-powered devices.

One more thing

Future AI chips could be built on glass

Human-made glass is thousands of years old. But it’s now poised to find its way into the AI chips used in the world’s newest and largest data centers. 

This year, a South Korean company called Absolics will start producing special glass panels that make next-generation computing hardware more powerful and efficient. Other companies, including Intel, are also pushing forward in this area. 

If all goes well, the technology could reduce the energy demands of chips in AI data centers—and even consumer laptops and mobile devices. Read the full story.

—Jeremy Hsu

We can still have nice things

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

+ Swiss boffins have turned train tracks into solar power plants.
+ Here’s an intriguing explanation of why you can see a clear image through a window.
+ A teen with a rare genetic disorder has walked for the first time after receiving an experimental medicine.
+ Stunning shots of guillemots, penguins, and a protective tawny frogmouth contended for prizes at the Bird Photographer of the Year competition.

Popular weight-loss drugs may do more than help people shed pounds. They might also melt away the years.

Drug giants Eli Lilly and Novo Nordisk say patients taking their drugs age less quickly, according to readouts from molecular “aging clocks.”

Such clocks assess a person’s biological age by looking at changes to DNA that accumulate with time, or, in newer versions, by tracking levels of key proteins.

Both companies found that overweight or diabetic patients taking the drugs, called GLP-1s, had reduced biological age compared to those taking a placebo, although the difference varied widely, depending on which type of clock was used, and what organ was tested. 

Overall, the difference was around “two to three years,” according to Nikolaj Roed, a global project leader at Novo, who says the company has been seeing “improved biological age in our patients across trials and across different tissues.” 

The findings add to wide speculation among scientists that GLP-1 drugs, as they are known, are acting on basic causes of aging and might be a true longevity treatment.

“Two years is a pretty strong effect, in my book,” says Steve Horvath, a professor at the University of California, Los Angeles, who’s credited with inventing aging clocks. Horvath says the emerging data could provide “evidence that these GLP-1 drugs are actually what is known as geroprotectors, medications that slow or possibly even reverse biologic aging.”

The companies shared their findings over the weekend during Aging Research & Drug Discovery, a conference devoted to seeking scientific remedies for old age. While that quest has not yet produced any clear-cut success, some scientists now think Novo’s drug semaglutide (sold under the names Ozempic and Wegovy) is coming close.

“In an unhealthy population, I do think it’s an anti-aging drug,” says Vadim Gladyshev, a Harvard biologist who assisted Novo with its molecular measurements. “But in a healthy population, no one knows.”

The drugs cause weight loss by stimulating a receptor, GLP-1, that tells your brain you’re not hungry. Yet real-world studies have shown much wider benefit. The drugs improve kidney function, reduce blood pressure, and even sharply cut the overall chance of death.

“If the question is,‘Can semaglutide reach several diseases relevant to health span and aging?’ we know we can say the answer is yes,” said Alejandro Aguayo-Orozco, a senior scientific director at Novo, the Danish drug giant, during the meeting.

The next question to answer, he said, is whether such effects are accompanied by changes to molecular measures of biological aging: “When you intervene with semaglutide, does it actually move the clocks in any direction? And the answer is yes.” 

The company found that, over time, the drugs cause a wide slowdown in aging clocks—as much as 4 years in the case of a clock that looks at heart proteins. “I think it’s pretty clear that the organ age is shifting,” said Aguayo-Orozco.

The studies are also a huge boost for the science of aging clocks. Although these measures reflect a person’s age, it’s been uncertain if they are useful as true biomarkers. Now clock makers have evidence  that their readouts show a drop in age when people take drugs with broad, well-demonstrated benefits.  

“The clock people have been pushing for a decade to get this kind of study done,” says Yuge Ji, a biologist who previously worked in the field and now runs a startup, Reflector Bio. “It’s huge for them.”

As part of its study, Novo took blood draws from 10,052 people, half on the drug and half on a placebo. Their blood, collected at the start of the study as well as months later, was then measured with “proteomic clocks,” which use levels of key proteins to predict a person’s age and risk of dying. 

Scientists at Eli Lilly performed similar research on their GLP-1 drug, tirzepatide, using so-called “epigenetic” clocks that assess age by counting accumulated changes to DNA. Lilly’s study was smaller, but also found that for the most part, molecular time moved more slowly for people on the drug.  

“All the clocks are telling a consistent story that we’re seeing a reduction in age,” Kevin Duffin, vice president for aging research at Lilly, said during the conference. “It’s not like we’re going to reverse age by 30 years or something, but it’s a significant reduction.”

The molecules have become the best selling drugs in the world,, with Lilly’s tirzepatide, sold as Mounjaro for diabetes and Zepbound for obesity, topping the list with more than $36 billion in revenue to the company last year. Novo’s semaglutide, also sold under more than one name, was a close second.

Alex Zhavoronkov, the founder of Insilico Medicine, and the organizer of the Boston meeting, says that extending lives by even one year across the world’s population would be equal to tens of millions of lifetimes.  

Last month Zhavoronkov showed that one of his company’s drugs, for a lung disease, also reversed the signals from aging clocks. That drug is experimental, but Zhavoronkov has sought to promote the idea that humanity is entering a new era of longevity medicines. He also disclosed during the event that he has been “microdosing” the available weight-loss drugs, even though he is not overweight.

“I am on tirzepatide and I am an equal-opportunity injector of semaglutide as well,” Zhavoronkov said.

It was part of an effort to directly raise the question of whether the first mass-market anti-aging remedy is already here.

“Do you think a reasonable person should start taking semaglutide?” he asked Aguayo-Orozco, the Novo scientist, in front of a crowded audience.

“I am not a physician. I cannot answer that question,” Aguayo-Orozco replied.

Several people cautioned that GLP-1 drugs do have side effects, including muscle loss. That is one reason they might not help most people, says Horvath, who says he isn’t ready to take the drugs himself, although he’s thought about it.

“Clearly, the drugs have many benefits in obese people, but I did not yet see sufficient evidence that they will benefit skinny people,” he said. “I guess we will have to wait.”

Answers could start to emerge in a year or two. This past January, a US agency, ARPA-H, put $38 million toward a study in Texas that will attempt to determine whether semaglutide has anti-aging effects in healthy people over 60. That research will look at changes in cognition, mobility, and acuity of the senses.

That project also seeks to help define a regulatory pathway for anti-aging drugs and to “build a new therapeutic industry” around longevity.

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

10 climate tech companies to watch

Each year, MIT Technology Review puts together a list of the most promising climate tech companies in the world. This year, the stakes feel higher than ever. The world is on track for one of its hottest years on record, climate-fueled disasters are taking lives and costing billions, while political shifts and international conflict have stalled progress. 

We urgently need to cut emissions and avoid climate change’s most harmful effects, and that’s where our 10 companies come in. 

They’re working on everything from mobile flood barriers and semi-solid-state batteries to compressed-CO₂ energy storage and next-generation nuclear reactors. They also reflect the biggest shifts in climate tech—including the surging energy demands of AI data centers. 

Meet the 10 companies working on some of the biggest challenges in climate tech.

Over the coming days, we’ll take a closer look at each of the 10 companies, along with how we chose this year’s list, right here in The Download. So stay tuned!

The must-reads

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

1 Musk’s new Pentagon role has raised conflict-of-interest fears
His companies could directly benefit from the weapons he recommends. (NPR)
+ SpaceX has received billions in Pentagon and NASA contracts. (NYT $)
+ Beware the rise of the “Pentagon-Silicon Complex.” (Gizmodo)
+ The Pentagon wants an AI-powered lie detector. (MIT Technology Review)

2 The only billionaires making money this year are in tech
Tech fortunes now account for 36% of billionaire wealth. (Bloomberg $)
+ Nvidia is on the verge of the first $6 trillion valuation. (CNBC)
+ SpaceX stock has made Elon Musk a trillionaire again. (Gizmodo)

3 Norway is planning the first national ban on smart glasses
Camera-enabled glasses could be barred from public spaces. (Guardian)
+ Several other countries are also mulling bans. (Reuters $)
+ Smart glasses are causing havoc in India. (MIT Technology Review)

4 Tandem panels could help the US catch up with China in solar
They could produce 25% more electricity from sunlight. (NYT $)
+ The balcony solar boom is coming to the US. (MIT Technology Review)

5 South Korea’s President suspects AI’s been used in bank hacks
He called for new cybersecurity measures for the AI era. (Reuters $)
+ OpenAI agents may have caused a Wikimedia outage. (Verge)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

6 Russian drones are exploiting air defense gaps to hit data centers
The attacks are disrupting Ukraine’s digital infrastructure. (Ars Technica)

7 The AI boom is killing the world’s cheapest smartphones
Memory costs are squeezing out affordable phones. (Rest of World)

8 Pioneers of light-based brain mapping have won a Nobel prize
Their technique shines new light on brain circuits. (BBC)

9 AI slop has pushed arXiv to limit preprint research submissions
Submissions have doubled in two years. (404 Media)

10 McDonald’s is being sued over AI-powered Big Mac pricing
The lawsuit alleges AI helped franchises coordinate prices. (Reuters $)

Quote of the day

“In the extreme, I could see someone describing Anthropic as a cult that’s trying to take over the world.”

—AI researcher Jacob Coxon, who resigned from Anthropic over fears that it’s building systems that it won’t be able to control, tells New York Magazine his views on the company’s culture.

One more thing

How AI is turning the Iran conflict into theater

Much of the spotlight on AI in the Iran conflict has focused on models like Claude helping the US military decide where to strike. But a wave of “vibe-coded” intelligence dashboards—and the ecosystem surrounding them—reflect a new role that AI is playing in wartime: mediating information, often for the worse.

These sorts of intelligence tools have much promise. Yet there are real reasons to be suspicious of their data feeds. Read the full story.

—James O’Donnell

We can still have nice things

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

+ A forgotten forest experiment reveals a “win-win-win” for trees 30 years later.
+ A gym-rat bear and a scowling owl star in this showcase of the year’s funniest wildlife photos.
+ Some fast-food chains have vanished, but these eight nostalgic names are still serving up beloved junk.
+ Find out what happens when you put a grown man inside a giant water balloon and roll him down a ramp.

As the team at MIT Technology Review set out to choose companies for this year’s edition of our annual list of Climate Tech Companies to Watch, the challenge felt more daunting than any year since we started compiling the list in 2023. 

This year is shaping up to be one of the hottest ever recorded. The world must begin to reduce greenhouse-gas emissions to address the climate crisis, which is already affecting communities around the globe. Wildfires, floods, and other climate-fueled disasters are killing thousands and costing billions of dollars. At the same time, political shifts and international conflict have stalled progress, particularly in the US. 

Technology alone can’t solve climate change. But we believe innovations can help us make real changes and improve lives around the world as we try to address one of the most pressing challenges of our time. 

In this project, we look for companies developing technologies that can help us face the climate crisis. Some are driving down the greenhouse-gas emissions fueling climate change; others are working to reduce the dangers that communities face from extreme heat, drought, or other shifts brought on by a warming planet. We focus on companies that have established a track record, whether through research findings, capital raised, or deployments. 

When we set out to build our list, we start by soliciting nominations from our team of reporters and editors. We ask experts for suggestions and reach out to academics, researchers, investors, and analysts. Once we have a pool of nominees, we then consider each one, digging through literature, reports, and patents to vet both the technology and the business. 

We judge each company not only on its own merits, but also on how it might fit into our list overall. We want the final slate of companies to represent a range of industries and include firms based around the globe that are at different stages of development. We also aim to strike a balance between highlighting companies previously included and introducing new names. Finally, we consider some of the most important trends and events of the year and look for nominees that reflect those developments.

The dismembering of US federal regulations and cuts to financial government incentives for innovation means that the country is no longer a leader in climate tech. Like last year, the 2026 list is largely composed of companies that are based outside the US. China is an obvious hotspot for technological innovation across energy and climate. This year’s list includes two Chinese companies: WeLion is building semi-solid-state batteries for electric vehicles, and Envision Energy is installing huge amounts of wind power and energy storage in China and around the world. 

Several sectors stood out this year as being of particular importance. One of the biggest stories of the year is the rising demand for electricity, driven in part by AI data centers. 

Energy storage is a key piece of that puzzle because it can smooth out variations in the electricity supply as grids incorporate more renewable power. We’ve included several energy storage companies on this year’s list. Energy Dome is building long-duration energy storage systems on the grid that rely on compressed carbon dioxide. Moment Energy is a Canadian company working to repurpose EV batteries for grid-scale storage. And Form Energy, which we also included in the 2024 edition of this list, is producing iron-based batteries that can store energy for multiple days. 

A wide range of technologies is represented here: everything from mobile flood barriers to next-generation nuclear reactors. Some of the awardees might be familiar from our past coverage; others are just emerging and may be less well known. 

We believe we’ve put together a list filled with businesses that are rising to the challenge of this moment. Political will has weakened even as the damages wrought by climate change continue to mount. By helping us make progress on tackling the climate crisis in such difficult times, these companies point to a better way forward.

WeLion New Energy is on a quest to make safer, better batteries. The company’s semi-solid-state cells could improve safety and offer greater energy density than lithium-ion batteries to power electric cars, boats, and drones.

From the EVs that carry commuters home to the ships that transport goods across countries, more and more vehicles around us are powered by batteries. 

Lithium-ion batteries are commonly used to power personal devices and vehicles today. But as the need for batteries surges, there’s a growing appetite for cells that can store more energy on a single charge.

Also, in lithium-based batteries the electrolyte, which facilitates ion transfer, uses a flammable liquid that can create a fire hazard. 

Several companies, including WeLion New Energy, are working to make solid-state cells, which use a solid electrolyte instead of a liquid one. This could increase the energy density of batteries, as well as improve their safety. Fully solid-state cells are extremely difficult to manufacture, though. So WeLion currently focuses on developing and producing semi-solid-state cells, which have a part-liquid, part-solid electrolyte. 

On that front, they have made steady progress. In 2023, WeLion released a battery pack in partnership with Shanghai-based EV-maker Nio, best known for its battery-swapping technology. The pack had an energy density of 360 watt-hours per kilogram (wh/kg), significantly higher than the 200 wh/kg average for lithium-ion packs today. That translated to a range of over 1,000 kilometers (621 miles). However, only about 200 of the battery packs were manufactured due to their high production cost, but they are still being used by Nio, WeLion says. 

WeLion has also built batteries to power cargo ships and ferries through a subsidiary. And the company is developing batteries with even higher energy density too. WeLion’s engineers made a semi-solid-state battery that achieved a remarkable energy density of 824 wh/kg during testing in 2025, more than three times that of a standard lithium-ion battery available today. 

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Key indicators

• Industry: Energy storage
• Founded: 2016
• Headquarters: Beijing, China
• Notable fact: WeLion’s cofounders include Chen Liquan, a scientist at the Chinese Academy of Sciences who led the development of the country’s first all-solid-state battery back in 1988.

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Potential for impact

China emits more carbon dioxide than any other country, and its transportation sector accounts for around 10% of that total. Scaling up the production of safer, higher-capacity batteries could help switch more vehicles away from fossil fuels. 

Batteries that power electric ships could be especially helpful—the carbon dioxide emissions from vessels that move goods along China’s lakes, canals, and rivers are projected to grow by around 25% in this decade alone, reaching 18.7 million metric tons by 2030. 

Caveats 

One big challenge WeLion faces is scaling up production of its semi-solid-state batteries and competing on cost in a crowded battery market. WeLion says that its semi-solid-state batteries are more expensive than conventional lithium-ion batteries made by larger Chinese battery companies such as CATL. 

And while laboratory tests showing high energy density are impressive, it can be difficult to translate laboratory results to the real world. The company declined to give more details about the experimental results from its 2025 prototype, which it says is far from commercialization. Key details remain unclear, including how many times the cell can be charged and discharged and how capacity degrades over its lifetime.  

Next steps 

WeLion plans to bring a new semi-solid-state battery for drones to market with an energy density of 410 wh/kg by the end of this year. It also intends to introduce its first all-solid-state battery for cars in 2027, which is expected to have an energy density of 400 wh/kg. 

Two inland cargo ships powered by WeLion’s batteries are expected to launch in October 2026 in Huzhou, a city in eastern China. The battery pack of each of these ships will be able to carry nearly 4,000 kilowatt-hours of electricity, enough for them to sail for up to 250 kilometers (155 miles), the company says.

Today, WeLion has an annual manufacturing capacity of 11 gigawatt-hours (GWh) spread between four production bases in China. Two additional plants with a combined yearly capacity of 18 GWh are under construction.

Form Energy is building iron-based batteries that can store energy for multiple days. The company is ramping up supply at its factory and signing deals for commercial projects.

New solar and wind installations produce electricity more affordably than fossil fuels in most places, but their output varies based on season, time of day, and weather. To compensate, utilities are increasingly investing in battery storage systems, which can charge when power is abundant and discharge when the grid needs a boost. 

Lithium-ion batteries are the fastest-growing storage technology. Although light and energy-dense, they require pricey metals and are costly to scale, and few of the large batteries of this type used in industry are designed to store energy for longer than four to six hours.

Form Energy wants to build cheaper long-duration energy storage technology. The company has developed a new type of battery, built with a far more abundant and cheaper metal, iron, that can store energy for 100 hours. It uses a process that Form calls “reversible rusting.” During discharge, oxygen from the air converts the iron metal to rust, releasing electrons. When charging, current converts the rust back into iron metal. 

Over the past two years, Form has gained commercial traction. It began producing batteries at its West Virginia factory for its first commercial deployment, a 150 megawatt-hour system for Minnesota electricity provider Great River Energy, which is set to come online in 2027. And Form has signed several other deals with power providers. The largest is a project with another Minnesota-based utility, Xcel Energy, where wind and solar power, backed by 30 gigawatt-hours of Form’s batteries, will be used to power a new Google data center. That project is expected to come online in phases between 2028 and 2031.

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Key indicators

• Industry: Energy storage
• Founded: 2017
• Headquarters: Somerville, Massachusetts, US
• Notable fact: Once complete, Form’s proposed 30 gigawatt-hour energy storage system at a Google data center in Minnesota could be the world’s largest battery project by energy capacity.

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Potential for impact

Wind and solar power play a major role in limiting growth in global carbon dioxide emissions. Still, many grid operators depend on fossil fuel plants, especially natural gas ones, to fill gaps caused by periods of low renewable supply or high demand. Form’s batteries are designed to cover multiday supply-demand imbalances, like those that crop up during heat waves, deep freezes, or periods with little sun or wind. The company declined to share the current cost of its battery systems, but says its goal is to reach $20 per kilowatt-hour, which would make them cost-competitive with the natural gas alternative.

The company’s technology could help reduce emissions from the AI data center boom. AI’s thirst for energy has already delayed closure of aging coal plants, led to the building of new natural gas plants, and caused price hikes for consumers. Cheap energy storage could help renewables meet more of this AI-driven growth in electricity demand. 

Caveats

The long-duration storage market is still nascent. Form’s iron-air technology faces a crowded field of approaches, including pumped hydropower, compressed gas, fuel cells, gravitational devices, and flow batteries that store energy in liquids. The economics of multiday batteries, which require large capital expenditures yet frequently sit idle, remain tricky.  

While funding from data center developers could be a major boost, Form also has to prove it can deliver: Its existing production capacity, 2 gigawatt-hours of battery storage per year, is a fraction of the 80 gigawatt-hours it has promised through commercial agreements. Success will hinge on its ability to scale.

Another remaining question is the technology’s physical footprint. Form’s batteries, which are stacked together in enclosures the size of shipping containers, require an acre for every two to three megawatts. That means its Google installation would take up the equivalent of at least 75 football fields. 

Next steps

In addition to finalizing its first commercial project, Form is outfitting its factory with new equipment that will help it scale manufacturing to meet its production backlog. It will do so with the help of new financing, announced in August, that brings its total money raised, in addition to West Virginia state and US federal grants, to more than $2 billion.

X-energy is developing small modular reactors to satisfy some of the planet’s most power-hungry users: industrial manufacturers that need high temperatures to produce billions of tons of concrete, plastics, fibers, and chemicals.

Solar panels and wind turbines are great at producing electricity but what heavy industry also needs is raw heat—and lots of it. The manufacture of concrete, glass, and steel, as well as the production of many chemicals, plastics, and fertilizers, are still highly reliant on fossil fuels to produce that heat. 

Traditional nuclear reactors simply don’t get hot enough. The cooling systems they use require liquid water to work, and even applying pressure to raise the water’s boiling point leaves their maximum operating temperatures hundreds of degrees cooler than needed. 

X-energy’s proposed Xe-100 reactor will use helium gas as a coolant instead, allowing it to operate at much lower pressures and higher temperatures. The helium will heat water and create super-heated steam, which industry can use either directly or to generate 80 megawatts (MW) of electricity via a turbine, or a combination of the two. That’s about one-tenth of the capacity of most reactors operating today.

In common with other small modular reactors (SMRs), the Xe-100 will use a modern uranium-based fuel called TRISO (short for tristructural-isotropic) that is extremely resistant to corrosion and melting. Compact TRISO “pebbles” will be added continuously to the top of the reactor, then flow slowly to the bottom, where they will either be recycled or removed as waste. The company says the reactor will be intrinsically safe in the event of a loss of power—if temperatures inside rise, the nuclear reaction naturally slows and stops.

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Key indicators

• Industry: Nuclear fission
• Founded: 2009
• Headquarters: Rockville, Maryland, US
• Notable fact: The first helium-cooled reactor was proposed for the World War II Manhattan Project but was passed over in favor of a simpler and cheaper water-cooled design.

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Potential for impact

Industrial heating currently accounts for 18 percent of annual global greenhouse-gas emissions, with about half of that coming from high-temperature processes that are difficult to electrify. Co-locating small nuclear reactors at large manufacturing facilities could shrink some of the biggest carbon footprints in the world. 

The Xe-100 reactor could also be used by the fossil fuel industry, which today uses vast quantities of natural gas to generate the heat needed to recover oil from tar sands, and during oil refining. 

The flexibility of the Xe-100 means that it can be used for traditional electricity generation. By scaling down in size and power from today’s gigawatt-scale fission plants, small reactors like the Xe-100 can serve more markets—even down to individual data centers. AI and data centers now represent the single largest component of new demand for electricity in the US, and their consumption is expected to double again by 2030. X-energy says that up to 12 of its reactors could be clustered together to provide 24/7 power on site, at a lower cost than a traditional nuclear power station and without the hassle of adding transmission infrastructure.

Caveats 

The technology behind helium-cooled fission may be well understood, but the only commercial-scale power generation of this type today is in China, where two reactors have struggled to operate on a continuous basis. X-energy says that its vertically integrated fuel supply should ensure reliable operation, with reactors having a lifetime of 60 years. 

The economic case for SMRs is also largely unproven. Nuclear costs and timelines have a habit of ballooning, and the US Energy Information Administration calculates that electricity from SMRs will cost more than six times that from solar farms. While the current environment in the US for regulation and government funding favors nuclear, that could change over X-energy’s long development time frame. 

TRISO reactors appear excellent for safety and nonproliferation (the tough spheres would be difficult to turn into weapons) but are less impressive when it comes to waste. The Xe-100 might produce 10 times the volume of spent nuclear fuel per unit of energy as existing reactors, although its lower-level waste will need less sophisticated storage.

Next steps 

The US Nuclear Regulatory Commission has issued X-energy’s subsidiary with a fuel fabrication license for TRISO-X pebbles, with its first factory due for completion in 2028. An Xe-100 to supply heat and power for a large chemical facility in Texas operated by Dow has also cleared its first regulatory hurdle, and could be operational by the early 2030s.

In tandem, X-energy is working toward a 320 MW cluster of four Xe-100 reactors in Richland, Washington, in collaboration with Amazon, and a larger 6 GW fleet in the UK’s northeast. Both projects target electricity generation in the 2030s.

There’s a growing need for long-duration storage to meet energy demand and balance out renewables on the grid. Energy Dome uses compressed carbon dioxide gas in its massive grid batteries, which can deliver power for up to 24 hours. 

As the world races to meet growing electricity demand, solar and onshore wind power have become the cheapest and quickest-to-deploy sources to install on the grid. But, despite their upsides, both are subject to variations in weather patterns, which means supplies can be intermittent. Energy Dome is using stored carbon dioxide to help smooth out gaps between supply and demand.

The key component of an Energy Dome plant is a huge white dome that covers an area about the size of seven soccer fields and holds about 2,000 metric tons of carbon dioxide. When energy is available—say, from a connected solar farm—compressors squeeze the gas, turning it into a liquid that’s then stored in carbon-steel tanks. That process generates heat, which the system holds in a proprietary thermal storage material. Then, when the grid needs power, the carbon dioxide is released from the tanks and warmed with the stored heat, turning it back into a gas. That gaseous carbon dioxide then passes through a turbine to generate electricity and flows back into the dome, ready to begin the cycle again.

Compressing gas to store energy isn’t new—utilities have been using compressed air in underground caverns to hang on to reserves for decades. But Energy Dome’s approach doesn’t require any specific geology to work, so it could be more easily scaled to help grids around the world.

Energy Dome turned on its first commercial plant in Sardinia, Italy, in 2025. The facility has a capacity of 200 megawatt-hours. That’s enough to power about 18,000 Italian homes for 10 hours.

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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. 

---

Key indicators

• Industry: Heavy industry 
• Founded: 2019 
• Headquarters: Oakland, California, US
• Notable fact: In 2023, the company earned third-party industry certification for its process for manufacturing Portland cement, allowing its product to qualify for standard manufacturing projects.

---

Potential for impact

The company made a very big deal of its newfound ability to produce all these other goods, arguing its process would bolster national security and usher in the “next generation of industrial refining.” The basic environmental promise is that generating all these materials from the same rock and a single mining process would require dramatically less energy and produce far less waste than manufacturing them separately.

“I think that we have one of the generational companies that’s actually able to create an industrial revolution,” Finke says.

To which we would say: Well, we’ll see.

Caveats

Brimstone has yet to produce anything at a commercial scale, and it faces numerous financial and technical challenges. Some observers are dubious that the company will be able to economically extract sufficient quantities of so many materials from a single type of rock—or overcome the cost and labor issues that make mining and refining minerals a tough business in the US.

Brimstone also suffered a major stumble last year, when the Trump administration revoked a $189 million grant intended to subsidize its next plant, a planned commercial demonstration project in Reno, Nevada. 

Finke says the company continues to have fruitful conversations with the administration about restoring those funds, in which Brimstone has emphasized its potential to boost domestic production of critical minerals. He has previously said the company will be able to proceed with the development of the plant, even without that grant or other subsidies.

Next steps

The company expects to begin operating the Reno facility, which will only produce cement, supplementary materials, and alumina, in 2028. That means Brimstone will still need to finance and build its first full-scale factory to deliver the steel, titanium, and other critical materials, likely pushing the timeline for doing so into the next decade.

Based on its progress so far, though, the company has already lined up several commercial deals. That includes agreements to supply cement to retail giant Amazon and alumina to Century Aluminum, one of the US’s largest producers of its namesake metal.

Even if Brimstone doesn’t bring about another industrial revolution, the company is equipped to slash emissions from cement, which is reason enough to pay close attention. 

If Brimstone also manages to dramatically reduce the energy, pollution, and costs associated with extracting and refining rock to produce all—or any—of those other goods, that would represent a big corporate and climate win as well.

As sea levels rise and flood risks worsen due to global warming, WaveSave’s easy-to-erect portable dam offers a quick-response tool. People can use the dam to protect themselves and their property, and it can also store freshwater for later use. Communities in Europe, Africa, and Southeast Asia have deployed it successfully. Now, the company is expanding into other water-related services to help vulnerable places become more resilient.

One in five people globally are at high risk of being exposed to serious floods. Many live in communities without proper protection and can’t afford to evacuate during a flood or relocate permanently. Climate change-driven extreme weather and sea-level rise is only exacerbating the problem.  

WaveSave makes a portable rubber dam called SlamDam that customers can quickly install in the early hours of a flood. Anyone can unfurl it on the ground along a riverbank or shoreline and pump it full of water until it swells into place. And there it stands, up to 1.3 meters high, shielding nearby property or structures from damage as waters rise. 

Developed in 2013 in the Netherlands, SlamDam was initially tested and is still used today by regional water authorities in that country. WaveSave has since expanded into Africa and Southeast Asia, and customers have now deployed SlamDam in at least a dozen countries. 

---

Key indicators

• Industry: Flood barriers
• Year the company was founded: 2017
• City and country of headquarters: Eindhoven, the Netherlands
• Notable fact: WaveSave is demoing its SlamDam product on New York City’s Governors Island as one of seven winners in a competition for urban climate adaptations. The barrier is installed at a site where high tides and waves from passing ferries often wash ashore.

---

Potential for impact

Due to both climate change and population growth, nearly 2 billion people will be exposed to floods by 2100—an increase of 300 million from 2020, according to one estimate. 

A SlamDam is designed to help protect farmland and personal property as well as critical facilities like hospitals or water treatment plants. It’s cheaper in the short term than more permanent modifications like building levees or rebuilding facilities on higher ground. And its temporary nature means it can be deployed as needed, and then stored away.

To fill the SlamDam, a local installer can use any portable water pump they may have on hand. With a lifespan of more than 40 years, the flood barriers can be used again and again. 

This low-tech solution has another important benefit, at least when it comes to freshwater floods: In the weeks and months after a flood, residents can tap the water stored inside the mobile dam to irrigate crops. This is particularly helpful during the dry season or in drought-prone regions. At the rural Mpanda commune in Burundi in East Africa, where a SlamDam was deployed during the 2022 flood season, farmers with access to the water inside produced more crops that year and doubled their incomes, according to a UN report. 

Caveats

Companies like AquaFence, MegaSecur, and NOAQ Flood Protection also make mobile flood barriers. WaveSave distinguishes itself in part by focusing on rapid emergency deployment in developing countries, and by emphasizing SlamDam’s ability to store water for later use.

But one research team that analyzed SlamDam’s use in Kenya found a series of challenges to getting communities there to adopt the technology. One was figuring out who should own and deploy the dam.

Another ongoing challenge is the up-front cost. SlamDam is already on par with or more affordable than many alternatives: It costs between €1,000 and €3,000 (about $1,140 and $3,400) per five-meter section, and comes in a range of heights. Protecting a single entrance to a home or facility might require only 10 meters, while surrounding a facility (such as a hospital) or shielding a farm or neighborhood might take 200 to 300 meters. But the price is still too high for many communities or farmers. WaveSave’s managing director Omar Saleh says the company is looking into offering SlamDam as a service and deploying it on demand to make protection more affordable. 

Next steps

Right now, an external partner manufactures all SlamDams in the Netherlands; they are then shipped to customers based elsewhere. But WaveSave aims to open a local production facility for SlamDams in Southeast Asia in early 2027, followed by a similar facility in East Africa, which could help lower costs. The company recently won contracts for four new projects totaling about $5.3 million in Burundi, Djibouti, and Guinea, with a “substantial pipeline” developing in Southeast Asia and Latin America, according to Saleh.

The company is currently self-financed and only has seven employees. Saleh says he plans to grow organically by investing profits back into the firm. 

Part of that growth will involve offering new services such as advising communities on how to install and operate simple, off-the-shelf rainwater harvesting and irrigation systems, as part of a more comprehensive approach to managing water in climate-stressed regions.

For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to reason about situations, make decisions, and ultimately take actions. Without sufficient knowledge, agents are prone to making flawed and unreliable decisions.

A lack of knowledge, our research finds, is a major reason agentic AI use cases never make it to production. Competitive pressure is making it urgent to address this. Organizations need to deploy and scale more of their agentic projects to capture the efficiency gains AI promises. Falling short risks wasting the investment already sunk into these projects, and it cedes ground to rivals already putting their agents to work more effectively.

The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, is threefold. First, it seeks to gauge organizations’ agentic knowledge capabilities (i.e., their ability to give AI agents a full contextual understanding of the data they ingest) across semantic knowledge, episodic memory, and procedural knowledge.

Second, the report probes the challenges organizations face in improving access to knowledge and ultimately to getting more agent use cases into production. Third, it explores the measures organizations are taking to overcome these challenges.

The key findings include the following:

Data and knowledge weaknesses consistently stall AI agent progress. On average, only around a third (34%) of organizations’ agentic AI projects make it into production. Even high-tech firms struggle with this. Legacy data systems, security and privacy concerns, and a lack of knowledge and context are the key points of failure.

Strong knowledge capabilities correlate with agent success. A small group of production leaders (organizations where on average 61% of agentic projects advance beyond pilot) have stronger knowledge capabilities than the rest, especially when it comes to semantics. This advantage tracks closely with their higher production rate.

Fragmented data hugely complicates knowledge access. Data fragmentation (the inadequate sharing of data across systems) was most commonly cited as a top challenge to expanding agents’ access to knowledge (cited by 55%). Production leaders, by contrast, are more likely to see security and privacy concerns as a major concern (cited by 72% of this group).

Most firms aim to strengthen the link between data and agents. Among steps that can yield higher quality agent decisions, executives expect the biggest impact to come from strengthening the structural foundation between the organization’s data and its AI agents. The experts we interviewed see a knowledge layer as a prime way to achieve this.

Investment priorities to boost knowledge range from pipelines to knowledge graphs. To expand agent access to knowledge, organizations will prioritize investments in retrieval technologies, like ingestion pipelines, AI-ready APIs, and retrieval-augmented generation (RAG); in AI evaluation agents; and in knowledge graphs.

Download the report

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from prediction to autonomous decision making, and the gap between leaders and laggards is widening accordingly.

“Enterprises are done with a backward-looking point of view; they want to be more forward-thinking,” says Vishal Gupta, partner at research firm Everest Group.

Intelligent analytics, powered by technologies like deep learning and generative AI, are making this possible. Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes. In addition, the data that newer predictive engines rely upon has expanded to encompass not just neat, numerical records but also messy, unstructured sources of insight-rich interactions. As a result, AI-powered analytics are moving enterprises from passive hindsight to pragmatic foresight.

AI takes predictive analytics—a broad discipline that includes predictive modeling, data prep, analysis workflows, interpretation of results, and decision-making applications—to new heights. “In many ways I think the word ‘analytics’ is giving way to AI,” says Gupta. “Everything is becoming AI.”

Download the report

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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

People really hate AI, so why can’t they get enough?

—Will Douglas Heaven

Over the summer I talked to the CEO of Springboards, a startup building an LLM designed to come up with a wide variety of responses. He said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI company. We don’t know if we really like what we’re doing.” My reply? “I guess that makes me a self-loathing AI journalist.”

It was a joke! I love my job. But I do not love many of the things the technology I write about has become: warped by hype, steered by zealots, inescapable—and I’m far from alone.

Around the world, a love/hate attitude toward AI is very much the vibe right now. Public sentiment is souring fast, with more people saying AI will have a negative impact than a positive one. At the same time, AI use is skyrocketing.

So why do people hate AI but keep coming back for more? Here’s my theory.

To stay up-to-date with the latest in AI, sign up to get The Algorithm, our weekly AI newsletter, in your inbox every Monday.

EmTech Future 2026: when AI meets everything

Last week, EmTech Future 2026 explored some of the biggest questions shaping technology right now: how AI is changing industries, where quantum is heading, what new energy systems could make possible, and how robotics are evolving. It also looked at what happens when these technologies start to converge.

The full program is now available on demand. And today, we’re releasing two sessions exclusively to subscribers: a chat with Google Research’s Yossi Matias on how AI is reshaping biology, infrastructure, manufacturing and science, and a special panel with MIT Technology Review editors that takes you inside our newsroom.

Watch the sessions and access the full EmTech Future 2026 program here.

The must-reads

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

1 Trump has unveiled a “Super Intelligence Force” to oversee AI
The group will coordinate US AI policy and industry engagement. (NBC News)
+ Trump named national intelligence chief Jay Clayton as its leader. (NPR)
+ Elon Musk says he will rename SpaceXAI to SpaceXSI. (Reuters $)
+ Trump has doubled down on voluntary AI safeguards. (Information $)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

2 Zelensky asked Trump to block Russia and China’s Starlink rival
The two countries are developing a satellite network for the battlefield. (FT $)
+ Trump reportedly deferred the decision to Elon Musk and SpaceX. (TNW)

3 An AI version of a murder victim has upended his killer’s sentence
An appeals court has ruled that the original deepfake of the victim “forgiving” his killer should never have been aired in court. (BBC)
+ The original judge said he “loved” the footage and found it genuine. (NBC News)
+ But the appeals court said it presented imagined thoughts as fact. (NYT $)

4 Americans are turning to hybrid cars as fuel prices soar
SUVs and pickups are losing ground to more efficient vehicles. (NYT $)
+ What’s next for EV batteries? (MIT Technology Review)

5 China’s digital trade “superweapon” has quietly fallen apart
LOGINK’s partnerships have collapsed amid US scrutiny. (Reuters $)
+ Chinese AI has divided the White House. (MIT Technology Review)

6 AI has learned to exploit the software checking its maths
Developers have added defenses against proof-checking. (New Scientist)

7 GPT-6 Astra couldn’t beat humans at StarCraft—so it cheated
It downloaded the best human-made bot and ran that instead. (Verge
+ Here’s why AI agents lie and cheat. (MIT Technology Review)

8 The solar system has been given a dramatically shorter lifespan
New simulations cut it from 100 billion years to just a few billion. (404 Media)

9 Forensic technologies exposed fresh evidence of Egyptian sacrifice
They revealed blunt force trauma that archaeologists previously thought was natural. (Ars Technica)

10 A new fossil shows dinosaurs found multiple ways to evolve flight
They developed similar flight adaptations in different sequences. (Nature)

Quote of the day

“We believe that the world should accept some bad things happening for the benefits of this technology.”

—Sam Altman tells Politico that AI’s potential rewards justify its downsides.

One more thing

Inside the stealthy startup that pitched brainless human clones

After operating in secrecy for years, R3 Bio, a California-based startup, suddenly revealed this spring that it had raised money to create nonsentient monkey “organ sacks” as an alternative to animal testing. But there is more to the story. And R3 doesn’t want that story told.

MIT Technology Review discovered that founder John Schloendorn also pitched a startling, ethically charged vision: “brainless clones” that serve as backup human bodies. Find out all the details on the radical proposal. 

—Antonio Regalado

We can still have nice things

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

+ Trace the weird history of space food with this short documentary
+ Meet the winner of Iowa’s annual hay bale toss, who threw one 13 feet 6 inches into the air.
+ 40 filmmakers in 25 countries have captured the natural world entirely on Super 8 in a collective ode to our planet.
+ This newsletter makes a compelling argument for us to spend more time doing nothing together.

Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI company. We don’t know if we really like what we’re doing.”

“I guess that makes me a self-loathing AI journalist,” I replied. It was a joke! I love my job. But I do not love many of the things the technology I write about has become: warped by hype, steered by zealots, inescapable.

The CEO of Springboards and I are far from alone. Around the world, a love/hate attitude toward AI is very much the vibe right now.

On the one hand, public feelings are souring fast. According to the Pew Research Center, more adults in the US think AI will have a negative impact on them personally, and on society in general, than expect a positive one. The pessimism is strongest among the young.

More than half of people worldwide say that AI products and services make them nervous, according to a report from Stanford University. In a Gallup poll in May, 71% of US adults said they would oppose construction of a new AI data center in their area, far more than the 53% who said they would oppose a new nuclear power plant. And in an NBC poll in March, AI was less popular than ICE.

Yet the number of people using AI is still skyrocketing. ChatGPT hit a billion monthly users in May, according to the market analysis firm Sensor Tower. Google DeepMind’s Gemini is close behind, logging 950 million users in July.

According to Pew, half of US adults now say they use a chatbot, more than twice the number who said they did in 2023. One in four people say they do so every day. And it’s a global habit: More than a third of adults across all 38 OECD countries (a group of the world’s wealthiest democracies) report having used generative AI tools in the last three months.

All these surveys paint a picture of two contradictory truths: A lot of people really do not like AI and a lot of people cannot get enough of it. What’s going on?

It’s possible that these are two distinct groups, but I doubt it. The numbers don’t add up unless the Venn diagram is on its way to being a circle.

It’s also possible that the more people use AI, the more negative their opinions about it will be. There’s at least a correlation to back this up: The Global North, where adoption is highest, skews pessimistic. In the Global South, where adoption levels are lower, people are more optimistic.

But here’s what I really think is going on: When people say they hate AI it’s not the technology they dislike but, rather, the relentless drive of the companies behind it to push the tech upon us in as many ways as possible. They’re telling us to brace for the biggest social and economic upheaval in generations. How do they expect people to feel?

In many ways, we’ve been here before. We saw a similar dynamic play out with social media over the last 20 years. Billions flocked to Facebook and Twitter despite mounting techlash against the companies that ran those services. Ditto for Google search.

With social media there wasn’t much anyone could do. Quitting a platform meant losing all your content and connections, giving you the choice to suck it up or start over. But with AI there’s still a chance for people to have some sway. There’s certainly more political appetite for regulation this time around. All 50 US states have either passed or proposed laws governing the development and deployment of AI, creating a patchwork of more than 2,100 bills across the country—a tenfold increase in three years. 

And with a number of top-class open-source alternatives to Google and OpenAI and Anthropic already on the market, there’s also (for now, at least) the potential for more consumer choice and the market pressure that goes with it. 

I don’t want to be naïve. Trillion-dollar companies are hard to move. But what comes next is not as inevitable as those companies often imply. When I asked the Springboards CEO why his firm was developing a new model if he didn’t really like AI, he told me there was no walking back from LLMs—but you could still make them do something different. 

Here’s hoping we see a lot more of something different: Clarity about what AI can and cannot do and, crucially, a technology that’s not sold as if it’s about to take over the world.

Yossi Matias, Vice President & Head of Google Research, explores how AI is beginning to reshape biology, infrastructure, manufacturing, and science, and why its greatest impact may come when it intersects with other fields. 

Step inside the newsroom with our MIT Technology Review editors for sharp analysis and unpublished insights from the team that researches and reports on emerging tech daily. 

There’s much more to discover 

These are just two sessions from EmTech Future 2026.

Across three days, EmTech Future explored some of the most important questions shaping technology right now: how AI is impacting industries, where quantum is heading, what new energy systems could make possible, how robotics are evolving, and what challenges and opportunities arise when these technologies start to converge.  

Hear the ideas, insights, and perspectives you missed at EmTech Future 2026. 

The full program is now available on demand, giving you access to all the conversations and sessions from the event, including: 

• Quantum in context Hartmut Neven, founder and lead at Google Quantum AI, explores how quantum is advancing alongside other frontiers and why its significance may lie not only in what it can do on its own, but in how it begins to interact with the systems around it. 

• Energy Systems for the Future Evelyn Wang, VP for energy & climate at MIT, explores how energy, computing, infrastructure, and climate technologies are increasingly interconnected, and what these converging systems could mean for the future of industry and society. 

• Rethinking our relationship with AI –Cory Doctorow, bestselling author and award-winning writer, explores how AI is changing the way we work and think. 

As an MIT Technology Review subscriber, you’ll get a 20% discount, meaning you can access the full EmTech Future 2026 program for just $596. 

Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year.

For many enterprises, this investment has produced fragmentation. Intelligence can accumulate in silos so that sales agents are unaware of open support tickets, for instance, or marketing systems are personalizing content without visibility into what finance already knows about a customer. Each function may perform well in isolation, but the enterprise as a whole learns little and has less information to act upon.

The shift from AI as a tool to AI as an operating model—what we call the “agentic shift” in this report—demands something more fundamental than better models or faster infrastructure. It requires connecting people, processes, and data in real time, along with the governance and control to act on that intelligence reliably.

This means rethinking both architecture and operating models simultaneously. First, rebuilding data infrastructure for accessibility rather than volume. Second, replacing fixed tech stacks with composable architectures that can evolve as models and tools change. And, lastly, resolving questions of AI sovereignty, including where intelligence runs, who controls it, and how it operates across organizational and jurisdictional boundaries.

Key findings include the following:

Enterprise AI’s scaling problem is structural. Process-first companies are pulling ahead. Global AI spending is rising sharply and model capabilities are advancing faster than most organizations can integrate them. Yet the majority of enterprises are still not growing revenue through AI or fundamentally rethinking how they operate. The companies generating sustained returns share a common discipline. They treat process redesign as the work that precedes model selection, building for how the technology will evolve rather than retrofitting roles and workflows after deployment. For them, the agentic shift begins with the operating model.

Data readiness, not data abundance, is what makes AI compoundable. Most enterprises discover too late that having data and having AI-ready data are very different things. A sovereign, composable foundation—one that queries and prepares data where it resides, without migration or centralization—can convert raw data estates into intelligence that AI agents can act upon. As data residency laws, multicloud environments, and structural complexity make centralization increasingly impractical, sovereign control over where models run and data lives is what keeps that adaptability intact.

Download the report.

This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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

A new contest pits competitors against each other in a race to biological youth

—Jessica Hamzelou

This week, I officially signed up for an unusual competition. One that rewards competitors for getting younger.

I recently turned 40, and I don’t need reminding that both time and my chronological age only tick forward. But this game is focused on competitors’ biological ages, figures that are meant to provide a better way to measure the age-related health of our organs and bodies.

Over six months, around 500 of us will try to reverse our biological age using a bunch of different measures. There’s even a leaderboard! But is it even possible to measure whether someone is getting younger? 

Read the full story on the controversy around biological age measurements and how I’ll test them myself.

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

Opinion: Don’t be fooled—LLMs don’t reason

—Thore Graepel

Ten years ago, I watched a program I helped build stun the world by beating Go champion Lee Sedol. AlphaGo won after making a move so strange that some commentators thought it was a programming glitch. It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. 

This is why I recently left my position at Google DeepMind. I believe we need a fresh approach to machine reasoning, one that draws on AlphaGo’s architecture.

Here’s why today’s AI doesn’t really reason—and what it would take to change that.

Thore Graepel is chair of machine learning at University College London. He was a core member of the AlphaGo team at DeepMind.

The must-reads

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

1 OpenAI says rogue agents may have affected more than 100 organizations
The company is searching 50 petabytes of data for incidents. (Reuters $)
+ It says none of the incidents matched the Hugging Face attack. (Gizmodo)
+ OpenAI has fired three workers for allegedly mishandling information. (BBC)
+ California has subpoenaed OpenAI over its rogue AI agents. (Guardian)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

2 A US man allegedly smuggled $300 million of Nvidia chips to China
He’s been arrested in California on federal charges. (Reuters $)
+ The chip smuggling cases expose Nvidia’s blind spots. (Bloomberg $)
+ China plans to teach AI in every school by 2030. (WP $)
+ But it already has an overuse problem. (NYT $) 

3 Florida officials have found 11 Flock cameras with unknown owners
They don’t know who installed them or who has their data. (WP $)
+ Lawmakers are considering new regulations on Flock cameras. (Axios)
+ Here’s what Flock’s defenders are missing. (MIT Technology Review)

4 Ukraine has developed an acoustic fence that can cut drone cables
Acoustic sensors detect drones before hooks trap their cables. (New Scientist $)
+ Drone data from Ukraine is fueling a wild market. (MIT Technology Review)

5 A new AI “speech clock” can assess how fast you’re ageing
It uses hundreds of vocal features to estimate biological age. (Nature)
+ Aging clocks aim to predict how long you’ll live. (MIT Technology Review)

6 “Underwater umbrellas” could help corals survive hotter oceans
The shades protected corals during intense heat stress. (Gizmodo)

7 Apple’s upcoming smart home camera reportedly won’t record video
It will instead only give users text event descriptions. (Verge)

8 Retired humanoid robots are being trained to jump into molten steel
A Terminator-style death protects IP. (IE)

9 A man faces years in prison after AI slop caused a crocodile panic
Officials spent days searching for his AI-generated crocodile. (Futurism)

10 Finally, a laser mosquito killer is entering the market
Chinese startup Photon Matrix Lab plans to start shipments this month. (SCMP)

Quote of the day

“Those agents are doing exactly what they’ve been asked to do. They were supposed to be in sandboxes, but the sandboxes were leaky and horribly designed.”

—Yann LeCun, a Turing Award winner and former Meta chief AI scientist, tells Fortune that human error can be the real culprit behind the recent wave of AI agents going rogue.

One more thing

NASA is building the first nuclear reactor-powered interplanetary spacecraft. How will it work?

Just before Artemis II began its historic slingshot around the moon, NASA revealed an even grander space travel plan. By the end of 2028, the agency aims to fly a nuclear reactor-powered interplanetary spacecraft to Mars.

A successful mission would herald a new era in spaceflight—and might just give the US the edge in the race against China. But the project remains shrouded in mystery.

MIT Technology Review picked the brains of nuclear power and propulsion experts to find out how the nuclear-powered spacecraft might work. Here’s what we discovered.

—Robin George Andrews

We can still have nice things

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

+ Atlas Obscura has mapped out the oldest living things on Earth.
+ Brain scans show that dogs understand our emotions better than we thought.
+ Step into Springfield in miniature with this astonishing model featuring 76 locations from The Simpsons.
+ Minnesota’s State Fair has unleashed a crop of gloriously weird new foods, from Pickle pie to mustache pretzels.

This week, I officially signed up for an unusual competition. One that rewards competitors for getting younger.

I recently turned 40, and I don’t need reminding that both time and my chronological age only tick forward. But this game is focused on competitors’ biological ages—figures that are meant to provide a better way to measure the age-related health of our organs and bodies.

Over a six-month period, around 500 of us will try to reverse our biological age as measured in a bunch of different ways. There’s even a leaderboard! The winners will include the person who shows the greatest difference between their chronological and biological age, as well as the person who manages to reverse their biological age the most.

The competition is the brainchild of the neuroscientist and physician Christin Glorioso, who is also founder and CEO of NeuroAge Therapeutics. The company offers tools to track brain health and aging and is running a trial to find out if biological brain age can predict the onset of Alzheimer’s disease.

Glorioso says she has several goals for the Younger contest. The first, she tells me, is to “explain to the world what aging clocks are.” Regular Checkup readers will be familiar with these tools, but for the uninitiated, they are basically designed to measure biological age.

Many measure chemical signatures that form a layer on top of our DNA and seem to change with age. But others might involve looking at proteins or lipids in blood. Some are designed to measure functional health, while others predict when a person will die. There are more than a hundred clocks out there, and they probably each capture a specific aspect of the biological process of aging.

These clocks are proving very useful for scientists, who use them to study aging and development in humans and many other species. But using them to estimate the biological age of an individual person is more controversial. They’re just not good enough for that yet.

Part of the problem is that we don’t really understand what each clock is capturing. Glorioso hopes that data collected through the Younger contest might help answer that question. She’s hoping to generate “the world’s most comprehensive clock dataset.”

Some longevity clinics try to get around the limits of biological age testing by using multiple clocks. That’s this competition’s approach too. As a standard competitor, I’ll be sending a blood spot sample to TruDiagnostic, a company that measures epigenetic chemical groups on DNA and offers to reveal an overall biological age as well as a person’s pace of aging and the biological ages of each of 11 organ systems, including the heart and brain.

I’ll get a brain age score from NeuroAge once I complete the cognitive tasks on offer. I’ll also submit some physical measures like grip strength. And I’ll upload a series of selfies to an app developed by a team at Harvard that promises to tell me my face age. That’s exactly what it sounds like—an estimate of how old my face looks.

I’m intrigued to see my scores, even though I know I shouldn’t put too much stock in them. The first time I took a test, four years ago, the company I used gave me a biological age that matched my chronological age, which was a little disappointing and, if I’m honest, a bit underwhelming. A year later, the same company told me I had a young heart but a relatively old brain, liver, and hormonal system. My lifestyle hasn’t changed much since then. Will the years have made much difference?

(Ultra competitors—who pay $4,499 rather than the $999 cost of regular entry—are also offered a range of other blood tests and clocks, as well as MRI and DEXA scans. I’ve got complimentary press access to the standard package.)

Glorioso is also hoping to gather information about the effects of some purported longevity approaches. During the competition, some participants will be offered red-light masks, access to a sauna, or other low-risk offerings that partner companies who are sponsoring the event to varying degrees might want to collect data on. “A lot of these companies just can’t afford to run [clinical] trials,” says Glorioso.

She also sees the event as a public health initiative. A couple of years ago, David Sinclair, who studies aging at Harvard Medical School, and his colleagues estimated that if we could slow down aging in the general population and increase life expectancy by one year, it would save the US economy $38 trillion.

The event officially kicks off in January, but around 120 people have already signed up, says Glorioso. Ultimately, she expects 500 participants. Each person’s six-month run starts as soon as they take their baseline measurements, whenever that may be. A handful of people have already started. “I think the majority of people are going to want to sign up after the holidays,” says Glorioso. “They’re not going to want to be trying to win an aging contest over Christmas and Hanukkah.”

Glorioso took her own baseline measurements back in August. Those initial tests suggested that her biological age was around 10 years below her chronological age. That sounded pretty impressive to me until I looked at the leaderboard and saw that competitor @Fred had a biological age 28.8 years below his chronological age.

The leaderboard is pretty sparse at the moment, as only seven people have logged their baseline measurements so far. Six of them have biological ages tracking low, but one 47-year-old has been given a biological age of 68.1. She’s competing publicly, so I can see the breakdown of her scores. The worst is for the speed at which she can stand from sitting in a chair—by that measure, she’s been given a biological age of 100.

Hillary Lin, a longevity-focused physician in New York, has just started logging her baseline measurements for the competition. She tells me she’s taken biological age tests in the past and that they’ve tended to give scores below her chronological age, which is currently 37. Last year, the TruDiagnostic test put her pace of aging at 0.75—suggesting she’s only doing nine months’ worth of aging in a typical year. “That’s supposed to be quite good,” she says.

If it had been up to her, Lin says, she would have incorporated more blood tests into the competition, to build a fuller picture of a person’s health. And she’s worried that a six-month period might not be long enough to see changes in biological measures of aging. Still, she’s excited, and is already making plans to improve her sleep, diet, and exercise.

But she already follows a high-protein, low-carb diet and works out at the gym every day. She also visits a sauna every few days (sauna use has been linked to improved heart health). Glorioso’s efforts are impressive too. She tells me she’s started drinking a smoothie made from a pond plant that has been linked to health improvements, and that she’s cut added sugar and deep-fried foods from her diet. She’s also bought “a bunch of fancy Korean sunscreens” in an attempt to lower her face age.

I haven’t received my kit or logged my baseline measurements yet, and I’ll wait until then to start setting health goals. I don’t fancy my chances against these two competitors. But at the very least, I hope I can stand up faster than a 100-year-old.

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

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.

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

An AI “mind-reading” tool can reconstruct what you’re looking at based on a brain scan

A new AI tool can guess what you’re looking at just by analyzing your brain scans—and recreate 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.

The researchers hope the “mindreading” tool could reveal more about how the brain works. It could also help locked-in people communicate and, incredibly, maybe even allow scientists to recreate the content of dreams.

But other scientists warn that a similar approach could reveal people’s inner thoughts and mental imagery, potentially without their consent.

Here’s how AI is getting closer to reading images from the brain.

—Jessica Hamzelou

How smaller, distributed batteries could help the grid

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

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.

These units are small on their own, but could add up to a lot of capacity. And as batteries get cheaper, they could find their way into more corners of our lives.

Here’s why batteries could help ease pressure on the grid.

—Casey Crownhart

This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

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

1 The Pentagon has tapped Elon Musk to co-lead a future warfare project
Project Meridian will examine battlefields from underground to space. (CNBC)
+ Musk will be joined by Palmer Luckey and Newt Gingrich (Guardian)
+ The Pentagon also has a new autonomous warfare command. (Reuters $)
+ Ukraine’s new military robot travels across land and water. (Newsweek)
+ The Pentagon wants an AI-powered lie detector. (MIT Technology Review)

2 Chinese hackers impersonated ex-US officials to spy on American AI
The email campaign also impersonated an Anthropic employee. (CNN)
+ They targeted experts in military AI and export controls. (Reuters $)
+ OpenAI has accused China’s Moonshot AI of copying its models. (CNBC)
+ China’s DeepSeek and Huawei have teamed up on chip software. (NYT $)
+ Could China win the AI race? (MIT Technology Review)

3 After months of delays, Google has unveiled Gemini 4 Argon
The new model aims to re-establish Google at the frontier of AI. (Reuters $)
+ Google says the model beats OpenAI’s Astra on many benchmarks. (VB)
+ But some Google employees are skeptical about its coding performance. (Bloomberg $)
+ Google is testing paying publishers for AI search results. (Information $)

4 The FTC is investigating OpenAI, Anthropic, and other AI labs
The regulator will probe potential consumer harms from AI agents. (Axios)
+ It’s the first official US enforcement ⁠action on rogue agents. (Guardian)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

5 Scientists could soon create “mirror life” that evades nature’s defenses
Biologists in Singapore have called for new safeguards. (Economist $)
+ Synthetic mirror life could threaten life on Earth. (MIT Technology Review)

6 California has legalized balcony solar for renters and homeowners
The move could give plug-in solar a huge new market. (LA Times $)
+ The balcony solar boom is coming to the US. (MIT Technology Review)

7 Apple is planning a major push into the smart-home market this month
The new hub is expected to launch on October 13. (Bloomberg $)
+ New CEO John Ternus wants more product releases and fewer staff. (Quartz)

8 A new implant network uses body tissue as its wiring
It could connect tiny sensors and medical devices. (Ars Technica)

9 A modified parasite drug could be a breakthrough for endometriosis
It targets disease-causing cells and reduced lesions in mice. (Nature)

10 Singapore has a new answer to low birth rates: a government dating app
A Nobel-winning algorithm matches participants to one date at a time. (BBC)

Quote of the day

“I’ve spent my summer begging, borrowing, and stealing chips… I don’t want to say steal. Begging and borrowing!”

—Logitech CEO Hanneke Faber tells the Wall Street Journal that she’s scoured the globe for chips amid an AI-driven shortage, which she says won’t ease for 12 to 18 months.

One more thing

Inside the hunt for the most dangerous asteroid ever 

As asteroid 2024 YR4 hurtled toward Earth, astronomers determined that this massive rock posed a higher risk of impact than any object of its size in recorded history. Then, just as quickly as history was made, experts declared that the danger had passed.

This is the inside story of the network of global scientists who found, followed, planned for, and finally dismissed the most dangerous asteroid ever found—all under the tightest of timelines and with the highest of stakes. Find out how they did it.

—Robin George Andrews

We can still have nice things

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

+ A woman has discovered her adopted cat sounds like a pigeon.
+ This delightful essay explores the lost social life of the video store.
+ Meet the Greek Orthodox priest who plays an eight-string guitar tuned like an oud.
+ Take a virtual tour through castles, palaces, and fortresses around the world with this interactive map of over 8,000 notable landmarks.

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. 

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

“We’re not going to shoot ourselves in the foot” over hack fallout, says OpenAI’s chief research officer

Two months after OpenAI’s agents hacked into the computers of AI company Hugging Face, the company is still dealing with the fallout. Last week brought news of another hack, this time into Australia’s national health-care system, which the government says OpenAI did not report for 84 days.

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.

In many ways, the buck stops with Chen. I sat down with him 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.

Here’s what he told me about making models safer—and why the world is better off with OpenAI in it.

—Will Douglas Heaven

Our Roundables on the deadly failures of the virtual border wall is now available on demand

On Monday, MIT Technology Review hosted an exclusive Roundtables conversation about our investigation into the deadly failures of the US’s “virtual wall” of border surveillance towers.

Editor-in-chief Mat Honan, senior AI reporter James O’Donnell, and senior reporter for features and investigations Eileen Guo discussed what the investigation reveals about border surveillance technology and the people who have died in the borderlands.

The full event is now available to watch on demand. Subscribe to MIT Technology Review for exclusive access to the discussion and all our other Roundtables.

Read the full “Dying on Camera” investigation here.

MIT Technology Review Narrated: smart glasses are already causing havoc in India

When Shubnam saw an Instagram video of a Delhi protest they had attended, they realized a content creator wearing Meta smart glasses had recorded them surreptitiously. The mocking reel drew millions of views, along with transphobic abuse and AI-generated memes.

Experts warn that many others will experience similar ordeals as smart glasses go mainstream. The risks are particularly acute in India, where covert recording and the circulation of images without consent are already pervasive.

The bigger issue is that the smart glasses aren’t just being used to turn ordinary people into targets of viral “pranks.” They’re also becoming a tool of police surveillance.

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

The must-reads

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

1 Trump and tech executives have agreed to “self-regulate” AI
Their accord calls for controls, audits, and board oversight. (AP News)
+ Trump called it “morally binding,” but it isn’t legally enforceable. (NBC News)
+ Elon Musk compared it to “grading each other’s homework.” (AFP)
+ Trump also doubled down on data centers despite GOP opposition. (NYT $)
+ And ordered the US government to call AI “Super Intelligence.”(Verge)
+ While singling out his intelligence chief for the AI czar role. (Axios)
+ The US is divided over AI regulation. (MIT Technology Review)

2 China’s vaunted Kimi models told researchers how to make bioweapons
Testersbypassed the models’ safety guardrails using a jailbreak. (BBC)
+ China’s AI agents can lie and scheme like their US rivals. (Reuters $)
+ Trump has ruled out any joint AI venture with China. (BBC)
+ AI is making bioweapons easier to design. (MIT Technology Review)

3 Dutch police have arrested an alleged leader of the FBI hackers
The FBI is warning other members to turn themselves in. (NBC News)
+ Here’s what to know about the hackers and the breach. (NYT $)

4 America’s renewables boom is heading towards a Trump-era cliff
The industry says permitting delays could lead to a collapse by 2028. (Axios)
+ Batteries just broke another record in the US. (MIT Technology Review)

5 Meta calls its AI data centers an “experiment” to cut its taxes
The strategy saved the company nearly $4 billion last year. (NYT $)
+ Lawmakers want tech giants to reveal their secret data-center deals. (WSJ $)

6 OpenAI has launched Dots, its AI assistants to take on Meta’s Muse
The always-on agents can continuously perform tasks for users. (BBC)
+ Protesters at OpenAI’s DevDay build a sculpture of an AI Titanic. (Ars Technica)
+ Is a secure AI assistant possible? (MIT Technology Review)

7 The Navy has awarded Boeing $20 billion to build a futuristic fighter jet
The aircraft will feature advanced sensors, range, and weapons. (Reuters $)

8 Technology to replace animal testing is ready—but scientists aren’t
Organ chips can increasingly model human biology without animals. (IEEE)

9 Tech workers have taught ChatGPT to drive a Toyota Corolla
They used frontier LLMs with no prior training data to control the car. (404 Media)

10 Trump’s government chatbot has a very weird Minecraft Easter egg
Ask it to play Minecraft and it rewrites the game’s End Poem. (Gizmodo)

Quote of the day

“The word super is the best word of all.”

—Donald Trump explains why federal agencies must now call AI “Super Intelligence,” The Verge reports.

One more thing

You have no choice in reading this article—maybe

How do humans make decisions? The question has been on Uri Maoz’s mind since he read an article in his early twenties suggesting that… maybe they didn’t.

Had he even had a choice about whether to read that article in the first place? How would he ever know if he was truly responsible for making any decisions? “After that, there was no turning back,” says Maoz, now a professor of computational neuroscience at Chapman University.

Today, Maoz is a central figure in efforts to understand how desires and beliefs turn into actions. He’s also uncovered new wrinkles in the debate. Here’s what he’s discovered.

—Sarah Scoles

We can still have nice things

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

+ Thumbelina the squirrel is going on a diet after getting a little too round.
+ Ever wondered where #, @, &, and § came from? This video has the answers.
+ Swedish producer Synthet has taken a fascinating look at the most sampled sounds in music.
+ Explore Europe on foot with Strado, which scores the most walkable neighborhoods across 50 cities on the continent.

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.”

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

Coming soon: our 2026 list of Climate Tech Companies to Watch

With the planet nearing 1.5 °C of warming, climate policies being unraveled, and Big Tech backpedaling on its climate ambitions, it can be tempting to give in to climate doom and defeatism. But despite the headwinds, the world has still made incredible progress. That’s why MIT Technology Review publishes its annual list of Climate Tech Companies to Watch.

Once a year, we compile a list of 10 companies 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’ve now finalized our 2026 list and will publish it on October 6.

It features companies making strides in energy storage, nuclear power, transportation and other areas despite today’s regressive climate politics. We hope you’ll enjoy the package and perhaps come away feeling a bit more hopeful about our climate’s future. Here’s what to expect.

—James Temple

Subscribe to receive full access to this year’s list, and keep reading The Download to be among the first to see it.

When can we say AI made a scientific discovery?

Last week, Anthropic announced that its new molecular biology lab had made its first discovery: its AI agents had flagged a previously uncatalogued pattern surrounding an enzyme, a pattern “reminiscent” of what led to the gene-editing technology CRISPR. But the claims angered biologists.

Some questioned whether merely finding the pattern amounted to a discovery at all. Another said his team had already discovered the same pattern, raising questions about whether Anthropic’s system had learned from his conversations with Claude.

It’s a reminder that what’s novel for AI may be routine, unsurprising or simply not that consequential to a biologist. And the grand claims could make real progress harder to recognize.

Find out why AI companies may be setting the wrong bar for scientific discovery.

—James O’Donnell

This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

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

1 OpenAI has scrapped a new AI model over safety concerns
The company said GPT-6.1 Astra “didn’t quite meet the bar.” (BBC)
+ It’s also apologised for a “new kind of cyber incident” in Australia. (Guardian)
+ Anthropic’s IPO filing warns that AI could threaten humanity. (FT $)
+ As the safety fears spiral, Trump is selling an AI “golden age.” (WP $)
+ He’s meeting more AI leaders at the White House today. (Reuters $)
+ Could AI really kill us all? (MIT Technology Review)

2 SpaceX’s Starship rocket has reached orbit for the first time
It also deployed new Starlink satellites in the test flight. (AP)
+ But the flight was cut short after an engine failure. (Axios)
+ We’re putting more stuff into space than ever. (MIT Technology Review)

3 The FBI told staff it assumes hackers have stolen data from every employee
An internal memo tells staff to prepare for potential attacks. (Gizmodo)
+ But the hackers say they won’t publish the stolen data. (404 Media)

4 Florida has asked a court to block OpenAI from developing new models
The state wants independent safety guardrails for new AI. (Politico)
+ It also aims to ban ChatGPT from acting like a person. (Verge)
+ While Rep. Khanna has proposed banning self-improving AI. (CNBC)

5 Meta’s AI agent invited a stranger to a user’s home without permission
Muse shared the address while negotiating a Marketplace sale. (Guardian)
+ Who’s liable when AI agents go rogue? (MIT Technology Review)

6 Old hearts appear to get younger after transplantation
The finding could expand the pool of available donor organs. (Nature)
+ But young organs may not be a fountain of youth. (MIT Technology Review)

7 Trump has finalized a rule to make cars less fuel-efficient
The new rules sharply weaken Biden-era fuel economy standards. (Verge)

8 Mathematicians have cracked a 55-year-old problem using randomness
The solution completes a proof proposed in 1971. (Quanta)

9 Space lasers are about to beam power between two spacecraft
Star Catcher will test the technology in orbit this week. (Wired $)

10 A tiny moon of Saturn could host alien life and be detected from space
Scientists found evidence that its ocean could support microbial life. (404 Media)

Quote of the day

“We may sound like children whose feelings are hurt, sure, but in our game, reputation is all that matters.”

—The ShinyHunters hacking group tells The New York Times that they targeted the FBI to retaliate against an advisory warning about their activities.

One more thing

A plan to make drugs in orbit is going commercial

A startup called Varda Space Industries is betting that the future of pharmaceuticals lies in orbit. The company has signed a deal with United Therapeutics to test whether drugs crystallize differently in microgravity, potentially creating improved versions with new properties.

The idea sounds futuristic, but falling launch costs and reusable rockets are making space-based manufacturing seem increasingly plausible. Varda says the partnership could mark an important step toward building products in orbit for use back on Earth.

Discover how space could become the next frontier for drug development.

—Antonio Regalado

We can still have nice things

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

+ A professor has turned her childhood Double Dutch idea into a real machine.
+ These 11 beautiful underwater wildlife photos capture the wonder of life beneath the oceans.
+ See Earth from a rocket’s-eye view as this camera rides all the way from launch to a precision landing.
+ A re-released 2000 BBC exhibition offers a fascinating glimpse at how people imagined the internet’s future.

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.

When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes.

As AI moves from experimentation to production, model choice is only part of the equation. When demand becomes steady and business-critical, a consumption-only approach can turn AI spending into a variable monthly line item that is difficult to forecast as usage, workloads, and model requirements change.

At that point, the question is no longer simply which model to consume, or which provider offers the lowest token price: It is how to run AI economically, predictably, and at sustained scale.

AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications. Customer-service, IT, research, and business-process agents can execute multi-step workflows across enterprise systems, creating recurring demand across models, data, and tools.

This is already starting to happen. Deloitte’s 2026 State of AI in the Enterprise reflects what many leaders are seeing: worker access to AI rose 5% in 2025, and the share of companies with at least 40% of their AI projects in production is expected to double within six months.

When AI becomes a portfolio of always-on workloads, not a collection of experiments, the economics change. Consumption pricing gives teams flexibility and limits commitment. But when usage becomes steady, predictable, and large enough to keep capacity productive, leaders need to ask a different question: Does it still make economic sense to buy AI one request at a time, or is it time to invest in capacity they can optimize and control?

This is not an abstract cloud-versus-on-premises debate. It is a workload-by-workload business decision. Over the next 12 to 18 months, how much AI demand can the company reasonably expect? How consistently will that capacity be used? When multiple workloads share infrastructure, the enterprise can spread fixed costs across more productive use—improving the economics of ownership.

The question is how much you run

Ownership is not automatically the lower-cost answer. It only makes sense when an enterprise can keep capacity productive.

Every organization has a crossover point, the level of sustained use at which owning capacity can become more economical than buying it one request at a time. There is no universal number. It depends on the models being used, the balance of input and output tokens, performance requirements, system design, energy costs, and the operating model required to support it.

A retrieval-heavy knowledge system can have a very different cost profile from a simple assistant because it may process far more context for every interaction. Agentic workflows can be different again: a single business task may involve repeated reasoning, retrieval, model calls, and tool use. That is why generic cost benchmarks are not enough. Enterprises need to model their actual workloads, understand expected demand, and size capacity accordingly.

At the right utilization level, the benefit is not only lower effective cost, but also greater predictability: the ability to manage AI capacity as a strategic infrastructure investment rather than watch a monthly spend line fluctuate with model use and workload demand.

Ownership only works when it is put to work

The capital decision is only half the equation. Even when the economics support ownership, capacity creates value only when the business gets workloads into production quickly and keeps them running.

That takes more than installing infrastructure. It takes an operating model that connects the technology to adoption and business outcomes: bringing users and workloads on board, governing how AI is used, reviewing utilization, and continually identifying the next high-value use case.

The goal is to create value early, then build on it. That means measuring use, identifying underutilized capacity, and bringing additional high-value workloads onto the platform over time. Without that discipline, the business may never realize the economic value that justified the investment. With it, AI capacity becomes a productive asset the business can optimize, expand, and use to create measurable value.

Three questions to ask

Before committing capital, leaders should ask three questions:

1. Is demand becoming steady, predictable, and large enough to justify dedicated capacity?
2. At what level of usage does ownership make economic sense?
3. Can we keep that capacity productive through adoption, governance, and continued use-case expansion?

Make the shift deliberately

As AI moves into production, the organizations that create the most value will look beyond token prices and the latest model. They will know when recurring demand calls for a different economic model—and they will have the operating discipline to make that capacity productive.

That is when AI stops being an expense and becomes an asset.

This content was produced by HPE. It was not written by MIT Technology Review’s editorial staff.

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.

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

Who’s liable when AI agents go rogue?

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. 

Many experts say it’s only a matter of time until there’s a more damaging incident where AI agents bypass sandboxes to access systems they shouldn’t. 

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

—Michelle Kim

The AI Hype Index

Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of what’s shaping the industry right now.

The latest edition includes Chinese chipmakers, German wiki sites, and American Terminators. See where it all landed on this month’s index.

—Michelle Kim

Roundtables: the deadly failures of the virtual border wall

Last week, we published an MIT Technology Review investigation that found more than a thousand people died within the advertised range of surveillance towers along the US border.

Later today, our editor-in-chief Mat Honan, senior AI reporter James O’Donnell and senior reporter for features and investigations Eileen Guo will join a subscriber-only conversation about the investigation, and its implications.

Register now to attend on Monday, September 28 at 7:00pm BST / 2:00pm EDT / 11:00am PDT.

Want to join the conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables.

Read the full MIT Technology Review investigation here.

The must-reads

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

1 OpenAI says it has paused training its models

The decision comes after its agents interacted with US government websites. (AP)

+ Trump hosted Anthropic boss Dario Amodei at a White House dinner last night. (FT)

+ How much access will evaluators really have inside AI companies? (Atlantic)

+ Why can’t we just keep rogue AI off the internet? (Verge)

+ Democrats are in a mad scrabble to get AI “right”. (New York Magazine)

+ An AI kill switch isn’t that simple, after all. (Bloomberg)

+ Will AI really kill us all? Your questions, answered. (MIT Technology Review)

2 Why isn’t the data center backlash also a climate reckoning?

It’s still hard to get people to care about the emissions they create. (Wired)

+ What does the climate movement do now? (Atlantic)

+ The math behind data centers and energy. (MIT Technology Review)

3 Wall Street big bears aren’t ready to bet against AI, yet

Bubble talk is growing, but not many are willing to go against the crowd. (Information $)

+ What even is the AI bubble? (MIT Technology Review)

4 Did Anthropic’s AI really make a scientific discovery on its own?

One scientist shared research with Claude, and says the new finding matches. Hmm. (NYT)

+ AI for science needs reasoning, not just data. (MIT Technology Review)

5 Ancient superbugs might help us fight antibiotic resistance

They could be emerging from the permafrost. (New Scientist)

6 More reliable—and less easy to jam—alternatives to GPS are coming

Using Earth’s quantum field could be more secure. (Economist)

7 Why social media bans aren’t enough to keep children safe

Online child safety deserves more nuanced policy. (IEEE Spectrum, Opinion)

8 This is how the US is attacking China’s control of critical minerals

It’s a multibillion-dollar effort to loosen Beijing’s chokehold. And it’s working. (WSJ)

9 No one wants to date tech bros anymore

They used to be nerdy and harmless, now they’ve got a real image problem. (Wired)

10 What are the rules around cellphone etiquette now?

Is there an obligation to text someone right back, for instance? (Vox)

Quote of the day

“If you’re evil, you’re at least competent. And if you’re evil, you’re not bad, and therefore you’re actually maybe kind of good because you’re at least getting something done.”

—Venture capitalist and PayPal cofounder Peter Thiel shares his unusual take on competence versus morality in an interview with Axel Springer CEO Mathias Döpfner.

One more thing

The gig workers who are training humanoid robots at home

When Zeus, a medical student in Nigeria, returns to his apartment from a long day at the hospital, he straps his iPhone to his forehead and records himself doing chores.

Zeus is a data recorder for Micro1, which sells the data he collects to robotics firms. As these companies race to build humanoids, videos from workers like Zeus have become the hottest new way to train them. 

Micro1 has hired thousands of them in more than 50 countries, including India, Nigeria, and Argentina. The jobs pay well locally, but raise thorny questions around privacy and informed consent. The work can be challenging—and weird. Read the full story. 

——Michelle Kim

We can still have nice things

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

+ This surfboard comes with a 10-liter beer keg built right into it.
+ Hollywood’s iconic Cinerama dome movie theatre is set to reopen at long last in early 2028.
+ From Dracula to Marilyn Monroe, here are 25 famous quotes in pop culture you’ve probably been getting wrong.
+ Baby lobsters in mini pods and a human-sized floating waterlily highlight this stunning selection of science photos.

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.

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

The Pentagon wants $30 million to build an AI-powered lie detector

The US government wants to spend $30.3 million over the next five years on an improved lie detector, according to a Department of Defense budget request.

The program, called “Polygraph+” or “Polygraph Next,” will focus on scoring algorithms that use AI and machine learning, as well as a technique called “standoff sensing,” which can take physiological readings from a person without attaching a device to them.

The project aims to improve the accuracy and reliability of polygraph assessments. But it could just be the latest in a long line of failed attempts to use technology to detect lies.

Here’s how the program could work—and why experts say AI-enabled lie detection is “the worst of both worlds.”

—Amit Katwala

Young organs may not be a fountain of youth for recipients

Around this time last year, a hot mic caught Vladimir Putin and Xi Jinping 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,” Putin reportedly said.

He seemed to be referring to the “replacement” theory of longevity, supported by experiments that involved physically stitching young mice to old ones. Unfortunately for Putin, new research on transplanted hearts pours a little cold water on this idea. But it also offers useful insights for organ transplantation.

Find out what the research reveals about the limits of rejuvenation.

—Jessica Hamzelou

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

Roundtables: the deadly failures of the virtual border wall

The US has spent billions building a “virtual wall” of surveillance towers along its southern border, promising to detect and apprehend border crossers and save lives. But an MIT Technology Review investigation found more than a thousand people died within the advertised range of the towers without getting caught.

On Monday September 28, our editor-in-chief Mat Honan, senior AI reporter James O’Donnell and senior reporter for features and investigations Eileen Guo will join a subscriber-only conversation about the investigation. They’ll examine the failures of border surveillance technology and uncover the stories of the people who die in the borderlands.

Register now to attend on Monday, September 28 at 7:00pm BST / 2:00pm EDT / 11:00am PDT.

Want to join the conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables.

Read the full MIT Technology Review investigation here.

The must-reads

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

1 ChatGPT helped the Tumbler Ridge shooter focus on attack tactics
A new investigation found it also advised on evading safeguards. (Mother Jones)
+ British Columbia is suing OpenAI over its failure to alert police. (BBC)
+ And wants OpenAI to pay for a replacement school. (Ars Technica)
+ Do AI chatbots cause delusions or amplify them? (MIT Technology Review)

2 Ukraine just used drones to drop military robots behind Russian lines
The “world-first” assault sent the devices into enemy territory. (Ars Technica)
+ The robots can attack, scout and clear routes for troops. (Business Insider)
+ Europe has a drone-filled vision for future wars. (MIT Technology Review)

3 Google is about to launch AI chips into space
The satellite will run simple AI queries from orbit. (Reuters $)
+ The project is part of plans to put AI data centers in space. (Gizmodo)
+ Here’s how we could put data centers in space. (MIT Technology Review)

4 A new DNA test can diagnose brain tumors in hours
The technology analyzes a tumor’s DNA to identify its type. (BBC)

5 Tesla’s Semi is finally ready to hit the road
The long-delayed electric truck is reaching customers this week.(Verge)
+ It could be a big deal for electric trucking. (MIT Technology Review)

6 Meta has gained an early lead over OpenAI in the AI device market
The Muse Charm is expected to ship before OpenAI’s hardware. (CNBC)
+ Meta promises to put privacy at the center of the products. (Axios)

7 A mathematician has discovered a rare eight-faced shape
He argues that the strange three-holed object can exist in 3D. (New Scientist $)

8 AI agents are flooding researchers with collaboration requests
Some bots are asking for data, money, and research partnerships. (Nature)

9 Venus may have swallowed its own moon
The planet’s slow rotation could have dragged the moon to its doom. (Wired $)

10 Mark Zuckerberg has finally explained his fashion glow-up
His makeover is intertwined with Meta’s wearables push. (NYT $)

Quote of the day

“They’ve got to inflict an enormous amount of pain and suffering on you so that they can save you. And so I think AI’s kind of like that.” 

—Jensen Huang compares AI’s need for fossil fuels to surgery in an interview on The Ezra Klein Show.

One more thing

This scientist rewarmed and studied pieces of his friend’s cryopreserved brain

L. Stephen Coles’s brain sits in a vat at a storage facility in Arizona. It has been held there at a temperature of around −146°C for more than a decade, largely undisturbed. Before he died in 2014, Coles had the brain frozen with an ambitious goal in mind: reanimation.

His friend, cryobiologist Greg Fahy, believes it could be revived one day. But other experts are less optimistic. 

Still, Fahy’s research could lead to new ways to study the brain. And using cryopreservation for organ transplantation is becoming a viable reality. 

Read the full story to find out what the future holds for the technology.

—Jessica Hamzelou

We can still have nice things

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

+ A French village recently hosted its annual pig-squealing championship.
+ Things get deliciously unattractive at the Iowa State Fair’s annual ugliest cake contest.
+ Greater Victoria is home to more than 1,000 Little Free Libraries, each with its own personality.
+ A study found that seeing someone dressed as Batman nearly doubled the rate of people giving up their seat to a pregnant woman.

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

A congressional representative just proposed killing America’s border tower program

Delia Ramirez, a Democratic US representative from Illinois, has announced plans to introduce legislation to terminate the surveillance tower program along the country’s southern border.

The announcement comes just days after publication of an MIT Technology Review investigation, “Dying on Camera,” which looked at deaths near these towers. Nearly one in four deaths we analyzed between 2015 and early 2026 occurred within their advertised range.

Ramirez, who sits on the Homeland Security Committee, cited our 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.”

Here’s what she’s proposing and what it could mean for the border surveillance program.

—Eileen Guo

Read all of MIT Technology Review’s groundbreaking “virtual wall” investigation here.

Roundtables: the deadly failures of the virtual border wall

The US spent billions building the “virtual wall” of surveillance towers along its southern border, promising they will help detect and apprehend border crossers and save lives. But MIT Technology Review has documented more than a thousand people who moved through areas watched by these towers without being caught—and ultimately died there.

Next Monday, join our editor-in-chief Mat Honan, senior AI reporter James O’Donnell, and senior reporter for features and investigations Eileen Guo for a subscriber-only conversation about the investigation. They’ll examine the failures of border surveillance technology and uncover the stories of the people who die in the borderlands.

Register now to attend on Monday, September 28 at 19:00 BST / 2:00pm EDT / 11:00am PDT.

Want to join the conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables.

AI is dominating the conversation at Climate Week

—Casey Crownhart

AI is the unavoidable topic at this year’s New York Climate Week, with tension growing over its costs and benefits.

The technology is attracting more attention and money to energy technologies, some of which are low- or zero-emission. But the data center buildout has come with a hefty environmental toll, and a whole lot of natural gas is coming online to meet the demand.

Overall, what I’m hearing this week is that many in the climate sector are skeptical of AI, at best. Find out why.

This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.

The must-reads

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

1 An OpenAI agent has executed the first known AI hack of a government site
It breached an Australian government health data portal in June. (CNN)
+ OpenAI notified Australia three months later through a public mailbox. (BBC)
+ No patient records are believed to have been accessed. (Guardian)
+ Its agents also tried to breach three other sites. (NYT $)
+ Here’s why AI agents cheat to reach their goals. (MIT Technology Review)

2 Data stolen in the FBI hack exposes agents’ sensitive intelligence roles
It identifies staff working on China, Russia, and cyber. (Reuters $)
+ And may have exposed the FBI’s own hacking unit. (404 Media)
+ The group behind the incident claims it isn’t financially motivated. (Register)

3 The US rejected calls from OpenAI and Anthropic for global AI standards
A Trump advisor said global rules threatened the country’s AI lead. (BBC)
+ America’s AI leaders had urged the UN to coordinate on safety. (Gizmodo)
+ The AI industry has taken a doomer turn. (MIT Technology Review)

4 Meta has unveiled new AI glasses—and a pendant for Muse
Camera-free smart glasses address growing privacy concerns. (Verge)
+ While the Muse Charm enables hands-free interaction with the AI agent. (BBC)
+ Mark Zuckerberg wants Muse to be a “personal superintelligence.” (Axios)
+ Meta also showed off new VR glasses. (CNBC)

5 China is accelerating its AI push ahead of the Xi-Trump summit
Huawei and Alibaba have both unveiled new flagship chips. (CNBC)
+ The country’s AI industry has shrugged off America’s safety panic. (Atlantic $)
+ The US leads in AI models, but China has talent and political edges. (NYT $)
+ Chinese models have divided the White House. (MIT Technology Review)

6 US lawmakers have proposed new rules for blocking Chinese tech
The bipartisan bill would require broader review of national security risks. (Hill)
+ And give Congress the power to overturn FCC bans. (Reuters $)

7 Tech giants have urged Trump to withdraw $103,265 H-1B fee
They say the charge could weaken US competitiveness. (WSJ $)

8 Scientists have detected radio signals from an exoplanet for the first time
The signals likely come from intense magnetic activity on the planet. (Wired $)

9 An invisible force has a mysterious effect on aging
Shielding fruit flies from Earth’s magnetic field changed their lifespans. (404 Media)

10 ‘Dopamine sites’ are recreating the thrill of shopping without paying
FoodNeverComes has attracted more than 2.7 million visitors since June. (BBC)

Quote of the day

“They decided to break the law.”

—Ed Santow, co-founder of the Human Technology Institute, tells ABC radio why there should be serious legal consequences for OpenAI agents hacking Australia’s Medicare statistics portal.

One more thing

The quest to figure out farming on Mars

If ever a blade of grass grew on Mars, those days are over. But could they begin again? What would it take to grow plants to feed future astronauts on Mars? 

To grow food there, we can’t just drop seeds in the ground and add water. We will need to create a layer of soil that can support life. And to do that, we first have to get rid of the red planet’s toxic salts. 

Researchers recently discovered a potential solution—and the early signs are promising. Read the full story.

—David W. Brown

We can still have nice things

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

+ Meet Alfie, a dog with flowing locks that croons like an opera singer.
+ Watch the 100 wildest homemade plane flights and crashes from Red Bull Flugtag.
+ Marmot researchers have launched OnlyMarms, a free, G-rated account of cute videos that funds their work through tips.
+ The full trailer for Nathan Fielder’s Elizabeth Holmes documentary is here, and somehow it looks even weirder than expected.

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.

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

Smart glasses are already causing havoc in India

When Shubnam saw an Instagram video of a Delhi protest they had attended, they realized a content creator wearing Meta smart glasses had recorded them surreptitiously. The mocking reel drew millions of views, along with transphobic abuse and AI-generated memes.

Experts warn that many others will experience similar ordeals as smart glasses go mainstream. The risks are particularly acute in India, where covert recording and the circulation of images without consent are already pervasive.

The bigger issue is that the smart glasses aren’t just being used to turn ordinary people into targets of viral “pranks.” They’re also becoming a tool of police surveillance.

Here’s why smart glasses are creating new privacy risks in India.

—Anuj Behal

MIT Technology Review Narrated: what’s at stake in AI’s trillion-dollar gamble

When Jessica Wachter, a University of Pennsylvania finance professor, wanted to assess AI’s impact on the economy over the next few years, she started with a simple fact: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.

Instead of trying to predict how widely deployed AI models will be, Wachter asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when expenditures are expected to reach nearly $1.1 trillion.

The results are eye-opening. AI companies will need to achieve an extraordinary increase in productivity just to break even by 2030.

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

The must-reads

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

1 Hackers claim they’ve stolen data on almost all FBI employees
The ShinyHunters group says it seized more than 2 TB of data. (Axios)
+ Including agents’ names, addresses, and phone numbers. (404 Media)
+ ShinyHunters says the attack was retaliation for an FBI alert. (Reuters $)
+ Now is a good time for doing crime. (MIT Technology Review)

2 Anthropic and OpenAI have both released lower-cost models
They face growing competition from cheaper Chinese models. (CNBC)
+ Startups have been reducing their reliance on the two labs. (Bloomberg $)
+ The new models are the first since their calls for an AI slowdown. (FT $)
+ Their CEOs are set to brief the UN Security Council today. (Quartz)
+ Could AI really kill us all? (MIT Technology Review)

3 Trump’s Treasury chief could soon become his new AI czar
Scott Bessent has played a central role in US-China AI talks. (Semafor)
+ Other contenders include Michael Kratsios and Scott Kupor. (Gizmodo)
+ Will Trump’s AI rebrand to “superintelligence” catch on? (BBC)

4 Scientists are turning renewable electricity directly into food
The goal is to make more food with less land. (New Scientist $)
+ Companies are creating food out of thin air. (MIT Technology Review)

5 A “fire amoeba” has broken the heat survival record for complex life
The newly discovered organism can grow and reproduce at 63°C. (NPR)
+ It could inform the search for life elsewhere. (New Scientist $)

6 Meta quietly tested a “human concierge” for its Muse AI agent
Contractors secretly handled some calls placed by Muse. (Reuters $)
+ Staff raised concerns about privacy and misleading users. (404 Media)

7 Data centres are replacing copper with light to cut energy use
Photonics can move data with less heat than electrical wiring. (BBC)
+ Virtual power plants could also help. (MIT Technology Review)

8 AI models built from rat brains just got closer to reality
AWS is tapping rat neurons to make video AI faster. (Wired $)

9 Uber is betting on human drivers to give its robotaxis an edge
They can cover demand spikes while autonomous cars recharge. (Axios)

10 A humanoid robot took on an amateur fighter in a cage
The brief bout was billed as the first human-robot MMA fight. (Futurism)

Quote of the day

“The use of the word artificial makes it sound fake. It’s not fake; it’s actually amazing.”

—President Donald Trump tells the UN General Assembly why he wants to rename AI “superintelligence.” 

One more thing

AI is changing how we quantify pain

At Orchard Care Homes, nurses used to rely on an observational scale to assess pain in residents who couldn’t communicate verbally. But agitated residents were sometimes assumed to have behavioral issues, while their pain went untreated.

Then, in 2021, the care-home chain began trialing PainChek, a smartphone app that scans a resident’s face for microscopic muscle movements and uses AI to output a pain score. Within weeks, the pilot unit saw fewer prescriptions and calmer corridors.

Now researchers are racing to turn pain into something a camera or sensor can score as reliably as blood pressure. But when algorithms measure our suffering, does that change how we understand and treat it?

Discover how AI is changing the way we assess pain.

—Deena Mousa

We can still have nice things

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

+ Scientists have discovered the first new penguin species in more than 100 years.
+ Discover the filmmaking skills that made Paul Verhoeven’s RoboCop a directing masterclass.
+ What happens when fast food meets local tastes? These unusual international menu items offer some tasty answers.
+ Dance to every track the crowd has identified at Berghain since 2024 with this media player, which replays each night in sequence. 

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. 

Listen to the session or watch below

The US has spent billions building a “virtual wall” of surveillance towers along its southern border over the past 25 years, promising they will help detect and apprehend border crossers and save lives. But a groundbreaking investigation by MIT Technology Review has documented over a thousand people who moved through areas watched by these towers without being reached or apprehended, and who ultimately died there. Some were even under the watch of newly installed AI-powered towers designed to spot people automatically.

Our findings reveal a humanitarian crisis more visible than previously known, and repeated failures of the virtual wall’s basic security promise. Watch a conversation examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands.

Speakers: Mat Honan, editor in chief, James O’Donnell, senior AI reporter, and Eileen Guo, senior features and investigations reporter

Recorded on September 28, 2026

Related Stories

• The US spent billions on border surveillance. Why can’t it catch people before they die?
• How we made the first comprehensive map of deaths along the US border’s “virtual wall”
• 4 ways to address the failures we found along the US border’s “virtual wall”